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Cloud computing has revolutionized the development and operations of hardware and software across diverse technological arenas , yet academic biomedical research has lagged behind despite the numerous and weighty advantages that cloud computing offers . Biomedical researchers who embrace cloud computing can reap rewards in cost reduction , decreased development and maintenance workload , increased reproducibility , ease of sharing data and software , enhanced security , horizontal and vertical scalability , high availability , a thriving technology partner ecosystem , and much more . Despite these advantages that cloud-based workflows offer , the majority of scientific software developed in academia does not utilize cloud computing and must be migrated to the cloud by the user . In this article , we present 11 quick tips for architecting biomedical informatics workflows on compute clouds , distilling knowledge gained from experience developing , operating , maintaining , and distributing software and virtualized appliances on the world’s largest cloud . Researchers who follow these tips stand to benefit immediately by migrating their workflows to cloud computing and embracing the paradigm of abstraction .
Cloud computing is the on-demand use of computational hardware , software , and networks provided by a third party [1] . The rise of the internet allowed companies to offer fully internet-based file storage services , including Amazon Web Services’ Simple Storage Service , which launched in 2006 [2] . Throughout the past decade , cloud computing has expanded from simple file and object storage to a comprehensive array of on-demand services ranging from bare metal servers and networks to fully managed databases and clusters of computers capable of data processing at a massive scale [3 , 4] . Modern cloud computing providers and the customers that utilize their services share responsibility for computer systems , with the cloud provider managing the physical hardware and virtualization software and the consumer utilizing the cloud services to architect workflows which may include applications , databases , systems and networks , storage , web servers , and much more [5 , 6] . In this way , cloud computing allows users to offload the burden of managing physical systems and focus on building and operating solutions . Cloud computing has revolutionized the way businesses operate . By using a cloud provider instead of operating private data centers , companies can reduce costs by paying for only the hardware they use and only when they use it . In addition , cloud-based technological solutions offer many important advantages when compared to conventional enterprise data centers , including the ability to dynamically scale up under increased load , recover from disaster incidents automatically , remotely monitor application states , automate hardware and software deployments , and manage security through code . In addition , many cloud providers operate multiple data centers across continents , providing redundancy across different locations in the world to increase fault tolerance and reduce latency . Finally , cloud computing has evolved a new paradigm of microservice-centric application design , wherein the traditional monolithic software stack is replaced with loosely coupled components which can each be scaled individually , updated individually , and even replaced with fully managed cloud services such as message passing services , serverless function execution services , managed databases and data lakes , and even container management services . Businesses have exploited these advantages of cloud computing to gain an edge in a competitive landscape , ushering in a new era of computing that emphasizes abstraction , agility , and virtualization . Scientific computing in academic research environments still mostly utilizes in-house enterprise compute systems such as High Performance Compute ( HPC ) clusters [7] . In these systems , all software , hardware , data storage , networking , and security are the responsibility of the institution , including compliance with applicable state and federal laws such as HIPAA and other regulations which govern data storage for protected health information and human genetic data . The fact that scientific institutions manage their own separate compute systems poses serious problems for reproducibility due to differences in hardware and software across institutions [8–10] . Additionally , the HPC model fails to allow researchers to capitalize on the innovations offered by cloud computing . For these reasons , we have compiled a set of eleven quick tips to help biomedical researchers and their teams architect solutions using cloud computing . We provide a high-level overview of some best practices for cloud computing with an emphasis on reproducibility , cost reduction , efficiency of development and operations , and ease of implementation .
Cloud computing providers such as Microsoft Azure , Google Cloud Platform , Amazon Web Services , and others have developed templating systems that allow users to describe a set of cloud infrastructure components in a declarative manner . These templates can be used to create a virtualized compute system in the cloud using a language such as JSON /or YAML , both of which are human-readable data formats [11] . Templates allow developers to manage infrastructure such as web servers , data storage , and fully configured networks and firewalls as code . These templates may be version-controlled and shared , allowing lateral transfer of full compute systems between academic institutions . Templatized infrastructure makes it is easy to reproduce the exact same system at any point in time , and this provides an important benefit to researchers who wish to implement generalizable solutions instead of simply sharing source code . Templates allow researchers to develop virtual applications that provide a control over hardware and networking that is difficult or impossible to achieve when researchers use their institutional HPC systems . Additionally , templates themselves are lightweight documents that are amenable to version control , providing additional utility . Finally , templates can be modified programmatically and without instantiating the computational stack they describe , allowing developers to modify and improve templates without invoking costs . Version-control systems such as Git give developers immense control over software changes , including branching and forking mechanisms , which allow developers to safely implement new features and make modifications [8] . Additionally , repository hosting services such as GitHub allow researchers to share workflows and source code , aiding in reproducibility and lateral transfer of software . In cloud computing , infrastructure of entire complex systems can be templatized . These templates can then be version-controlled , allowing researchers and developers to keep a record of prior versions of their software and providing a mechanism to roll back to an earlier version in the event of a failure such as an automated test failure . Version control therefore plays a vital role in architecting workflows on cloud computing because it applies not only to the software , but also to templates that describe virtualized hardware and networks . Academic scientists who work in isolated compute environments such as institutional HPC clusters might not employ version control at all , instead opting to develop and operate applications and workflows entirely within the cluster . This practice is undesirable in that it fails to keep a record of code changes , fails to provide a mechanism for distribution of source code to other researchers , and fails to provide a mechanism by which collections of code can be migrated to other systems . It is strongly encouraged that absolutely every piece of code and infrastructure template be version-controlled , and further , that version control becomes a first step in all bioinformatics workflow development . Cloud computing providers often offer fully managed services for version-control hosting , allowing researchers , teams , and even whole institutions to maintain private collections of repositories without the need to manage a version-control server or use a third-party version-control service like GitHub . An example of a cloud-based virtual appliance which uses a version-controlled template to recreate infrastructure is EVE [12] . EVE is a cloud application that utilizes snapshots of software and reference data to perform reproducible annotation of human genetic variants . The appliance’s infrastructure is declared in a CloudFormation template which can be shared , modified offline , and used to instantiate an exact copy of the same hardware–software stack for annotation , a bioinformatics workflow which is difficult to reproduce across varying compute environments that are not controlled for software and reference data versions across space and time . EVE is an example of how templatized infrastructure and imaged software and reference data allow cloud computing to enhance reproducibility of biomedical informatics workflows .
The on-demand nature of cloud computing has driven innovation in imaging technology as well as templating technology . In contrast to local data centers , cloud computing encourages users to expand computational capacity when needed , and users do not need to leave a server running all the time . Instead , users can instantiate the hardware they need only when they need it and shut it down afterwards , thus ending the operational expense . This ephemeral approach to computing has spurred development of imaging and snapshotting services . An important element of cloud providers is their ability to take snapshots and images of data storage volumes which can be used to later recreate the internal state of a server . A user can install software and data onto a virtual server and then create an image of the block storage devices that server uses , including the operating system , file system , partitions , user accounts , and all data . The ability to image data and software provides tremendous utility to biomedical researchers who wish to develop reproducible workflows . External data sources upon which biomedical workflows depend may change over time; for example , databases of genetic polymorphisms are updated regularly , and genome assemblies are revised as more genotype data is accrued . Imaging the reference data that is used in a particular biomedical workflow is an excellent way to provide a snapshot in time which will not mutate , providing a reproducible workflow by controlling software and data . When combined with templatized infrastructure , snapshots and images can fully recreate the state of a virtual appliance without the requirement that the end user copies data or installs and configures any software whatsoever .
Containers are software systems that provide the ability to wrap software and data in an isolated logical unit that can be deployed stably in a variety of computing environments [13] . Containers play an important role in the development of distributed systems by allowing tasks to be broken up into isolated units that can be scaled by increasing the number of containers running simultaneously . Additionally , containers can be leveraged for reproducible computational analysis [14] . Importantly , cloud providers often offer integration with containers such as Docker , allowing developers to manage and scale a containerized application across a cluster of servers . A compelling example of containerized applications for biomedical informatics workflows is presented by Polanski et al . , who implement 14 useful bioinformatics workflows as isolated Docker images that are provided both directly and integrated into the CyVerse Discovery Environment [15] , which is an NSF-funded cyberinfrastructure initiative formerly known as iPlant [16] . These images , shared on both GitHub and DockerHub , are useful not only within the CyVerse Discovery Environment but also via managed Docker services including Amazon Web Services ( AWS ) Elastic Container Service , Microsoft Azure Container Service , Google Kubernetes Engine , and others .
Cloud providers often operate under a shared responsibility model for security , in which the cloud providers are responsible for the physical security of the cloud platform and the users are responsible for the security of their applications , configurations , and networks [17] . While this imposes new responsibilities on users who otherwise would operate entirely within an institutional compute system such as an HPC , it also creates opportunities to take control of security as code . Much like servers and storage volumes , firewalls and account control in cloud computing are expressed as code , which may be version-controlled and updated continuously . Cloud computing and the infrastructure-as-code paradigm allow developers to configure and deploy firewalls , logical networks , and authentication/authorization mechanisms in a declarative manner . This allows developers to focus on security in the same way as hardware and software and pushes security into a central position in the process of development and operations of cloud applications . Cloud computing also allows automated security testing , an important component of agile software development . In addition , privacy settings are also amenable to programmatic and automated management in cloud computing . Access to specific cloud resources is controlled by provider-specific mechanisms , including role-based account management and resource-specific access control . Users are encouraged to manage privacy by a principle of minimum privilege , complying with all applicable regulations . Cloud computing providers make it easy to control which users can access which resources , including sensitive datasets . In addition , access logs for cloud-based data storage and built-in encryption mechanisms offer fine-grained auditing capabilities for researchers to demonstrate compliance .
Cloud providers compete with each other to offer convenient and cost-saving managed services to perform common tasks without the user having to implement them [18] . These include message passing , email , notification services , monitoring and logging , authentication , managed databases and data lakes , cluster management tools such as for Apache Spark and Hadoop , and much more . Utilizing these services is not only cost-effective but also offloads the burden of development and maintenance . Additionally , these services are often implemented in a distributed and highly available manner , utilizing redundancy and cross-data center replication technology . All of this is provided and maintained by the cloud service provider , and effective utilization of managed services can yield tremendous gains for very little investment . Some crucial examples of managed services which can greatly accelerate the pace of development for biomedical workflows include managed data analysis clusters such as Apache Spark . Apache Spark is a powerful and easy-to-use distributed data processing engine that has found use cases in bioinformatics , especially when working with very large datasets . Multiple major cloud providers offer a managed Apache Spark service , allowing users to skip over installing and configuring Apache Spark and even spin up an entire cluster of preconfigured Spark nodes with a few clicks . This allows scientists to go directly from raw data to distributed processing , and these services often additionally offer convenient integration with cloud storage . Another example comes in the form of managed database services , most notably Google’s BigTable and Amazon Web Service’s DynamoDB , which are both NoSQL databases that the user accesses directly through an application programming interface ( API ) . This means that cloud users can simply put data into a database table without having to spin up a server for the database and install and manage the database itself; instead , the database is already running as a managed service in the cloud , and the user can directly call it to store and retrieve data . BigTable and DynamoDB are implemented in a distributed manner behind the scenes , providing the advantages of a high-availability system with built-in redundancy and the accompanying low latency and high durability . Using managed services like distributed computing systems and databases reduces developer burden and provides a technologically advanced solution that need not be reinvented . An example of a cloud-based biomedical informatics workflow which benefits from managed services is Myrna , which is a pipeline for alignment of RNA-seq reads and investigation of differential transcript expression [19] . Myrna utilizes Elastic MapReduce ( EMR ) , a managed Hadoop service offered by Amazon Web Services , as a distributed computing engine . While users could install and configure their own Hadoop environments starting from raw cloud resources , the managed service offloads the burden of configuring and managing Hadoop clusters , and has convenient features for automatic or manual scaling . Services such as EMR are great examples of ways in which cloud computing services can reduce management burden while simultaneously providing useful features that users do not need to reimplement .
The advent of cloud computing has spawned the creation of a new paradigm for web applications: serverless computing . Serverless computing is a model in which the user does not create and operate a web server but instead creates abstract functions that are logically connected to each other to perform all of the logic of the application . Instead of the user managing a server which runs the application’s logic , the cloud provider dynamically manages each function in real time , allocating resources and executing code . The user only pays for code that is executed and does not need to keep a web server running continuously . Additionally , serverless applications are easier to scale up because the functions which define the application’s logic are executed in isolated containers . This means that there can be many functions executing simultaneously and asynchronously without overwhelming any one server or saturating any one network environment . Serverless computing can also be blended with traditional servers instead of purely serverless applications that do not utilize any provisioned servers . Serverless computing is a new paradigm for application development and operations that is considerably more abstract than creating and operating an application on a provisioned cloud server . In designing serverless applications , developers do not need to manage memory , application state on disk , or software dependencies . Instead , programmers define pure logic in the form of functions and the events that trigger them . This way of thinking is a challenge to adopt , but the rewards are incredible in that applications can scale without autoscaling policies that allow conventional server-bound applications to scale . Additionally , serverless applications do not continuously bill the user’s account in the way that continuous operation of a server would . Finally , serverless computing may reduce development time and cost by removing the responsibility of managing servers and their resources from the developer . As an example , Villamizar et al . recently implemented a real-world web application in a traditional monolithic design , a user-managed microservice design , and a fully serverless design which uses AWS Lambda functions [20] . Cost comparisons showed that the serverless implementation reduced costs by over 50% while simultaneously providing agility and fine-grained scalability within the application . While serverless computing is a new paradigm that has yet to see widespread adoption in biomedical informatics , this example illustrates the capability of serverless applications to transform the way biomedical informatics workflows are developed and operated .
Agile development is an emerging set of principles for software development and deployment that emphasizes flexibility , small releases , and adaptivity to change . Instead of focusing on large releases with monolithic changes to large features in a software application , agile teams focus on a nearly continuous stream of small updates . This allows teams to respond to changes in project scope , design criteria , and process changes more effectively than while building toward a major release . Additionally , agile development has brought special emphasis to techniques such as automated testing , continuous integration and delivery , and test-driven development . Cloud computing is a great fit for agile development , and agile development is a great fit for cloud computing . With cloud computing , deploying new servers and calling new managed services is fast , allowing developers and teams to iterate quickly . Cloud computing offers developers fast and on-demand access to a variety of different testing environments , which can aid in automated testing and blue-green deployments for uninterrupted services during updates . In addition , many cloud providers offer managed services for continuous integration and continuous delivery . These services can automatically build , test , and deploy software every time a change to the source code is made . In many ways , the agile paradigm can enhance productivity for biomedical research teams , and cloud computing offers many avenues for agile development .
Decoupling is the process by which separate components of a system are rendered less interdependent . For example , an application which utilizes a database and a web server can benefit from migrating the database to a different virtualized server . The decoupled database and web server can then be individually updated and maintained without affecting each other . In addition , the database and web server components can be individually scaled and extended , imparting elasticity into the entire application . Finally , the decoupled system is less fault-tolerant and less prone to resource competition , including processors , memory , disk read/write , and network throughput . Decoupled systems are modular in their nature , and cloud computing provides the ability to decouple components through message passing , virtualized networking capabilities , and managed services . For example , database tiers can be replaced by managed database services . This allows total decoupling of the database and the web server in the above example , so if one experiences a fault , the other is not affected . Additionally , a conventional database tier can be maintained but on a separate server or group of servers than the web server itself , and virtualized networking can allow these two components to access each other over the same subnet . Some cloud providers even offer managed services to design and operate decoupled systems , allowing developers to focus on the components without having to design message passing and handling logic . Decoupled systems are an important part of design best practice for highly available cloud architectures and as such are an active area of development .
Cloud providers often offer mechanisms by which researchers can share components of cloud systems simply by making them public instead of private . For example , images of servers and snapshots of storage volumes can be made public by changing their permissions . Additionally , single users can be added without making the image or snapshot public , for example to provide the ability for a peer reviewer to access components of a cloud system without opening it to the general public . In another example , datasets stored in cloud-based object storage can be shared with specific user accounts or made generally public . Examples of this include the Cancer Genome Atlas and the 1000 Genomes Project , both of which offer publicly available data which utilizes cloud storage . Researchers and developers can also develop templates of cloud systems which utilize snapshots and images that are then made public , allowing other users to instantiate perfect copies of a reproducible computing environment . An example is a researcher who architects a workflow , then saves a snapshot of the storage volume that contains installed and configured software alongside any reference datasets used . The researcher can then create a template that references these images and make that public , thereby creating a fully reproducible virtual application that has tremendous advantages over simply disseminating source code and referring to versions of publicly available datasets . The ability for components of cloud systems to be shared simply by changing settings to allow specific or general access is an advantage of cloud computing .
In contrast to traditional academic compute systems which are constructed under large , up-front capital expenses , cloud computing requires little or no up-front cost and is billed as a recurring operational expense . Users of cloud computing services are billed for what they use , often on a monthly cycle . This shift in billing methods can lead to researchers being shocked with a bill that is much higher than anticipated . It is the responsibility of the user to track expenses in real time , manage costs , and adhere to budgets , which is often not a concern that academic compute users have had to monitor , especially when academic compute systems are entirely covered by indirect costs . In addition , cloud computing providers often charge for services that academics are not used to paying for , such as data transfer and storage . These unexpected costs , coupled with the operational expense nature of cloud computing , can result in researchers receiving “bill shock” at the end of the first period of active cloud computing utilization . Cloud providers have developed several mechanisms by which users can manage costs and budgets . First , most major cloud providers have services that provide a real-time breakdown of expenses categorized by service type , including data transfer , data storage , networking expenses , compute time , managed services , and more . In addition , cloud providers offer budget calculators such as the AWS Simple Calculator that allow users to estimate costs before launching any services . Finally , some cloud providers offer full budget management suites such as AWS Budgets which allows users to set custom alerts for budget thresholds and provides usage-to-date monitoring functionalities to maintain a tight command over spending . While the use of cloud services to monitor budgeting and expenses requires extra effort on behalf of the user , new features such as daily and per-minute quotas offered by Google Cloud Platform’s App Engine offer fine-grained control over cost management by setting hard limits on resource utilization . While academic computing is often covered under indirect costs of grant funding , cloud computing invokes expenses as it is used , providing the opportunity for users to lose track of their spending rate . However , diligent and regular utilization of built-in budget and cost-management tools is a necessary part of cloud computing . In addition to cost-management and budget tools , government research sponsors such as NIH and NSF have launched cloud computing initiatives such as CyVerse to speed adoption of cloud computing in academia [16] . Finally , cloud providers themselves often provide free credits for researchers , such as the AWS Cloud Credits for Research award and the Microsoft Azure for Research program .
Much of the activity in the cloud computing ecosystem takes place outside of the realm of academic research . The tech community hosts a diverse series of conferences ranging from massive international gatherings such as re:Invent to distributed , local meetups such as Python User Groups , data science groups , and DevOps Days . The latter is an example of a conference in which scientists have the opportunity to present their research and development and simultaneously interact with leading technologists , from whom scientists and researchers can benefit by exposure to the latest tools and design patterns that are driving innovation in the tech sector but have yet to reach adoption in academia . In addition to conferences and meetups , much of the discussion of technological advances in cloud computing takes place on social media platforms . In both cases , scientists stand to benefit by interfacing and interacting with the tech sector and may find a lot more common ground than expected . Developers and engineers in the tech sector are often keenly interested in scientific research , and if scientists and academics can immerse themselves into the tech culture instead of merely attending scientific conferences and meetings , substantial mutual benefit may be obtained . Finally , tech meetups and conferences are a great way to network and source new talent with accompanying new ideas and cutting-edge skills .
Cloud computing offers the potential to completely transform biomedical computing by fundamentally shifting computing from local hardware and software to on-demand use of virtualized infrastructure in an environment which is accessible to all other researchers . However , many challenges and barriers to adoption remain pertinent to biomedical informatics and other scientific disciplines . Existing software and code bases might not easily migrate from academic computing centers to cloud providers , and performance of existing software might be negatively impacted by deployment in the cloud; for example , network latency between filesystem and CPU and network bandwidth between database and application tiers could be considerably slower in a cloud deployment when compared to a single data center . To mitigate this and ease transition from local to cloud deployment , containerization systems such as Docker and Kubernetes are promising candidates for software deployment in diverse environments . In addition to migration , cloud computing utilizes a different billing model when compared to academic computing centers , which are often funded by large , up-front capital expense with recurring expenses that might be covered by indirect costs . In contrast , cloud computing providers frequently require no up-front capital expense , and instead , users are billed for on-demand uses purely as operational expenses . This can result in surprise “billing shock” when users are taken off-guard with a monthly bill that is much higher than expected . While use of cloud computing services for cost management is an advisable use pattern , it’s the user’s responsibility to proactively manage costs and maintain a budget in real time . Finally , while academic computing centers are often compliant with government regulations concerning sensitive data such as protected health information , cloud computing can present a considerable privacy and security risk when used in a manner which compromises data privacy , for example by accidentally making data publicly accessible by changing privacy settings via a cloud provider’s web console . In academic computing , users have no control over the firewall and authentication/authorization of the compute system , but in the cloud the user is entirely responsible for data privacy and security for systems they create and utilize . This shift in responsibility is a grave concern for users with sensitive , protected , and regulated data , and users of cloud computing must manage their own compliance with international , federal , state , and local laws . An example of this is the Database of Genotypes and Phenotypes , which recently has added cloud computing to the Authorized Access mechanism for research use of deidentified genotype and phenotype data [21] . As cloud computing technology continues to innovate at a rapid pace , the future holds exciting possibilities for biomedical informaticians . The pace of data acquisition in biology and medicine continues to increase at an unprecedented rate , and the vertical and horizontal scaling capabilities of cloud computing are an ideal fit . Despite this , the concerns for privacy and security in biology and medicine demand the advent of managed services specifically tailored to clinicians and researchers . While cloud providers are responsible for security of the physical hardware and the underlying software used to provide cloud computing services to the end user , users are responsible for data security and privacy for infrastructure they provision and use . For this reason , advances in cloud security and privacy for sensitive data are needed to bridge the gap between on-premise academic compute environments , which often have their own dedicated IT staff , and cloud environments , where no such staff currently exists at many institutions and universities . In addition to security and privacy concerns for the future of biomedical cloud computing , education and academic support for cloud computing is an area which can benefit from increased investment and development on behalf of cloud providers , academic institutions , grant-funding agencies , and individual research groups .
Cloud computing holds the potential to completely change the way biomedical informatics workflows are developed , tested , secured , operated , and disseminated . By following these 11 quick tips , researchers can ease their transition to the cloud and reap the rewards that cloud computing offers . | Cloud computing has revolutionized the tech sector , but academia is slow to adopt . These 11 quick tips are geared towards helping academic researchers and their teams harness the power of cloud computing by utilizing the design patterns that have evolved in the past decade . Cloud computing can increase reproducibility , scalability , resilience , fault-tolerance , security , ease of use , cost- and time-efficiency , and much more . | [
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| 2018 | Eleven quick tips for architecting biomedical informatics workflows with cloud computing |
While the recognition of microbial infection often occurs at the cell surface via Toll-like receptors , the cytosol of the cell is also under surveillance for microbial products that breach the cell membrane . An important outcome of cytosolic recognition is the induction of IFNα and IFNβ , which are critical mediators of immunity against both bacteria and viruses . Like many intracellular pathogens , a significant fraction of the transcriptional response to Mycobacterium tuberculosis infection depends on these type I interferons , but the recognition pathways responsible remain elusive . In this work , we demonstrate that intraphagosomal M . tuberculosis stimulates the cytosolic Nod2 pathway that responds to bacterial peptidoglycan , and this event requires membrane damage that is actively inflicted by the bacterium . Unexpectedly , this recognition triggers the expression of type I interferons in a Tbk1- and Irf5-dependent manner . This response is only partially impaired by the loss of Irf3 and therefore , differs fundamentally from those stimulated by bacterial DNA , which depend entirely on this transcription factor . This difference appears to result from the unusual peptidoglycan produced by mycobacteria , which we show is a uniquely potent agonist of the Nod2/Rip2/Irf5 pathway . Thus , the Nod2 system is specialized to recognize bacteria that actively perturb host membranes and is remarkably sensitive to mycobacteria , perhaps reflecting the strong evolutionary pressure exerted by these pathogens on the mammalian immune system .
Mycobacterium tuberculosis ( Mtb ) , the causative agent of human tuberculosis , is an exquisitely adapted obligate human pathogen that is thought to persist within as many as one billion individuals worldwide [1] . This bacterium's ability to survive and replicate inside a modified phagosomal compartment of host macrophages is central to the pathogenesis of this disease [2] . While residing at this site , Mtb is able to persist for decades . However , a robust cell-mediated immune response effectively inhibits bacterial replication in approximately 90% of otherwise healthy individuals , and the infection can be controlled indefinitely . Deficits in this immune response result in progressive bacterial replication , necrosis of infected lung tissue , and spread to other individuals . Thus , like many other pathogens that cause chronic infections , the long-term survival of Mtb , depends on a delicate balance between bacterial virulence and host immunity . Immunity to tuberculosis depends on both the innate and adaptive responses of the host . Initial recognition of the bacterium is mediated by pattern recognition receptors ( PRR ) such as Toll-like receptors ( TLRs ) [3] , [4] or nucleotide binding oligomerization domain ( NOD ) -like receptors ( NLRs ) [5] , [6] , both of which recognize conserved microbial structures known as pathogen associated molecular patterns ( PAMPs ) . TLRs monitor the extracellular environment and endosomal compartments , and recognize a variety of microbial components including bacterial lipoprotein , peptidoglycan , CpG DNA , and double- and single-stranded RNA [4] . NLRs constitute a more diverse family of approximately 25 proteins , including the caspase-recruiting domain ( CARD ) -containing Nod1 , Nod2 and NLRCs , the pyrin ( PYR ) domain-containing NLRPs and the baculovirus-inhibitor-of-apoptosis-repeats ( BIRs ) -containing NLRBs . Nod1 and Nod2 reside in the cytosol and recognize microbial products in this compartment [7] . While the functions of most NLR's remain undefined , the Nod1 and Nod2 proteins have been shown to respond to bacterial cell wall fragments . The Nod1 protein recognizes a fragment of peptidoglycan ( PGN ) containing the dipeptide γ-d-glutamyl-meso-diaminopimelic acid ( iE-DAP ) produced by Gram-negative and some Gram-positive bacteria . Nod2 recognizes muramyl dipeptide ( MDP ) present on most types of PGN [8] , [9] , [10] , [11] . While the recognition of these common forms of peptidoglycan have been extensively studied , bacteria modify their cell walls in a myriad of ways and the effects of these modifications on Nod1/2 recognition are only beginning to be appreciated ( reviewed in [12] , [13] , [14] ) . For example , Listeria monocytogenes removes a common N-acetyl moiety from the glucosamine of its peptidoglycan , which renders the cell wall resistant to host lysozyme and thereby inhibits bacterial recognition by Nod1 [15] . In contrast , mycobacteria , replace the N-acetyl group of the muramic acid of MDP with a N-glycolyl moiety[16] , [17] , and this modification significantly increases the potency of this compound as a Nod2 agonist ( Coulombe , F . and Behr , M . A . unpublished data ) . Nod1 and Nod2 functions depend on a downstream signaling component , Rip2 , which belongs to a protein family currently consisting of 7 members [18] . Like the prototype Rip1 , Rip2 contains an N-terminal serine threonine kinase domain followed by an intermediate region and a C-terminal caspase recruitment domain ( CARD ) . Rip2 has been shown to be essential for cytosolic Nod1/2 signaling , and its overexpression stimulates NF-κB activity and induces apoptosis [19] , [20] . We have shown that Rip2 is stably modified with ubiquitin in cells treated with the Nod2 agonist MDP [21] . This modification is required for Nod1-mediated NF-κB activation [22] , indicating that stable polyubiquitination is a critical component of this signaling cascade . Intact Mtb bacilli are recognized by both TLRs and NLRs , which cooperatively respond to infection and synergistically induce NF-κB activation [23] . However , a large fraction of the transcriptional response to Mtb , including many immunologically important proteins , such as the chemokines RANTES and IP-10 , and the inducible nitric oxide synthase enzyme NOS2 that is critical for mycobacterial immunity , are induced independently of TLR2/4 and the adapter proteins MyD88 , MAL and TRIF . Instead these responses rely on autocrine or paracrine signaling via type I interferons ( IFNα/β ) , which are induced through largely undefined pathways [24] . Despite the ability of cell surface localized TLR4 to trigger IFNα and IFNβ transcription , existing evidence indicates that during genuine bacterial infections , this response instead requires the recognition of bacterial products in the cytosol . This has been most clearly demonstrated for pathogens that replicate in the host cell cytosol , such as Listeria monocytogenes and Francisella tularensis . In both cases , the bacterium must disrupt the phagosomal membrane and escape into the cytosol in order to trigger the type I IFN response in resting macrophages [25] , [26] , [27] . Despite its residence in the phagosome , Mtb still induces rapid and robust IFNα/β transcription , and this response depends on a specialized secretion system of the bacterium , ESX1 [28] . This system has been suggested to contribute to the perturbation of the phagosomal membrane [29] , [30] , [31] , indicating that cytosolic recognition might be critical for IFNα/β responses to diverse bacterial pathogens including Mtb . The primary pathways leading to IFNα/β induction upon bacterial infection remain obscure . Since transfection of DNA into the cytosol of macrophages can induce a Tbk-1 and Irf3-dependent IFNα/β response similar to that seen upon L . monocytogenes infection , bacterial DNA has been implicated as the eliciting stimulus [32] . Two different cytosolic DNA sensors have been identified , DAI [33] and AIM2 [34] , but their importance during bacterial infections remains to be demonstrated . While Nod2 recognition of MDP is not absolutely required for IFNα/β production [35] , it has been shown to synergize with the cytosolic DNA response and enhance IFN production during both L . monocytogenes and Mtb infection [36] . However , Nod2 stimulation alone is thought to be insufficient to induce type I IFN production [36] . In sum , while a large fraction of the macrophage response to Mtb infection depends on type I IFN [24] and therefore is likely to rely on a cytosolic signaling pathway , the bacterial products recognized and the pathways involved remain unknown . We previously found that Mtb infection of macrophages triggers Rip2 polyubiquitination in a TLR and MyD88 independent manner [21] . We now show that this stimulation is due to the ESX1-dependent entry of bacterial products into the cytosol where they are recognized by Nod2 , implicating MDP as the relevant PAMP . Unexpectedly , this results in IFNα/β production that is dependent on a novel pathway consisting of Nod2 , Rip2 , Tbk1 , and Irf5 . This work is the first to implicate NLRs in IRF activation and to suggest a role for Irf5 in anti-bacterial innate immune responses . Furthermore , we found that the unusual N-glycolyl MDP produced by Mtb was 10–100 fold more potent than the commonly studied N-acetylated MDP produced by most bacteria , and that only N-glycolyl MDP could stimulate Rip2-dependent IFNα/β transcription in the absence of other stimulants . Thus , the mammalian Nod2 pathway appears to be remarkably sensitive to mycobacterial MDP and responds to infection by triggering the production of type I interferon , which is responsible for a significant component of the transcriptional response to Mtb infection .
The ability of Mtb to rapidly modify macrophage signaling and vesicular sorting pathways [2] suggests that bacterial products gain access to the cytosol soon after phagocytosis . These products are , in turn , likely to be sensed by the host and trigger the innate immune response . Previously , we demonstrated that Mtb rapidly induces the TLR2/4 independent polyubiquitination of the Rip2 protein [21] , an event that could represent the initiation of cytosolic recognition . To characterize these events in more detail , we infected the mouse macrophage cell line RAW 264 . 7 or primary bone marrow derived macrophages ( BMDM ) with live or heat killed Mtb . In both cell types , we observed that infection with live , but not heat-killed , Mtb stimulated the rapid polyubiquitination of Rip2 . The Mtb-induced ubiquitin modification reached maximal levels within 1 hour post-infection and declined by 4 hours ( Figure 1A ) . Furthermore , pretreatment of cells with cytochalasin D to inhibit phagocytosis reduced Rip2 polyubiquitination in a dose-dependent manner ( Figure 1B ) , indicating that the bacteria must be both live and intracellular to initiate this response . Since Nod1 and Nod2 have been implicated in the cytosolic recognition of mycobacterial components [23] , we sought to determine if Rip2 polyubiquitination depended on these proteins . In contrast to cells from wild type mice , inducible Rip2 polyubiquitination was not observed in macrophages derived from mice lacking Nod1 and Nod2 , and was greatly reduced in cells lacking only Nod2 ( Figure 1C ) . These data confirmed that intracellular Mtb is recognized by a Nod2-dependent pathway and that this protein is required for the stable ubiquitination of Rip2 . Live intracellular Mycobacteria were required to stimulate the Nod-Rip2 pathway , indicating that the bacterium actively participated in this process , likely via the translocation of bacterial products into the cytosol . A specialized protein secretion system , encoded by the ESX1 locus , has been implicated in the perturbation of the host membranes [29] , [30] , [37] and for stimulation of the type I IFN response [28] and inflammasome activation [38] , suggesting that this system might contribute to cytosolic recognition via Nod proteins . In order to test this hypothesis , we infected the mouse macrophage cell line RAW 264 . 7 with wild type Mtb or mutants lacking ESX1 function . No induction in Rip2 polyubiquitination was observed upon infection with a strain of Mtb harboring the “RD1” mutation , which deletes a portion of the ESX1 locus [39] . Similarly , a mutant lacking espA , a distally-encoded gene that is required for ESX1-mediated secretion [40] , also failed to elicit this response ( Figure 2 ) . The phenotype of the latter mutant could be complemented by the expression of espA from a plasmid vector , demonstrating that the inability to stimulate Rip2 polyubiquitination was linked to the espA mutation . Furthermore , M . bovis BCG , an attenuated vaccine strain carrying the RD1 deletion and therefore lacking ESX1 function [39] , was unable to stimulate Rip2 polyubiquitination . While all of these ESX1 mutants are less virulent than wild type bacteria , the lack of Nod2-Rip2 stimulation did not appear to be a nonspecific effect of attenuation . Two unrelated bacterial mutants that are unable to grow intracellularly , a biotin auxotroph ( ΔbioF [41] ) and a small molecule efflux mutant ( TN::rv1410c [42] ) , robustly stimulated this response ( Figure 2 ) . Taken together , these observations indicate that a functional ESX1 secretion system is specifically required for Nod2 stimulation . Since the Mtb-induced Rip2 polyubiquitination required ESX1 , we hypothesized that this system might be responsible for the release of Nod2 ligands into the cytosol , perhaps via the disruption of vacuolar membrane integrity . However , it also remained possible that ESX1-deficient strains simply lacked a critical PAMP or other Nod2 stimulating activity . To distinguish between these possibilities , we investigated whether ESX1 function could be complemented by two exogenous membrane-disruptive activities . Streptolysin O ( SLO ) is a cholesterol-dependent toxin that introduces pores directly into mammalian membranes . Pores can also be introduced by adding ATP to macrophages , resulting in stimulation of the P2X7 receptor and the subsequent opening of the hemichannel , pannexin-1 ( PANX1 ) [43] . We observed that membrane perturbation by either of these two methods resulted in robust Rip2 polyubiquitination upon infection with espA-deficient bacteria , which were otherwise unable to induce this response ( Figure 3 ) . The involvement of PANX1 in the ATP-facilitated Rip2 ubiquitination was verified by the addition of a competitive inhibitory peptide of the PANX1 pore . This peptide , but not a scrambled control peptide , inhibited Rip2 polyubiquitination to levels observed in cells infected with the ΔespA mutant ( Figure 3 ) . While the K+ flux subsequent to membrane damage has been found to stimulate NLRs in some circumstances [5] , we found that the addition of ATP or SLO alone resulted in a minimal response . These data indicate that SLO , PANX1 and ESX1 are all likely to promote Nod2 pathway activation via a similar mechanism , by facilitating the release of a stimulatory mycobacterial component into the cytosol . Since this pathway appears to be specific for peptidoglycan fragments , mycobacterial MDP-containing fragments were the most likely candidates . The inability of ESX1 mutants to stimulate either the Nod2-Rip2 pathway or the type I IFN response [28] led us to hypothesize that the Nod2 pathway may mediate type I IFN expression in this system . To investigate a potential link between Nod2 and IFNα/β , we infected Nod2- or Rip2-deficient macrophages with Mtb , and measured the induction of IFNα and IFNβ mRNAs using real time PCR ( qRT-PCR ) . In the absence of Rip2 , IFNβ induction was reproducibly reduced approximately 3-fold , whereas IFNα induction was almost completely abrogated ( Figure 4A and B ) . Nod2 deficiency had a similar effect on both IFNα and IFNβ transcription , consistent with its requirement for Rip2 polyubiquitination . Nod1 appears to play no role in this pathway , as nod1−/− macrophages produced wild type levels of IFNβ ( Figure S1 ) . The decreases in mRNA abundance observed in rip2−/− and nod2−/− cells were reflected in a similar decrease in protein production , as measured by ELISA ( Figure 4C and D ) . In order to assess the importance of Nod2 and Rip2 to the downstream IFNα/β-dependent macrophage response , we quantified the induction of RANTES mRNA , which depends on type I IFN secretion and signaling via the IFNαβ receptor ( IFNAR1 ) in this infection model [24] . We found that in the absence of Rip2 or Nod2 , Mtb infection failed to induce RANTES expression ( Figure 4E ) . These data suggest that the effect of a Rip2 deficiency on downstream type I IFN responses may be even more pronounced than the IFNβ mRNA levels indicate . In contrast , TNFα mRNA levels were unaffected by Nod2- or Rip2-deficiency ( Figure 4F ) indicating that other pattern recognition pathways remained responsive to Mtb in these cells . Consistent with previous work [28] , we found that infection with ESX1 mutant bacteria induced significantly less IFNβ and RANTES expression than wild type bacteria ( Figure 5 ) . To test whether ESX1-mediated type I IFN expression was mediated solely via Rip2 , we infected Rip2-deficient macrophages with ESX1 mutant bacteria and quantified IFNβ and RANTES mRNA levels . We found that in the absence of Rip2 , the loss of ESX1 function resulted in a further decrease in IFNβ mRNA levels ( Figure 5A ) , suggesting the presence of an additional host pathway ( s ) that contribute to IFNβ induction . However , Rip2 deletion had no significant effect in the absence of ESX1 ( Figure 5B ) , supporting our biochemical evidence that NOD2 stimulation depends entirely the ESX1-dependent delivery of stimulants into the cytosol . While our data indicated that a significant fraction of the IFNα/β response could be attributed to the Nod2-Rip2 pathway , it has been suggested that MDP stimulation alone is unable to induce type I IFNs and can only augment responses triggered by other pathways [36] . Indeed , we also found that the N-acetylated MDP that is commonly used to stimulate Nod2 was a very poor inducer of IFNβ and RANTES expression ( Figure 6A and B ) . However , our preliminary studies investigating Rip2 polyubiquitination indicated that Mtb was a particularly potent stimulator of this response [21] , and therefore we reasoned that this could be due to the N-glycolylated form of MDP produced by Mtb . To determine if this form of MDP was sufficient to induce type I IFN responses , we compared the ability of N-acetyl- and N-glycolyl-MDP to stimulate IFNβ expression . In contrast to N-acetyl MDP , treatment with the N-glycolylated form stimulated a robust IFNβ response , which was entirely dependent on Rip2 and Nod2 ( Figure 6 ) . In addition , at least 30-fold less N-glycolyl-MDP was necessary to stimulate the IFNβ transcription . Thus , the Nod2/Rip2 pathway alone is sufficient to induce the production of the IFN response when stimulated with this potent form of MDP . Listeria monocytogenes infection induces a potent host type I IFN response mediated by the Tbk1 kinase and Irf3 [27] , [32] , [35] , [44] . To test whether Mtb infection triggered similar pathways , we infected Irf3-deficient and Tbk1/Tnfr1-deficient macrophages with Mtb and measured IFN induction . The Tnfr1 deficiency was necessary to suppress the embryonic lethality of Tbk1 deletion [45] . Similar to the L . monocytogenes model , we found that IFNβ induction by Mtb infection was completely dependent upon Tbk1 , and the loss of Tnfr1 had little effect ( Figure 7A ) . However , in contrast to the complete dependence on Irf3 observed for L . monocytogenes [27] , [32] , [35] , we found IFNβ expression was reduced , but not ablated when Irf3-deficient macrophages were infected with M . tuberculosis ( Figure 7A ) . This partial dependence on Irf3 was not changed by varying the multiplicity of infection ( Figure S2 ) . These data prompted us to test whether other IRFs mediate Nod2-dependent type I IFN responses . Induction of IFNβ expression is dependent on the formation of the enhancesome which includes the NF-κB , ATF-2 , c-jun , Irf3 and Irf7 transcription factors [46] . Irf5 is a related family member that has also been shown to contribute to induction of type I IFN responses triggered by TLRs , and overexpression of MyD88 has been shown to synergize with Irf5 to induce IFNβ expression [47] . Based on these studies , we tested whether RIP2 collaborates with IRF5 or IRF3 to stimulate IFNβ luciferase reporter activity . HEK293 cells were transfected with an IFNβ-luciferase reporter construct , along with increasing amounts of expression plasmids encoding RIP2 , MyD88 , IRF3 or IRF5 . RIP2 and IRF5 coexpression stimulated IFNβ promoter activity in a dose dependent manner and to a similar extent as MyD88 and IRF5 ( Figure 7B ) . In contrast , RIP2 and IRF3 expression failed to induce this robust response ( Figure 7C ) . RIP2 and IRF5 expression also stimulated IFNα4 promoter activity as well as a reporter construct containing multimerized ISRE elements ( data not shown ) . To further investigate the contribution of Irf5 to the anti-bacterial type I IFN response , we infected macrophages from Irf5-deficient mice and control littermates with either Mtb or L . monocytogenes , and measured IFNβ expression . Consistent with the luciferase reporter studies , we found that Mtb-induced IFNβ ( Figure 7D ) and IFNα ( Figure S3 ) expression was impaired in the absence of Irf5 . In contrast , the response to Listeria was unaffected by the loss of Irf5 ( Figure 7D ) . While the related Rip1 adaptor protein regulates Irf7 activity in innate anti-viral signaling [48] , we found that IFNβ induction after Mtb infection was unaffected by Irf7 deficiency ( data not shown ) . To rule out the possibility that Irf3 expression levels may also be affected in irf5−/− macrophages , we verified that the Irf3 protein level was unchanged in Irf5-deficient cells ( Figure S4 ) . These results indicated that Mtb infection stimulates type I IFN expression via a pathway that depends on Nod2 , Rip2 , Tbk1 , and Irf5 . This contrasts with the pathway triggered by L . monocytogenes , which depends entirely on Irf3 and not Irf5 . We reasoned that this dependence on different Irf proteins might be explained by the preferential stimulation of a Nod2-Rip2-Irf5 pathway by mycobacterial peptidoglycan . Consistent with this model , we found that the IFNβ induction triggered by N-glycolyl MDP was entirely dependent on Irf5 and independent of Irf3 ( Figure 8 ) , functionally linking Irf5 with the Nod2 pathway .
Mammals first detect microbial infections via an array of PRRs that include both cell surface TLRs and cytosolic NLRs . However , not all microbial interactions represent a pathological state , and the immune system must be able to discriminate to some degree between colonization by commensal organisms and dangerous infection . One level of discrimination is provided by the desensitization or anatomical sequestration of TLRs at sites of chronic stimulation , such as the gut , which presumably allows for tolerance to normal flora [49] , [50] . Bacterial pathogens can still be recognized at these sites via NLRs , since these systems rely on the specific ability of pathogens to translocate PAMPs into the host cytosol . The concept that NLRs are specific for pathogenic organisms that disrupt host membranes is supported in a number of bacterial systems in which the loss of specific virulence functions abrogates NLR signaling . For example , in resting macrophages , cytosolic recognition of L . monocytogenes requires the pore-forming toxin , listeriolysin O [26] , [27] . Similarly , Helicobacter pylori [51] and Legionella pneumophila [32] mutants lacking a functional type IV secretion system ( T4SS ) , and Shigella flexneri [52] or Salmonella enterica serovar typhimurium [52] mutants lacking a functional type III secretion system ( T3SS ) fail to stimulate NLR pathways . In each case , the virulence system in question is responsible for host membrane damage and the likely translocation of bacterial products into the cytosol where they can be recognized by NLRs and/or other cytosolic surveillance systems . Similarly , we found that the ESX1 specialized protein secretion system of Mtb is required for Nod2 recognition . While it has been suggested that type I IFN induction via ESX1 might represent a specific immunomodulatory virulence strategy [28] , analogies to these other pathogens suggests that perhaps NLR recognition is simply a byproduct of a membrane damaging function that allows bacterial products to enter the cytosol . This model is supported by our observations that other membrane perturbing agents , such as SLO and PANX1 can substitute for ESX1 function and allow cytosolic recognition . Thus , in a number of cases it appears that NLRs can be considered as sentinels for pathogens that rely on membrane damage as a pathogenic strategy . Based on their common role in protein secretion and in facilitating cytosolic recognition , it is tempting to speculate that ESX1 and Gram-negative T3SS and T4SS function analogously to deliver effector proteins into the host cytosol . Despite these similarities , the role played by ESX1 during infection remains unclear , since no translocated effectors have been identified to date . In both Mtb and M . marinum , a related pathogen of ectotherms , ESX1 has been implicated in host membrane disruption and one of the major substrates of this system , EsxA , has been proposed to possess a membrane-lytic activity [30] , [37] . This single activity could be sufficient to account for the delivery of MDP and other PAMPs to the cytosol . It remains to be determined whether perturbing host membranes is the only role played by ESX1 during infection , or if this system also serves additional functions analogous to the specialized secretion systems of other pathogens . A major consequence of the cytosolic recognition of Mtb is the induction of type I IFN . While the importance of this response in viral defense is clear and virtually universal , its role in antibacterial immunity appears to vary . Mice deficient in the type I IFN receptor , Ifnar1 , are significantly more susceptible to several Gram-positive and -negative bacterial infections [53] , [54] , [55] , [56] , indicating that IFNα/β are important for immunity to many bacteria . However , Ifnar1 mutation has the opposite effect on the outcome of L . monocytogenes infection [57] , suggesting that IFNα/β can also exacerbate disease . The role played by IFNα/β in Mtb infection remains somewhat uncertain . The induction of several immunologically important genes , including NOS2 , depend on IFNα/β , suggesting a protective role . Initial studies of mouse and human infections appeared to support this view [58] , [59] . However , like the L . monocytogenes system , mutation of the IFNα/β receptor has in most cases been associated with decreased bacterial burden in mouse models of tuberculosis [28] , [59] , [60] , [61] , [62] . IFNα/β may fail to protect against disease because Mtb inhibits the response to these cytokines in infected macrophages [63] . The ultimate influence of IFNα/β on Mtb infection appears to depend on a number of experimental factors , which might include host species , bacterial strain , route of infection and dose . Despite these differences however , some important themes emerge from these studies . Most importantly , the effect of IFNα/β is most apparent after the onset of adaptive immunity and not before , suggesting that the major role-played by type I IFNs during tuberculosis may be to instruct the priming or maintenance of the adaptive immune response and perhaps to control the differentiation of regulatory T cells [59] . A variety of bacterial pathogens trigger the type I IFN response , and a paradigm has begun to emerge regarding the induction of this response by bacteria . One current model suggests that bacterial DNA translocated into the host cytosol is the major eliciting agent . This model is based largely on the observations that infection with L . monocytogenes or L . pneumophilla , or transfection of DNA into the cytosol induces a similar IFNβ response that is Rip2 independent , and Tbk1- and Irf3-dependent [32] . Other PAMPs , such as MDP , can provide a synergistic IFN-inducing stimulus , but have not appeared to be sufficient for induction of IFNβ in the absence of other triggers [36] . In contrast , our data support a model whereby Nod2 stimulation by Mtb infection induces the polyubiquitination of Rip2 , which acts via the Tbk1 kinase to stimulate the activity of Irf5 and induce transcription of IFNα/β . This differs from the pathway triggered by other bacteria such as L . monocytogenes , which depends entirely on Irf3 in resting macrophages [32] and does not involve Irf5 ( Figure 7 ) . Although Irf5 has previously been shown to be activated by the MyD88-dependent TLR7 and TLR9 pathways , this work reveals a novel role for this protein in Nod2 signaling , and a new link between Nod proteins and the type I IFN response . Furthermore , we found that unlike the N-acetylated MDP found in many bacteria , stimulation with the N-glycolylated MDP derivative found in mycobacteria was sufficient to stimulate the IFN response in the absence of other stimuli . A significant component of IFNβ induction remains intact upon Mtb infection of Rip2-deficient macrophages ( Figures 4 and 5 ) , indicating that additional pathways are also involved . Since virtually all IFNβ expression is ESX1-dependent , it appears that the residual induction observed in rip2−/− macrophages also depends on cytosolic recognition pathways . These pathways could certainly include a DNA sensor that acts via Irf3 , as proposed for other infections , since Irf3 deficiency had a moderate effect on IFNβ expression in our experiments ( Figure 7A and S2 ) . Thus , our data do not imply that Mtb is stimulating IFNα/β in a fundamentally different manner from other bacteria . Instead , it is likely that bacterial pathogens stimulate the IFN response via multiple , partially redundant pathways , and that the relative importance of each is determined by the unique biology of the infection . In the case of Mtb , we speculate that the N-glycolylation of its peptidoglycan , and perhaps a paucity of other stimulants such as DNA , favor recognition via Nod2 . It is also possible that the balance of these pathways might be affected by the activation state of the macrophage . When resting macrophages are infected with L . monocytogenes , the IFN response requires LLO and is completely Irf3 dependent . In contrast , IFNγ-stimulated cells are able to deliver this bacterium to the lysosome , where the cell wall is degraded to produce abundant peptidoglycan fragments . In this situation , a significant component of the IFNβ induction depends on Nod2 and not Irf3 [64] . While Irf5 was not investigated in this study , it is possible that this represents another situation in which robust Nod2 signaling promotes a Nod2- and Irf5- dependent type I IFN response . While we found that loss of Nod2-Rip2 signaling only partially reduces the induction of IFNβ , Rip2 deletion completely abrogated IFNα and RANTES expression . These results can be explained by the structure of the IFN regulatory circuit . Initially , only IFNβ is expressed , and subsequently IFNα and other interferon regulated genes ( IRGs ) , such as RANTES , are induced via an Ifnar1 and Irf7-dependent autocrine/paracrine signaling pathway [65] . Thus , it appears that the decrease in IFNβ expression that we observe is sufficient to severely impair downstream IRG induction , at least in this cell culture model . Multiple steps of this pathway are likely to depend on stable ubiquitin modifications . Not only did we observe that Rip2 is polyubiquitinated upon infection , but we also found that a Rip2 point mutant that cannot be stably ubiquitin modified is unable to mediate IFNα/β induction in response to Mtb infection ( Figure S5 ) . Collectively , these data suggest that polyubiquitinated Rip2 is required for Mtb-induced type I IFN expression via Irf5 . Interestingly , MyD88-dependent activation of Irf5 involves formation of a tertiary complex that includes the E3 ubiquitin ligase , Traf6 [66] , [67] . This E3 ubiquitin ligase associates with Rip2 upon MDP stimulation , raising the possibility that a Rip2-Traf6-Irf5 complex might exist and that the activity of Irf5 might also be regulated by ubiquitin . The specificity of the innate immune system has been shaped by the very powerful natural selection imposed by microbial pathogens . Our work suggests that upon infection with Mtb , a particularly potent form of MDP is translocated into the host cell cytosol where it triggers a novel signaling pathway leading to the robust induction of the type I IFN response . It is unlikely to be coincidental that the active component of our most potent adjuvant , complete Freund's adjuvant ( CFA ) , consists of mycobacterial cell fragments . The specific pathway described in this work might play a major role in this adjuvant's effectiveness , since IFNα/β production is required for CFA to promote antigen-specific immune responses ( 55 ) . Thus , while PAMPs are often regarded as invariant microbial components , it is clear that functionally important pathogen-specific differences exist in the composition of these molecules , and that the immune system can differentiate these subtly distinct structures . Given the potent adjuvant activity of mycobacterial components , it is somewhat surprising that the attenuated vaccine strain M . bovis BCG , which produces the same PAMPs present in CFA , provides poor protection against pulmonary TB in adults [68] , [69] . The lack of ESX1 function in this strain appears to be at least partially responsible , since the reconstitution of ESX1 improves the efficacy of this vaccine [70] , [71] . While this effect has previously been attributed to either the secretion of additional antigens or altered antigen presentation , it is also possible that ESX1 activity improves immunity by delivering crucial PAMPs into the cytosol where they are optimally recognized . Understanding both the details of PAMP trafficking , as well as the precise specificity of PAMP recognition , promises to aid in both the design of improved adjuvants and more effective tuberculosis vaccines .
C57BL/6 mice ages 8–12 weeks were obtained from the Jackson Laboratory . rip2−/− mice were a kind gift from Dr . Vishva M . Dixit ( Genentech , Inc . South San Francisco , CA ) . nod2−/− mice were provided by Dr . Peter J . Murray ( Department of Infectious Diseases , St . Jude Children's Research Hospital , Memphis , TN ) . nod1−/− and nod1−/−nod2−/− mice were provided by Dr . Gabriel Nunez ( University of Michigan Medical School , Ann Arbor , MI ) . irf3−/− , irf5−/− , tbk1+/+tnfr1−/− and tbk1−/−tnfr1−/− mice and their littermate controls were provided by Dr . Kate A . Fitzgerald ( University of Massachusetts Medical School , Worcester , MA ) . Mice were housed under specific pathogen-free conditions , and in accordance with the University of Massachusetts Medical School , IACUC guidelines . The WT strain of M . tuberculosis used in these studies was the H37Rv strain . All the mutants were derived from the wild type strain . ΔESX-1 was obtained from D . Sherman ( SBRI , Seattle . WA ) [39] . ΔBioF , ΔRv3616 and ΔRv3616-complemented strains have been described previously [40] , [41] . TN::Rv1410 contains a himar-1 transposon inserted at nucleotide #688 of the 1557 bp predicted open reading frame [72] . All strains were cultured in 7H9 medium containing 0 . 05% Tween 80 and OADC enrichment ( Becton Dickinson ) . Pre-titered stocks of Listeria monocytogenes strain 10403 stored at −80°C ( kindly provided by Victor Boyartchuk ) were recovered for 1 hr at 37°C in 9 ml of Tryptic Soy Broth ( BD Biosciences ) . Bacteria were then washed and resuspended in PBS prior to infection . Anti-Rip2 ( Rabbit ) and anti-ubiquitin ( Mouse ) antibodies were obtained from Santa Cruz Biotechnology . Anti-Irf3 antibody was obtained from Zymed . Anti-Irf5 antibody was obtained from Abcam . Anti-β-actin antibody was obtained from Sigma . MDP was obtained from InvivoGen . Mouse TNF-α was obtained from Sigma . LPS derived from Escherichia coli strain 0111 . B4 was purchased from Sigma , dissolved , treated with deoxycholate , and re-extracted with phenol/chloroform as described in [73] . The pannexin-1 mimetic blocking peptides panx1 ( WRQAAFVDSY ) and the scrambled peptide control were synthesized by GeneScript Corporation ( Piscataway , NJ ) and have been described previously [74] . Streptolysin O ( SLO ) a pore forming protein derived from Streptococcus and Adenosine 5′- triphosphate ( ATP ) were purchased from Sigma . N-glycolyl muramyl dipeptide ( N-glycolyl MDP ) was custom synthesized ( Carbohydrate Synthesis , Oxford , UK ) and shown to be more than 95% pure by NMR spectrometry . This preparation was found to be free of endotoxin contamination using the Limulus amebocyte lysate assay ( Pyrotell , Cape Cod Inc . , MA ) . Bone marrow from 8- to 10-week-old mice was harvested from femurs and differentiated into macrophages for 7 days in Dulbecco's modified Eagle medium ( DMEM ) supplemented with 10% L929-cell conditioned medium , 10% fetal bovine serum , 2 mM L-glutamine and 1 mM sodium pyruvate . After 7 days in culture , bone marrow derived macrophages ( BMDMs ) were washed with phosphate-buffered saline ( PBS ) and seeded into tissue culture plates for infection . RAW 264 . 7 macrophage cell line was cultured in Dulbecco's modified Eagle medium ( DMEM ) supplemented with 10% fetal bovine serum . All Mtb strains were cultivated in 7H9 broth , grown to exponential phase and washed thoroughly in DMEM media prior to infection . Bacterial clumps were removed by passing the washed suspension through a 5 µm syringe filter . For the peptide blocking studies , the cells were pre incubated with the desired peptides for 30 minutes followed by ATP or SLO for additional 15 minutes . Macrophages were infected at an MOI of 10 for 1 or 2 hours after which filtered cell lysates were immunoprecipitated with anti-Rip2 antibody ( Santa Cruz ) . Heat inactivation was achieved by incubating the bacteria at 80°C for 30 minutes . Immortalized macrophage cell lines from wild type , rip2−/− , nod2−/− and nod1−/−nod2−/− mice were established by infecting bone marrow cells with a v-raf/mil and v-myc retrovirus in the presence of GM-CSF and polybrene [75] , [76] . These rip2−/− , nod2−/− and nod1−/−nod2−/− macrophage cell lines express CD11b and Gr-1 and are capable of phagocytosing antibody coated beads . To determine the effect of cytochalasin D on the phagocytic function of the macrophages , we used the Vybrant phagocytosis assay kit to quantify the uptake of fluorescent E . coli . This assay was performed according to the protocol provided by the manufacturer . For the immunoprecipitation and ubiquitination assays , cell lysates were prepared in radioimmune precipitation assay ( RIPA ) buffer ( 150 mM NaCl , 50 mM Tris-HCl ( pH 7 . 5 ) , 1% NP40 , 0 . 25% deoxycholate , 0 . 1% SDS , 1 mM EDTA ) , supplemented with protease inhibitors ( Roche Applied Science ) and 5 mM N-Ethylmaleimide ( Sigma ) , immunoprecipitated with anti-Rip2 antibody ( Santa Cruz ) . Polyubiquitinated Rip2 proteins were detected by immunoblotting with an anti-ubiquitin antibody ( Santa Cruz ) . Total immunoprecipitated Rip2 protein was measured by immunoblotting with anti-Rip2 antibodies ( Santa Cruz ) . HEK293 cells ( 2×104 ) seeded in 96 well plates were transfected with 40 ng of the IFNβ luciferase reporter plasmid together with a total of 100 ng of various expression plasmids using GeneJuice ( Novagen ) . The total amounts of transfected DNA were kept constant in all experiments by adjustment with empty vector . Luciferase activity was measured 24 h later using Dual Luciferase reporter assay system ( Promega ) . The Renilla luciferase gene ( 40 ng ) was co-transfected and used as an internal control plasmid . IFNβ luciferase reporter activity was normalized to Renilla luciferase reporter activity . Each experiment was repeated three times . Data are expressed as mean±s . d . of three replicates . To measure IFNα/β mRNA levels upon MDP treatment or Mtb infection , total RNA was extracted from the macrophage cultures using Trizol reagent ( Invitrogen ) according to the manufacturer's directions . cDNA was prepared from 2 µg of total RNA and quantitative real-time PCR performed using SYBR green as a label with the following primers: mIFNα-F , 5′-AAGATGCCCTGCTGGCTG; mIFNα-R , 5′-TTCTGCTCTGACCACCTCCC; mIFNβ-F , 5′-CGTCTCCTGGATGAACTCCAC; mIFNβ-R , TGAGGACATCTCCCACGTCA; β-actin-F , 5′-CGAGGCCCAGAGCAAGAGAG; β-actin-R , 5′-CGGTTGGCCTTAGGGTTCAG; mTNFα-F , CAGTTCTATGGCCCAGACCCT; mTNFα-R , CGGACTCCGCAAAGTCTAAG; mRANTES-F , GCCCACGTCAAGGAGTATTTCTA; mRANTES-R , ACACACTTGGCGGTTCCTTC . Results shown are representative of more than three separate infection experiments , with each PCR performed in triplicate . All values reported were in the linear range of the experiment and were normalized to β-actin values . Standard curves were generated by linear dilution of a cDNA sample generated from poly I∶C-stimulated macrophages . IFNα protein in cell culture supernatants was performed using a custom ELISA as described previously [77] . IFNα concentrations were calculated using a recombinant IFNα ( HyCult , Biotechnology , Uden , Netherlands ) standard curve performed in quadruplicate using linear regression , and expressed in units per ml . IFNβ protein in cell culture supernatants was measured similarly using a custom ELISA as described in [78] . | Bacterial and viral infection stimulates production of several cytokines and chemokines that are thought to protect the host against infection . The bacterial strain known to cause tuberculosis elicits production of type I interferons , yet it was unclear how the bacteria isolated within the cell was capable of stimulating this host response . This study reveals that the bacteria use a specialized system to cause damage to these cellular compartments and release bacterial products that activate intracellular innate immune pathways . In this work , we demonstrate that Nod2 , Rip2 , Tbk-1 , Irf3 and Irf5 proteins cooperate to produce type I interferons . Understanding how these pathways are mediated is likely to aid in the design of more effective tuberculosis vaccines . | [
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| 2009 | NOD2, RIP2 and IRF5 Play a Critical Role in the Type I Interferon Response to Mycobacterium tuberculosis |
Cells in organ primordia undergo active proliferation at an early stage to generate sufficient number , before exiting proliferation and entering differentiation . However , how the actively proliferating cells are developmentally reprogrammed to acquire differentiation potential during organ maturation is unclear . Here , we induced a microRNA-resistant form of TCP4 at various developmental stages of Arabidopsis leaf primordium that lacked the activity of TCP4 and its homologues and followed its effect on growth kinematics . By combining this with spatio-temporal gene expression analysis , we show that TCP4 commits leaf cells within the transition zone to exit proliferation and enter differentiation . A 24-hour pulse of TCP4 activity was sufficient to impart irreversible differentiation competence to the actively dividing cells . A combination of biochemical and genetic analyses revealed that TCP4 imparts differentiation competence by promoting auxin response as well as by directly activating HAT2 , a HD-ZIP II transcription factor-encoding gene that also acts downstream to auxin response . Our study offers a molecular link between the two major organ maturation factors , CIN-like TCPs and HD-ZIP II transcription factors and explains how TCP activity restricts the cell number and final size in a leaf .
Cells in an early organ primordium undergo active proliferation to generate sufficient number , while increasingly more cells exit division cycle and enter differentiation at later stages of organ growth , thus ensuring a mature organ with defined size [1 , 2] . While genes that drive cell cycle progression have been identified and studied in detail , developmental regulatory factors that promote the exit from cell division and entry to differentiation during organ morphogenesis are less known . Arabidopsis leaf epidermis acts as an ideal system to study this process due to its easy accessibility and since proliferation and differentiation take place in different regions of the same primordium during its development [3–5] . Moreover , the absence of cell migration and cell death as contributory factors to leaf epidermal morphogenesis makes the leaf surface , and the constituent cells , easily traceable during blade expansion [6 , 7] . Early leaf growth is sustained by cell division and mitotic cell expansion , collectively referred to as proliferation , which takes place throughout the primordium . As the primordium increases in size , the domain of this proliferation zone increases initially and is then maintained at constant size [4] . As cell number increases within the proliferation zone , the distal cells lose division potential and form a transition zone where they acquire differentiation competence [8] . Once the transition to differentiation is acquired , the pavement cells , which are the primary constituent cells of the lamina , do not revert back to division cycle under normal circumstances and start expanding in size to form the distal-most expansion zone . As more cells exit the transition zone , the relative size of the expansion zone increases and that of the proliferation zone decreases , giving the transition zone an appearance of a moving front in the basipetal direction [4 , 9] . All these three growth zones have diffused boundaries that overlap with the adjacent zones . Cell number in a mature leaf depends on the size of the proliferation zone , the rate of proliferation within it and the rate at which cells exit proliferation and enter differentiation within the transition zone; perturbation in any of these parameters leads to altered cell number and final leaf size [10 , 11] . Many transcription factors have been identified over the years that promote cell proliferation and leaf size , the important ones being AINTEGUMENTA ( ANT ) , GROWTH-REGULATING FACTORS ( GRF ) and GRF-INTERACTING FACTORS ( GIF ) [10 , 12 , 13] . Their inactivation results in smaller leaves with fewer cells while their over-expression yields larger leaves due to longer/ faster proliferation . The proteins that suppress cell proliferation in a leaf primordium have also been identified and include DA1/DAR , BIG BROTHER and the class II TCP ( TEOSINTE BRANCHED 1 , CYCLOIDEA , PROLIFERATING CELL FACTOR1/2 ) transcription factors [14–18] . The TCP genes are predicted to encode non-canonical , basic helix-loop-helix transcription factors [19 , 20] that regulate multiple aspects of plant development [21] . Five miR319-regutated class II TCP proteins in Arabidopsis , namely TCP2 , 3 , 4 , 10 and 24 , and their orthologues in snapdragon and tomato , regulate leaf morphogenesis by limiting the number of pavement cells [8 , 14 , 15] . In the Arabidopsis jaw-D plants where the level of these five TCP transcripts are reduced due to the overexpression of endogenous miR319a , leaf size is increased as a result of excess number of pavement cells , whereas leaves with elevated level or activity of these TCP proteins are smaller in size with reduced cell number due to precocious differentiation [9 , 17 , 22 , 23] . Based on these functional studies , together with the localization of their transcripts in the transition zone [14 , 15] , it has been hypothesized that these class II TCP proteins promote differentiation potential in the proliferating leaf cells within their expression domain [24] . However , so far this has not been tested experimentally , primarily due to the high degree of functional redundancy among these proteins . TCP-mediated cell differentiation and organ maturation has been linked to the biosynthesis and response pathways of the major phytohormone , auxin [21] . Auxin controls cell proliferation and maturation in a context-dependent manner . Plants expressing a dominant , repressive form of TCP15 display up-regulation of multiple auxin biosynthesis genes YUCCA1 , 4 and 6 , suggesting that TCP15 is a negative regulator of auxin biosynthesis [25] . Similarly , TCP3 inhibits auxin response by inducing IAA3 ( INDOLE-3-ACETIC ACID INDUCIBLE3 ) / SHY2 ( SHORT HYPOCOTYL2 ) , a negative regulator of auxin signaling , and PIN1 ( PIN-FORMED1 ) , 5 , 6 , AUX1 ( AUXIN RESISTANT1 ) , the polar auxin transporters [21 , 26] . By contrast , TCP4 induces auxin response by activating YUC5 , an auxin biosynthetic gene [27] . Even though the regulation of auxin response by TCP proteins has been studied in some detail , the role of auxin in cell maturation and transition zone formation has been studied only in roots [1] but not in leaves . The role of the plant-specific homeodomain-leucine zipper ( HD-ZIP ) transcription factors has been implicated in leaf morphogenesis . The HD-ZIP members are classified into four sub-groups ( I to IV ) based on sequence similarity within the DNA-binding homeodomain [28 , 29] . Function of the class III HD-ZIP proteins such as REVOLUTA ( REV ) , PHABOLUSA ( PHB ) , PHAVOLUTA ( PHV ) , ARABIDOPSIS THALIANA HOMEOBOX 8 ( ATHB8 ) and ATHB15 is the most well-studied among the family members and their role has been linked to the establishment of leaf/ embryo polarity and meristem maintenance [30–32] . Members of the class II subgroup including HOMEODOMAIN ARABIDOPSIS THALIANA1 ( HAT1 ) and HAT2 have been implicated in growth adaptation to the environmental signals and in organ maturation in response to hormone signaling [33–35] . Recent reports show that the class II HD-ZIP proteins ATHB4 and HAT3 physically interact with REV and form a functional repression complex which suppresses the miR165/166 expression , thereby establishing the adaxial/abaxial polarity in leaves [36] . Interestingly , ATHB4 and HAT3 are downstream targets of REV , pointing to a complex interconnection among the HD-ZIP genes during leaf morphogenesis . In addition , some members of the class II HD-ZIP genes , specifically HAT1/2 , are activated by auxin response and promote cell expansion and organ maturation [33 , 34 , 37 , 38] . Plants over-expressing either HAT1 or HAT2 produce smaller leaves with reduced cell number and elongated hypocotyl cells [34 , 37 , 38] , which is also seen in plants with gain-of-function of class II TCP genes [22 , 23 , 27] . However , it is not clear whether and how the HD-ZIP II proteins are linked to other cell differentiation factors in promoting organ maturation . Here , we induced a dominant form of TCP4 protein at various developmental stages of Arabidopsis leaf primordium that lacked class II TCP function and followed its effect on leaf growth kinematics . By combining this with spatio-temporal expression of endogenous TCP4 , we show that the class II TCP proteins irreversibly reprogram the mitotic cells to exit division and acquire differentiation competence within the transition zone . Induction of cell maturation is mediated by auxin response as well as by direct activation of HAT2 transcription by TCP4 .
The class II TCP genes are heterochronic regulators of leaf development and their function has been implicated in the repression of cell division [22] and the promotion of differentiation [17] . In order to examine the specific role of the five redundant , miR319-targeted , class II TCP genes–TCP2 , 3 , 4 , 10 & 24—in cell division/ differentiation of Arabidopsis leaves , we compared the mature first leaf pair and their epidermal cells of wild type ( Col-0 ) with those in the loss-of-function mutants tcp2;4;10 [27] and jaw-D , where all the five TCP transcripts are down-regulated due to the over-expression of miR319a [15] . The tcp2;4;10 and jaw-D leaves measured >1 . 5 times larger than the Col-0 leaves ( Fig 1A ) , as was reported previously [9 , 17] . The number of the abaxial epidermal cells in the mutant leaves increased ~2 . 5-fold ( Fig 1B ) , suggesting that the increased mutant leaf area is due to an excess of cell proliferation . To determine if class II TCP activity is sufficient to repress proliferation , we induced a miR319-resistant , dexamethasone ( DEX ) -inducible form of TCP4 protein ( mTCP4-GR ) expressed under ProTCP4 and Pro35S [27] . DEX-grown , homozygous ProTCP4:mTCP4:GR or Pro35S:mTCP4:GR plants in the Col-0 background ( hereafter referred to as Col-0;GR or 35S;GR , respectively ) showed smaller leaves with reduced cell number ( Fig 1C and 1D and S1A–S1D Fig ) . A more pronounced relative reduction in the final leaf size ( by ~70%; Fig 1C and 1E ) and cell number ( by >80%; Fig 1D and 1F ) were observed in the DEX-grown ProTCP4:mTCP4:GR plants in the jaw-D background ( hereafter referred to as jaw-D;GR ) compared to the mock treatment , demonstrating that TCP4 is a strong cell proliferation inhibitor . Similar negative effect of TCP4 on leaf size and cell number was also observed in three independent transgenic lines where three different gain-of-function forms of TCP4 were expressed constitutively under a leaf primordia-specific promoter ( ProBLS:rTCP4:GFP ) [17] , a ubiquitous promoter ( Pro35S:TCP4:3F6H ) [39] or the endogenous promoter ( ProTCP4:TCP4:VP16 ) [23] ( S1F–S1I Fig ) . Concomitant with the increased number , the area of mature pavement cells reduced to almost half in jaw-D and tcp2;4;10 leaves ( Fig 1G and S2A Fig ) . Induction of TCP4 activity , however , had little effect on the final cell area in the Col-0;GR and 35S;GR leaves ( Fig 1H and 1I and S1E Fig ) and merely restored the cell size defect found in the jaw-D;GR leaves to the wild-type level ( Fig 1I and S2B Fig ) . Pavement cell area also remained unaltered in the three other dominant lines of TCP4 , ProBLS:rTCP4:GFP , Pro35S:TCP4:3F6H and ProTCP4:TCP4:VP16 ( S1J and S1K Fig ) . These results suggest that the class II TCP proteins are required for pavement cell maturation during leaf development but are not sufficient to promote pavement cell expansion per se , even though they do so in the hypocotyl [27] . Contrary to the miR319-resistant inducible form of TCP4 , constitutive expression of a miR319-susceptible form of TCP4 protein in the homozygous Pro35S:TCP4:GR plants ( hereafter referred to as 35S;sGR ) showed no effects on leaf size , cell number and pavement cell area ( Fig 1C , 1D and 1I ) , demonstrating that the effects of mTCP4:GR induction described above resulted specifically from miR319 activity on the class II TCP transcripts and not from artifacts due to GR fusion or to the site of insertion [27] . The requirement of TCP genes for developmental cell maturation was also apparent in the reduced differentiation status of the pavement cells in the TCP loss-of-function leaves . The abaxial surface of mature Col-0 leaf is composed mostly of large epidermal cells with jigsaw shape ( Fig 1H , S2A and S2C Fig ) , a characteristic marker for differentiation [17 , 40] . By contrast , mature tcp2;4;10 and jaw-D leaves were composed mostly of smaller cells ( Fig 1J , S2A and S2C Fig ) , a differentiation defect that was totally rescued by TCP4 induction ( Fig 1K ) . However , induction of TCP4 in the Col-0 background did not alter the cell size distribution ( S2D Fig ) . The results described above suggest that the class II TCP proteins restrict cell number and promote cell maturation during leaf development . To examine their role in cell proliferation/ maturation at early growth phase , we compared the kinematics of growth of the first pair of jaw-D;GR leaves under TCP4 inductive and non-inductive conditions . Col-0 leaf blade expanded slowly at an early growth stage , from 6–10 days after stratification ( DAS ) , during which the average area of the abaxial pavement cells remained small ( ~500 μm2 or less ) and their number increased exponentially ( Fig 2A–2C , S3A and S3B Fig ) . At 10 DAS , the blade acquired ~10% of its final area and ~75% of its final pavement cell number . Later , the blade expanded at an exponential rate up to 16–18 DAS , before reaching a plateau at ~22 DAS ( Fig 2A and S3A Fig ) . The pavement cells also expanded faster during this period , and continued to do so beyond 22 DAS ( Fig 2C and S3B Fig ) . The number of pavement cells reached its maximum at the mid-log phase of blade expansion and no further proliferation was observed beyond 12 DAS ( Fig 2B ) , at which stage the blade acquired approximately half of its final area . These kinematics results are in general agreement with the earlier reports on Arabidopsis leaf published by other laboratories [5 , 41] . The overall trend of the growth kinematics of jaw-D leaf blade was by and large similar to Col-0 , with a slow ( 6–10 DAS ) , exponential ( 10–18 DAS ) and saturating ( 18–22 DAS ) phase . However , the jaw-D blades started expanding at a rate faster than that of Col-0 from the earliest stage measured ( 6 DAS; inset in Fig 2A ) , and consequently its area remained higher throughout the growth . The increased jaw-D blade area was due to excess number , and despite reduced size , of the pavement cells . The jaw-D cells accumulated at a faster rate than Col-0 , but the proliferation ceased at almost similar growth stage ( ~12 DAS ) in both the genotypes , resulting in ~2 . 5 times more cells in the mature jaw-D leaf compared to Col-0 ( Figs 1B and 2B ) . As in Col-0 , the size of the jaw-D pavement cells remained small in the slow phase ( 6–10 DAS ) and then increased exponentially until 16 DAS , before saturating rather abruptly ( Fig 2C ) . However , the average area of jaw-D pavement cells remained 1 . 6 ± 0 . 16 fold smaller than that in Col-0 throughout the growth phase of the leaf ( S1 Table ) . All the cellular defects of the jaw-D leaf were rescued by continuous induction of TCP4 throughout the growth phases ( Fig 2A–2C and S3A Fig ) . The DEX-grown jaw-D;GR blade expanded at the same rate as Col-0 until 12 DAS and then at a slower rate , before ceasing to expand at 18 DAS to a final area that was smaller than the Col-0 blade ( Fig 2A and S3A Fig ) . Upon TCP4 induction , the pavement cells accumulated in the jaw-D;GR blade for almost the same duration ( till ~12 DAS ) as in Col-0 , but at a slower rate , resulting in fewer cells at maturity compared to Col-0 ( Figs 2B and 1D ) . While both blade area and cell number were smaller than the Col-0 values in the jaw-D;GR leaves grown under DEX , the average size of the pavement cells was rescued up to the Col-0 value at all stages of leaf growth ( Figs 2C and 1I ) . The growth kinematics described above shows that class II TCP activity is essential for reducing the total number , and for increasing the average size , of the pavements cells during leaf development . A growing leaf blade , however , is composed of cells of heterogeneous size and proliferation status; the smaller , dividing cells are located at the proximal end and the larger , differentiating cells towards more distal side [4] . To examine if TCP4 activity is specific to a stage of cell maturation , we determined the proportions of smaller ( arbitrarily defined as <1500 μm2 in area ) and larger ( >3000 μm2 ) pavement cells in expanding Col-0 lamina ( S2C Fig ) and compared with jaw-D . During early growth ( 6–8 DAS ) , nearly all Col-0 pavement cells measured <1500 μm2 , and their proportion sharply declined to ~30% at 22 DAS as the blade expanded ( Fig 2D and S3C Fig ) . The larger cells started appearing at 10 DAS and steadily increased to ~40% at 22 DAS ( Fig 2E ) . The fraction of the smaller cells in the jaw-D;GR blade without TCP4 induction , however , declined at a slower rate and stabilized at >60% at 16 DAS ( Fig 2D and S3D Fig ) . Consequently , the jaw-D;GR larger cells increased in proportion rather slowly and reached the maximum value of little over 10% at 16 DAS ( Fig 2E ) . TCP4 induction in the jaw-D;GR blade restored the proportions of smaller and larger cells close to the Col-0 levels at all growth stages ( Fig 2D and 2E and S3E Fig ) . Taken together , these kinematic results suggest that a major function of the class II TCP proteins is to convert the smaller pavement cells to larger cells , possibly by promoting their proliferation to differentiation competence . The differentiation-inducing activity of the TCP proteins is also apparent in the altered stomatal index ( the proportion of epidermal cells assuming stomatal lineage ) , a measure of leaf maturation [41] , in the mutant leaves . 20 . 5 ± 1 . 2% Col-0 epidermal cells assumed stomatal identity at 6 DAS , a value that steadily increased during leaf maturation and reached to a maximum value of 37 . 4 ± 2 . 6% at 22 DAS ( Fig 2F ) . However , only 13 . 8 ± 2 . 7% jaw-D;GR cells were converted to stomatal lineage at 6 DAS , and this value increased to a maximum of 26 . 7 ± 2 . 8% at 22 DAS . Induction of TCP4 activity in the jaw-D;GR leaves nearly restored the defect in stomatal index to wild-type level . All jaw-D;GR rosette leaves were smaller with smoother margin when grown with continuous presence of DEX ( Fig 1L ) , indicating that TCP4 induction is active throughout the vegetative growth . However , when jaw-D;GR seedlings were grown under inductive condition for first 10 days and then transferred to non-inductive medium , the first few leaves resembled those grown under continuous DEX , but the leaves that emerged later showed progressively more jaw-D-like phenotype . Conversely , when the seedlings were grown under non-inductive condition for first 10 days and then transferred to inductive condition , the first few leaves resembled jaw-D leaves but the later leaves were smaller with smoother margin ( Fig 1L ) . This suggests that DEX application can be used to induce TCP4 activity at any point of time during plant development . To determine the developmental timing of TCP4 function during leaf maturation , we induced TCP4 activity in the jaw-D;GR leaves after growing them in the non-inductive medium for various number of days after stratification ( 4 to 15 DAS; Fig 3 and S4A Fig ) and compared their blade area , cell number and cell size at maturity ( 29 DAS ) . Without TCP4 induction , the jaw-D;GR lamina grew to 32 . 6 ± 2 . 5 mm2 that contained 17450 ± 1336 pavement cells of average area 1870 ± 170 μm2 ( Fig 3 ) . On the other hand , the lamina under continuous TCP4 induction grew to 12 . 0 ± 1 . 7 mm2 with much fewer cells ( 4130 ± 579 ) of the average area 2920 ± 140 μm2 . The blade area progressively increased from 12 . 0 ± 1 . 7 mm2 to the non-inductive value of 32 . 6 ± 2 . 5 mm2 when TCP4 was induced at 4 , 5 or 6 DAS , even though their final cell numbers were much fewer than the non-inductive value . When TCP4 was induced at 6 DAS , its blade area at maturity was close to the jaw-D value ( 30 . 4 ± 2 . 3 mm2 ) , even though its cell number was almost half of that in jaw-D leaf ( Fig 3A–3C ) . This is because the defect in the pavement cell size was rescued in these leaves to the wild-type value ( 3130 ± 170 μm2 ) ( Fig 3D ) . When TCP4 was induced at 7 , 8 , 9 or 10 DAS , the final jaw-D;GR blade area progressively increased to a maximum of 46 mm2 , nearly 1 . 5-fold bigger than the jaw-D blade ( Fig 3A and 3B and S5A and S5B Fig ) . When TCP4 was induced at 10 DAS , the mature first leaf had pavement cell number ( 15420 ± 1220 ) that was close to the jaw-D value , but the cells ( 2940 ± 240 μm2 ) were larger than the jaw-D cells ( 1870 ± 170 μm2 ) , ultimately yielding a mature leaf that was bigger than even the jaw-D leaf . It may be noted here that the jaw-D leaves were at the peak of its exponential phase of proliferation at 10 DAS , when almost all pavement cells were smaller in size ( <1500 mm2; Fig 2B and 2D ) . When TCP4 was induced at 11 , 12 , 13 , 14 and 15 DAS , the mature blade area progressively reduced to the jaw-D value ( Fig 3A and 3B ) . At 15 DAS of TCP4 induction , the blade area , cell number and cell area were comparable to the respective jaw-D values , demonstrating that TCP4 induction at 15 DAS or later failed to rescue the size defect of the jaw-D pavement cells . At 15 DAS , the jaw-D cells ceased to proliferate and the proportion of the smaller cells are at its minimum level ( Fig 2B and 2D ) . When TCP4 was induced at various developmental stages of Col-0;GR and 35S;GR leaves , similar effect on final blade area was observed ( S5C–S5E Fig ) . Leaves of both genotypes grew to ~26 mm2 under non-inductive condition and to 9 . 2 ± 2 . 0 mm2 ( Col-0;GR ) and 12 . 7 ± 1 . 0 mm2 ( 35S;GR ) upon continuous TCP4 induction ( Fig 1C and S5C–S5E Fig ) . When TCP4 was induced at 4 , 6 , 8 , 9 and 10 DAS in these plants , the final blade area progressively increased to the un-induced value but , unlike in jaw-D;GR , never surpassed it . This is consistent with the observation that TCP4 failed to promote pavement cell expansion beyond the wild type level ( Figs 1I and 3D ) . On the contrary to the Col-0;GR and 35S;GR leaves , when DEX-induction was performed in the expanding 35S;sGR blades , the final blade area remained more or less unaffected at all stages of TCP4 induction ( S5C and S5F Fig ) , implying that the collective effects of TCP4 induction on jaw-D;GR , Col-0;GR and 35S;GR leaf growth dynamics described above resulted from miR319 activity on the TCP4 transcript . These results , together with those shown in Fig 2 , strongly suggest that TCP4 promotes maturation in proliferating pavement cells , perhaps by committing them to differentiation , within the developmental window of the proliferative phase . Developmental cell maturation is a unidirectional process where the fully expanded pavement cells do not revert back to proliferative fate under normal circumstances . The transcripts of the miR319-regulated class II TCP genes that induce leaf differentiation are abundant in young lamina and their level steadily declines as the lamina matures [42] . Therefore , it is possible that these TCP genes activate the proliferation→differentiation transition in the pavement cells in an irreversible manner . To test this , we grew jaw-D;GR seedlings under TCP4 inductive condition for 0 . 5 , 1 , 2 , 3 , 4 , 5 or 6 DAS , shifted them to non-inductive condition until 29 DAS ( S4B Fig ) and monitored leaf area and cell number of their mature first leaves ( Fig 4A–4D ) . Arabidopsis seeds germinated ( emergence of radicle ) at 1 DAS and the first leaf pair initiated at 2 DAS ( Fig 4A ) . When the plants were grown for 0 . 5 or 1 DAS in the presence of DEX , there was no effect of TCP4 induction on the final leaf area ( Fig 4B and 4C ) , possibly because the leaf primordia were barely formed at 1 DAS . When TCP4 was induced for 2 DAS , the leaf margin defect was totally rescued ( Fig 4B ) and leaf area reduced to almost half ( Fig 4C ) . TCP4 induction for 4 DAS yielded mature leaf as small as what was observed with continuous DEX induction ( Fig 4B and 4C ) , suggesting that miR319-mediated inhibition of class II TCP transcripts for the first 2 days post leaf initiation ( that is , 4 DAS ) , when the average length of leaf primordium was 274 ± 35 μm ( Figs 4A and 5B ) , is crucial for normal leaf growth . Consistent with the reduction of blade area , TCP4 induction for 4 DAS also reduced the pavement cell number to 4550 ± 910 , similar to the value obtained with continuous TCP4 induction ( Fig 4D ) . Lower cell proliferation activity was also reflected in the reduction of the cell cycle marker CyclinD3;2 [43] in young leaves . Up to 1 DAS of TCP4 induction did not reduce the GUS activity to a noticeable extent in 8-day old jaw-D;ProTCP4:mTCP4:GR X ProCyclinD3;2:GUS seedlings ( Fig 4E ) . However , the GUS activity much reduced when TCP4 was induced for 2 or 4 DAS , and totally disappeared at 6 DAS of induction . In order to determine the minimum duration of TCP4 activity that is sufficient to induce differentiation commitment in proliferating cells , we provided pulse of TCP4 function of various durations from 0 to 48 hours in 4-day old jaw-D;GR seedlings and compared their mature leaf area at 29 DAS . Leaves without TCP4 induction grew to a maximum area of 36 ± 3 . 8 mm2 ( Fig 4F ) . A 3-hour pulse of TCP4 induction reduced the area to a noticeable extent , and the area continued to reduce with longer pulses , finally stabilizing to a minimum value ( 23 . 2 ± 2 . 0 mm2 ) with a 24-hour pulse . This suggests that TCP4 activity for the duration of 24 hours is sufficient to impart commitment to differentiation in a dividing cell . Consequently , a 24-hour DEX pulse at 4 , 6 , 8 , 9 , 10 and 12 DAS ( S6 Fig ) altered the mature leaf area of mock-grown jaw-D;GR seedlings to nearly the same extent ( Fig 4G ) as in continuous DEX-induction starting from the respective DAS ( Fig 3B ) . When we treated 3-day old jaw-D;GR seedlings with hydroxyurea , a widely used cell cycle inhibitor that induces reversible G1/S arrest [44 , 45] , only for a duration of 36 hours , the mature first leaf grew as big as the untreated control ( Fig 4H ) , implying that reversible cell arrest for a short while does not adversely affect the overall cell proliferation in a growing leaf . However , similar treatment with DEX reduced the final leaf size to a great extent ( Fig 4H ) , suggesting that TCP4 initiates irreversible cell cycle arrest in a proliferating leaf . When TCP4 activity was induced in the 35S:GR seedlings for 0 . 5 , 1 , 2 , 3 , 4 , 5 or 6 DAS , the final leaf area progressively reduced with increased duration of induction ( Fig 4I , 4J and S7 Fig ) , as observed for jaw-D;GR leaves ( Fig 4B and 4C ) . However , when similar induction experiment was performed on 35S;sGR seedlings , the final leaf area remained unaltered , suggesting that miR319 activity at early stage of leaf growth contributes to blade expansion . Increased cell maturation upon TCP4 induction was also reflected in the elevated level of nuclear ploidy , a typical cell differentiation marker [46] , in cells of DEX-induced leaves . Flow cytometry analysis of the nuclei isolated from 8-day old Col-0 leaves revealed a ploidy distribution of 57 . 5% 2C , 39 . 6% 4C and 2 . 9% 8C cells ( Fig 4K ) . In the jaw-D;GR leaves grown in mock condition , the proportion of 2C nuclei increased modestly to 69 . 5% and that of 4C ( 28 . 7% ) and 8C ( 1 . 7% ) nuclei decreased , suggesting a less differentiation status of this cell population . Conversely , when TCP4 function was induced in these leaves , the fraction of 2C nuclei reduced to 34 . 8% , with a concomitant increase in the proportion of 4C ( 55 . 7% ) and 8C ( 9 . 5% ) nuclei . Together these results demonstrate that TCP4 makes the proliferative cells exit division cycle and enter into endoreplication . To correlate the function of the miR319-TCP module with their expression dynamics in expanding leaf blade , we localized miR319c and TCP4 promoter activity and TCP4 protein at various growth stages of transgenic leaves expressing GUS reporter . In a 19-day old ProTCP4:GUS seedling , strong GUS activity was detected throughout the blade of the 9th rosette leaf ( 1 . 2 mm long ) , which was progressively restricted towards the proximal end in the more mature 8th , 7th and 6th leaves ( Fig 5A ) . Since the maturation states of younger to older leaves in an Arabidopsis rosette recapitulate the ontogeny of a leaf on a specific node [17] , it can be concluded that TCP4 promoter activity progressively gets restricted towards leaf base as the blade differentiates , before disappearing completely , as observed previously for TCP4 and its orthologues [8 , 14 , 47] . GUS activity in the ProCyclinD3;2:GUS leaves [43] resembled that of ProTCP4:GUS leaves ( Fig 5A ) , suggesting that TCP4 promoter is active in the proliferative zone of the expanding leaf blade . GUS reporter was detected in a small domain at the base of the young 9th leaf ( 0 . 8 mm long ) of PromiR319c:GUS seedlings ( Fig 5A ) , and expression was reduced and restricted to more proximal region in the 8th leaf ( 1 . 9 mm long ) ; no detectable GUS activity was found in the 7th and the 6th leaves . These results are in agreement with the earlier reports [48] , and suggests that miR319c promoter is active during a small temporal window at the earliest stage of leaf growth . Consequently , the TCP4:GUS reporter activity in young ProTCP4:TCP4:GUS leaf at the 9th position was not detected in the corresponding basal region where miR319c promoter is active at this stage , suggesting that miR319 activity excludes TCP4 transcript [15] and its protein product ( Fig 5A ) from its expression domain . To compare the relative expression domains of miR319c , TCP4 transcript and TCP4 protein in young leaf primordia where miR319 promoter is active , we studied the GUS expression pattern in 4-day old transgenic seedlings where the first leaf pair just initiated ( Fig 4A ) . In ~300 μm long first leaf , TCP4 and CyclinD3;2 promoters were active throughout the leaf , whereas the miR319 promoter was detected in a small region at the very base ( Fig 5B and S8 Fig ) . Activity of the TCP4:GUS fusion protein was , however , detected only in the distal half of young leaf primordia ( Fig 5B and 5D and S8 Fig ) , and was excluded from the proximal region where miR319c promoter is active . Similar expression patterns have been reported for endogenous TCP4 transcript in wild-type [15] , and for a miR319-susceptible form of ProTCP4:GUS transcript in transgenic leaf primordia [47] . In young fifth leaf primordium ( ~300 μm long ) , barely detectable TCP4:GUS activity was found towards the distal extreme ( Fig 5C ) , implying that miR319 activity is present in a broader basal domain in this leaf . In agreement with this , strong GUS activity was detected across the basal half of a PromiR319c:GUS leaf primordium of similar length ( Fig 5C ) . At a later growth stage , PromiR319c activity was restricted more towards the base , where it reduced the TCP4:GUS signal . Since TCP4:GUS activity retracted from the distal end in these leaves owing to the lack of ProTCP4 activity in differentiated cells , the TCP4:GUS protein was detected in a band-like domain in the middle of the leaf with diffused proximal and distal boundaries ( Fig 5A , 5C and 5D ) , which has been proposed as the transition zone where proliferating cells acquire differentiation competence [3 , 4 , 8 , 24] . Ectopic expression of miR319 throughout the leaf blade completely abolished the TCP4:GUS activity in the jaw-D;GR X ProTCP4:TCP4:GUS seedlings ( Fig 5D and S8 Fig ) , highlighting the importance of class II TCP activity within the transition zone in leaf maturation . Cells proximal to the transition zone are expected to remain proliferative whereas the distal cells would differentiate . In agreement with this , the promoter activity of YUCCA5 ( YUC5 ) , a known differentiation marker [27 , 49] , was initiated at the distal end in a ~300 μm long first leaf of 4-day old ProYUC5:GUS seedlings , and the expression extended towards more proximal direction as the leaf matured ( Fig 5E ) . The GUS signal reduced when the class II TCP genes were down-regulated in the jaw-D;GR X ProYUC5:GUS leaves ( Fig 5F ) , and increased when gain-of-function TCP4 activity was induced by DEX application , suggesting that the distal expression of YUC5 in young leaves is indeed mediated by TCP4 activity . The expression dynamics of TCP4 described above , together with the kinematic data ( Figs 2–4 ) , suggests that TCP4 activity imparts irreversible differentiation fate to the proliferating cells within the transition zone of an expanding leaf blade . Auxin is known to play a key role in tissue maturation by coordinating the cell division with differentiation [1 , 50] . TCP4 function has been linked to the auxin biosynthesis and response in hypocotyl cells [27] . We observed that TCP4 activation increased GUS reporter activity in both jaw-D;GR X ProYUC5:GUS and jaw-D;GR X ProDR5:GUS rosettes ( Figs 5F and 6A ) , demonstrating that TCP4 promotes YUC5 transcription and auxin response in developing leaves . In agreement with this observation , the levels of many SAUR and HD-ZIP II transcripts , which are known targets of auxin signaling [34 , 51 , 52] , were altered upon TCP4 induction ( S9 Fig ) when a previously reported jaw-D;GR microarray dataset was analyzed [27] . Fourteen SAUR transcripts were also elevated upon heightened auxin response or upon external application of auxin ( Fig 6B ) in the microarray datasets available in the GENEVESTIGATOR database [33 , 34 , 51–53] . The SAUR and HD-ZIP II genes are known to be involved in multiple developmental events including cell proliferation and differentiation [34 , 37 , 50 , 51] , suggesting a link between TCP4 function and auxin responsive genes in cell differentiation . RT-qPCR analysis demonstrated that the transcripts of SAUR19 , 20 , 21 , 24 , 62 and 63 increased in the jaw-D;GR seedlings grown under constitutive TCP4 activation ( Fig 6C ) . In addition , GUS reporter activity in the jaw-D;GR X ProSAUR63:GUS seedlings increased upon dexamethasone treatment ( Fig 6D ) , suggesting that TCP4 activates the SAUR genes in planta . SAUR activation was abolished in the jaw-D;GR seedlings when TCP4 was induced for 4 h in the presence of cycloheximide , a general protein synthesis inhibitor ( Fig 6E ) [54] , suggesting that the promotion of SAUR by TCP4 is an indirect effect , possibly mediated by elevated auxin response . To examine whether the elevated auxin response by TCP4 is capable of inducing leaf cell maturity , we studied the effect of TCP4 induction on pavement cell area/number in short hypocotyl2-2 ( shy2-2 ) , where cells are insensitive to auxin response due to the sequestration of AUXIN RESPONSE FACTORs ( ARF6 and 8 ) by a dominant , non-degradable form of AUX/INDOLE-3-ACETIC ACID3 ( IAA3 ) inhibitor protein [55] . Mature first leaves of jaw-D;GR X Col-0 heterozygous seedlings grew to 29 ± 2 . 6 mm2 , consisting of 18850 ± 1670 pavement cells with average area of 1550 ± 165 μm2 ( Fig 6F , 6G and 6H ) . TCP4 induction in these leaves reduced the blade area to 19 . 0 ± 2 . 2 mm2 and cell number to 5240 ± 650 , while increased the average cell area to 3490 ± 180 μm2 . The average area of mature jaw-D;GR X shy2-2 leaves ( Fig 6I ) under non-inductive condition was 18 . 0 ± 2 . 3 mm2 , with the total cell number 14080 ± 1660 and average cell area 1350 ± 200 μm2 ( Fig 6F , 6G and 6H ) , possibly indicating a TCP-independent role for auxin response in leaf blade expansion . TCP4 induction , however , failed to reduce the jaw-D;GR X shy2-2 leaf area to any noticeable extent ( Fig 6F ) , even though it increased the pavement cell area ~2-fold , as in jaw-D;GR X Col-0 control leaves ( Fig 6H ) . While TCP4 induction reduced the total pavement cell number by ~75% in the jaw-D;GR X Col-0 leaves , it reduced the jaw-D;GR X shy2-2 cell number by ~50% ( Fig 6G and 6J ) , suggesting that at least a part of the TCP4 effect on the repression of cell number is mediated by IAA3-dependent suppression of auxin signaling . The pavement cell population of the mature jaw-D;GR X Col-0 and jaw-D;GR X shy2-2 leaves showed similar size distribution under un-induced condition ( Fig 6K ) , with ~70% cells of smaller area ( <1500 μm2 ) and <10% cells of larger area ( >3000 μm2 ) . Upon TCP4 induction , the fraction of the smaller cells decreased to ~25% and that of larger cells increased to ~50% in jaw-D;GR X Col-0 , indicating a rescue of the cell size defect by TCP4 activity . The extent of rescue was , however , less ( ~40% smaller cells and ~30% larger cells ) in the jaw-D;GR X shy2-2 leaves , suggesting that TCP4 partly requires auxin response to induce cell maturity . HAT2 and its closest homologue HAT1 have been recognized as immediate auxin responsive genes and control cell proliferation in leaves and cell elongation in hypocotyls , functions that overlap with miR319-targeted TCP genes [22 , 27 , 33 , 34] . HAT1 and HAT2 transcripts were consistently up-regulated in multiple independent microarray datasets performed upon external auxin application ( Fig 7A ) [34] . HAT2 transcript was also up-regulated at both 2 and 4 h of TCP4 induction in an earlier microarray experiment ( S9 Fig ) [27] . RT-qPCR analysis showed that the transcript of HAT2 alone , and not of HAT1 or the clade members , was up-regulated upon constitutive TCP4 induction in the Pro35S:mTCP4:GR seedlings ( Fig 7B ) . HAT2 was induced within 1 h of TCP4 induction ( Fig 7C ) , and the level was maintained for at least 4 h . In addition , HAT2 level was elevated in TCP4:VP16 seedlings , a gain-of-function line of TCP4 with enhanced transcriptional activity [23] , while that was significantly reduced in the loss-of-function line jaw-D ( Fig 7D ) . Together , these results suggest that HAT2 is an early downstream target of TCP4 . Since class II TCP proteins induce auxin biosynthesis [27 , 56] and HAT2 is an early auxin responsive gene , it would appear that HAT2 activation is an indirect effect of TCP4 induction . However , we found that TCP4 induction activates HAT2 , but not the SAUR genes , in the jaw-D;ProTCP4:mTCP4:GR seedlings even when both auxin biosynthesis and polar auxin transport were inhibited by administering a combination of L-kynurenine and N-1-naphthylphthalamic acid ( Fig 7E ) [57] . Moreover , TCP4 induction in the jaw-D;GR X shy2-2 seedlings , where auxin response was genetically depleted by expressing a degradation-resistant form of IAA3 [55] , also promoted HAT2 expression ( Fig 7F ) . These results suggest that TCP4 is capable of promoting HAT2 expression independent of auxin response , and possibly by direct transcriptional activation . Consistent with this hypothesis , TCP4 induction increased HAT2 transcript level in jaw-D;ProTCP4:mTCP4:GR and Pro35S:mTCP4:GR seedlings even when new protein synthesis was blocked by applying cycloheximide ( Fig 7G and 7H ) . This suggests that TCP4 directly activates HAT2 possibly by binding to its genomic locus . Analysis of HAT2 genomic sequence yielded three putative TCP4 binding elements ( BS1-3 ) ; two in the promoter region and the third in the coding region ( Fig 7I ) . Electrophoretic mobility shift assay showed that recombinant MBP ( maltose binding protein ) -tagged TCP4 protein [20] binds to oligonucleotides corresponding to these elements with sequence specificity ( Fig 7J ) . To test whether the TCP4 protein is recruited to the HAT2 chromatin in planta , we performed chromatin immuno-precipitation ( ChIP ) assay in the Pro35S:TCP4:3F6H transgenic line using anti-FLAG antibody [39] . TCP4 recruitment was observed in the HAT2 upstream regulatory regions that corresponded to the TCP4 binding motifs ( Fig 7K ) , but no recruitment was found in the HAT2 coding regions . Consistent with this result , formaldehyde-assisted isolation of regulatory elements ( FAIRE ) experiment [58] suggested that the HAT2 chromatin assumed more open conformation in the regions corresponding to the putative TCP4-binding sites and not in a distant region when TCP4 was activated in the Pro35S:mTCP4:GR seedlings by dexamethasone treatment ( S10A Fig ) . Even though we identified one putative TCP4 binding site in the HAT1 upstream regulatory region and another in the coding region ( S10B Fig ) , we suspected that HAT1 is not a target of TCP4 since its transcript level remained unaltered upon TCP4 induction ( Fig 7B ) . ChIP assay also suggested that TCP4 protein is not recruited at the HAT1 locus ( S10C Fig ) . Based on these results , and on the observation of a general overlap in the expression levels of TCP4 and HAT2 at various developmental stages ( S11 Fig ) , we conclude that HAT2 , and not HAT1 , is a direct target of TCP4 . Both TCP4 and HAT2 restrict cell number in leaves and promote cell expansion in hypocotyl [27 , 34] . Since TCP4 directly activates HAT2 ( Fig 7 ) , it is likely that TCP4 requires HAT2 activity in planta to promote developmental cell maturation . To test this , we crossed the jaw-D;GR line to the hat1;hat2 double mutant , since there is a high degree of functional redundancy between HAT1 and HAT2 and their single mutants cause little phenotypic alterations [59] . However , the dominant jaw-D phenotype was suppressed in the jaw-D;GR X hat1;hat2 F1 individuals possibly due to transgene silencing , a phenomenon that commonly occurs with the viral 35S promoter [60 , 61] . To overcome this , we established the Col-0;GR;hat1;hat2 line and analyzed its leaf parameters upon TCP4 induction . The Col-0;GR leaves , which resemble Col-0 under non-inductive condition [27] , were reduced to ~38% in area upon TCP4 induction , primarily due to a corresponding reduction in the number of pavement cells ( Fig 8A–8C ) that were mildly but significantly larger than the un-induced cells ( Fig 8D ) . Though the mature hat1;hat2 leaves resembled wild type leaves in shape and size ( Fig 8A ) , they were made up of an increased number of pavement cells that were smaller in area ( Fig 8B and 8D ) , an indication of differentiation defect frequently observed in the mutants of CIN-TCP genes ( Fig 1 ) [17] . When TCP4 was activated in the absence of HAT1 and HAT2 gene function in the hat1;2;GR line , leaf blade area reduced only to ~70% with a corresponding reduction in the pavement cell number . Further , TCP4 activation failed to sustain the increase in the pavement cell size in the absence of HAT function ( Fig 8D ) . Taken together , these results suggest that TCP4 requires HAT2 , and perhaps HAT1 as well , to limit cell proliferation and leaf size . It has been shown that overexpression of HAT2 inhibits the leaf size and promotes the cell elongation in hypocotyl similar to that of TCP4 activity in leaf and hypocotyl , respectively [27 , 34 , 59] . Pro35S:HAT2 line shows only a mild effect in reducing leaf size and cell number in the wild-type background ( 60 ) . However , this line reduced the size of the jaw-D leaf by ~30% and its pavement cell number by ~50% ( Fig 8E–8G ) . Further , Pro35S:HAT2 rescued the small-size phenotype of the jaw-D pavement cells nearly to the wild type level ( Fig 8H and 8I ) . Since HAT1 and HAT2 are functionally redundant partners and their overexpression yields comparable changes in leaf and hypocotyl [33–35 , 37] , we tested whether HAT1 overexpression can also rescue jaw-D phenotype . Similar to TCP4 dominant effect ( Fig 8A ) , overexpression of HAT1 in the Pro35S:JAIBA:GFP line ( JAB:GFP ) [37] reduced leaf size to a third of the wild type value with ~60% decrease in pavement cell number ( S12A–S12C Fig ) , demonstrating that HAT1 is a strong cell proliferation inhibitor in developing leaves . Moreover , HAT1 overexpression rescued the effect of class II TCP down-regulation on the leaf size and pavement cell number of the jaw-D;GR leaves nearly to the Col-0 values ( S12A–S12D Fig ) . Intriguingly , overexpression of HAT1 resulted in a significant decrease in pavement cell area ( S12E and S12F Fig ) , a phenotype that contrasts TCP4 overexpression . This negative effect of HAT1 on pavement cell size remained unaltered even in the jaw-D;GR background , possibly indicating an effect of HAT1 independent of TCP4 . Taken together , these genetic interaction studies show that TCP4 induces pavement cell maturation in part via direct activation of HAT2 , and not HAT1 . However , HAT1 ( along with HAT2 ) is likely to be an indirect target of TCP4 as it is an auxin response gene ( Fig 7A ) [34] and TCP4 is known to promote auxin level through YUC5 activation ( Fig 6A ) [27] . Consequently , overexpression of each of these clade-members ( HAT1 and HAT2 ) rescued jaw-D phenotype ( Fig 8E–8I and S12 Fig ) .
Constitutive down-regulation of class II TCP activity resulted in the accumulation of more cells with smaller average area while increased TCP function reduced cell number without affecting the average size ( Figs 1–3 ) [22] , thus confounding the exact effect of TCP proteins on cell proliferation and expansion . Frequency distribution of the pavement cell population based on cell area identified increased proportion of smaller cells in the jaw-D and tcp2;4;10 leaves , a defect that was rescued by TCP4 induction ( Figs 1 and 2 ) . Cell size in an organ primordium remains small as long as cells proliferate and starts to expand when they differentiate [2] , suggesting that TCP4 converts the dividing cells to differentiation . This is further supported by our growth kinematics analysis that demonstrated that the effect of TCP4 on cell number is confined within the proliferation phase and TCP4 induction beyond this phase had no effects on final cell number and leaf area ( Fig 3 and S5 Fig ) [17] , thus discounting any role for TCP4 in regulating differentiation-associated leaf cell expansion . TCP4 activity for the first two days of leaf initiation , when the primordium measured <300 μm in length , had the same effect on cell number and leaf size as observed with continuous TCP4 activity throughout the growth phase ( Fig 4 ) . Further , a 24-hour pulse of TCP4 activity in the leaf primordium reduced the final leaf area as much as the continuous TCP4 activity did ( Fig 4 ) . These results suggest that TCP4 imparts irreversible differentiation competence to the dividing cells of leaf primordium . The mature miR319 is active at the base of young leaves ( Fig 5A–5C ) [48] , restricting its target TCP transcripts towards more distal region of the blade [15 , 47] . Since the promoter activity of these TCP genes are excluded from the distal differentiated zone ( Fig 5A ) [62] , their transcripts and protein products are restricted to a broad transition zone within a growing leaf blade that interfaces , and overlaps with , the more distal differentiation zone and the proximal proliferation zone , where they provide morphogenetic competence to the leaf blade ( Fig 8J ) [8 , 24] . To explain the precise role of class II TCP proteins in leaf cell proliferation/ maturation , here we propose a model ( Fig 8J ) that incorporates the expression domain of the miR319/TCP module and the function of the TCP proteins based on the growth kinematics results . The transcripts of class II TCP genes are undetectable at the base of the leaf primordia due to the activity of mature miR319 , even though their promoters are active in this zone . Lack of TCP activity helps the basal cells maintain their mitotic status [4] . Distal to this proliferation zone , TCP proteins are active in the transition zone where they commit the dividing cells to exit division and acquire differentiation competence . As the basal region expands due to cell proliferation , more dividing cells are incorporated within the transition zone where they encounter TCP proteins that commit them to exit cell division cycle . Since TCP activity imparts irreversible differentiation status to the leaf cells in the transition zone ( Figs 3 and 4 ) , the cells in the distal differentiation zone , though devoid of TCP activity , remain committed to differentiation and start expanding . Our model explains the leaf phenotype with reduced or elevated TCP activity . Due to the absence of TCP proteins from jaw-D leaves ( Fig 5D ) , increased proportion of cells within the transition zone remain mitotic and therefore smaller in size ( Fig 2D ) since fewer cells exit the division cycle ( Fig 2E ) . This yields a lower cell area when averaged over the entire lamina ( Fig 2C ) . Higher proportion of mitotic cells in the jaw-D leaves ultimately results in increased cell number at maturity ( Fig 2B ) . When TCP4 activity was induced in the transition zone of the jaw-D;ProTCP4:mTCP4:GR leaves , the proportion of cells exiting proliferation was restored to the wild-type level ( Fig 2D and 2E ) , resulting in the correction of the defects in cell size and number . However , since jaw-D;ProTCP4:mTCP4:GR leaves express a dominant form of TCP4 that cannot be degraded by miR319 [15] , the ectopic TCP4 activity at the base prematurely converts the proliferating cells to differentiated fate , thereby reducing the final cell number and leaf size at maturity ( Fig 2 ) . Young leaf primordia with or without TCP4 induction contained nearly similar number of pavement cells ( Fig 2B ) , but the TCP4-induced leaves had higher fraction of cells with increased ploidy compared to the un-induced leaves ( Fig 4K ) . Increased nuclear ploidy resulting from endoreplication cycle is used as a differentiation marker since it commits the cells at G2 phase to bypass mitosis and directly enter into the S-phase [46] . The cyclin-dependent kinase inhibitors KRP1 and KRP2 inhibit entry to mitosis and promote the onset of endoreplication in leaves [63 , 64] . Further , KRP1 is directly activated by TCP4 during leaf development [22] , suggesting that KRP-induced endoreplication forms an important component of TCP-mediated onset of cell differentiation within the transition zone . Suppression of the GRFs that redundantly promote cell proliferation in the leaf primordia is yet another likely mechanism of TCP-mediated restriction of cell number in leaves [12 , 65 , 66] . This possibility is further supported by a recent observation that the promoter of miR396 , a microRNA that degrades the transcripts of several GRF genes , is activated by TCP4 [22] . However , the effects of the miR319-TCP module on cell proliferation and differentiation are distinct from those of the miR396-GRF module in several aspects . First , TCP activation restricts the pavement cell number without a noticeable increase in size , whereas lack of GRF products results in a reduction in pavement cell number with a concomitant increase in cell size . Second , lack of TCP activity increases the cell number as well as decreases its average size , whereas overexpression of GRF proteins increase cell number without affecting the size . Third , while the class II TCP proteins are expressed only within the transition zone , the activity of miR396 promoter in the leaf primordia is initiated in the distal expansion zone and that of its target proteins is detected in the entire leaf base including the proliferation zone [12 , 22 , 66] . These differences point to a GRF-independent function of TCP proteins in promoting cell differentiation . Nevertheless , a yet unidentified link between the activities of TCP and GRF proteins within the transition zone cannot be ruled out . The GRF-independent TCP function is likely mediated by multiple parallel pathways that possibly converge to the commitment of proliferating cells to differentiation within the transition zone . One arm of this function is via auxin response originating from TCP-mediated YUCCA activation [27 , 56] . An auxin minimum has been shown to promote a cellular switch from division to differentiation in root transition zone [50] , whereas auxin response appears to be required for TCP-mediated differentiation commitment ( Figs 6 and 8J ) . Auxin response activates the transcription of HAT2 , a gene that restricts cell number and leaf size in Arabidopsis [33 , 34 , 37 , 38] . Consistent with this , hat1;hat2 leaves contain excess cells with reduced size ( Fig 8B and 8D ) . Interestingly , the class II TCP proteins also promote division to differentiation switch in leaf pavement cells independent of auxin by directly activating HAT2 transcription ( Fig 7 ) . These parallel pathways possibly ensure a robust HAT activation by TCP proteins . The downstream events of HAT activation triggering cell differentiation are currently unclear . The possibility of the HAT transcription factors directly promoting the KRP cell cycle inhibitors to initiate endoreduplication , as shown for TCP4 [22] , needs to be tested .
Col-0 was used as the wild type control in all experiments . The mutant lines tcp2;4;10 , jaw-D , shy2-2 and hat1;2 were reported previously [9 , 15 , 33 , 55] . The Col-0;Pro35S:mTCP4:GR #2 line was screened and established in the T2 generation . The cloning methodology and primers information was previously reported [27] . The homozygous transgenic lines Col-0;ProTCP4:mTCP4:GR , jaw-D;ProTCP4:mTCP4:GR , Col-0;Pro35S:mTCP4:GR , Col-0;Pro35S:TCP4:GR , ProYUC5:GUS , ProDR5:GUS , ProCyclinD3;2:GUS , PromiR319c:GUS , ProSAUR63:GUS , Pro35S:JAB:GFP , Pro35S:HAT2 , Pro35S:TCP4:3F6H , ProBLS:rTCP4:GFP and ProTCP4:TCP4:VP16 used in this study are all established lines reported previously [17 , 23 , 27 , 37 , 39 , 43 , 48 , 59 , 67 , 68] . The F1 genetic crosses of jaw-D;ProTCP4:mTCP4:GR X ProYUC5:GUS , Col-0 X ProTCP4:GUS , jaw-D;ProTCP4:mTCP4:GR X ProTCP4:GUS , Col-0 X ProTCP4:TCP4:GUS , jaw-D;ProTCP4:mTCP4:GR X ProTCP4:TCP4:GUS , jaw-D;ProTCP4:mTCP4:GR X ProDR5:GUS , jaw-D;ProTCP4:mTCP4:GR X ProSAUR63:GUS , Col-0 X Pro35S:HAT2 and jaw-D;ProTCP4:mTCP4:GR X Pro35S:HAT2 were used in this study . Col-0;ProTCP4:mTCP4:GR line was established in the hat1;2 background by genetic cross in F4 generation . The homozygous jaw-D;ProTCP4:mTCP4:GR;Pro35S:JAB:GFP line was established by genetic cross in F3 generation . All experiments were performed under long day conditions ( 16 hours light/8 hours dark at 22°C ) . 12 μM DEX was used for TCP4 induction in all the experiments unless specified otherwise . The 2 . 16 kb upstream region of TCP4 including 5’ untranslated region was amplified using the forward primer 5’-AATTGACCCTTTTCTATCATGC-3’ and the reverse primer 5’-TGGTAGAGCATATTCGTCGAGA-3’ , pfu DNA polymerase and Col-0 genomic DNA and cloned into pGEMT-Easy vector by TA cloning ( Promega; pGEMT-Easy-ProTCP4 ) [27] and then moved into pCAMBIA 1304 using NcoI and SacI digestion to generate ProTCP4:GUS construct . The TCP4 cDNA fragment of pBSKS-TCP4:GR [27] was digested with the SalI and BamHI and moved into pCAMBIA-1391Xa to generate TCP4:GUS cassette . The NotI released 2 . 16 kb ProTCP4 fragment of pGEMT-Easy-ProTCP4 , described above , was end filled with Klenow DNA polymerase and cloned upstream of the TCP4:GUS cassette by blunt end ligation to generate ProTCP4:TCP4:GUS construct . The transgenic lines were generated in Col-0 background by Agrobacterium tumefaciens-mediated floral dip method [69] . For the measurement of leaf size the crinkled leaves were flattened and photographed using a Canon PowerShot S110 camera . Leaf area measurement was made in the photographs using Image J software ( rsbweb . nih . gov/ij/ ) . The first leaf from 29-day old plants were cleared with 70% alcohol and processed in choral hydrate:water:glycerol ( 8:2:1 ) solution and lactic acid for one to three weeks . In another method , leaves were collected in 70% ethanol followed by shifting them to ethanol:glacial acetic acid ( 7:1 ) and kept overnight ( 12–14 h ) at room temperature . After clearing , the leaf tissues were treated with 1 M KOH for maximum 30 min in constant rotating followed by two times washing with double-distilled water . Image of abaxial epidermal cells were taken using differential interference contrast microscope ( Olympus , USA ) and analyzed with Image J software . For cell size analysis , 400–750 cells area was measured from different fields of 4–7 leaves and averaged to obtain the final cell size . The total number of cells per leaf was calculated by using the average cell size and leaf area . The kinematic analysis of Col-0 and mock and DEX-grown jaw-D;GR leaf development was performed as described earlier [5 , 9 , 41 , 70] . Initially seedlings were grown on Murashige Skoog ( MS ) medium up to 10 days and then the plants were transplanted to soil . For DEX induction , the MS medium was supplemented with 12 μM DEX or ethanol control . For continuous DEX induction , seedlings were transplanted to DEX ( 12 μM ) - or ethanol ( 0 . 04% ) -treated soil and sprayed with DEX or ethanol on alternate days until experiment was completed . One of the first leaf pair from at least 9–12 plants was used for each time point of kinematic study . The leaf area , cell number and cell size were analyzed as described above . The stomatal index was analyzed as described earlier [5 , 41] . Total RNA samples were isolated by Trizol ( Sigma , USA ) method after treatment with ethanol solvent ( Mock ) or 12 μM dexamethasone . To check the relative expression of respective genes , quantitative RT-PCR was performed using SYBER Green qPCR kit ( SensiFAST SYBR Lo-ROX Kit , Bioline , USA ) according to the manufacturer’s instructions . Results were analyzed using ABI Prism 7900HT SDS software ( Applied Biosystems , USA ) and ΔΔCT values were determined after normalization with internal control . Intensity ratios were calculated using the formula 2-ΔΔCT . Detailed experimental protocols for dexamethasone induction and primer sequences for RT-qPCR were described earlier [27 , 70] . GUS assay , EMSA and FAIRE experiments were performed according to the earlier protocols [9 , 27] . The list of primers used in this study is provided in S2 Table . 1st pair of leaves from 8-day old seedlings of indicated genotypes were collected after removing hypocotyl and cotyledons . Ploidy levels were measured using the protocol provided earlier [71 , 72] . In brief , leaves were placed in ice-cold Galbraith’s buffer ( 45 mM MgCl2 , 30 mM sodium citrate , 20 mM 4-morpholinepropane sulfonate , and 1% Triton X-100 , pH 7 . 0 ) and finely chopped using sharp razor blade followed by homogenizing in the buffer . The homogenate was filtered through Miracloth ( 22–25 μm ) and treated with 10 μl/ml RNaseA ( 1mg/ml stock ) for 20–30 min followed by nuclear DNA staining with 50 μl/ml of propidium iodide ( 1 mg/ml stock ) and kept in dark at 4°C for 30 min . Samples were run through BD FACS verse ( USA ) flow cytometer and analyzed using BD FACS unit software . It was considered that the left-most peak corresponds to 2C [71 , 72] ChIP protocol was followed as mentioned earlier [39] . In brief , approximately 1 . 5–2 gm of fresh 10-day old Pro35S:TCP4:3F6H seedlings were harvested and crushed into fine powder in liquid nitrogen . The powder was homogenized in the nuclei extraction buffer 1 ( 0 . 4 M sucrose , 10 mM Tris-HCl pH 8 . 0 , 10 mM MgCl2 , 5 mM β-mercaptoethanol , 0 . 1 mM PMSF , 1 mM Na3VO4 , 1 mM NaF , and Complete protease inhibitor cocktail tablets [Roche] ) and proceeded for cross-linking . 37% formaldehyde was used for crosslinking by gentle rotating in 4°C for 10 min and 2 M glycine was added to stop the reaction . After quenching the cross-linked samples were filtered through two layers of Miracloth and centrifuged for 10 min at 10 k rpm at 4°C . The pellets containing the nuclei were resuspended and washed twice with extraction buffer 2 ( 0 . 25 M sucrose , 10 mM Tris-HCl pH 8 . 0 , 10 mM MgCl2 , 1% ( w/v ) Triton X-100 , 5 mM β-mercaptoethanol , 0 . 1 mM PMSF , 1 mM Na3VO4 , 1 mM NaF , and Complete protease inhibitor cocktail tablets ) and transferred to ice-cold 1 . 5mL Eppendorf tubes followed by centrifugation at maximum rotor speed for 10 min at 4°C to isolate the nuclei . The isolated nuclei were lysed using Nuclei lysis buffer ( 50 mM Tris-HCl pH 8 . 0 , 10 mM EDTA , 1% SDS , 1 mM PMSF , and Complete protease inhibitor cocktail tablets ) and proceeded for sonication to shear the chromatin ( ~ 500 bp to 1 kb size fragments ) using Bioruptor ( Condition; 30 sec ON 45 sec OFF , 45 cycles ) . The sheared chromatin were diluted in ChIP dilution buffer ( 16 . 7 mM Tris-HCl pH 8 . 0 , 167 mM NaCl , 1 . 1% Triton X-100 , 1 . 2 mM EDTA , 0 . 1 mM PMSF , 1 mM Na3VO4 , 1 mM NaF , and Complete protease inhibitor cocktail tablets ) to make the volume up to 1 . 5 mL and centrifuged at maximum speed for 5 min at 4°C . From the collected chromatin solution , 30 μL ( 2% ) was kept as input for the assay and rest of the solution was divided into two parts from which one was immunoprecipited using anti-FLAG antibody ( Sigma; F1804; 1 mg/mL ) and the other using Anti-IgG antibody ( Sigma; A9044 ) . Magnetic Dynabeads Protein G ( Life technologies ) were used for Immunoprecipitation for which first the beads were pretreated with the anti-FLAG antibody ( Sigma; F1804; 1 mg/mL ) or anti-IgG antibody for 20 min by rotating at room temperature and diluted in ChIP dilution buffer after washing twice with 1X PBST . The antibody-bound beads were incubated with chromatin solution ( 26 μL Dynabeads and 3 μL antibody per 600 μL of chromatin solution ) for 20 min in ice followed by 2 h rotating at 4°C . After immunoprecipitation , the immunocomplex was washed using low salt buffer ( 20 mM Tris-HCl pH 8 . 0 , 150 mM NaCl , 0 . 1% SDS , 1% Triton X-100 , 2 mM EDTA ) and high salt buffer ( 20 mM Tris-HCl pH 8 . 0 , 500 mM NaCl , 0 . 1% SDS , 1% Triton X-100 , 2 mM EDTA ) and TE buffer ( 10 mM Tris-HCl pH 8 . 0 , 1 mM EDTA ) . To elute the immunocomplex from the beads , 50 μL of nuclei lysis buffer was added and incubated for 20 min at 65°C and the elution was repeated twice . Eluted immunocomplex along with the input samples were reverse cross-linked by adding 6 μL of 5 M NaCl and incubating at 65°C overnight . Proteinase K and RNase A was treated to digest all proteins and RNA , respectively , and finally DNA were cleaned up by phenol:chloroform:isoamyl alcohol ( 15:24:1 ) followed by precipitation using sodium acetate and 100% ethanol . The final extracted DNA was dissolved in 50 μL of nuclease-free water and proceeded for qPCR using 0 . 8 μL DNA as template per reaction . To quantify the relative enrichment on the respective gene element , qPCR was performed using 0 . 8 μL of immunoprecipited DNA and SYBER Green qPCR kit ( SensiFAST SYBR Lo-ROX Kit , Bioline , USA ) in a 10 μL reaction volume according to manufacturer’s protocol . Relative enrichment was calculated using the formula 0 . 02 X 2 ( Ct input- Ct IP ) X 100 and normalized to IgG control . The final fold enrichment was plotted in relative to Col-0 value . | Cells in a young organ primordium proliferate to generate sufficient number , before they exit division cycle and enter differentiation programme at later stages . While factors that drive cell cycle progression have been identified and studied in detail in diverse eukaryotic species , developmental factors that promote exit from division and entry into differentiation are less known , especially in the plant kingdom . Here , we show that the class II TCP proteins , notably TCP4 , irreversibly reprogram the mitotic cells to exit division and acquire differentiation competence by auxin response as well as direct activation of HAT2 transcription . Our work offers a molecular link between class II TCP and HD-ZIP II genes during the cell differentiation and leaf maturation . | [
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| 2019 | The CIN-TCP transcription factors promote commitment to differentiation in Arabidopsis leaf pavement cells via both auxin-dependent and independent pathways |
Next to the two-component and quorum sensing systems , cell-surface signaling ( CSS ) has been recently identified as an important regulatory system in Pseudomonas aeruginosa . CSS systems sense signals from outside the cell and transmit them into the cytoplasm . They generally consist of a TonB-dependent outer membrane receptor , a sigma factor regulator ( or anti-sigma factor ) in the cytoplasmic membrane , and an extracytoplasmic function ( ECF ) sigma factor . Upon perception of the extracellular signal by the receptor the ECF sigma factor is activated and promotes the transcription of a specific set of gene ( s ) . Although most P . aeruginosa CSS systems are involved in the regulation of iron uptake , we have identified a novel system involved in the regulation of virulence . This CSS system , which has been designated PUMA3 , has a number of unusual characteristics . The most obvious difference is the receptor component which is considerably smaller than that of other CSS outer membrane receptors and lacks a β-barrel domain . Homology modeling of PA0674 shows that this receptor is predicted to be a bilobal protein , with an N-terminal domain that resembles the N-terminal periplasmic signaling domain of CSS receptors , and a C-terminal domain that resembles the periplasmic C-terminal domains of the TolA/TonB proteins . Furthermore , the sigma factor regulator both inhibits the function of the ECF sigma factor and is required for its activity . By microarray analysis we show that PUMA3 regulates the expression of a number of genes encoding potential virulence factors , including a two-partner secretion ( TPS ) system . Using zebrafish ( Danio rerio ) embryos as a host we have demonstrated that the P . aeruginosa PUMA3-induced strain is more virulent than the wild-type . PUMA3 represents the first CSS system dedicated to the transcriptional activation of virulence functions in a human pathogen .
The human opportunistic pathogen Pseudomonas aeruginosa is known for a high proportion of regulatory genes in its genome [1] . This is not only due to the number of two-component regulatory systems , but this bacterium also contain a large number of different cell-surface signaling ( CSS ) systems [2] , [3] . CSS is a regulatory mechanism used by bacteria to sense signals from the extracellular medium and transmit them into the cytoplasm . CSS systems are generally composed of three different components , an alternative σ70 factor of the extracytoplasmic function ( ECF ) family , a sigma factor regulator located in the cytoplasmic membrane and an outer membrane receptor [2] , [4] , [5] . Sigma factors are essential subunits of prokaryotic RNA polymerase , they are involved in promoter recognition and transcription initiation . The primary sigma factor ( RpoD ) , which is responsible for the majority of mRNA synthesis in exponentially growing cells , belongs to the σ70 family . This family also includes many alternative sigma factors that are nonessential proteins required only under certain circumstances [6] , [7] . The largest and most diverged group within this family is the one including the ECF subfamily of sigma factors . ECF sigma factors are specially abundant in P . aeruginosa [8] . The outer membrane receptor of CSS systems is usually a member of the TonB-dependent receptor family . These receptors are mostly involved in the transport of iron-siderophore complexes across the outer membrane . To accomplish this task these receptors need to be energized by a protein complex in the cytoplasmic membrane . This protein complex is composed of TonB , ExbB and ExbD , of which the TonB protein is the one that actually makes contact with the outer membrane receptor , hence the name TonB-dependent receptors [9] , [10] . TonB interacts with a specific region of the TonB-dependent receptors , generally known as the TonB box [11] . Coupling with the cytoplasmic membrane is necessary because the iron-siderophore complex has to be actively transported across the outer membrane , where there is no source of energy available . All TonB-dependent receptors possess the same structural components: a 22 antiparallel stranded β-barrel , an N-terminal globular domain known as the cork or plug domain that occludes the opening of the β-barrel and a TonB box that extends into the periplasm [10] . However , not all TonB-dependent receptors are involved in CSS , only a subfamily known as TonB-dependent transducers [12] . This subfamily can be easily distinguished from other TonB-dependent receptors on the basis of an N-terminal extension of approximately 70–80 amino acids [13] . This extension determines the specificity of the transduction pathway , but has no effect on the binding and transport of the siderophore [14] . This domain is thought to interact with the sigma factor regulator , which is located in the cytoplasmic membrane . For P . aeruginosa's own siderophore pyoverdine the signal transduction pathway of CSS starts with binding of the inducing signal Fe-pyoverdine to its outer membrane receptor FpvA , which results in the activation of two ECF sigma factors , PvdS and FpvI . Upon activation , PvdS binds the RNA polymerase core enzyme and directs it to the promoter upstream of the genes required for pyoverdine production and also of the genes encoding the virulence factors exotoxin A and PrpL [15] . Activated FpvI bound to the RNA polymerase initiates transcription of fpvA [16] . In addition to FpvI and PvdS , P . aeruginosa contains another twelve iron starvation sigma factors [17] that are probably part of a CSS pathway [2] , [3] . Most of these P . aeruginosa iron starvation sigma factors control iron uptake via haem , via citrate or via heterologous siderophores , such as ferrichrome , ferrioxamine B and mycobactin [3] , [18]–[20] . There are also two P . aeruginosa iron-starvation sigma factors that seem to regulate the uptake of a metal ion ( s ) different than iron , probably zinc or manganese [3] . The last P . aeruginosa iron starvation sigma factor is the one encoded by the PA0675 gene ( named pigD in the Pseudomonas Genome Project database ) . This gene is clustered with a gene encoding a putative sigma factor regulator ( PA0676 or pigE ) and with one encoding a putative receptor ( PA0674 or pigC ) . In silico analysis of this CSS system , which has been designated PUMA3 , showed that it has a number of specific and unusual characteristics . The most obvious difference is the receptor component . The PA0674 receptor is considerably smaller ( 23 KDa ) than that of other CSS outer membrane receptors ( 75–85 KDa ) . It contains the N-terminal extension typical of TonB-dependent receptors involved in signaling ( Figure S1 , Supporting Information ) , but does not have the C-terminal β-barrel domain typical of these receptors . Moreover , PA0674 seems to form a single operon with the ECF-encoding gene PA0675 , while the sigma factor regulator gene PA0676 seems to form a different transcriptional unit . This is in contrast to all other CSS systems in which the genes encoding the sigma factor and the sigma factor regulator are forming an operon [3] . Interestingly , the synthesis of the PA0674 receptor is induced upon interaction of P . aeruginosa with human airway epithelial cells [21] , [22] , which suggests that this CSS system could be active in vivo . This work was aimed at characterizing this novel P . aeruginosa CSS system . To get more information about its unusual receptor component , a homology model for the PA0674 protein has been constructed . The PUMA3 target genes were identified by microarray analysis of cells overexpressing the PA0675 ECF sigma factor . These analyses show that this CSS system is involved in the regulation of at least 27 genes , including genes encoding secreted proteins and components of secretion systems . Although the role of most of these regulated genes has not been established yet , we have demonstrated , using zebrafish ( Danio rerio ) embryos as an infection model , that PUMA3 is involved in the regulation of P . aeruginosa virulence . Therefore , we propose to rename the components of this system VreA ( PA0674 ) , VreI ( PA0675 ) and VreR ( PA0676 ) ( from virulence regulator involving ECF sigma factor ) .
Bioinformatic analysis predicts that the VreA receptor contains a signal sequence ( SS ) of 25 amino acids and separate amino- ( N- ) and carboxy- ( C ) -terminal domains ( NTD and CTD , respectively ) ( Figure S2 , Supporting Information ) . The predicted mature domain of VreA was submitted to several secondary structure prediction servers , including PSI-PRED [23] . The consensus results indicate that the NTD consists of residues 29–115 and the CTD of residues 133–238 , which are separated by a short linker ( residues 116–132 ) . BLASTp results of the full-length VreA sequence against the Protein Databank revealed that residues 60–115 have a strong structural homology to the Secretin and TonB N-terminus ( STN ) domain superfamily . The highest ranked homologous structure ( 30% sequence identity ) corresponds to the periplasmic signaling domain ( residues 1–117 ) of the P . aeruginosa ferripyoverdine receptor FpvA . Since the structure of this protein is known , FpvA was used as a template for model building using a structure-based sequence alignment . Superimposition of residues 39–120 of the VreA/NTD structure to residues 1–82 of the signaling domain structure of FpvA reveals that the two structures are nearly identical , with a backbone Cα RMSD of 0 . 09 Å ( Figure 1A ) . The structure of the VreA/NTD displays a β-α-β fold , with two α-helices positioned side-by-side and sandwiched between two-stranded and three-stranded β-sheets . Despite the high degree of homology between the model and template structures , an interesting difference arises with respect to the location of the expected TonB-box of VreA . In the structure of FpvA , the signaling domain is located before the TonB-box [24] , while in the VreA/NTD structure the predicted TonB-box ( 88-DALTR-92 ) is found in α-helix 3 of the signaling domain ( Figure 1A ) . Submission of the C-terminal domain ( CTD ) sequence to the SUPERFAMILY server [25] revealed a strong structural homology to the C-terminal domain of the TolA/TonB protein superfamily , which was not detected by BLASTp or the Protein Model Portal [26] . The SUPERFAMILY server method is optimized to find homologues of protein sequences with low sequence identity . The crystal structure of the C-terminal periplasmic domain of P . aeruginosa TolA protein was identified as the closest structural domain despite a low overall sequence identity of 9 . 3% . TolA is part of the Tol-Pal ( Tol-OprL in Pseudomonas ) membrane complex , which is mainly involved in maintaining the integrity of the outer membrane [27] , [28] . TolA is structurally and functionally related to the TonB protein , both of which belong to the TolA/TonB protein superfamily . The final homology model of VreA/CTD encompasses residues 124–233 and includes a portion of the short linker region . Structural alignment of the C-terminal domains of VreA and TolA reveals a backbone Cα RMSD of 7 . 04 Å ( Figure 1B ) . The VreA and TolA C-terminal domains both adopt the same central secondary structure fold , β ( 2 ) -α-β , in which the three-stranded β-sheet is packed against two α-helices . The VreA/CTD homology model differs primarily from the TolA/CTD with respect to its shorter α-helix 1 and β-strand 3 , which could have functional implications since both are directly involved in the interaction of TolA with other proteins [29] . These predictions would indicate that this putative VreA receptor is not located in the outer membrane , but in the periplasm . To study this in more detail we generated an influenza hemagglutinin ( HA ) epitope-tagged version of VreA and expressed this chimeric gene at low levels in the wild-type strain and in the PA0676 mutant , which does not produce the putative inner membrane regulator VreR . Although VreA is partially membrane associated , the majority is soluble ( Figure S3 ) and therefore probably located in the periplasm . Furthermore , the presence or absence of the inner membrane regulator VreR did not affect stability and localization of VreA . Remarkably , the apparent molecular weight of VreA was higher than expected ( i . e . 34 kD in stead of 23 ) , which means that VreA is posttranslationally modified or has a secondary structure that affects migration in SDS-PAGE . To identify genes whose transcription might be regulated by the VreI ECF sigma factor , total RNA from P . aeruginosa cells overexpressing the vreI gene from the pMUM3 plasmid was isolated and subjected to cDNA microarray analysis . Overexpression of ECF sigma factors usually results in the expression of the sigma-dependent genes in the absence of the inducing signal [3] , [14] , [16] , [19] . As listed in Table 1 , overexpression of vreI upregulates 30 genes ( including the vreI gene itself that was overexpressed , and vreA and vreR that were also partially present on the pMUM3 plasmid and therefore overexpressed ) . Most regulated genes are located immediately downstream to the PUMA3 locus ( Figure 2 ) , as is often the case of genes regulated by ECF sigma factors . These genes encode: components of the Hxc type II secretion system ( PA0677-PA0687 ) involved in the secretion of alkaline phosphatase ( PA0688 ) [30] , a putative two-partner secretion system ( TPS ) ( PA0690 and PA0692 ) , a putative transposase ( PA0691 ) , exbBD homologues ( PA0693 and PA0694 ) , three hypothetical proteins ( PA0696 , PA0697 , and PA0698 ) two of them containing predicted signal peptides , and a putative peptididyl-prolyl cis-trans isomerase ( PA0699 ) ( Table 1 ) . The putative secreted protein of the two partner secretion system ( PA0690 ) belongs to a family of high-molecular-weight surface-exposed proteins involved in cell adhesion and pathogen dissemination [31] , [32] . In addition , VreI seems to control the expression of a small number of other genes located in different loci of the P . aeruginosa genome . These include genes encoding an ECF sigma factor ( PA0149 ) , a hypothetical protein ( PA0532 ) , three putative cytoplasmic membrane proteins ( PA1652 , PA2404 and PA2784 ) , two putative ATPases of ABC-transport systems ( PA0716 and PA4192 ) , two putative lipoproteins ( PA2349 and PA5405 ) , a homologue to the Fur regulator ( PA2384 ) , and a putative transcriptional regulator ( PA5403 ) . To validate the microarray results , the expression of some VreI-regulated genes was analyzed by RT-PCR . Primers within two VreI-regulated genes , PA0691 and PA0692 , were designed to determine the mRNA levels in P . aeruginosa cells overexpressing the VreI ECF sigma factor . As shown in Figure 3A , the expression of both genes was induced by VreI , but not the expression of the control gene PA0636 . VreI-mediated induction of PA0691 was also confirmed using a transcriptional fusion of the PA0691 promoter region to lacZ . Overexpression of the sigma factor vreI leads to a 25-fold increase in the PA0691 promoter activity when cells are cultured in presence of 1 mM IPTG ( Figure 3B ) . Since vreI is under control of the Ptac promoter , this inducing condition is expected to result in an increased expression of the PA0675-regulated genes . Previous experiments have shown that the PUMA3 CSS system appears to be induced in vivo , since interaction of P . aeruginosa with human airway epithelial cells induces the expression of many VreI-regulated genes ( Tables S1 and S2 , Supporting Information ) [21] , [22] . In order to determine whether VreI-regulated genes are synthesized in vivo , we analyzed the presence of antibodies against VreI-regulated proteins in the serum of P . aeruginosa infected patients . To this end , predicted highly antigenic internal fragments of the PA0690 ( TpsA ) , PA0692 ( TpsB ) and PA0697 genes were fused to a glutathione S-transferase ( GST ) gene and overproduced in E . coli ( Figure 4A ) . The fusion proteins were then purified using Glutathione Sepharose 4B columns . Subsequently , these purified chimera proteins were used to detect the presence of antibodies in the serum of P . aeruginosa infected patients . We tested in total the serum of 25 different patients , 7 with positive blood culture for P . aeruginosa and 18 cystic fibrosis ( CF ) patients . Antibodies against the secreted component of the TPS system , the PA0690/TpsA protein , were present in the serum of 5 of the 7 patients with positive blood culture for P . aeruginosa ( 71 . 4% ) and in the serum of 12 of the 18 CF patients tested ( 66 . 7% ) ( Figure 4B ) . However , antibodies against the second component of the TPS system , the outer membrane transporter PA0692/TpsB could not be detected with any of these sera ( Figure 4B ) . The third protein tested , PA0697 , which contains a putative signal sequence , was detected with 4 ( 57 . 1% ) of the sera from patients with positive blood culture for P . aeruginosa and with 10 ( 55 . 5% ) of the CF patients sera ( Figure 4B ) . The presence of antibodies against these proteins indicates that they are being expressed during infection . Since mRNA levels of these genes are extremely low under non-inducing conditions , this result also suggests that the PUMA3 CSS system is induced in these patients . In order to determine the role of the sigma factor regulator VreR in the PUMA3 signaling pathway , we analyzed the stability and activity of the VreI ECF sigma factor in a vreR mutant . To analyze the stability of this sigma factor , we first constructed the pMUM3RσHA-tag plasmid in which the vreI gene is C-terminal tagged with the HA epitope . This plasmid and the control plasmid pMUM3 were then transferred to the P . aeruginosa PAO1 wild-type ( WT ) strain and the vreR mutant ( sigma factor regulator mutant ) . The presence of the VreI-HAtag protein was analyzed by Western blot using an anti-HAtag antibody . As shown in Figure 5A , the VreI-HAtag protein ( 22 kDa ) could be detected in strains bearing the pMUM3RσHA-tag plasmid , whereas it could not be detected in strains bearing the control plasmid ( data not shown ) . The addition of 1 mM IPTG slightly increases VreI-HAtag production , which is under control of the Ptac promoter ( Figure 5A , upper panel ) . Interestingly , the VreI ECF sigma factor seems to be more stable in absence of the sigma factor regulator as the amount of this protein in the vreR mutant is considerably higher that in the wild-type strain ( Figure 5A ) . Analysis of the cytosol and membrane fractions of both strains showed that VreI is associated to the membrane through the VreR sigma factor regulator since the VreI-HAtag protein could not be detected in the membrane fraction in absence of this protein ( Figure 5A , lower panel ) . Although there is more VreI sigma factor in absence of VreR , this sigma factor is not active in this condition ( Figure 5B ) , since overexpression of vreI in the vreR mutant does not increase PA0691 promoter activity , while it does in the wild-type strain ( Figure 5B ) . Overexpression of the whole PUMA3 system ( receptor , ECF sigma factor and sigma factor regulator ) from the pMMB-PUMA3 plasmid does not increase PA0691 promoter activity ( Figure 5B ) , possibly due to the simultaneous overexpression of the vreR gene encoding the sigma factor regulator component . In conclusion , VreR is an anti-sigma regulator for VreI that is both required for the function of VreI and inhibits its activity under non-inducing conditions . Although the role of most P . aeruginosa PUMA3-induced genes has not been established yet , the fact that some of them encode secreted proteins and components of secretion systems suggests that the PUMA3 CSS system could be involved in regulation of virulence . For this reason we decided to analyze P . aeruginosa virulence . Therefore , we used a novel infection model for P . aeruginosa using zebrafish ( Danio rerio ) embryos as a host . The zebrafish model has a number of advantages over other models of infection [33] . One of them is that zebrafish embryos are transparent , which allows the analysis of bacterial infections in situ , in real time and at a high resolution by using fluorescent microorganisms . Recently , zebrafish embryos have been reported to be a suitable model for P . aeruginosa [34] , [35] . In order to set up the model , we analyzed first whether P . aeruginosa could infect 28–30 hours-post-fertilization ( hpf ) embryos . To this end we introduced P . aeruginosa PAO1 wild-type strain into the zebrafish embryo by microinjection in the caudal vein . We observed that P . aeruginosa was able to lethally infect the embryos in a dose dependent manner ( Figure 6A ) . Embryos were resistant to low doses of bacteria ( 150–200 colony forming units , CFU ) , but increased mortality was observed with larger inocula ( ∼400–1300 CFU ) ( Figure 6A ) . These experiments also showed that P . aeruginosa kills the embryos within the first two days-post-infection ( dpi ) ; embryos that were alive after this time usually were able to clear the P . aeruginosa infection and developed normally . Then , we analyzed the virulence of the P . aeruginosa PUMA3-induced strain , by overexpression of the vreI ECF sigma factor , in zebrafish embryos . As shown in Figure 7 , infection with the P . aeruginosa PUMA3-induced strain resulted in a significant increase of zebrafish embryo mortality . This effect was repeatedly shown in 5 different experiments using groups of 20 embryos . To demonstrate that this effect is specific for PUMA3 induction , we also infected the embryos with the vreR sigma factor regulator mutant bearing the pMUM3 plasmid that overexpresses the VreI sigma factor . We have shown previously that overexpression of vreI in this mutant does not lead to upregulation of the PUMA3-controlled genes ( Figure 5B ) . As expected , induction of PUMA3 in the vreR mutant did not result in an increase in P . aeruginosa virulence ( Figure 6B ) , which confirms the direct involvement of VreI in the increased P . aeruginosa virulence . We next assessed the role of different PUMA3-regulated genes in VreI-induced virulence . To this end , the pMUM3 plasmid was introduced in transposon insertion mutants of PUMA3-regulated genes encoding potential virulence factors , such as both components of the TPS system tpsA and tpsB ( PA0690 and PA0692 , respectively ) , the tonB homologue PA0695 and the putative secreted protein PA0696 . Subsequently , these different mutants were injected in zebrafish embryos . Unfortunately , all mutants , except the vreR sigma factor regulator mutant described previously , were as virulent as the wild-type strain ( Figure 6C ) . This means that none of these potential virulent factors is by itself responsible of the VreI-induced lethality in zebrafish embryos . Possibly a combination of these factors is responsible for this phenotype , or some of the other VreI-regulated genes . Zebrafish embryos infected with red fluorescent P . aeruginosa ( PAO1/RFP ) bearing the pMMB67EH ( empty plasmid ) or the pMUM3 ( overexpressing vreI ) plasmid were microscopically observed to follow the progression of the infection ( Figure 8A ) . In the first hours after infection , fluorescence was undetectable ( data not shown ) , but with the progression of the infection , fluorescent bacteria were clearly detectable in the embryos at 20–24 hpi ( Figure 8A ) . The amount of fluorescence in the embryos correlated with a slower blood circulation and decreased heartbeat when compared with healthy embryos ( in which fluorescence was undetectable ) , and also with severe damages of the tissues , mainly in the tail ( Figure 8A ) . Affected embryos presented increasing red fluorescence and normally died within the first 24–30 hpi , whereas infected but apparently healthy embryos were able to survive and develop normally . There were no obvious differences between the phenotype of moribund embryos infected with the PUMA3-induced strain or with the non-induced strains ( data not shown ) . Both strains produce a similar necrotic cell death that starts in the tail and extends to other tissues . The difference between both strains lies in the fact that a considerably higher percentage of embryos infected with the induced strain died due to the proliferation of the P . aeruginosa infection ( Figure 6B and 8A ) . The microscopy studies also showed that P . aeruginosa , which was microinjected in the blood stream , was able to extravasate and infect other tissues , mainly what appears to be brain and spinal cord of the embryos ( Figure 8A ) . By whole mount immunohistochemistry of embryos infected with PAO1/RFP using an antibody that specifically recognizes Acetylated tubulin ( AcTub ) present in neurons and axons of the embryo , we observed co-localization of bacteria with fluorescent brain and spinal cord tissues ( Figure 8B ) . More in depth analysis with confocal microscopy clearly showed many bacteria in the center and around the nerve bundles of the spinal cord ( Figure 8C ) . No colocalization with axons or neuronal cell bodies was observed ( Figure 8C , middle panel ) , which suggests that P . aeruginosa bacteria reside in non-neuronal cells or extracellularly . In addition single bacteria were observed in the muscles ( Figure 8C , middle panel ) .
In this work , we report the identification and characterization of a novel CSS system , designated PUMA3 , which has a number of specific and unusual characteristics , and is involved in the regulation of P . aeruginosa virulence . The most obvious difference of PUMA3 with all other CSS systems is the receptor component VreA . The structural modeling of VreA predicts a bilobal protein ( Figure S2 ) , with an N-terminal domain ( NTD ) that resembles the N-terminal periplasmic signaling domain of TonB-dependent transducers , such as FpvA , and a C-terminal domain ( CTD ) that resembles the periplasmic C-terminal domain of the TolA/TonB protein superfamily . The signaling domain of TonB-dependent transducers is the domain that interacts with the sigma factor regulator [36] . Therefore , it is likely that VreA/NTD interacts with the sigma factor regulator VreR , as illustrated in Figure S4 . Although the structures of VreA/NTD and the signaling domain of FpvA are nearly identical ( Figure 1A ) , an interesting difference is the location of the expected TonB-box of VreA . In FpvA , the signaling domain is located N-terminal to its TonB-box . In the apo-FpvA structure , the TonB-box is buried between the signaling domain and the plug/barrel domains , and forms a β-strand that interacts with the signaling domain . Upon ferric-pyoverdine binding this β-strand is displaced and free to interact with TonB in a β-strand lock-exchange mechanism [24] . In the VreA/NTD structure , the predicted TonB-box is found in α-helix 3 of the signaling domain fold ( Figure 1A ) . If the VreA TonB-box interacts with the TonB protein in a similar mixed four-stranded β-sheet fashion as other reported TonB-dependent proteins a significant conformational change would be required . The VreA/CTD showed , despite the low sequence identity , strong structural homology to the C-terminal domain of the TolA protein ( Figure 1B ) . The Tol-Pal ( Tol-OprL ) system is organized into two protein complexes: a cytoplasmic membrane complex that consists of the TolQ , TolR , and TolA proteins , and an outer membrane-associated complex composed of TolB and Pal . TolA plays a central role by providing a bridge between the cytoplasmic and outer membranes via its interaction with the Pal lipoprotein [37] . The Tol proteins are parasitized by filamentous ( Ff ) bacteriophages and group A colicins [38] , [39] . The N-terminal domain of the Ff phage g3p protein and the translocation domains of colicins interact directly with TolA during the processes of import through the cell envelope [40] . The TolA protein has functional analogy with the TonB protein . Especially the interaction of the C-terminal domain of TolA and the Ff phage g3p protein is similar with that of the C-terminal domain of TonB and the TonB-box of TonB-dependent receptors [11] , [41] . Since the C-terminal domain of VreA is similar to TolA/TonB , it is tempting to speculate that VreA/CTD could interact with other partner proteins in the outer membrane ( Figure S4 ) . Based on bioinformatics analysis , it is clear that the predicted domain architecture of VreA is unique and has yet to be reported . Significantly , both domains of VreA are predicted to resemble proteins that form essential interactions with partner proteins required for signal transduction and bacterial virulence . By microarray analysis of P . aeruginosa cells overexpressing the PUMA3 ECF sigma factor VreI , we have identified the genes regulated by this novel CSS system . It was shown previously that overexpression of the sigma factors results in the specific induction of the sigma-dependent genes in the absence of the inducing signal [3] , [14] , [16] , [19] . The microarray analysis shows that the PUMA3 system controls the expression of 27 genes ( Table 1 ) , most of which are located directly downstream of the PUMA3 locus ( Figure 2 ) . As observed previously for other ECF sigma factors [3] , overexpression of vreI does not result in an unspecific response and does not affect house-keeping genes . This is probably due to the fact that the RNA polymerase has a higher affinity for the house-keeping sigma factor RpoD ( σ70 ) than for alternative sigma factors [42] . The interaction network of the P . aeruginosa VreA receptor with other proteins using the STRING database [43] shows the physical and functional connectivity of this protein not only with the other two components of the PUMA3 system VreI and VreR , but also with most PUMA3-regulated proteins such as both components of the TPS system , ExbBD2 , and the PA0696-PA0697-PA0699 proteins ( Figure S5A , Supporting Information ) . PUMA3 homologues are found in the genome of both Pseudomonas fluorescens Pf5 and Pf01 , Pseudomonas entomophila , Burkholderia vietnamiensis , Rhodopseudomonas palustris and Janthinobacterium sp ( Figure S5B ) . Interestingly , in all these bacteria , the PUMA3 gene cluster is associated with homologues of the TPS secretion system , of the ExbBD system and of PA0696 . P . fluorescens contains an Hxc-like type II secretion system immediately downstream of PUMA3 and also a tpsA-like gene . In B . vietnamiensis , homologues of some of the hxc-like genes are located upstream of PUMA3 , interrupted by a tpsA-like gene . The second component of the TPS pathway is located downstream of PUMA3 , associated with exbBD homologues . In R . palustris and Janthinobacterium sp . , the PUMA3 cluster is repeated four times . Three of these clusters are followed by a tpsA homologue , and the fourth one by a tpsB-like gene associated with exbBD and PA0696 homologues . This gene association further suggests a role for PUMA3 in the regulation of the genes , especially tpsA . Although most CSS systems described to date control the expression of their cognate TonB-dependent transducers , this does not seem to be the case of the PUMA3 CSS system . Microarray analysis did show an increase in vreA mRNA levels in cells overexpressing the VreI sigma factor ( Table 1 ) . However , because part of the vreA gene is also partially present on the vreI overexpressing plasmid this does not mean that vreA is induced upon activation of PUMA3 . Direct analysis of vreA expression using a vreA::lacZ transcriptional fusion showed no differences between cells overexpressing or non-overexpressing vreI ( data not shown ) . This difference between PUMA3 and other CSS systems is probably related to the unusual genetic organization of the PUMA3 system . In contrast to all CSS systems in which the sigma and sigma factor regulator genes are arranged in an operon , the sigma factor gene of the PUMA3 system seems to form an operon with the receptor gene ( these genes overlap 4 bp ) , while the sigma factor regulator seems to be part of a different transcriptional unit ( Figure 2 ) . In this situation , regulation of the VreA receptor expression by the VreI sigma factor would imply that VreI also induces its own expression , which is unusual for ECF sigma factors . Another interesting characteristic of PUMA3 is the role of the sigma factor regulator VreR in the signaling pathway . The function of this integral cytoplasmic membrane protein is to couple the signal perceived by the TonB-dependent transducer to the ECF sigma factor in the cytoplasm . The large periplasmic C-terminal part interacts with the receptor in the outer membrane , whereas the short cytoplasmic tail binds the ECF sigma factor [44] , [45] . Currently , there is no structural data available for any member of this protein family , and the molecular mechanism by which these proteins work is not completely understood . It is generally considered an anti-sigma factor , based on the fact that overexpression of the ECF sigma factor results in constitutive induction of the CSS system [3] , [19] . In accordance with this , overexpression of the sigma factor regulator results in a strongly reduced induction upon the presence of the extracellular signal [16] . However , for the PUMA3 CSS system the sigma factor regulator is in fact essential for VreI sigma factor activity ( Figure 5B ) . This is also the case for the sigma factor regulator FecR of the E . coli ferric-citrate CSS system [4] . Our experiments also show that the PUMA3 ECF sigma factor is more stable in the absence of the sigma factor regulator and relocates to the cytosol ( Figure 5A ) . A similar situation has been described for the sigma factor PvdS and its regulator FpvR; overexpression of fpvR results in increased degradation of the ECF sigma factor PvdS and possibly also FpvI [45] , [46] and PvdS relocates partially to the cytosol in absence of the inner membrane regulator [47] . This means that these sigma factor regulators not only retain the ECF sigma factors at the cytoplasmic membrane in an inactive form , but possibly also deliver these to a specific endoprotease . Future experiments are necessary to show which protease is involved and what the exact role of the PUMA3 sigma factor regulator . The PUMA3 CSS system appears to be induced in vivo , since the serum of the majority of P . aeruginosa-infected patients , including patients with positive blood culture for P . aeruginosa and cystic fibrosis patients , contains antibodies directed against PUMA3-regulated proteins ( i . e . TpsA and PA0697 ) ( Figure 4B ) . This means that these proteins are synthesized in vivo during infection and that the PUMA3 system is probably induced . This further suggests its activation by a host signal , which is consistent with the previous reports showing that interaction of P . aeruginosa with human airway epithelial cells induces the expression of many PUMA3-regulated genes [21] , [22] ( Tables S1 and S2 ) . Although the role of most P . aeruginosa PUMA3-induced genes has not been established yet , we have demonstrated that this CSS system is involved in the regulation of P . aeruginosa virulence . Infection of zebrafish embryos with the P . aeruginosa PUMA3-induced strain by overexpression of the vreI sigma factor results in a significant increase of embryo mortality ( Figure 6B ) . This effect is VreI specific since its overexpression in a mutant in the sigma factor regulator , which is necessary for VreI activity ( Figure 5B ) , did not result in increased virulence ( Figure 6B ) . The induced lethality was visible after the first day of infection as the embryos that were alive at this point usually were able to clear the infection . Zebrafish embryos have been recently reported to be a suitable model for P . aeruginosa [34] , [35] . Known attenuated P . aeruginosa mutants , such as mutants in type III secretion or in quorum sensing , are less virulent in zebrafish embryos . Moreover , key host determinants , such as phagocytosis , play an important role in zebrafish embryos pathogenesis , and , as in humans , phagocyte depletion increases the susceptibility of the embryos to P . aeruginosa infection [34] . Neutrophils and macrophages rapidly phagocytosed and killed P . aeruginosa , but if the amount of cells injected exceeds the phagocytic capacity of the embryo bacteria survive and grow causing the death of the embryo [34] . The PUMA3 CSS system seems to play a role in the first hours of infection . Induction of PUMA3 may result in P . aeruginosa resistance to phagocytosis leading to a lower survival of the embryos . Alternatively , the PUMA3-induced strain may replicate faster in the embryo , although the growth rate of these strains in vitro showed no difference ( data not shown ) . The identification of the upregulated factor ( s ) responsible of the PUMA3-induced virulence will be essential to understand the mechanisms by which this CSS system induces virulence . Using fluorescence microscopy , we have shown that P . aeruginosa is able to extravasate and infect other tissues , mainly the brain and spinal cord of the embryos ( Figure 8 ) . This pattern of infection seems to be specific for P . aeruginosa and completely different to the one caused by , for example , Salmonella typhimurium , which replicates either inside macrophages or extracellularly but always within the vascular system , or Mycobacterium marinum , which is located in clustered macrophages [33] , [48] . In summary , in this work we have identified and characterized a novel CSS system that triggers expression of virulence factors , probably in response to a host signal . A similar function for a CSS system has only been described in the plant pathogen Ralstonia solanacearum , which uses a CSS system to regulate virulence in response to a non-diffusible plant signal [49] , [50] . However , whereas the Ralstonia CSS system has all the characteristics of a normal CSS system , PUMA3 shows a number of new and unusual characteristics , and represents the first CSS system dedicated to the activation of virulence factors in a human pathogen . Our work also shows that CSS systems can be used for a broader purpose than the regulation of iron uptake .
A homology model of the N-terminal and C-terminal domains of VreA ( VreA/NTD and VreA/CTD , respectively ) was built using the SWISS-MODEL server [51] . The VreA/NTD homology model was constructed using a structure-based sequence alignment ( SBSA ) and the crystal structure of the P . aeruginosa ferripyoverdine receptor FpvA ( PDB ID: 2O5P ) as template . Other structural homologues of FpvA , including the periplasmic signaling domains from the E . coli ferric citrate receptor FecA ( PDB IDs: 2D1U and 1ZZV ) and P . putida pseudobactin 358 receptor PupA ( PDB ID: 2A02 ) were included in the SBSA . For the VreA/CTD domain , the template for model building was the C-terminal domain of TolA from P . aeruginosa ( PDB ID: 1LRO ) . Other structural homologues including the C-terminal domains of TolA ( PDB ID: 1S62 ) and TonB ( PDB ID: 1XX3 ) from E . coli were used to construct an SBSA upon which the C-terminal domain sequence of VreA was initially threaded using the program Deepview ( version 4 . 0 ) , prior to submission to the SWISS-MODEL server . Sequence analysis of the PAO1 genome was performed at http://www . pseudomonas . com [52] . Signal peptides were predicted using the SignalP 3 . 0 Server available at http://www . cbs . dtu . dk/services/SignalP/ [53] . The functional associations of PUMA3 were predicted using the STRING 8 database at http://string . embl . de/ [43] . The bacterial strains and plasmids used are listed in Table 2 . P . aeruginosa PAO1 wild-type strain and all the P . aeruginosa transposon insertion mutants used were from the comprehensive P . aeruginosa transposon mutant library at the University of Washington Genome Center [54] . The locations of the mutations were confirmed by PCR with primers flanking the insertion sites . The strain ID ( unique identifier ) is given on the table , and further information on these mutants can be found at http://www . genome . washington . edu/UWGC/Pseudomonas/index . cfm . Bacterial strains were routinely grown in liquid Luria-Bertani ( LB ) medium [55] at 37°C on a rotary shaker operated at 200 revolutions per min . When required , antibiotics were used at the following final concentrations ( µg mL−1 ) : ampicillin ( Ap ) , 100; chloramphenicol ( Cm ) , 30; kanamycin ( Km ) , 25 for E . coli and 200 for P . aeruginosa; piperacillin ( Pip ) , 25; tetracycline ( Tc ) , 10 for E . coli and 20 for P . aeruginosa . Standard molecular biology techniques were used for DNA manipulations [55] . PCR amplifications and DNA sequencing were performed as described previously [19] . The sequences of the oligonucleotide primers used in this study are listed in Table S3 ( Supporting Information ) . Transcriptional fusions to lacZ were made by cloning the promoter regions , amplified by PCR as EcoRI-XbaI or BglII-KpnI fragments , into the EcoRI-XbaI or BglII-KpnI sites of pMP220 [56] . The fusion constructs were confirmed by DNA sequencing , and transferred from E . coli DH5α to P . aeruginosa by triparental mating using the helper plasmid pRK600 as described before [57] . The influenza HA tag was cloned in the XbaI-HindIII sites of the pMUM3Rσno-stop plasmid ( Table 2 ) , which carries the vreI gene without stop codon , as an adapter that was the result of a primer dimer formed by the primers HAtagF-X and HAtagR-H ( Table S3 ) . This introduces the HA tag epitope YPYDVPDYAC* at the C-terminal end of the VreI protein . The resulting plasmid , in which the XbaI site is not restored , was designated pMUM3RσHA-tag . The HA tag was introduced at the end of the vreA gene , replacing the stop codon , by a PCR with primers PA0674F-E and EndPA06742 . This introduces the HA tag epitope YPYDVPDYAC* at the C-terminal end of the VreA protein and an additional EcoRI and HindIII cloning site . These restriction enzymes were used to digest the PCR product after gel extraction and clone it in the brood host range vector pMMB67EH . The resulting plasmid was designated pMMB674HA . P . aeruginosa cells bearing the plasmid pMMB67EH or pMUM3 were grown in quadruplicate in 300 mL Erlenmeyer flask with 30 mL LB and 25 µg/ml piperacillin at 37°C and 200 revolutions per min . In these conditions , the growth rate of the wild-type strain and the vreI overexpressing strain was similar ( not shown ) , therefore cell density-dependent regulatory circuits are not affected . When the optical density at 600 nm reached 0 . 7–0 . 8 , cultures were induced with 1 mM IPTG . After 45 min of incubation , a total of 50 ml of cells from two independent cultures were harvested by centrifugation at 4°C , and total RNA was isolated as described before [3] . RNA quantity was assessed by UV absorption at 260 nm in a ND-1000 Spectrophotometer ( NanoDrop Technologies , USA ) . RNA quality was monitored by 1 . 5% ( wt/v ) agarose gel electrophoresis containing 2 , 2 M formaldehyde as denaturing agent . The cDNA probes were prepared according to the protocol supplied by the manufacturer ( Affymetrix ) as described before [3] . Target hybridization , washing , staining and scanning were performed by the Affymetrix Core Facility using a GeneChip® hybridization oven , a Fluidics station and MICROARRAY SUITE software ( Affymetrix ) at the Leiden Genome Technology Center ( LGTC® ) ( Leiden , The Netherlands ) as described previously [3] . Genes were considered differentially regulated if the relative change ( n-fold ) was >2 . 5 and the P-value was <0 . 05 . Microarray data sets are available from the NCBI Geo Database under accession number GSE15697 . RT-PCR analyses were performed by using the Titan One-Tube RT-PCR kit ( Roche ) in accordance with the manufacturer's recommendations . For each reaction , 1 µg of total RNA was used . The annealing temperature in each reaction was determined according to the composition of the primers included . DNA contamination of the RNA samples was ruled out by inactivation of the reverse transcriptase at 94°C for 4 min prior the RT-PCR reaction . The sequences of the primers used for the RT-PCR are listed in Table S4 ( Supporting Information ) . ß-galactosidase activities in soluble cell extracts were determined using ONPG ( Sigma-Aldrich ) as described previously [19] . Each assay was run in duplicate at least three times and the data given are the average . The β-galactosidase activity is expressed in Miller Units . Overnight cultures of E . coli DH5α cells bearing the pRP270 ( empty plasmid ) , or the pGST-0690 , pGST-0692 and pGST-0697 plasmids ( containing the indicated GST fusions ) in LB liquid medium supplemented with ampicillin were subcultured 1∶10 in 500 ml of the same medium , grown until log-phase and incubated 3 h with 0 . 1 mM IPTG . The cells were harvested by centrifugation , resuspended in 10 ml of 1% ( v/v ) Triton X-100 in phosphate-buffered saline ( PBS ) , and ultrasonically disrupted . The total bacterial lysate was centrifuged ( 12 . 000×g , 15 min , 4°C ) and the supernatants loaded on Glutathione Sepharose 4B column ( Pharmacia ) equilibrated with 3 column volumes of PBS . GST-fused proteins were eluted with 10 mM glutathione in 50 mM Tris-HCl pH 8 . 0 in a fraction of 750 µl ( 3× times with 250 µl ) . Protein concentration was determined by the BCA protein assay ( Pierce ) using BSA as a standard . Purified proteins were separated on a 12 . 5% ( w/v ) acrylamide SDS-PAGE gel and electrotransferred onto nitrocellulose and immunodetected with the serum of 25 different P . aeruginosa infected patients . The second antibody , horseradish peroxidase-conjugated goat anti-human , was visualized using 4-chloronaphtol/3 , 3-diaminobenzidine staining [55] . P . aeruginosa cells were grown in LB until late log phase and cultures were then centrifuged ( 10 min at 15 , 000×g ) . The cell pellets were solubilized in Laemmli sample buffer [58] and heated for 5 min at 95°C ( total protein fraction ) . To separate cytosol and membrane fractions , cell pellets were first ultrasonically disrupted and centrifuged 5 min at 2 , 000×g to remove unbroken cells . The resulting supernatant was then centrifuged during 45–60 min at 12 . 000×g , 4°C . The pellet from this centrifugation step ( membrane fraction ) was solubilized in Laemmli buffer , and the proteins from the supernatant ( cytosol fraction ) were precipitated with 10% ( w/v ) trichloroacetic acid . Proteins from cell lysates , membrane and cytosol fractions were separated by SDS-PAGE containing 15% acrylamide . Proteins were electrotransferred onto nitrocellulose and immunodetected with a monoclonal antibody directed against the HA epitope . The second antibody , horseradish peroxidase-conjugated goat anti-mouse , was visualized using ECL detection ( Pierce ) . Quantification was performed on a Fluor-S MultiImager ( Bio-Rad ) using Bio-Rad multianalyst software , version 1 . 0 . 2 . Zebrafish embryos were collected from a laboratory-breeding colony kept at 24°C on a 12∶12 h light/dark rhythm as previously described [48] . Embryos were staged at 28 hours post-fertilization ( hpf ) dechorionated and anaesthetised in 0 . 02% buffered 3-aminobenzoic acid methyl ester ( MS222 , Sigma ) . Overnight cultures of P . aeruginosa cells bearing the pMMB67EH empty plasmid or the pMUM3 plasmid overexpressing vreI in LB liquid medium supplemented with piperacillin were subcultured 1∶50 in the same medium , grown until log-phase and incubated 1 h with 1 mM IPTG to induce vreI expression from the Ptac promoter . Then , 2 ml of the log-phase bacteria was pelleted by centrifugation , washed twice with PBS and diluted in phenol red containing PBS ( Sigma ) at the desired bacterial density . Embryos were individually infected by microinjection ( 2 nl ) of P . aeruginosa in the caudal vein near the blood island and the urogenital opening as previously described [48] . To determine the number of CFU microinjected in each set of embryos , bacteria were also microinjected in PBS and plated on LB-agar . The mCherry variant of DsRed [59] was obtained as derivative of the pRSET-B plasmid ( Invitrogen ) . In order to optimize for high gene expression in bacteria a new Shine/Dalgarno ( SD ) sequence was introduced upstream of the original ATG initiation codon . This was achieved by cloning an adapter that was the result of a primer dimer formed by the primers WBcherF ( 5′-GATCCAAGCTTGAGGAGGA-3′ ) and WBcherR ( 5′-GATCTCCTCCTCAAGCTTG-3′ ) in the BamHI site of the pRSET-B mCherry plasmid . This cloning introduced a HindIII site ( underlined ) and a SD sequence ( GGAGGA ) ( shown in bold ) in front of the mCherry gene . In order to centre the new SD on the -10 position from the ATG start codon , a 7 bp fragment between the SD and the ATG codon was then deleted . This was achieved by an inverse PCR reaction using DNA from this new plasmid as template and the primers Cherry-TomatoF ( 5′-CATGGTCAGCAAGGGCCAGG-3′ ) and WBcherR . The obtained PCR product was then re-ligated and transformed in E . coli DH5α cells . The resulting mCherry gene was then cloned as a HindIII-EcoRI fragment in the pBBR-PoprF plasmid , a derivative of the broad-host range pBBR1MCS-5 plasmid [60] ( Table 2 ) . This plasmid contains the promoter region of the P . aeruginosa oprF gene to maximize mCherry expression . This final construct , designated pBPF-mCherry , was introduced in P . aeruginosa by triparental mating [57] . For whole-mount immunohistochemistry , embryos were fixed in 4% ( w/v ) paraformaldehyde overnight at 4°C , rinsed in PBS and incubated in blocking solution for 1 hour ( PBS+0 . 1% ( v/v ) Triton X-100+10% ( v/v ) normal goat serum ) . The primary antibody , a monoclonal anti-acetylated tubulin ( Sigma , clone 6-11B-1 ) was diluted 1∶250 in PBS with 0 . 1% ( v/v ) Triton X-100 and 1% ( v/v ) normal goat serum , and incubated overnight at room temperature with slow agitation . After extensive washing with 0 . 1% ( v/v ) Triton X-100 in PBS , embryos were incubated overnight at room temperature in PBS with 0 . 1% ( v/v ) Triton X-100 , 1% ( v/v ) normal goat serum and 1∶250 diluted secondary antibody , a goat anti-mouse conjugated to Alexa 480 ( Invitrogen ) . After extensive washing in PBS with 0 . 1% ( v/v ) Triton X-100 , embryos were transferred to Vectashield mounting medium ( Vector Laboratories ) and examined by microscopy . Fixed or live zebrafish embryos , anaesthetised in 0 . 02% buffered MS222 , were examined with a Leica MZ16FA stereomicroscope equipped with a DFC420C digital camera . Photographs were taken with the Leica Application Suite software ( version 2 . 8 . 1 © Leica Microsystems ) . In addition , whole-mount immuno labeled zebrafish embryos were examined with a Leica DMIRE2 confocal microscope using the Leica confocal software ( version 2 . 6 . 1 © Leica Microsystems ) . | Pseudomonas aeruginosa is a versatile pathogen; these bacteria are able to cause an infection in humans and other mammals , zebrafish , insects , nematodes and even plants . P . aeruginosa evolved an impressive amount of gene regulation systems to be able to express the right virulence genes under the right circumstances . The best studied examples of these are the two-component systems and the autoinducers . In addition , P . aeruginosa is also able to regulate virulence genes using the pyoverdine cell-surface signaling system ( CSS ) . Genome analysis shows that there are multiple putative CSS systems in P . aeruginosa . In this paper we have studied a novel CSS system with a number of remarkable characteristics and show that this system is involved in the regulation of several putative virulence factors . Induction of this system leads to increased virulence in our zebrafish embryo infection model . Our study provides new insights into the regulation of virulence by P . aeruginosa . | [
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| 2009 | A Novel Extracytoplasmic Function (ECF) Sigma Factor Regulates Virulence in Pseudomonas aeruginosa |
Signal transducer and activator of transcription 3 ( Stat3 ) transduces signals of many peptide hormones from the cell surface to the nucleus and functions as an oncoprotein in many types of cancers , yet little is known about how it achieves its native folded state within the cell . Here we show that Stat3 is a novel substrate of the ring-shaped hetero-oligomeric eukaryotic chaperonin , TRiC/CCT , which contributes to its biosynthesis and activity in vitro and in vivo . TRiC binding to Stat3 was mediated , at least in part , by TRiC subunit CCT3 . Stat3 binding to TRiC mapped predominantly to the β-strand rich , DNA-binding domain of Stat3 . Notably , enhancing Stat3 binding to TRiC by engineering an additional TRiC-binding domain from the von Hippel-Lindau protein ( vTBD ) , at the N-terminus of Stat3 , further increased its affinity for TRiC as well as its function , as determined by Stat3's ability to bind to its phosphotyrosyl-peptide ligand , an interaction critical for Stat3 activation . Thus , Stat3 levels and function are regulated by TRiC and can be modulated by manipulating its interaction with TRiC .
Signal transducer and activator of transcription 3 ( Stat3 ) is a member of a family of seven closely related proteins responsible for the transmission of peptide hormone signals from the extracellular surface of cells to the nucleus [1] , [2] . Mice deficient in Stat3 died during early embryogenesis , indicating its critical role in development [3] . Conditional gene targeting of Stat3 in several types of tissues revealed important roles for Stat3 in key biological functions including cell survival and growth , immunity , and inflammation [3] , [4] . These findings in mice have been recapitulated , in part , in patients with autosomal-dominant hyper-IgE syndrome ( AD-HIES ) or Job's syndrome , a rare immunodeficiency syndrome , which results from loss-of-function mutations in the Stat3 gene [5] . At the opposite end of the spectrum , gain-of-function mutations or increased wild-type Stat3 activity has been implicated in the pathogenesis of up to 50% of hematological and solid tumors [6]–[8] . Despite its importance to normal physiology and pathophysiology , however , little is known about how Stat3 achieves its native state within the cell , information that potentially could be exploited to develop novel therapies for AD-HIES and/or cancer . For the majority of eukaryotic proteins , folding to the native and functional conformation requires the assistance of elaborate cellular machinery composed of proteins known as molecular chaperones [9] , [10] . The chaperonins comprise a class of oligomeric , double-ring , high molecular weight , ATP-dependent chaperones with the unique ability to fold certain proteins that cannot be folded by simpler chaperone systems [11] , [12] . The primary cytosolic eukaryotic chaperonin is the tailless complex protein-1 ( TCP-1 ) ring complex ( commonly abbreviated TRiC , and also known as complex containing TCP-1 or CCT ) . TRiC is a large complex ( 1 MDa ) composed of eight homologous but distinct subunits ( CCT 1–8 ) , arranged in two stacked octameric rings to form two interior chambers in which substrate proteins can be encapsulated and folded . TRiC is essential for the de novo folding of approximately 10% of newly synthesized proteins in the eukaryotic cell and for refolding proteins that become denatured following stress [13] . These substrate proteins extend up to 120 kDa in size , and most appear to contain regions with β-strands [14] . Well-characterized clients of TRiC include the cytosolic proteins actin , tubulin , and the tumor-suppressor von Hippel-Lindau protein ( pVHL ) . In contrast , details regarding the contribution of TRiC to the folding and function of transcription factors are limited . Here we demonstrate that TRiC binds the oncogenic transcription factor , Stat3 , and contributes to its biosynthesis , refolding , and activity in vitro and within cells . TRiC binding to Stat3 was mediated , at least in part , by TRiC subunit CCT3 . Stat3 binding to TRiC mapped primarily to its β-strand-rich , DNA-binding domain ( DBD ) . Addition of a second TRiC-binding domain ( TBD ) to the N-terminus of Stat3 ( the TBD of pVHL , vTBD ) further increased the affinity of Stat3 for TRiC and its function . Thus , the structure and function of Stat3 is regulated by the major eukaryotic chaperonin TRiC/CCT and can be modulated through manipulation of TRiC levels or substrate affinity .
To determine if Stat3 interacts with TRiC , we performed TRiC immunoprecipitation in a cell-free mammalian translation system used previously [13] to identify new members of the TRiC interactome ( rabbit reticulocyte lysates , RRLs ) . Similar to pVHL , radiolabeled Stat3 , both full-length and nascent polypeptide chains , immunoprecipitated with TRiC ( Figure 1A ) , indicating that Stat3 is a member of the TRiC interactome and suggesting that its interaction with TRiC occurs co-translationally , as described previously for other TRiC client proteins [15] . To assess if the TRiC interaction with nascent Stat3 polypeptides is coupled to their biogenesis , RRLs were immunodepleted of TRiC and tested for the ability to generate Stat3 . TRiC depletion of RRLs ( Figure 1B ) markedly reduced its ability to generate Stat3 ( Figure 1C ) . Importantly , add-back of purified bovine TRiC to TRiC-depleted RRL reconstituted Stat3 protein synthesis in a dose-dependent manner ( Figure 1D ) . These findings indicate that TRiC is required for Stat3 protein biogenesis within RRLs and suggests that the co-translational interaction TRiC with Stat3 is coupled to Stat3 biogenesis . In addition to folding newly synthesized proteins , TRiC also functions to refold native proteins within the cell that become denatured under stress . To determine if TRiC refolds unfolded Stat3 in vitro , Stat3 protein chemically denatured in guanidine hydrochloride was added to RRLs before and after ATP depletion and the levels of refolded Stat3 assessed by native PAGE ( Figure 2 ) . RRL extracts before and after in vitro Stat3 translation were run on the same gel to indicate the location of native or refolded Stat3 and aggregated Stat3 . RRLs contained low levels of native endogenous Stat3 , which , as expected , increased markedly following in vitro translation ( Figure 2A , lanes 1 and 2 ) . Also , a small amount of endogenous Stat3 is unable to enter the gel , indicative of aggregate formation; in vitro translation increased the amount of aggregate detected , in addition to the amount of native folded Stat3 . Addition of denatured Stat3 into RRLs depleted of ATP resulted in a marked increase in the amount of Stat3 aggregate detected , as well as a decrease in the levels of endogenous native Stat3 detected ( Figure 2A , lane 3 ) ; the latter is most likely due to co-aggregation of native endogenous Stat3 with exogenous denatured Stat3 . In contrast , addition of denatured Stat3 to RRLs not depleted of ATP resulted in the reappearance of folded Stat3 and a marked decrease in Stat3 aggregate detected ( Figure 2A , lane 4 ) . Thus , TRiC-containing RRLs are capable of folding denatured Stat3 in the presence of ATP . In addition to bands representing native Stat3 and aggregated Stat3 , a third Stat3-containing band was detected particularly within the RRL samples containing the greatest levels of folded Stat3 ( Figure 2A , lanes 2 and 4 ) . Immunoblotting indicated that this Stat3-containing band co-migrated with TRiC ( Figure 2B ) , indicating that it most likely represents a complex of TRiC binding to folding intermediates of Stat3 . Of note , TRiC does not bind to aggregates of Stat3 ( Figure 2A , lane 3; Figure 2B , lane 3 ) , in contrast to aggregated proteins containing expanded polyglutamine repeats ( Tam et al . , 2006 ) [16] . These results strongly suggest that , in addition to de novo Stat3 protein folding , TRiC within RRL participates in the refolding of denatured Stat3 protein in vitro . Having established that TRiC interacts with newly translated and denatured Stat3 in RRLs , promoting its synthesis and folding in vitro , we asked if this interaction could be detected in vivo within mammalian cells . We transfected murine embryonic fibroblast cells in which Stat3 was deleted using Cre-Lox technology ( MEF/Stat3Δ/Δ cells ) [17] with Flag-tagged Stat3 . TRiC was immunoprecipitated from lysates of these cells in the absence or presence of exogenous ATP ( 2 mM ) . Stat3 co-immunoprecipitated with TRiC in the absence of ATP ( Figure 3A and B ) . In addition , the amount of Stat3 that co-immunoprecipitated with TRiC was reduced substantially in the presence of ATP ( Figure 3B ) , indicating that , similar to other TRiC clients , the interaction of Stat3 with TRiC is ATP dependent . To determine the contribution of TRiC to the levels of total and activated Stat3 ( Stat3 phosphorylated on Y705 , pStat3 ) within cells , we used shRNA targeting CCT2 to reduce the levels of TRiC within two cells lines ( HS-578T and HepG2 ) , both previously demonstrated to have constitutively activated Stat3 [18] , [19] . Immunoblotting demonstrated 90% and 70% reduction of TRiC CCT2 in HS-578T and HepG2 cells , respectively , following stable transfection with CCT2 shRNA vector compared to control cells ( Figure 4A and 4E ) . In addition to reduction in levels of CCT2 , other CCT subunits not targeted by shRNA ( CCT1 and 5 ) were similarly decreased in CCT2 knockdown cells ( Figure S1 ) , confirming previous observations that excess CCT subunits not within TRiC complexes are degraded [20] . Knockdown of TRiC in HS-578T cells decreased constitutively activated Stat3 ( pStat3 ) levels by 74% ( p = 0 . 0026; Figure 4B ) . The decrease in pStat3 levels resulted from both a decrease in the pool of total Stat3 , which was decreased by 41% ( p = 0 . 0056; Figure 4C ) , and a decrease in the proportion of total Stat3 that was phosphorylated , which was decreased by 56% ( p = 0 . 0072; Figure 4D ) . Similarly , knockdown of TRiC in HepG2 cells decreased pStat3 levels by 46% ( p = 0 . 0176; Figure 4F ) . In contrast to HS-578T , however , there was no decrease in total Stat3 ( Figure 4G ) ; rather , the decrease in pStat3 levels was due entirely to a 44% decrease in the proportion of total Stat3 that was phosphorylated ( p = 0 . 0124; Figure 4H ) . Thus , levels of constitutively activated Stat3 ( pStat3 ) and total Stat3 are decreased in cells in which TRiC levels are reduced; pStat3 levels are more sensitive to reduced TRiC than total Stat3 levels , which appear to require 90% or greater reduction in TRiC to be affected . To determine if levels of cytokine-activated Stat3 are affected by TRiC reduction in addition to constitutively activated Stat3 , we examined the levels of pStat3 in IL-6 stimulated control and TRiC knockdown HepG2 cells . Use of the HepG2 control and TRiC knockdown cells , as opposed to their HS-578T counterparts , allowed us to examine the levels of cytokine-activated Stat3 independent of levels of total Stat3 . HepG2 cells were treated for 30 min with increasing concentrations of IL-6 in the most sensitive portion of the dose–response curve ( 0 , 0 . 1 , 0 . 3 , and 1 ng/ml ) ( Figure 5 ) . Levels of pStat3 in knockdown cells normalized to total Stat3 were reduced at 0 . 1 ng/ml and 0 . 3 ng/ml IL-6 concentrations ( p<0 . 05 for both ) . These results indicated that TRiC contributes to maximal levels of ligand-activated Stat3 within cells at sub-maximal ligand concentrations . Thus , levels of both constitutively activated and ligand-activated Stat3 are sensitive to decreased TRiC levels within cells . Stat3 consists of six distinct functional domains ( oligomerization or tetramerization , coiled-coil , DNA-binding , linker , Src-homology 2 , and transactivation; Figure 6 ) [2] , [21] . X-ray crystallography of the core Stat3 protein established the structural features of the four central domains [22] and demonstrated that two of the four domains ( DNA binding and Src-homology 2 ) contain β strands , the structural motif within TRiC client proteins shown previously to be preferentially bound by TRiC [23] , [24] . To identify Stat3 domains that participate in TRiC binding , we performed TRiC immunoprecipitation from RRLs that expressed individual Stat3 domains . Of the individual domains , the DBD had the strongest association with TRiC ( Figure 6 ) followed by the linker domain , the N-terminal domain , and the SH2 domain . Fifty-seven percent of the secondary structure of the Stat3 DBD consists of β strands , which form a β barrel similar to the DBD of other members of the NF-κB/NF-AT superfamily of which Stat3 is a member . Thus , the Stat3 TBD maps mainly to its β strand-rich DBD , which is consistent with the known client motif-binding preference of TRiC . There is compelling evidence that the subunits of TRiC differ in their substrate specificity , which suggests that Stat3 binding to TRiC likely is limited to a subset of CCT subunits [23] . To examine this hypothesis and to identify which CCT subunit ( s ) bind Stat3 , we mixed RRLs expressing a single radiolabeled TRiC subunit with RRLs expressing either unlabeled Flag-tagged Stat3 or no specific protein; each RRL mixture was incubated with a reversible protein cross-linker ( DSP ) followed by immunoprecipitation with M2 anti-Flag antibody-bound agarose beads . The immunoprecipitated and cross-linked protein complexes were incubated with β-mercaptoethanol ( to reverse the cross-linking ) and analyzed by SDS-PAGE and autoradiography ( Figure 7A ) . These studies revealed that only CCT3 consistently bound to and cross-linked with Stat3 . To confirm and extend these results in vivo , we transiently expressed CCT3 or CCT7 ( negative control ) each tagged with mCherry into MEF cells stably expressing GFP-tagged Stat3α ( MEF/GFP-Stat3α ) [25] and examined them by confocal fluorescent microscopy . We previously demonstrated that the intracellular distribution of GFP-Stat3α in MEF/GFP-Stat3α cells changed from predominantly cytoplasmic in unstimulated cells to predominantly nuclear upon IL-6 receptor stimulation [25] . Both CCT3 and CCT7 were distributed predominantly within the cytoplasm in co-transfected cells in the absence of IL-6 receptor simulation ( Figure 7B ) . An increased fraction of CCT3 , but not CCT7 , co-translocated with GFP-Stat3α in the nucleus with IL-6 receptor stimulation , as assessed by image merging , which demonstrated a change in the color of the nucleus from green to yellow . We also performed ImagePro analysis to assess co-localization of GFP-Stat3α and CCT3 versus CCT7 in the absence or presence of IL-6 receptor stimulation . Using ImagePro to evaluate random microscopic fields , Pearson's correlation of co-localization within the nucleus for the CCT7 without IL-6 receptor stimulation ( Rr = 0 . 512 ) did not change with IL-6 receptor stimulation ( Rr = 0 . 493 ) ; in contrast , Pearson's correlation of co-localization within the nucleus for CCT3 increased markedly from Rr = 0 . 457 without IL-6 receptor stimulation to Rr = 0 . 800 with IL-6 receptor stimulation . In addition , ImagePro analysis of multiple fields of cells co-expressing GFP-Stat3α and either CCT3 or CCT7 and stimulated with IL-6/sIL-6Rα revealed a significantly higher Pearson's coefficient of co-localization within the nucleus for cells expressing CCT3 ( 0 . 90±0 . 06 ) versus CCT7 ( 0 . 62±0 . 21; p = 0 . 04 ) . The TBD of pVHL previously mapped to a 55 amino-acid residue , β-strand-rich [23] region within pVHL; addition of the TBD of pVHL ( vTBD ) to the N-terminus of dihydrofolate reductase ( DHFR ) , a protein that does not naturally bind to TRiC , conferred TRiC binding [23] . We hypothesized that adding the vTBD to Stat3 would increase binding of the resultant hybrid protein , vTBD-Stat3 , provided the vTBD bound to TRiC via CCT subunits that are distinct from those that bind the TBD of Stat3 ( sTBD ) ; pVHL binding to TRiC previously mapped to CCT1 and CCT7 [26] . To test this hypothesis , we placed the vTBD at the N-terminal end of Stat3 and examined its ability to bind to TRiC compared to wild-type Stat3 . The amount of vTBD-Stat3 that co-precipitated with TRiC from RRLs was increased over 2-fold compared to wild-type Stat3 ( Figures 1A and 8A ) . Binding of the vTBD to TRiC previously mapped to specific residues within two β strands located at the N-terminal end ( Box 1 ) and C-terminal end ( Box 2 ) of the vTBD [23] , [26] . A combination of point mutations of critical residues within Box 1 and 2 of the vTBD portion of vTBD-Stat3 reduced its ability to co-immunoprecipitate with TRiC to levels similar to those of wild-type Stat3 ( Figure 8A ) . Thus , addition of the vTBD to Stat3 increased its binding to TRiC; the increased binding , not unexpectedly , was mediated through Box 1 and Box 2 within vTBD . To assess if increased interaction of Stat3 with TRiC affects the function of Stat3 , we compared the activity of in vitro translated vTBD-Stat3 to wild-type Stat3 . We previously demonstrated that Stat3 bound with high affinity to a phosphotyrosyl-dodecapeptide ( p1068 ) through its Src-homology ( SH ) 2 domain [27] . This dodecapeptide is based on residues 1063 to 1074 within the epidermal growth factor receptor and contains a Y residue at position 1068 , which when phosphorylated was shown to recruit Stat3 [27] . Phosphopeptide pull-down assays demonstrated a 2 . 3-fold increase in the amount of vTBD-Stat3 that binds to phosphopeptide p1068 compared to wild-type Stat3 ( p<0 . 0028; Figure 8B ) . These results strongly suggest that addition of the vTBD to Stat3 not only increases its interaction with TRiC but also increases the fraction of SH2 domains within vTBD-Stat3 that are fully folded and functional compared to wild-type Stat3 .
Our understanding of how transcription factors achieve their folded and functional conformations within cells is incomplete . Here we demonstrate Stat3 , a transcription factor activated in up to 50% of cancers , binds TRiC/CCT chaperonin and that TRiC is required for Stat3 biosynthesis and activity in vitro and within cells . TRiC binding to Stat3 was mediated , at least in part , by TRiC subunit CCT3 . Stat3 binding to TRiC mapped primarily to its β-strand rich , DBD . Addition of a second TBD vTBD further increased its affinity for TRiC , as well as its function . Thus , Stat3 levels and function are regulated by TRiC and can be modulated through manipulation of substrate affinity and TRiC levels within the cell . Approaches used successfully to identify TRiC clients have included both directed and global strategies . The earliest recognized TRiC clients—β-actin [28] , the α and β subunits of tubulin [29]–[31] , and firefly luciferase [31] —were identified in TRiC complexes using nondenatured gel electrophoresis and co-immunoprecipitation of denatured recombinant proteins in solution or from RRLs . Additional TRiC clients were subsequently identified by ( i ) co-elution or co-immunoprecipitation with TRiC in RRLs ( actin-related protein and γ-tubulin [32] , G-transducin [33] , myosin heavy chain [34] , and histone deacetylase 3 [35] ) , ( ii ) TRiC binding in native gels ( cofilin , actin-depolymerization factor-1 , cofactor A , Cyclin B , cap-binding protein , CCT1 , H-ras , and c-Myc [36] , [37] ) , ( iii ) yeast two-hybrid screens or co-immunoprecipitation with TRiC from yeast cell lysates ( cyclin E , [38] , Cdc20 and Cdh1 [39] , polo-like kinase [40] , and sphingosine kinase 1 [41] ) , ( iv ) co-immunoprecipitation with TRiC following overexpression in human cell lines ( pVHL , [42] ) , and ( v ) mass spectrometry analysis to detect TRiC/CCT peptides within selected immunoprecipitates ( p53 , [43] ) . Taking a more global approach [13] , [24] , [44] , we recently used pulse-chase analysis involving TRiC immunoprecipitation , 2-D gel electrophoresis , and mass spectrometry to identify highly abundant TRiC clients including the WD repeat-containing translation initiation factor-3 and GAPDH [13] . In addition , we screened 2 , 600 clones from a murine C2C12 cell cDNA library using small-pool expression cloning in RRLs to identify low-abundant TRiC clients [13] . This approach identified 167 clients including proteins involved in cell cycle , cytoskeleton , protein degradation , DNA metabolism , meiosis , mitosis , carbohydrate metabolism , RNA processing , signal transduction , protein trafficking , transcription , and translation . Stat3 was not among the 167 TRiC clients identified in this screen most likely because of its lower abundance in the cell compared to other TRiC clients such as actin and tubulin and also due to the limited number of cDNAs screened . Our report represents the most comprehensive demonstration that an oncoprotein , Stat3 , is a TRiC client . As noted above , earlier investigators [36] demonstrated binding of radiolabeled , denatured H-ras and c-Myc binding to TRiC in native gel assays . However , the affinity of binding was low , the results were not confirmed by other methods , and the contribution of TRiC binding to H-ras or c-Myc biogenesis and function was not examined . There have been few studies that simultaneously compare the effects of targeting TRiC on the levels and function of multiple TRiC clients . In this regard , it is noteworthy that despite 90% reduction of TRiC within cells , levels of the TRiC clients , β-actin , GAPDH , and β-tubulin were not reduced ( Figures 4 and S1 , and unpublished data ) , in contrast to levels of pStat3 and total Stat3 ( Figure 4 ) . These results suggest a stricter requirement for TRiC to assist in Stat3 biogenesis and folding versus β-actin , GAPDH , or β-tubulin . Earlier reports have supported a contribution of chaperones to the biology of Stat3 . Stat3 previously was identified within the cytosol of unstimulated cells ( HepG3 ) , predominantly within high-molecular weight complexes in the size range of 200–400 kDa ( Statosome I ) and 1–2 MDa ( Statosome II ) [45] . Antibody-subtracted differential protein display using anti-Stat3 antibody identified the chaperone GRP58/ER-60/ERp57 as a major component , along with Stat3 , of Statosome I [45] . The composition of Statosome II was not determined; however , it is of interest , in light of our current findings , that its size is consistent with that of TRiC . Stat3 was demonstrated to directly interact with Hsp90 [46] , [47] , and its activation was linked to Hsp90 following IL-6 stimulation [48] . In addition , inhibition of Hsp90 using pharmacological inhibitors ( 17-DMAG ) or siRNA decreased levels of pStat3 in human primary hepatocytes and levels of total Stat3 in multiple myeloma cells . We have shown by immunoprecipitation studies in RRLs that the interaction between TRiC and Stat3 occurs predominantly through the CCT3 of TRiC binding to the DBD of Stat3 . Other domains of Stat3 , including the N-terminal domain , linker domain , and SH2 domain , also were pulled down with TRiC , although at lower levels than the DBD indicative of lower affinity interactions . Of these domains , only the SH domain contains β-strands . We demonstrate in this report that chaperonin–client interactions can be modulated not only to reduce client function but also to increase it . Knockdown of TRiC using shRNA reduced total Stat3 protein levels , reduced constitutively activated Stat3 , and reduced the sensitivity of Stat3 to IL-6–mediated activation . These findings provide proof-of-principle that targeting TRiC , or more specifically targeting the interaction between TRiC and Stat3 , may provide a novel approach to reducing levels of activated Stat3 for therapeutic benefit in cancer . Alternatively , addition of a new TRiC binding site to Stat3 through a covalent modification significantly improved its affinity for TRiC and its function . This result serves as proof-of-concept that future development of noncovalent modulators that increase TRiC–Stat3 interactions may be a novel approach to increasing Stat3 activity in the setting of acute injuries such as myocardial infarct [49] or traumatic injury where enhanced Stat3 has been demonstrated to prevent apoptosis of parenchymal cells within critical organs including cardiomyocytes , alveolar epithelial cells , and hepatocytes [50]–[52] .
All cell lines were maintained at 37°C in 5% CO2 in DMEM with 10% fetal bovine serum , 1% penicillin/streptomycin , and Glutamax . MEF/Stat3Δ/Δ cells were provided as a generous gift from Dr . Valeria Poli [17] . MEF/Stat3Δ/Δ cells stably transfected with GFP-Stat3α were generated as described [53] . HepG2 and HS578T cells in which TRiC was stably knocked down were generated by lentiviral transduction of shRNA targeting TCP1β ( CCT2 ) within lentiviral transduction particles used according to the manufacturer's protocols ( Sigma-Aldrich ) and selection with puromycin . The coding sequence for human Flag-tagged pVHL and Stat3 were subcloned into a modified pSG5 vector as XhoI/HindIII and HindIII/NotI fragments , respectively . Stat3 was also engineered to contain the 55 amino-acid TBD of pVHL ( vTBD ) at the N-terminal end to generate vTBD-Stat3 . The sequence encoding the 55 amino acids was generated by PCR using VHL cDNA as template and subcloned into the mammalian expression vector pSG5 . For mapping the TBD within Stat3 , sequences encoding each of the six domains of Stat3 were generated by PCR and cloned into pSG5 vector . All inserts were sequenced to ensure fidelity . In vitro transcription and translation reactions ( 50 µl ) were carried out using an RRL system ( TNTT7 Quick Coupled Transcription/Translation System; Promega , Madison , WI ) with 1 µg of pSG5 vector containing the insert of interest according to the manufacturer's instructions for 30 min at 30°C , and terminated by the addition of 2 mM puromycin , 5 mM EDTA , and 1 mM azide . Protein translation was monitored by SDS-PAGE followed by autoradiography . Where indicated , RRLs were depleted of ATP by incubation for 10 min at RT with apyrase ( 50 U/ml ) . TRiC immunoprecipitations were performed as described [42] . Briefly , 10 µl of each RRL reaction was diluted to 50 µl with buffer ( 25 mM Tris , pH 7 . 5 , 10% glycerol , 5 mM ETDA , 100 mM NaCl , 1 mM azide ) and treated with antibodies to TRiC ( mixture of rabbit anti-CCT2 and anti-CCT5 ) or rabbit anti-human IgG control antibody ( Abcam , Cambridge , MA ) for 45 min on ice . The mixture was incubated with 30 µl of Protein A-Sepharose and gently agitated on ice for 40 min . The immune complexes were pelleted and then washed 4 times with buffer containing 0 . 5% Triton X-100 . The protein samples were separated by SDS-PAGE and visualized by autoradiography . For immunodepletion of TRiC , 6 µg of rabbit anti-CCT1 antibody or rabbit anti-human IgG control antibody was added to RRLs and incubated at 4°C for 4 h . The immune complexes were bound by Protein A-agarose and removed by centrifugation . For immunoprecipitation analysis of Stat3/TRiC interactions in mammalian cells , murine embryonic fibroblast cells in which Stat3 was deleted using Cre/Lox technology ( MEF/Stat3Δ/Δ cells ) [17] were plated in 100 mm plates and transfected with PSG5 Flag-Stat3 construct using GeneJuice transfection reagent per the manufacturer's instructions ( EMD Millipore ) ; 48 h after transfection , the cells were washed and harvested in ice-cold PBS or ATP depletion buffer ( PBS containing 2 mM deoxyglucose , 1 mM sodium azide , and 5 mM EDTA ) . Lysates were prepared by dounce homogenization ( 70 strokes; pestle B ) in 1 ml of lysis buffer A ( 20 mM HEPES , pH 7 . 5 , 100 mM NaCl , 5 mM EDTA , 5% glycerol ) or Buffer B without 5 mM EDTA , containing instead 2 mM ATP and 5 mM MgCl2 . The lysates were either used immediately or snap frozen in liquid nitrogen and stored at −80°C . Lysate ( 100 µl ) was incubated on ice for 1–2 h with 3 µl each of rabbit antibodies to CCT2 and CCT5 or rabbit IgG anti-human control antibody . Magnetic protein A beads ( 50 µl equivalent; Invitrogen ) were resuspended in the antibody/lysate mixtures and incubated for 10 min on ice . Beads were washed two times with 500 µl of buffer A and two times with buffer A containing 1% Triton ×100 . Bound proteins were separated by SDS-PAGE and immunoblotted . RRL reactions containing 35S-methionine-labeled TRiC subunits were mixed with RRLs containing either an unlabeled Flag-tagged Stat3 construct or no expression construct; each RRL mixture then was incubated with the reversible protein cross-linker DSP ( dithiobis[succinimydyl-propionate] ) at a final concentration of 2 mM . Samples were quenched by 10-fold dilution in 20 mM Tris pH 7 . 5 containing 10% glycerol prior to immunoprecipitation with M2 anti-Flag antibody-bound agarose beads . The immunoprecipitated and cross-linked protein complexes then were incubated with β-mercaptoethanol ( 5 mM ) to reverse the cross-linking and analyzed by SDS-PAGE and autoradiography . Biotin-dodecapeptide 1068 or biotin-phosphododecapeptide p1068 ( 10 µl at 10 mg/ml ) were synthesized commercially based on the sequence within the cytoplasmic portion of the EGFR surrounding Y1068 previously shown to bind Stat3 through its SH2 domain [27] . Each peptide was added to 100 µl of 50% ( v/v ) Neutravidin beads ( Thermo Fisher Scientific , Rockford , IL ) in PBS and incubated overnight . The beads were washed 3 times with PBS and resuspended in 100 µl buffer 25 mM HEPES pH 7 . 5 containing 20% glycerol , Phostop ( Roche ) , and protease inhibitor cocktail ( Sigma-Aldrich , St . Louis , MO ) . RRL volumes were adjusted to match input protein concentration based on densitometry results . The lysates were diluted 50-fold in 100 µl buffer and treated with Neutravidin beads immobilized with either p1068 or control 1068 dodecapeptide . The samples were incubated for 1 h at 4°C . The beads were then washed 4 times with 500 µl of HEPES buffer containing 0 . 5% NP40 and analyzed by SDS-PAGE and autoradiography . To reduce TRiC levels within cells , MISSION shRNA lentiviral transduction particles targeting CCT2 sequence ( SHCLNV-NM-006431 ) and control shRNA lentiviral particles were obtained from Sigma-Aldrich . HepG2 and HS-578T cells were transduced with CCT2 shRNA or control shRNA lentiviral particles at an MOI of 5 and grown for 48 h . Stable TRiC knockdown cells were established by replating the cells in selection media containing puromycin at 3 µg/ml . CCT2 knockdown or control HepG2 cells were cultured in six-well plates overnight in DMEM supplemented with 10% fetal bovine serum ( FBS , GIBCO-BRL , Invitrogen , Carlsbad , CA ) containing 2 mM Glutamax ( GIBCO-BRL ) , 10 mg/ml penicillin , and 10 mg/ml streptomycin ( GIBCO-BRL ) at 37°C in 5% CO2 . The cells were then treated with IL-6 at 0 , 0 . 1 , 0 . 3 , and 1 ng/ml for 30 min at 37°C , then lysed in M-PER lysis buffer ( Thermo Fisher Scientific ) supplemented with phosphatase and protease inhibitor cocktails . For immunoblotting , total protein concentration was measured within M-PER lysates using the Coomassie assay kit ( Bio-Rad , Hercules , CA ) with BSA as a standard . Total cell lysates containing 20 µg of total protein were separated by 7 . 5% SDS-PAGE and transferred onto PVDF membranes . The membranes were blocked with 5% BSA in 0 . 1% TBS-T ( 0 . 1% Tween-20 in TBS ) for 1 h and incubated overnight at 4°C in blocking buffer with one of the primary antibodies against total Stat3 , pStat3 ( BD-Biosciences ) , β-actin , GAPDH , CCT1 , CCT2 , and CCT5 ( Abcam ) ; washed; incubated with appropriate secondary antibodies conjugated to horseradish peroxidase; washed; and developed with ECL substrate ( Thermo Scientific ) . For Luminex bead assays , cells were lysed and assayed for pStat3 , total Stat3 , and GAPDH using Millipore ( Milliplex ) kits as described by the manufacturer . Levels of each protein analyte were determined and analyzed using the Bio-Plex suspension array system ( Bio-Rad ) . Transformed MEF cells stably expressing GFP-Stat3α ( MEF/GFP-Stat3α ) were plated on polylysine-coated glass coverslips . After 12 h the cells were transfected with mCherry-CCT3 or mCherry-CCT7 fusion constructs using GeneJuice reagent ( EMD4 Biosciences ) according to the manufacturer's instructions . Forty-eight hours later , cells were treated with or without IL-6/sIL-6R ( ∼250 ng/ml ) for 30 min . The coverslips were rinsed with ice-cold PBS twice and fixed for 30 min with 4% paraformaldehyde in PEM buffer ( 80 mM potassium PIPE , pH 6 . 8 , 5 mM EGTA , and 2 mM MgCl2 ) at 4°C , rinsed 3× with PEM buffer , quenched in 1 mg/ml NaBH4 ( Sigma ) in PEM buffer for 5 min , then permeabilized with 0 . 05% Triton X-100 in PBS for 5 min , counterstained with 4′ , 6-diamidino-2-phenylindole ( DAPI , Invitrogen ) , mounted onto slides , and imaged using confocal fluorescence microscopy . TRiC was purified from bovine testes essentially according to previously established procedures [23] , [54] . To achieve extra purity and remove co-purifying bound substrates , prepared TRiC fractions were incubated for 15 min at 37°C in the presence of 1 mM ATP and then re-applied to MonoQ HR 16/10 ( GE Healthcare , USA ) and Superose 6 10/300 GL column ( GE Healthcare , USA ) in sequence . Ultra-purified TRiC retained its full capacity to fold substrate as assessed in a luciferase refolding assay performed as described [55] . The sequence encoding the human full-length Stat3 was expressed in Escherichia coli as a fusion to a C-terminal His10-tag in pET28a ( Novagen ) expression vector . The transformed cells were induced with 1 mM isopropyl-β-d-thiogalactopyranoside ( IPTG ) at an optical density of 0 . 7 at 600 nm . After growing for 5 h at 37°C , cells were harvested; resuspended in buffer containing 20 mM Tris-HCl , pH 8 . 0 , 150 mM NaCl , 1 mM DTT , 1% Triton X-100 , and 100 µg/ml lysozyme; and lysed by sonication . The insoluble fraction containing Stat3 as inclusion bodies was collected by centrifugation and washed extensively with a buffer containing 2 M urea . The washed pellet was solubilized in 0 . 1 M NaH2PO4 , 10 mM Tris-HCl , pH 8 . 0 , 300 mM NaCl , 5 mM imidazole , 10% glycerol , and 6 M guanidinium hydrochloride ( Gn-HCl ) . After centrifugation , the supernatant was applied onto a Ni-NTA column ( Qiagen ) equilibrated in the same buffer . The column was washed successively with buffer A ( 0 . 1 M NaH2PO4 , 10 mM Tris-HCl , pH 6 . 3 , 300 mM NaCl , 6 M Gn-HCl ) and buffer B ( buffer A at pH 5 . 9 ) . Proteins were eluted from the column with buffer C ( buffer A at pH 4 . 5 ) , concentrated , and buffer exchanged into buffer C containing 1 mM DTT . Proteins were stored at −80°C until use . | Stat3 is a multidomain transcription factor that contributes to many cellular functions by transmitting signals for over 40 peptide hormones from the cell surface to the nucleus . Understanding how multidomain proteins achieve their fully folded and functional state is of substantial biological interest . As Stat3 signaling is up-regulated in many pathological conditions , including cancer and inflammatory diseases , insight into what controls its folding may be useful for the identification of vulnerabilities that can be therapeutically exploited . We demonstrate that the major protein-folding machine or chaperonin within eukaryotic cells , TRiC/CCT , is required for Stat3 to fold during its synthesis and for Stat3 to be fully functional within the cell . We also find that TRiC can refold chemically denatured Stat3 and provide evidence that the CCT3 subunit of TRiC binds to the DNA-binding domain of Stat3 . We also show that Stat3 activity is decreased by down-modulating levels of TRiC and can be increased by increasing Stat3's interaction with TRiC . TRiC therefore regulates both Stat3 protein levels and its function , making Stat3 modulation by manipulation of its interaction with TRiC a potential approach for the treatment of cancer and inflammatory diseases . | [
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| 2014 | Modulation of STAT3 Folding and Function by TRiC/CCT Chaperonin |
Rhizobial infection of legume root hairs requires a rearrangement of the actin cytoskeleton to enable the establishment of plant-made infection structures called infection threads . In the SCAR/WAVE ( Suppressor of cAMP receptor defect/WASP family verpolin homologous protein ) actin regulatory complex , the conserved N-terminal domains of SCAR proteins interact with other components of the SCAR/WAVE complex . The conserved C-terminal domains of SCAR proteins bind to and activate the actin-related protein 2/3 ( ARP2/3 ) complex , which can bind to actin filaments catalyzing new actin filament formation by nucleating actin branching . We have identified , SCARN ( SCAR-Nodulation ) , a gene required for root hair infection of Lotus japonicus by Mesorhizobium loti . Although the SCARN protein is related to Arabidopsis thaliana SCAR2 and SCAR4 , it belongs to a distinct legume-sub clade . We identified other SCARN-like proteins in legumes and phylogeny analyses suggested that SCARN may have arisen from a gene duplication and acquired specialized functions in root nodule symbiosis . Mutation of SCARN reduced formation of infection-threads and their extension into the root cortex and slightly reduced root-hair length . Surprisingly two of the scarn mutants showed constitutive branching of root hairs in uninoculated plants . However we observed no effect of scarn mutations on trichome development or on the early actin cytoskeletal accumulation that is normally seen in root hair tips shortly after M . loti inoculation , distinguishing them from other symbiosis mutations affecting actin nucleation . The C-terminal domain of SCARN binds to ARPC3 and ectopic expression of the N-terminal SCAR-homology domain ( but not the full length protein ) inhibited nodulation . In addition , we found that SCARN expression is enhanced by M . loti in epidermal cells and that this is directly regulated by the NODULE INCEPTION ( NIN ) transcription factor .
In most eukaryotic cells , the reversible association of G-actin subunits can result in the dynamic formation of actin filaments that play essential roles in changing cell shape and controlling nuclear localization . In addition , actin filaments form a framework along which vesicles and proteins can be trafficked to localized sites on the plasma membrane . In non-plant cells , cellular protrusions of the plasma membrane are critical for cell migration and are induced by polymerization and branching of actin [1] . A complex containing actin-related proteins 2/3 ( ARP2/3 ) induces nucleation of branched actin that can drive the cellular protrusions . In the plant kingdom , the actin nucleation activity of the ARP2/3 complex is solely regulated by the SCAR/WAVE ( Suppressor of cAMP receptor defect/WASP family verprolin-homologous protein ) complex [1] . Due to the presence of the cell wall , membrane protrusions do not normally occur in plant cells , but nevertheless there are highly dynamic changes to the actin filaments catalyzed by nucleation of actin branching and actin polymerization and de-polymerization [2] . However , in contrast to non-plant cells , cytoskeletal control of plant-cell shape must be indirect , primarily mediated by the delivery of cell-wall remodeling proteins to localized sites in the cell periphery [3] . The SCAR/WAVE-ARP2/3 system plays an important role in polar growth of some plant cells such as trichomes and root hairs where mutations cause developmental phenotypes . In legumes , identification of infection-defective mutants has revealed that there is a special requirement for the SCAR/WAVE-ARP2/3 system during nodule infection . During the bacterial initiation of nitrogen-fixing nodules on the roots of legumes , the rhizobia gain entry to the root via plant-made structures called infection threads along which the bacteria grow [4] . The infection thread , which is usually initiated in a root hair , is essentially an intracellular tube formed by an invagination of the plant cell wall and membrane [4 , 5] . This requires a new type of inwardly-directed polar growth that appears to be controlled by the position of the root-hair nucleus [6] . Based on the patterns of induction of cell-cycle related genes and analysis of nuclear enlargement , infection thread growth appears to be associated with the activation of a partial cell cycle that involves nuclear endoreduplication but not nuclear division [7] . Establishment of infection threads in root hairs requires rhizobial production of signals called Nod factors . The first plant morphological response to Nod factors is for the root hair to deform , sometimes curling back on itself , enclosing the bacteria in an infection pocket; this involves rearrangements in the actin cytoskeleton [8–11] . A transient increase in rapidly-growing actin microfilaments is induced in root hairs 2–5 min after Nod-factor addition [12] . After rhizobial entrapment , a new phase of development in the root hair leads to the initiation of growth of the infection thread [13 , 14] and this involves a second phase of redistribution of actin polymerization sites , this time to the point of rhizobial entry [12] . As the infection proceeds , a second developmental program is activated in the root cortex leading to morphogenesis of the nodule structure , that will eventually be infected following the extended growth of the infection threads through the root hair cells into the root cortex [15 , 16] . Critical to the activation of the infection and nodule development programs is a signaling pathway activated by rhizobial Nod factors [17 , 18] . Following Nod-factor binding to plasma-membrane receptors , calcium oscillations are induced in and around the root-hair nuclei and these oscillations are detected by a calcium and calmodulin-dependent kinase that activates a transcription factor called CYCLOPS [19 , 20] . Downstream of CYCLOPS there is activation of another transcription factor NIN , which is needed for infection-pocket and infection-thread development [21] . After rhizobial entrapment , a second signaling pathway is activated; this appears to require both higher levels and higher structural specificity of Nod factors and induces a calcium influx across the plasma membrane; if the appropriate host-specific decorations are absent from the Nod factors , both the calcium influx and infection thread initiation are significantly reduced [22 , 23] . It has been suggested that this pathway may be activated by the ROP-GAP pathway and is also related to the production of reactive oxygen species via an NADPH oxidase on the plasma membrane [18 , 22] . Although there is high signaling specificity , it is evident that some endophytic rhizobia-like bacteria ( lacking nod genes ) can gain entry using infection threads initiated by nodulation-competent rhizobia[24] . Several nodulation-defective mutants have been identified in which infection pockets are initiated or established , but most infection thread growth is blocked; some of these mutations are in regulatory genes such as , NIN , LIN , RPG , ERN1 and NFYA1 [21 , 25–29] . However other mutations are in genes that appear likely to be involved with structural requirements associated with infection thread initiation . One of these is in a nodulation-specific pectate lyase gene ( NPL ) the product of which is thought to locally degrade plant cell walls to allow initiation of infection thread growth . The NPL gene is directly regulated by NIN in response to Nod factor signaling [30] . Three mutants with similar phenotypes to the npl mutant are in the genes NAP ( for Nck-associated protein 1 ) [31 , 32] , PIR ( for 121F-specific p53 inducible RNA ) [31] and ARPC1 ( for actin related protein complex 1 ) [33] . These proteins participate in promoting actin nucleation via the SCAR/WAVE-ARP2/3 complex . One possibility is that the SCAR/WAVE-ARP2/3 complex directs to the sites of infection thread initiation and growth , the cell-wall degrading enzymes ( such as NPL ) and the cell-wall and cell-membrane synthesis enzymes that will be required to establish an infection thread . If this model is correct , analysis of genes required for infection-thread initiation may give us insights not only into how legumes accommodate infection by rhizobia , but also into aspects of how the SCAR/WAVE-ARP2/3 complex operates and can be adapted for special situations in plant development . One of the questions with regard to the operation of the SCAR/WAVE-ARP2/3 complex in legume infection is whether it simply uses existing components or whether there is induction of components to facilitate rhizobial infection . In this work , we have identified a novel gene SCARN ( SCAR-Nodulation ) required for initiation of infection threads and this gene contains domains typical of SCAR proteins . However , although the gene product is conserved in legumes , it is novel because it is not closely related to any of the SCAR-family proteins identified in Arabidopsis . This gene is directly regulated by the nodulation transcription factor NIN . Intriguingly , two of the mutations in SCARN resulted in constitutive induction of root-hair branching , a phenotype normally induced by Nod-factors .
To understand the molecular mechanisms of infection thread formation during rhizobial infection of L . japonicus roots , we screened an ethyl-methane-sulfonate ( EMS ) mutagenized population [34–36] of L . japonicus Gifu B-129 for defects in infection by M . loti . Mutants defective for nitrogen fixation were initially identified based on nitrogen starvation ( yellowing ) of the leaves of plants grown under limiting nitrogen and then screening for the absence of nodules , or the presence of white nodules , indicative of an ineffective symbiosis . Candidate mutants were then inoculated with a strain of M . loti expressing a β-galactosidase ( lacZ ) gene and nodules formed were then stained with X-gal to determine if they were , or were not infected . The various mutants that appeared to have mostly uninfected nodules were crossed with the wild-type Miyakojima ( MG20 ) to generate mapping populations . Rough mapping using established DNA markers http://www . kazusa . or . jp/lotus/ revealed that four of the mutations were localized on L . japonicus linkage group 3 , between markers TM0111 and TM0707 . These mutations were in lines SL2654-3 , SL5737-2 , SL6119-2 and SL1058-2 and the F2 populations generated from crosses with MG20 were scored for nodulation defects . All of the mutations segregated 3:1 ( p < 0 . 05 ) based on the numbers ( shown in parentheses ) of WT and mutant plants in the F2 populations from crosses with SL2654-3 ( 246 Nod+/78 Nod- , x2 value = 0 . 0337 ) ; SL5737-2 ( 144 Nod+/33 Nod- , x2 value = 1 . 8165 ) ; SL6119-2 ( 236 Nod+/67 Nod- , x2 value = 0 . 5631 ) and SL1058-2 ( 200 Nod+/59 Nod- , x2 value = 0 . 244 ) . These data suggested that we had identified four allelic monogenic recessive mutations and so allelism was tested in reciprocal crosses . When SL2654-3 was crossed with SL5737-2 , SL1058-2 and SL6119-2 , or SL6119-2 was crossed with SL5737-2 , all the F1 plants produced no nodules or only small white bumps ( S1 Table ) . The results of these reciprocal crosses confirmed that the four mutations are allelic , and so fine mapping for positional cloning and analyses of phenotypes were done primarily with one mutant ( SL2654-3 ) . From crosses between SL2654-3 and MG20 , 439 mutant progeny were identified . Genotyping of these mutants delimited the mutated locus in SL2654-3 between markers TM1465 and TM0116; there was no recombination with marker BM2233 ( S1 Fig ) . We analyzed the predicted protein sequences of genes in this region and identified a large gene composed of nine exons ( Fig 1A ) encoding a predicted 1627 amino-acid protein , that has domains with similarity to domains typically found in SCAR ( suppressor of cAMP receptor ) proteins . SCAR proteins are components of the SCAR/WAVE complex , which is known to participate in the regulation of actin nucleation in Arabidopsis thaliana [37] . Since other components of the SCAR/WAVE complex had previously been identified as being required in legumes for root-hair infection by rhizobia , we thought the gene encoding a protein with domains showing similarity to SCAR protein was a good candidate . Amplification and sequencing of this gene from SL2654-3 revealed a C-T transition , leading to a stop codon replacing Q1199 . We amplified and sequenced the gene from the other three mutants and all had mutations ( Table 1 and Fig 1A ) : SL5737-2 has a G-A transition at the third exon splice acceptor site , SL6119-2 has a C- T transition causing a premature stop replacing at Q1100 , and SL1058-2 has 1 base pair deletion in the sixth exon causing a frame shift . Some time after the detailed analyses of these mutants , we also obtained a LORE1 insertion ( 30053577 ) in this gene from the L . japonicus LORE1 retrotransposon mutagenesis pool at Aarhus University , Denmark [38 , 39]; this mutant was also defective for infection and nodulation . The wild-type gene was amplified from the genome of Gifu B-129 and cloned behind the ubiquitin promoter in the vector pUB-GW-GFP [40] . The construct was introduced into roots of SL2654-3 by Agrobacterium rhizogenes-mediated hairy root transformation . Normal nodulation was restored in the SL2654-3 mutant ( Fig 1C and Table 2 ) . Taken together , it is clear from the multiple alleles and complementation data , that mutations in the SCAR-like gene caused the defect in nodule infection . As described below , although there are conserved domains , the overall predicted protein sequence was not strongly homologous to any of the Arabidopsis SCAR proteins . Furthermore the identified gene is increased in expression during nodule infection and so we named the gene SCARN ( SCAR-Nodulation ) . Accordingly we named the alleles in SL2654-3 , SL5737-2 , SL6119-2 and SL1058-2 scarn-1 , scarn-2 , scarn-3 and scarn-4 respectively and we named the LORE1 retrotransposon mutant allele scarn-5 ( Table 1 ) . Although the scarn mutants showed symptoms of nitrogen-starvation on nitrogen-deficient nutrient medium ( Fig 2A ) , they grew normally when supplied with nitrate ( S2A Fig ) . All five scarn mutants produced small white nodules ( hereafter called bumps ) three weeks after inoculation with M . loti ( Fig 2C and S2B Fig ) . The scarn-5 mutant had the most severe phenotype , producing only a couple of white nodule-like bumps four weeks after inoculation ( S2C Fig ) . However , 4–5 weeks after inoculation , some scarn mutants produced a few pale-pink nodules ( Fig 2D ) . Sections of the bumps and pale-pink nodules were examined by light microscopy showing that the bumps were uninfected nodules ( Fig 2F ) , whereas the wild type formed fully infected nodules ( Fig 2E ) . The occasional pale-pink nodules were partially infected and some had the characteristic ‘butterfly’ structure ( Fig 2G ) , indicative of infections via intercellular entry [41] . The net effects of the scarn mutations on nodulation were to greatly reduce the numbers of pink nodules and to induce the formation of more white bumps compared with wild type plants ( Fig 2H ) . Since the scarn1-4 mutants were similar , and we did not obtain the scarn-5 mutant until relatively late in the project , we used the scarn-1 mutant in all subsequent experiments except where noted . Some legume nodulation mutants are also defective for the arbuscular mycorrhizal symbiosis . Therefore , we also scored the scarn-1 mutant for infection by the mycorrhizal fungus Glomus intraradices . Normal infection and arbuscule formation was observed ( S3A–S3C Fig ) . The absence of bacteria in most of the nodules of the scarn mutants suggested that rhizobial infection was abnormal . Assays of infection by M . loti constitutively expressing a green fluorescent protein ( GFP ) or a β-galactosidase ( lacZ ) marker gene showed that the scarn-1 mutant was defective for infection thread formation . Unlike the wild-type , in which infection threads initiated from curled root hairs and extended through the epidermal cells ( Fig 3A ) , most of the infections of scarn-1 were blocked at the stage of formation of infection pockets ( Fig 3B ) and occasionally , some infection threads grew within root hairs of the scarn-1 mutant ( Fig 3C ) . In some of the few infections that did occur in the mutant , it appeared that the bacteria were released into the root hair cell ( Fig 3D and 3E ) , a phenomenon that has been noted in several infection mutants [42] . After 5–7 days , infection threads in wild-type plants ramified into the cortex ( Fig 3F ) , but in the scarn-1 mutant , although occasional infection threads extended to the base of the root hair , they did not extend deep into the cortex ( Fig 3G ) . Analyses of infection of the scarn-1 mutant 1 and 2 weeks after inoculation ( Fig 3H ) revealed that the total number of initiated infections was not reduced in the scarn-1 mutant , but most of these events were arrested in the infection foci in root hairs . During the initiation of the rhizobial-legume symbiosis , several genes including NIN , NPL and ENOD40-1 , are induced in response to rhizobia or Nod factors . These genes may be involved in rhizobial infection and/or nodule organogenesis and so we used quantitative real-time PCR to assay their induction in the scarn-1 mutant by M . loti . Five days after inoculation with M . loti , the NIN , ENOD40-1 and NPL genes were strongly and similarly induced in both the scarn-1 mutant and wild-type ( S4 Fig ) showing that scarn is not required for their expression . In Arabidopsis thaliana , mutations in SCAR2 cause distorted trichomes ( AtSCAR2 is also called DISTORTED3 or IRREGULAR TRICHOME BRANCH1 ) [43 , 44] . Mutations in some other components ( nap , pir and arpc1 ) of the SCAR/WAVE-ARP2/3 complex in legumes also cause distorted trichomes , and affect root-hair growth and pod and seed development [31–33] . Visual inspection did not reveal obvious differences in trichomes in the scarn mutants ( S5A Fig ) and scanning electron microscopy revealed that the trichomes in wild type and scarn-1 were indistinguishable ( S5B Fig ) . Moreover , there were no apparent defects in pod or seed development of scarn mutants compared with wild type ( S5C Fig ) . The scarn mutants formed slightly shorter root hairs than wild type ( S5D Fig ) . Interestingly , the scarn-1 and scarn-3 mutants produced branched root hairs in the mature zone even in the absence of M . loti ( Fig 4A ) . We assayed M . loti-induced root-hair deformation with the scarn mutants and saw that they had altered root hair deformation , with more branching and swelling of the root hairs ( Fig 4B ) . It appeared that the root hair swelling was associated with reduced root hair growth . These observations suggest that SCARN is required for both normal tip growth following exposure to M . loti and for the establishment of polar growth of infection threads . The other subunits of the SCAR/WAVE complex , LjPIR , LjNAP , MtNAP and LjARPC1 , are not only required for rhizobial infection , but are also required for normal trichome and root-hair growth , and seed development , as observed with their homologues in Arabidopsis . However , unlike the NAP , PIR and ARPC1 genes , SCARN is not required for the development of trichomes or normal pods and seeds in legumes . We analyzed SCARN transcript levels in different organs by quantitative real-time PCR and found no significant difference in expression in shoots , leaves , flowers and roots ( S6A Fig ) . We analyzed SCARN expression in roots at different time points after inoculation with M . loti: SCARN expression was slightly increased 1 and 7 days after inoculation , but its expression returned to basal levels 14 days after inoculation ( Fig 5A ) . This suggested that SCARN expression is enhanced during rhizobial infection . To further investigate this we analyzed the spatial and temporal expression of SCARN by generating A . rhizogenes-induced transgenic hairy roots carrying the β-glucuronidase ( GUS ) gene behind the SCARN promoter ( pSCARN:GUS ) . When inoculated with M . loti , we detected weak expression in epidermal cells under infection foci or elongated infection threads in the transformed roots ( Fig 5B ) confirming that SCARN is up-regulated during rhizobial infection . The strongest GUS expression was detected in young nodules ( Fig 5C and S6B Fig ) , sections of which indicated there was GUS activity in the epidermal and outer cortical cells but not in the central tissue ( Fig 5D ) . The observation that the scarn-1 and scarn-3 alleles caused root-hair branching ( Fig 4A ) implies that SCARN must be expressed in root hairs . Furthermore , the altered pattern of M . loti-induced root-hair deformation also implies that SCARN must be expressed in root hairs . However the SCARN expression must be below the level of detection by the pSCARN:GUS fusion . In mature nodules ( about 2 week post inoculation ) , the GUS activity was primarily seen at nodule vascular bundles ( Fig 5E and S6B Fig ) . In the absence of M . loti , pSCARN:GUS expression was detected in primary and lateral root tips , and sites of lateral root initiation , including pericycle cells and the lateral root meristem ( S6C–S6F Fig ) . Since ( a ) mutation of SCARN blocked most infections at the epidermis , and ( b ) SCARN was primarily expressed in epidermal cells of infected roots and in the epidermis of young nodules , we tested if epidermal-specific expression could rescue nodule infection in the scarn-1 mutant . We made a construct in which the epidermal-specific promoter pEpi [45] is upstream of the SCARN genomic DNA lacking its own promoter . We then generated A . rhizogenes-transformed hairy roots in the scarn-1 mutant using this construct ( pEpi-SCARN ) . The observed complementation ( Fig 1C and Table 2 ) revealed that pEpi-SCARN can rescue the infection deficiency , confirming that expression of SCARN behind an epidermal-specific promoter is sufficient to permit rhizobial infection . Mutation of the NODULE PECTATE LYASE ( NPL ) gene induced a block in nodule infection similar to that seen with the scarn mutants . The expression of NPL is regulated by the NIN-encoded transcription factor that is also required for nodulation and infection [30] . Furthermore , the pattern of NIN expression is similar to that described here for SCARN [46] . We measured ( by quantitative RT-PCR ) SCARN expression in the nin-1 mutant , revealing that the increased SCARN expression caused by M . loti requires NIN ( Fig 5A ) . The NIN-binding nucleotide sequence has been identified [46 , 47] and analysis of the DNA sequence upstream of SCARN revealed three putative NIN-binding sites: one was 1 . 9 Kb upstream of the predicted translation start and two overlapping sequences of predicted NIN-binding sites are present about 50 bp upstream of the translation start ( Fig 5F ) . We used an electrophoresis mobility shift assay to test if NIN could bind to these SCARN promoter regions . Specific retardation was observed with synthetic oligonucleotides corresponding to each of the three SCARN promoter regions when they were incubated with the carboxyl-terminal half of the NIN recombinant protein . This region of NIN contains the RWP-RK domain responsible for DNA binding [21] . Gel shift analyses and competition assays confirmed that NIN bound specifically to these promoter regions ( Fig 5G ) . We also co-expressed 35S:GFP-NIN with the reporter fusions pSCARN:GUS or pNIN:GUS in Nicotiana benthamiana leaf cells and GUS activity was determined histochemically and quantitatively in leaf discs . The results indicate that NIN can induce the SCARN , but not the NIN promoter ( Fig 5H ) . Taken together , these data demonstrate that NIN directly binds to the SCARN promoter to activate its expression . The predicted SCARN protein ( 1627 amino acids ) is much longer than the predicted A . thaliana SCAR1 , SCAR2 , SCAR3 and SCAR4 proteins ( 821 , 1399 , 1020 and 1170 amino-acids respectively ) [48] . As with other plant SCAR family proteins , SCARN has an N-terminal conserved SCAR homology domain ( SHD ) , which may mediate the assembly of the SCAR/WAVE complex . It also has a C-terminal predicted WH2 domain connected to an acidic domain ( A ) which has the potential to bind to G-actin and activate the Actin-Related Proteins ARP2/3 ( Fig 1B and S7A Fig ) . The full-length SCARN protein has only 30% and 26% identity with AtSCAR2 and AtSCAR4 respectively . ( For reference , the L . japonicus and Arabidopsis NAP1 proteins are 77% identical and the PIR proteins are 83% identical; [31] ) . The SCARN N-terminal SHD and C-terminal WA domains share 67% and 69% identity with AtSCAR2 , and 91% and 86% identity respectively with its putative homologue in M . truncatula . As with the other plant SCAR proteins , SCARN lacks the poly-proline region ( PPR ) that promotes binding to G-actin binding protein and is normally found in non-plant WASP/SCAR/WAVE family members . Within most plant SCAR proteins there are plant-specific conserved motifs referred to as SCAR of plants central region ( SPC ) [3 , 49] . SCARN contains conserved SPC1 and SPC4 domains , but lacks the SPC2 and SPC3 conserved domains ( Fig 1B and S7B Fig ) . We searched for proteins containing conserved SCAR domains in L . japonicus and other legumes; this identified only three proteins , two of which fell into the same legume-specific clade of proteins . This clade could be split into two subclades , one containing SCARN and another containing a protein clearly related to SCARN . In each sub-clade there is one representative from L . japonicus , M . truncatula and Phaseolus vulgaris and ( as would be expected due to its tetraploid nature ) two representatives from Glycine max ( Fig 6 ) . The SCARN-like protein from L . japonicus ( Lj4g3v2151410 ) is predicted to be 1461 amino acids long and is 48% identical with SCARN over its full length . The presence in legumes of two subclades of proteins related to SCARN fits with the suggestion [50] that ancestral polyploidy events can lead to enhanced root nodule symbiosis in the Papilionoideae . Possibly a gene duplication event may have allowed evolution of SCARN with a specialized role in legume infection . The ARP2/3 complex nucleates branched actin filament networks , but requires nucleation-promoting factors to stimulate this activity . In plants , ARP2/3 activation relies on the SCAR/WAVE family and in Arabidopsis the C-terminal WA domain of SCAR2 interacts with the ARP2/3 complex , activating actin nucleation . We tested if the WA domain of SCARN could associate with components of the ARP2/3 complex using a yeast-two-hybrid assay . Since the LjARPC1 gene is required for legume infection [33] , we first tested if the WA domain of SCARN could interact with LjARPC1 in a yeast-two-hybrid assay . Co-expression of the SCARN WA domain fused to the GAL4 binding domain and LjARPC1 fused to the Gal4 activation domain did not permit growth in the absence of histidine . The reciprocal fusion constructs also did not permit growth in the absence of histidine ( Fig 7A ) . These results indicate there is no interaction between the SCARN WA domain and LjARPC1 in the yeast-two-hybrid assay . However , when the GAL4 activation domain fused to the WA domain of SCARN was expressed together with the GAL4 binding domain fused to LjARPC3 ( Lj4g3v1934510 . 1 ) the yeast strain grew well in the absence of histidine , even in the presence of 10 mM 3-amino-1 , 2 , 4-triazole . This indicates LjARPC3 binds to the WA domain of SCARN . Quantitative analysis of the expression of the lacZ gene under the control of the GAL4 promoter confirmed that co-expression of the SCARN WA-GAL4 binding domain and LjARPC1-Gal4 activation domain increased β-galactosidase ( LacZ ) activity from a background of 5 . 3 ± 0 . 5 to 18 . 1 ± 2 . 1 units confirming the interaction . A direct interaction between the SCARN-WA domain and APRC3 was confirmed by in vitro pull-down assays . We expressed in Escherichia coli a recombinant glutathione S-transferase ( GST ) fused to the SCARN-WA domain and purified this GST-SCARN-WA recombinant protein using Glutathione Sepharose TM 4B beads . His-tagged APRC3 was purified from E . coli , immobilized on nickel–NTA beads and then incubated with the purified GST-SCARN-WA . After washes , proteins retained on the beads were eluted and resolved by SDS-PAGE . The GST-SCARN-WA fusion protein was observed in the eluate by immunoblotting with anti-GST antibody ( Fig 7B ) . The interaction was specific because LjAPRC3 was not pulled down by the GST protein alone ( Fig 7B ) . These results show that the SCARN-WA can interact with ARPC3 , suggesting a role for SCARN in the activation of the ARP2/3 complex . Both rhizobial inoculation and addition of purified Nod-factors rapidly induced accumulation of fine bundles of actin filaments in the apical/subapical region of the responding root hairs [9 , 12 , 51] and mutation of L . japonicus NAP and PIR genes blocked this effect [31] . Alexa-phalloidin staining of WT and the scarn-1 root hairs showed similar long cables of actin filaments aligned longitudinally ( S8A and S8B Fig ) , suggesting normal actin arrangements in the scarn-1 mutant . M . loti induced an accumulation of actin bundles in region II root-hair tips within 30 min of inoculation of both WT and scarn-1 mutant; we did not observe any difference in this actin rearrangement in mutant and wild type . We also checked the M . loti-induced actin rearrangement in root hairs of the scarn-4 and scarn-5 mutants using the same technique . About 55% root hairs showed normal accumulation of the actin cytoskeleton and this is consistent with wildtype and scarn-1 root-hair deformation responses ( S8C and S8H Fig and S2 Table ) . We used the L . japonicus pir mutant as a control and observed that under the same conditions the pir mutation blocked the formation of almost all actin fine bundles in all observed root hair tips ( S8D and S8I Fig ) . Since NIN regulates M . loti-induced SCARN expression , we also checked the M . loti-induced response in the nin-1 mutant; this revealed that 60% of the root hairs showed actin accumulation in their tips ( S8E and S8J Fig and S2 Table ) . These results suggest that SCARN is not required for the early phase of rhizobial-induced actin cytoskeletal rearrangement in root hairs and is consistent with the observed root-hair deformation in the scarn mutants . To investigate the importance of the SHD and C-terminal WA domains , we generated constructs that could express either of these SCARN domains using the ubiquitin promoter and introduced them into wild type roots by A . rhizogenes-mediated hairy root transformation . The constructs encoding the full length SCARN or the C-terminal WH2 domain had no observed effect on nodule formation compared with the empty vector control ( Fig 8A ) . However the expression of the N-terminal SHD domain of SCARN reduced nodule formation ( Fig 8A ) , with about one third of the transformed plants producing no nodules or inducing the formation of only one white bump ( Fig 8B ) . Some of the plants transformed with the SHD domain produced a few pink nodules , but on average the numbers of pink nodules were consistently much lower than that seen with the control plants transformed with the vector Ubi:mCherry lacking the SCARN SHD domain ( Fig 8A ) . These results show that strong expression of the SHD domain of SCARN can interfere with normal nodulation , although we observed no effect on root-hair growth . Similar dominant-negative effects have been seen with strong expression of the AtSCAR2 SHD domain causing abnormal trichome development [44] . The observation of a dominant negative effect of the SCARN SHD domain on nodulation ( a ) confirms the importance of this domain for function and ( b ) adds weight to the observation that SCARN plays an important role in legume nodulation .
The actin cytoskeleton plays an important role in the polar growth of plant cells , particularly polar-growing cells such as root hairs , trichomes and pollen tubes [52] . There are dynamic interactions and cooperation between the actin cytoskeleton and microtubules [53] . Actin rearrangements are initiated via ARP2/3-mediated nucleation of new actin filaments onto the existing actin filaments; for this to occur the ARP2/3 complex must be itself be activated and this occurs by interactions between the ARP2/3 complex and the SCAR components of the SCAR/WAVE complex [54] . In plants this has been best analyzed in A . thaliana in which the C-terminal WA domain of the AtSCAR proteins bind to the ARP2/3 complex [43] . There are four SCAR components in Arabidopsis [43 , 48]; searches for WA domains in Arabidopsis did not identify any additional SCAR components or other potential ARP2/3 activation proteins [3] . There appears to be a degree of functional redundancy between different AtSCAR proteins because double and triple mutations increase the severity of phenotypes [48] . Mutations in AtSCAR2 have the most severe phenotype , causing mildly distorted trichomes [43 , 44] . The sequence of SCARN suggests it is distinct from the four Arabidopsis SCAR proteins , both in terms of its length and its overall sequence . This distinctiveness fits with the observation that one of its main roles in L . japonicus is to do with initiation and growth of infection threads during legume infection and nodulation by rhizobia . This conclusion is based on the absence of normal infections in the mutants and by the observation that overexpression of the N-terminal SHD domain of SCARN strongly inhibited nodule infection . In Arabidopsis , it is this SHD domain that enables it to interact with the BRK and ABIL1 components of the SCAR/WAVE complex [44 , 55] . Presumably the expression of this SHD domain in L . japonicus root-hairs can titre out the normal SCARN-binding site in the SCAR/WAVE complex , thereby reducing binding by the wild-type SCARN protein . In turn this could decrease the coupling of the SCAR/WAVE complex with the ARP2/3 complex , because we have shown that SCARN can bind to ARPC3 via the conserved C-terminal WA domain in SCARN . There are two SCARN-like sub-clades in legumes , implying an ancient gene duplication in legumes . Ancestral polyploidization has been proposed to have enhanced development of root nodule symbioses in the papilionoideae [50] . Sequence analysis other SCAR/WAVE complex subunits revealed that orthologues of NAP , PIR or HSPC300 show very high sequence similarity [56] , whereas the WASP family proteins ( WASH , SCAR etc . ) appear to have evolved more rapidly [49 , 56] . Analysis of SCARN-like proteins suggests that a species-specific gene duplication probably occurred to generate SCARN and a related protein . In terms of the rearrangements of the actin cytoskeleton , the mutations in SCARN appear not to affect the early M . loti-induced response , whereas mutations in LjNAP and LjPIR cause defects in the M . loti-induced rearrangements in the actin cytoskeleton in root hair tip [31] . This difference could be due to some genetic redundancy of SCAR proteins . In Arabidopsis there appears to be some cell-type specificity in the function of SCAR components , probably mostly related to the expression of different SCAR genes in different cell types [37] . It appears from mutant phenotypes and analysis of a reporter-GUS fusion that LjSCARN is weakly expressed in the root epidermis and in root hairs , and was more strongly expressed in the epidermis of young nodules . This nodule expression was transient such that in mature nodules expression was only observed in vascular bundles . This is rather different from NAP , PIR and ARPC1 expression which was unaffected by M . loti inoculation [31 , 33] . The relatively strong LjSCARN expression in developing nodules suggests that SCARN plays an important role during nodule development and this role could be related to nodule morphogenesis and/or nodule infection . Although the analyses of actin rearrangements in root hairs of the scarn mutants may imply a degree of redundancy , it is nevertheless the case that the Ljscarn mutants have a severe infection-thread deficiency . There are two phases of actin nucleation in root hairs [12] , one associated with root-hair deformation ( mostly changes in the fine actin filaments within 30 min of perception of Nod factors ) and one associated with the development of infection threads ( within 72 hours after rhizobial inoculation ) . The lack of infections in the scarn mutant may indicate that SCARN plays a significant role in this latter phase of actin rearrangements . It is very difficult to image actin accumulation at this stage in infection-defective mutants , because there are few sites where infections are being initiated and those abnormal infections that do form may be delayed . A few infection threads do initiate in scarn mutants , and this is also observed in nap and pir mutants that are defective for the SCAR/WAVE complex [30] . Understanding how this fits with the late phase of actin rearrangements associated with initiation of infection threads remains to be understood . One of the unexpected phenotypes of two of the scarn mutants was that the root-hairs were branched . The scarn-1 and scarn-3 alleles causing this phenotype would both introduce missense translational stops and would be predicted to produce proteins of 1198 and 1099 residues respectively . The observation that the branching was seen in two independent mutants suggests that the phenotype is caused by these alleles . The fact that the phenotype was observed without rhizobial inoculation implies that the SCARN gene must be expressed in root hairs , but the level of expression must be low because we were unable to detect it using the pSCARN:GUS fusion . The absence of root-hair branching in the other alleles implies that the phenotype is caused by the presence of the truncated gene product . Intriguingly , branching of root hairs is induced by rhizobial Nod factors and is usually considered to be a consequence of re-initiation of root-hair growth following a pause in growth [8] . The growth of root hairs in some of the mutants appears to be somewhat slower than normal and so it is possible that scarn-1 and scarn-3 mutants can periodically show transient inhibition of root hair growth and that pauses in growth stimulate root-hair branching . Why this should be specific to these two but not the other alleles implies an unknown ( and unexpected ) role for the truncated gene products . Root-hair branching has been associated with action of microtubules rather than actin [57] . Interestingly , in Dictyostelium , it was found that the direction of cell migration depended on the action of a microtubule-binding protein to direct SCAR localization , revealing a role for SCAR proteins at the functional interface between actin and microtubules [58] . The root hair branching phenotype observed may therefore indicate a link between SCARN and microtubule function . The regulation of SCARN probably occurs both during transcription and at the level of activation of the SCAR/WAVE complex . Although the SCARN gene appears to be expressed in epidermal cells prior to the symbiotic interaction , it is induced in response to M . loti . This is somewhat different from the expression pattern of the NAP , PIR or ARPC1genes which are also required for infection , but appear not to be induced during the symbiosis [31 , 33] . The enhanced expression of SCARN is regulated by NIN , which is required for the establishment of both nodule development and the formation of the infection foci that precede infection thread growth in root hairs . We checked if other predicted components of the SCAR/WAVE complex are induced during symbiotic interactions and noted that ABIL1 is induced by Nod factors in M . truncatula root hairs [7] . This implies that in addition to the normal expression of SCAR/WAVE and ARP2/3 complex proteins , the expression of some of the components is enhanced during early stages of the symbiotic interaction via activation of NIN . NIN is induced by CYCLOPS , which is activated by CCaMK in response to Nod-factor-induced calcium spiking [19] . This NIN-mediated regulation of SCARN is consistent with normal mycorrhization in scarn mutants , because NIN is required for rhizobial but not mycorrhizal symbioses [59] . Post-translational regulation of the ARP2/3 complex occurs in plants and animals [3 , 60] , but in plants the regulation is thought to be less complex in this respect and appears to requires only the SCAR/WAVE nucleation promoting factors and the SCAR/WAVE regulatory complex [3] . One aspect of this regulation in Arabidopsis involves the binding of Rho-related GTP-binding protein ROP2 to PIR1 [61] . ROP-GTPases coordinate vesicular trafficking and cytoskeletal rearrangements during polar growth in Arabidopsis [62] . Nothing is known about the regulation of the ARP2/3 complex in legumes during rhizobial infection , but there is evidence for the action of ROP-GTPases in root-hair growth and rhizobial infection . In L . japonicus the Nod-factor receptor NFR5 interacts with ROP6 , which is involved in infection-thread growth [63] . ROP6 also interacts with clathrin heavy chain1 which is required for normal infection and nodule development , indicating a role for endocytosis during infection and nodulation [64] . In M . truncatula the Nod-factor receptor NFP interacts with MtROP10 to regulate root hair deformation; overexpression of MtROP10 , or a constitutively-active mutant form of MtROP10 , leads to depolarized growth of root hairs [65] . More work will be required to establish if these or other ROPs regulate actin nucleation during rhizobial infection and if so , which actin nucleation promoting proteins they bind to . Although the need for actin rearrangements during rhizobial infection is now well established , the physiological requirements for these are not fully known . One possibility is that the actin cytoskeleton is required for the vesicle trafficking and associated protein targeting required for initiation and maintenance of infection thread growth . If this is correct then the nodulation related pectate lysase NPL would be a likely cargo as would cell-wall and cell-membrane biosynthesis proteins . Another possibility is that actin rearrangements are required for the endocytosis that appears to be required for establishing the symbiosis [64] . Possibly the induced expression of SCARN in both the root epidermis and developing nodule cells points to multiple functions for the actin cytoskeleton during infection and nodule development . It appears likely that one of the plant SCAR proteins has been recruited to take on a specialized role in nucleating actin cytoskeletal changes that play an important role in legume infection and nodule development .
The L . japonicus mutants SL2654-3 , SL5737-2 , SL1058-2 and SL6119-2 were isolated from an EMS mutagenized population of Gifu B-129 [36] . The scarn-5 allele was obtained from a pool of L . japonicus mutants with LORE1 transposon insertions [38 , 39] . L . japonicus seeds were scarified with sandpaper or immersed for 5–7 min in concentrated H2SO4 then surface sterilized with 10% NaClO , washed with sterile water and then left to imbibe water . The seeds were then germinated for 4–5 days at 22°C on water agar plates . Seedlings were planted in vermiculite and perlite ( 1:1 ) mixed with N-free nutrient ( FP ) solution [66] and grown under a 16-h/8-h light/dark regime at 23°C . After 5–7 days growth , seedlings were inoculated with M . loti R7A carrying pXLGD4 ( lacZ ) or pMP2444 ( GFP ) . These strains were grown for 2 days at 28°C in TY liquid containing 5μg/ml tetracycline , at OD600≈1 . 0 , pelleted by centrifugation and resuspended in water at OD600≈0 . 01 . For hairy root transformation of L . japonicus roots , A . rhizogenes strain AR1193 was used , while for N . benthamiana transient expression , A . tumefaciens strain EHA105 was used . Mutants were crossed with MG20 , and nodulation phenotype was scored in F2 progeny from self-pollinated F1 plants . Genomic DNA was isolated and SSR markers were scanned for co-segregation with nodulation defects . Primer sequences and marker information were retrieved from the miyakogusa . jp website ( http://www . kazusa . or . jp/lotus/ ) . Rough mapping results indicated that four of the mutations fell within a similar region , so allelism was tested in the crosses described . Fine-mapping was done using SL2654-3 crossed with MG20 . To score the time course of nodulation , seedlings were grown in N-deficient medium and the numbers of nodules were counted 4 and 5 weeks after inoculation . The number of infection events was determined by microscopy of whole root stained with 5-bromo-4-chloro-3-indolyl-beta-D-galacto-pyranoside ( X-Gal ) , 7 and 14 days after inoculation with M . loti R7A carrying pXLGD4 ( lacZ ) using at least 15 plants at each time point . For staining , whole roots were immersed in fixative solution ( 0 . 1 M potassium phosphate buffer containing 1 . 25% glutaraldehyde ) for 1 h , washed twice for 10 min in 0 . 1 M potassium phosphate buffer and stained for β-galactosidase activity using staining solution ( 200 μl 100 mM K4Fe ( CN ) 6 , 200 μl 100 mM K3Fe ( CN ) 6 , 120 μl 2% X-Gal in dimethyl formamide , 3 . 2 ml 0 . 1 M phosphate buffer ) . After staining in the dark overnight at room temperature , roots were rinsed 3 times with water , cleared in 1% NaClO for 1min , then washed 5 times with water . Stained roots were observed using Nikon Eclipse Ni light microscopy under bright-field illumination . Individual infection events were imaged by Nikon digital sight with lacZ-marked M . loti . GFP-marked M . loti-inoculated roots were counterstained with propidium iodide and analyzed by laser scanning confocal microscopy ( Olympus FV1000 ) . Light microscopy of nodule sections was done with nodules fixed in 2 . 5% ( v/v ) glutaraldehyde as described [67] . After fixation , tissues were embedded in Technovit 7100 ( Kulzer GmbH ) resin according to the manufacturer’s instructions and 10 μm transverse sections were taken . Sections were stained with 0 . 5% ( w/v ) toluidine blue O in 0 . 5% ( w/v ) sodium tetraborate buffer before taking pictures under Nikon Eclipse Ni light microscopy . For mycorrhizal analysis , the L . japonicus seedlings were grown in pots with sand and vermiculite ( 1:1 ) with sterile G . intraradices spores at 22°C under a 16h-light /8h-dark cycle . Five weeks after inoculation , roots were treated with 10% KOH for 6 min at 95°C , followed by 3 min in ink at 95°C . Root length colonization was quantified using the grid line intersect method [68] using a Nikon Eclipse Ni light microscopy under bright-field illumination . 15 plants were analyzed for each treatment . Phalloidin staining and microscopy of actin rearrangements was done as described previously [31] . Seedlings prepared and grown as described previously [69] were transferred to slides containing 1ml liquid FP medium and left overnight . The seedlings were inoculated by adding fresh FP medium containing M . loti R7A ( OD600 about 0 . 01 ) and then left in the dark for approximately 18 h before analysis . Images were taken using a Nikon DS-Fi2 camera mounted on a Nikon Eclipse Ni light microscope . The branched root hairs observed in the un-inoculated slides were photographed 2 days after transfer of the seedlings to the sterile FP liquid medium slides . SCARN genomic DNA was amplified from Gifu B-129 leaves using the primer SCARN-attFL-F and SCARN-attFL-R ( S3 Table ) . PCR products were cloned into gateway entry vector pDONR207 and recombined into the destination vector pUB-GW-GFP [40] or pEpi-GFP [45] . A 2kb region upstream of the SCARN translation start was amplified from Gifu B-129 leaf genomic DNA using primers SCARN-Pro-F and SCARN-Pro-R . The PCR products were cloned into pDONR207 and then recombined into the destination vector pKGWFS7 . 0 to form pSCARN:GUS . The region of SCARN encoding the N-terminal SHD domain was amplified by primers SCARN-attFL-F and SCARN-N-R; C-terminal WA domain by primer LjSCARN C F and SCARN-attFL-R from Gifu cDNA library . The PCR products were cloned into pDONR207 and then recombined into the destination vector pUB-GW-GFP . All constructs in pDONR207 were confirmed by DNA sequencing , introduced into A . rhizogenes AR1193 by electroporation and then introduced into roots of wild type or scarn mutants by hairy root transformation on half strength B5 medium . The transformed chimeric plants were transplanted into vermiculite/perlite pot and after 5–7 days inoculated with M . loti R7A containing lacZ . The nodulation phenotypes were scored 3 weeks after inoculation after staining for β-glucuronidase or β-galactosidase activity . The SCARN C-terminal WA coding domain , ARPC1 and ARPC3 were all amplified from Gifu cDNA library using the high proofreading enzyme KOD Plus ( Toyobo ) and the primers LjSCARN-C-F and SCARN-attFL-R , ARPC1-attB-F and ARPC1-attB-R , or ARPC3- attB-F and ARPC3-attB-R respectively . PCR products were cloned into pDONR207 , their fidelity was confirmed by DNA sequencing and were then recombined into pDEST-GBKT7 or pDEST-GADT7 . The yeast strain AH109 was transformed with the constructs using lithium acetate transformation ( Yeast Protocols Handbook PT3024-1 , Clontech ) . Immunoblots were used to validate protein expression using Anti-Gal4 AD ( Upstate , Cat . #06–283 ) and Anti-Gal4 BD ( Sigma-Aldrich , 080M4814 ) antiserum . β-galactosidase activity was assayed following standard methods ( Yeast Protocols Handbook PT3024-1 , Clontech ) . The SCARN C-terminal WA coding domain was amplified from cDNA using the primers SCARN C ( SalI ) -F and SCARN ( Not1 ) -R and the PCR product was inserted into SalI and NotI digested pGEX4T-1 to form SCARN-WA pGEX4T-1 for expressing a GST-SCARN-WA fusion protein . The ARPC3 full-length cDNA was amplified using primers ARPC3 ( BamH1 ) -F and ARPC3 ( BamH1 ) -R and the PCR product was cloned into BamHI-digested pET28b , to form ARPC3 pET28b encoding His-tagged APRC3 . Plasmids expressing SCARN-WA , ARPC3 and the negative control pGEX4T-1were introduced into E . coli BL21-Codon Plus ( DE3 ) -RIL ( Stratagene ) . Proteins were induced during exponential growth using 0 . 5 mM IPTG for 4h at 28°C . The GST-tagged SCARN-WA protein was purified using Glutathione Sepharose TM 4B beads ( GE Healthcare ) under native conditions . The purified protein was incubated with soluble His-tagged ARPC3 in 1 mL of interaction buffer ( 20 mM Tris-HCl , 100 mM NaCl , 0 . 1 mM EDTA , and 0 . 2% Triton X-100 , pH7 . 4 ) for 1 h on ice with gentle shaking and then the mixture was incubated with Glutathione Sepharose TM 4B beads . The beads were then washed three times with 1 . 0 mL of NETN100 buffer ( 20 mM Tris-HCl , 100 mM NaCl , 0 . 1 mM EDTA , and 0 . 5% NP40 , pH 7 . 4 ) and three times with 1 . 0 mL of NETN300 buffer ( 20 mM Tris-HCl , 300 mM NaCl , 0 . 1 mM EDTA , and 0 . 5% NP40 , pH 7 . 4 ) . Retained proteins were eluted following incubation at 100°C for 5 min in 1X SDS sample buffer and analyzed by SDS-PAGE using 12% acrylamide gels . Proteins were transferred from the gel to a PVDF membrane for detection using anti-His antiserum ( Abmart ) . Total RNA was extracted with TRIpure Isolation Reagent ( Aid lab , China ) according to the instruction manual and quantified using a Nano-Drop 2000 ( Thermo ) . Reverse transcript first-strand cDNA was synthesized using TransScript one-step gDNA Removal and cDNA synthesis SuperMix ( Trans Gen Biotech ) . Real-time RT-PCR was done with TOYOBO SYBR Green Realtime PCR Master Mix ( TOYOBO ) and detected using an ABI step-one Plus PCR system . Nodulation marker gene expression samples were generated from whole root of about 10 seedlings of wild type Gifu or scarn-1 that had been grown for 2 weeks on N-free nutrient solution [66] agar plates and then incubated a further 5 days after inoculation with M . loti R7A . For analysis of tissue-specific expression , plants were grown in vermiculite-perlite . All of the primers used for qRT-PCR of target transcripts are described in S3 Table quantified relative to the ubiquitin gene as internal control . Data was analyzed as described [30] . The NIN carrying a C-terminal His tag was purified was described previously [30] . The synthetic biotin-labelled oligonucleotides used for tests of DNA binding are shown in S3 Table . After electrophoresis the gels were developed using the Light Shift chemiluminescent EMSA Kit ( Thermo ) following the manufacturer’s instructions and the chemiluminescent images were captured using a Tanon-ttoo CCD ( Tanon company , China ) . The full-length NIN cDNA was amplified by primers LjNIN-attB-F and LjNIN-attB-R using L . japonicus cDNA library as template . PCR products were cloned into gateway entry vector pDONR207 and sequenced , then recombined into pK7WGF2 to form LjNIN pK7WGF2 . LjNIN and pSCARN:GUS were introduced into A . tumefaciens strain EHA105 and infiltrated into N . benthamiana leaves . Samples were harvested 2 days after agro-infiltration . The GUS activity was measured by GUS staining at least 5 leaves each pair . For fluorimetric GUS assays , about 20 mg frozen leaf tissue was ground by mortar and pestle and protein was extracted using 20 microlitres extraction buffer ( 50 mM K/NaPO4 buffer pH 7 . 0 , containing 10 mM EDTA ( pH 8 . 0 ) , 10 mM β-mercaptoethanol , 0 . 1% sarcosyl and 0 . 1% Triton X-100 ) . Extracts were centrifuged ( 10000 g , 15 min , 4°C ) and the supernatant was used for GUS activity measurement as described with 4-methylumbelliferyl-β-D-glucuronide as substrate ( Sigma-Aldrich ) . GUS activities were measured using DyNA Quantity200 ( Hoefer , China ) . Mean values and standard deviations were determined from three biological replicates . The A . thaliana protein sequences were obtained from TAIR ( http://www . arabidopsis . org/ ) and the L . japonicas protein sequence were obtained from miyakogusa . jp website ( http://www . kazusa . or . jp/lotus/ ) Version 3 . 0 , and other legume SCAR protein sequences were obtained from Phytozome 10 . 1 ( http://phytozome . jgi . doe . gov/pz/portal . html ) . All the protein sequences are imported into MEGA6 . 0 [70] for complete alignment using ClustalW2 ( http://www . ebi . ac . uk/Tools/msa/clustalw2/ ) . The phylogenetic tree was built using MEGA6 . 0 [70] and using the neighbor-joining method with the bootstrapping value set at 1000 replications . | Characterization of Lotus japonicus mutants defective for nodule infection by rhizobia led to the identification of a gene we named SCARN . Two of the five alleles caused formation of branched root-hairs in uninoculated seedlings , suggesting SCARN plays a role in the microtubule and actin-regulated polar growth of root hairs . SCARN is one of three L . japonicus proteins containing the conserved N and C terminal domains predicted to be required for rearrangement of the actin cytoskeleton . SCARN expression is induced in response to rhizobial nodulation factors by the NIN ( NODULE INCEPTION ) transcription factor and appears to be adapted to promoting rhizobial infection , possibly arising from a gene duplication event . SCARN binds to ARPC3 , one of the predicted components in the actin-related protein complex involved in the activation of actin nucleation . | [
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| 2015 | SCARN a Novel Class of SCAR Protein That Is Required for Root-Hair Infection during Legume Nodulation |
Regions of the genome that have been the target of positive selection specifically along the human lineage are of special importance in human biology . We used high throughput sequencing combined with methods to enrich human genomic samples for particular targets to obtain the sequence of 22 chromosomal samples at high depth in 40 kb neighborhoods of 49 previously identified 100–400 bp elements that show evidence for human accelerated evolution . In addition to selection , the pattern of nucleotide substitutions in several of these elements suggested an historical bias favoring the conversion of weak ( A or T ) alleles into strong ( G or C ) alleles . Here we found strong evidence in the derived allele frequency spectra of many of these 40 kb regions for ongoing weak-to-strong fixation bias . Comparison of the nucleotide composition at polymorphic loci to the composition at sites of fixed substitutions additionally reveals the signature of historical weak-to-strong fixation bias in a subset of these regions . Most of the regions with evidence for historical bias do not also have signatures of ongoing bias , suggesting that the evolutionary forces generating weak-to-strong bias are not constant over time . To investigate the role of selection in shaping these regions , we analyzed the spatial pattern of polymorphism in our samples . We found no significant evidence for selective sweeps , possibly because the signal of such sweeps has decayed beyond the power of our tests to detect them . Together , these results do not rule out functional roles for the observed changes in these regions—indeed there is good evidence that the first two are functional elements in humans—but they suggest that a fixation process ( such as biased gene conversion ) that is biased at the nucleotide level , but is otherwise selectively neutral , could be an important evolutionary force at play in them , both historically and at present .
Understanding the forces that have shaped the evolution of the human genome is one of the most exciting problems in modern genomics . Two approaches to this problem are focused on identification and characterization of those genomic regions that have evolved the slowest and fastest along the human lineage [1]–[5] . The slowest evolving regions may contain elements that cannot be disturbed without disrupting essential function . The fastest evolving regions may harbor elements whose function is unique to our species lineage . To eliminate non-functional regions , both of these complementary approaches begin with a search for regions that are conserved throughout mammalian history or longer . The ultra conserved elements [1] maintain this conservation along the human lineage , and have been shown to be under purifying ( negative ) selection [6] , strongly suggesting that they are functionally important to our species , although in ways that are still largely unknown . By contrast , several groups have searched for positive selection along the human lineage by focusing on those previously slowly evolving regions of the genome that have evolved most quickly along the human lineage [4] , [5] , [7] . These regions , such as those in the set of Human Accelerated Regions ( HARs ) [7] , may include some of the genetic changes that make our species biologically unique . Indeed , biological characterization of the topmost elements on this list of candidates has proven fruitful: HAR1 is part of a novel RNA gene ( HAR1F ) that is expressed during neocortical development [3]; HAR2 ( or HACNS1 ) is a conserved non-coding sequence that has been shown to function as an enhancer in the developing limb bud with the human-specific sequence enhancing expression in the presumptive anterior wrist and proximal thumb [8] . Since the HARs were identified based on an excess of fixed differences between the human reference genome and sequences that are highly conserved among chimp , mouse and rat , such differences could have arisen at any time within the 5 million years that have elapsed since our common ancestor with the chimpanzee . As such it is important to recognize that even if such differences resulted from positive selection for advantageous mutations , they may have occurred so long ago that we have little power to find evidence for such selection using only the present day sequences available to us . Furthermore , as previously noted [7] , positive selection might not be the sole explanation for the rapid evolution that is evident in the HARs . Biased gene conversion ( BGC ) may also hasten the fixation of mutations in a local manner independent of any fitness benefits [9] , [10] . BGC arises as a byproduct of recombination between homologous chromosomal regions . In this process DNA double stranded breaks are repaired and the alleles from one chromosome are copied to the other , with a bias for conversion of A or T ( weak hydrogen bonding ) alleles to G or C ( strong hydrogen bonding ) alleles [11]–[14] . A neutral locus can thus mimic the rapid evolution of loci under positive selection [9] , [10] , and furthermore , BGC may in fact drive fixation of deleterious alleles [15] , the precise opposite of a positive , adaptive evolutionary effect . One of the most powerful tools for identifying those regions that have been subjected to directional selection comes from examining the distribution of allele frequencies segregating within a species . For example , analysis of this distribution , known as the site frequency spectrum ( SFS ) , allows for the identification of loci that have been involved in selective sweeps in the last few hundred thousand years . Analysis of the SFS has been used to identify targets of natural selection that may be responsible for genetic traits that are uniquely human , such as language [16] or cognition [17] . In the current work we investigate the top 49 HARs that were identified as having a 5% false discovery rate [3] . But rather than restricting our attention to the core elements , which are 100–400bp in length , we consider the polymorphism in a set of 22 chromosomal human samples in a 40kb neighborhood of each of these HAR elements , with an eye to capturing perturbations in the SFS at linked sites , and/or regionally biased patterns of allele fixation . Our samples are drawn from a single population , the Yoruba from Ibidan , Nigeria in order to avoid confounding issues of population admixture as well as to take advantage of a greater degree of variation in this population . We use an adaptation of several techniques previously developed [18]–[21] to enrich genomic DNA from our sample individuals for the target genomic neighborhoods . The enriched DNA is then subject to high throughput sequencing followed by genome-wide mapping of many overlapping sequences to determine genotypes at sites in the target regions , and hence derive the site frequency spectra . With these spectra in hand , it is possible to test for the hallmarks of BGC . We employed an approach that compares the separate site frequency spectra for the weak-to-strong ( i . e . A or T to G or C ) ( W2S ) and strong-to-weak ( S2W ) mutations to determine if any shift towards high frequency , normally characteristic of a selective sweep , is biased towards one of the two sets of mutations . This signal would indicate an ongoing process in the current human population . Similarly , one can compare the proportion of W2S changes among already fixed substitutions on the human or chimp lineage to that among the still segregating sites . A W2S bias in fixed differences relative to polymorphisms would indicate that the regions have historically been subject to a BGC-like biased process . On the other hand , the spectra may contain evidence of positive selection . Various techniques have emerged in recent years to search for signatures of positive selection using population genetic data [22]–[24] , but many are based on the phenomenon of genetic hitchhiking [25] , [26] in which fixation of beneficial mutations results in a skew in the site frequency spectrum . One such approach [27] is based on the composite likelihood of allele frequencies wherein the probability of the observed allele frequency at each polymorphic position is calculated based on its distance from a site under putative positive selection . This probability explicitly takes into account the strength of recombination and selection . A variant of this approach has been implemented [28] in the SweepFinder program that we use herein . It has been previously used [29] in a genome-wide search for sweeps at the scale of 500kb , since that study's data was restricted to loci with common polymorphisms . The power of that approach is probably limited to finding sweeps not much older than 200 , 000 years but has the attractive property that it is robust to demographic history [29] . Unlike other approaches that have been used [22] , [23] , [30] , [31] it also does not require that the sweep be ongoing or differentially concluded in separate populations . Since we have discovered many novel polymorphisms by resequencing our samples , we use this method to take a more focused look at our 40kb HAR neighborhoods in search of adaptive evolutionary forces .
When the HARs were first described , a strong W2S substitution bias was noted in the human-specific substitutions in these elements [3] , [7] . This bias was extremely pronounced in HARs 1 , 2 , 3 , and 5 , but also noticeable as a general trend in the entire set 1–49 . This evidence suggested that BGC could have had a historical role in the evolution of the HARs . Here we analyze our list of segregating sites in the 40kb HAR neighborhoods to determine if such bias is still ongoing in the human population . In each region , we separately computed the derived allele frequency spectra for the W2S mutations and the S2W mutations . We then tested for an offset in the spectra between the two categories with a two-sided Mann Whitney U ( MWU ) test ( see Materials and Methods ) . This test has been shown to have good power to detect fixation bias [34] . We found a significant ( p 0 . 05 ) difference in 11 out of 49 harseq regions ( Table 1 ) . In all 11 significant harseq regions , the offset was for the W2S mutations to be segregating at higher derived allele frequencies than S2W . This implies that regardless of the rate of introduction of W2S or S2W mutations , it is the W2S mutations that are more likely to reach high frequency and eventually fix in the human population . This is certainly consistent with a mechanism of gene conversion that favors selection of G or C alleles from a heterozygote or some other selective force generally favoring higher GC content . The novel features of this result are that it indicates that this process is ongoing and not confined to the core 100–400bp HAR elements . This ongoing W2S fixation bias distinguishes the harseq regions from ctlreg regions and Seattle SNPs regions . Significant MWU tests were observed at none of ctrlreg50-62 ( Supplementary Table 2 in Text S1 ) and five out of 62 Seattle SNPs regions ( Supplementary Table 4 in Text S1 ) , one of which has higher derived allele frequencies in S2W compared to W2S mutations . The distribution of the MWU test statistic in the test regions is also biased towards W2S mutations compared to the 62 Seattle SNPs regions ( Supplementary Figure 6 in Text S1 ) . Applying the MWU test to simulations of a neutral coalescent model ( see Materials and Methods ) showed that the p-values from this test accurately reflect the fraction expected by chance from a neutrally evolving locus ( Supplementary Figure 2 in Text S1 ) . The two harseq regions with the greatest offset were harseq21 and harseq34 ( Figure 1 ) . We note that across these two 40kb regions , the ratio of W2S to S2W segregating sites is not extreme; it is the W2S shift towards higher frequency in the population that is significant . These ratios are consistent with the smoothed ratios reported in the top several thousand conserved candidate HAR elements [7] even though our 40kb regions do not comprise largely conserved regions . It is natural to theorize that BGC will be a stronger driving force in areas of high recombination and studies have shown there to be a good correlation with the male recombination rate in particular [35] . We examined the recombination rates in the enclosing 1Mb windows as determined by the deCODE project [36] . Harseq21 is an outlier in that it is contained in a genomic region of extremely high recombination rate ( male 4 . 29 cM/Mb , sex-averaged 3 . 43 cm/Mb , in contrast to genome-wide averages of 0 . 93 cM/Mb and 1 . 29 cm/Mb respectively ) . But this is not true of harseq34 ( 0 male and 1 . 42 cm/Mb sex-averaged ) . For the remainder of the regions with a significant p-value on the MWU test , the rates vary . An additional ( not unrelated ) factor that has been strongly correlated with biased substitutions is chromosomal position near telomeres [35] . With the exception of harseq1 this is not the case for the regions with significantly shifted W2S spectra ( Table 1 ) . We also performed the MWU test for the shift in W2S sites toward higher frequencies after pooling all the segregating sites in our 49 harseq regions , and found a p-value . By contrast , the test was not significant ( p = 0 . 26 ) for the pooled data in our 13 control regions , thereby controlling for possible systematic bias in our sequencing and genotyping techniques . We thus conclude that in many of the neighborhoods of the top 49 HARs there is an ongoing force driving W2S mutations to higher frequency in the human population . Since the HARs were essentially defined based on fixed differences between the human and chimp reference genomes in otherwise strongly conserved elements [7] , we compared such human/chimp fixed differences to the segregating sites in our human samples . Our set of fixed differences was based on high quality base calls from the reciprocal best alignments [37] of human and chimp genomes . We further restricted this list to the locations within our regions for which we had the above-mentioned 35-fold coverage for an individual in our sample . Finally , we removed from this initial fixed difference list those positions that we found to be segregating in our samples , or that appeared in the dbSNP129 database [38] ( see Materials and Methods ) . The latter two filters removed 6 . 7% of the fixed differences at high coverage positions . Since we do not have information on sites that are segregating in the chimp population , we could not remove those , but would expect the number to be similarly small . We separated the mutations in our segregating site set into the categories: W2S , S2W , and neither . We similarly divided the set of fixed differences , regardless of whether the substitution occurred on the chimp or human lineage . As in reference [35] , we performed a variant of the McDonald-Kreitman ( MK ) test to compare the W2S∶S2W ratios in the sets of mutations and the sets of substitutions ( see Materials and Methods ) . We found a significant ( p 0 . 05 ) difference between the substitution patterns of segregating versus fixed sites in 11 of the 49 harseq regions ( Table 2 ) . In all but one ( harseq39 ) of these 11 , the fixed substitutions had relatively more W2S mutations ( compared to S2W ) from the ancestral form than did the segregating sites . Four of the 11 fell in the 40kb neighborhoods of the top 11 HARs , and indeed the strongest result ( p = 0 . 00015 ) was for harseq1 . This is not surprising , since the HAR1 element has 18 fixed differences , all W2S [3] . By contrast , none of the ctlreg regions and nine out of 62 Seattle SNPs regions had a significant p-value on this test ( Supplementary Tables 2 and 4 in Text S1 ) . All of the nine significant Seattle SNPs regions had a higher W2S∶S2W ratio in fixed differences than in segregating sites . Thus , the harseq regions have a much stronger signal for historical fixation bias than our control regions and a somewhat stronger signal than the genic Seattle SNPs regions . Applying the MK test to simulations of a neutrally evolving primate phylogeny ( see Materials and Methods ) showed that the p-values from this test accurately reflect the fraction expected by chance from a neutrally evolving locus ( Supplementary Figure 3 in Text S1 ) . We also recapitulate and expand the finding [7] that this bias towards W2S fixation is associated with telomeres , since 7 of the 11 significant regions under the MK test were found either in the karyotype band containing a telomere or the one immediately adjacent . ( This count includes the two cases of harseq19 and harseq36 from chromosome 2 that fall adjacent to the ancestral telomeric fusion event at 2q14 . 1 . ) Although many of the 11 regions ( including ones near telomeres ) have elevated recombination rates , it is also worth noting that 5 of the 11 are in regions with much lower than average male recombination rates ( including the two near the chromosome 2 fusion site , and harseq11 on chrX ) . One noteworthy negative result from the MK test , ( and the MWU test as well ) is harseq2 . It had been noted [7] that the core HAR2 element showed a strong bias towards W2S fixations , and that this extended to a region of 1kb . Here we find no significant signal for either the MK or MWU test in our 40kb neighborhood of that element ( Supplementary Table 2 in Text S1 ) . Another noteworthy negative result on these tests is the harseq6 region , which has an extremely elevated rate of mutation as estimated either by nucleotide diversity [39] or by the number of segregating sites [40] ( Supplementary Table 1 in Text S1 ) , but apparently no strong bias towards weak-to-strong fixation ( Supplementary Table 2 in Text S1 ) . Although the MK test , like the MWU test , is consistent with the mechanism of BGC favoring fixation of G or C alleles , in fact only one of our regions ( harseq1 ) had significant results in both tests . The complementary evidence from the MWU test ( a total of 20 of our 49 test regions are W2S-significant on one test or the other ) indicates that the ongoing bias in favor of W2S mutations has also probably led to human specific substitutions in otherwise conserved elements . Because BGC is posited to operate on a scale much smaller than the 40kb of our target sequencing regions , perhaps operating at localized recombination hotspots , and because the ( 100–400bp ) HAR elements at the core of our target regions were suspected of arising in part due to BGC [7] , we wanted to test whether the MWU and MK signals depended on these core elements . We therefore performed the same tests after masking out the central 500bp , 1kb , 5kb or 10kb of each region . We found that signals of both ongoing and historical fixation bias are fairly robust to removing sequences including and flanking the core HAR element . For the MWU test , all but two ( harseq1 , harseq18 ) of the 11 regions that were significant at the 5% level for the MWU test were still significant at that level with the central 5kb omitted . Seven of the 11 remained significant even with 10kb omitted ( Table 1 ) . For the MK test , the results were slightly more sensitive to masking . Considering the 11 regions with a significant result on that test ( including the one favoring S2W fixation ) , 9 ( 7 ) of these were still significant with the central 1kb ( 5kb ) masked out ( Table 2 ) . We conclude that the evolutionary forces behind these results is not confined to the small HAR elements themselves , but rather that any bias in the substitutions found in the HARs is likely a byproduct of the forces acting at a larger scale . To test whether there might be other localized elements within the 40kb regions driving these results we performed the tests under a set of fifteen overlapping 5kb masks ( centered at a 2 . 5kb spacing along each region ) . Among the 11 MWU significant regions , 5 were still significant at 10% under this regime , while 3 completely lost significance ( p 20% ) for at least one such mask ( Table 1 ) . Of the 11 MK significant regions , 5 were still significant at 10% ( including harseq1 ) under this regime , while 1 completely lost significance ( p 20% ) under at least one mask ( Table 2 ) . It is worth noting that the harseq1 result reflects the fact that of the 105 segregating sites we found in that 40kb neighborhood , 71 were S2W and only 19 W2S ( Supplementary Table 2 in Text S1 ) . We conclude from this set of tests that the evolutionary forces behind W2S fixation bias are not necessarily highly local . If fixation bias relies on recombination hotspots and BGC , we have to posit that such hotspots extend over a long range of bases , or are somehow temporally and spatially variable ( cf . [41] ) . To determine if the results for the MWU and MK tests on the HAR neighborhoods are consistent with a model of GC-biased evolution , we performed simulations under a model of BGC ( see Materials and Methods ) . Of 499 simulations , for the MK test 130 were significant ( p ) with W2S bias ( none significant with S2W bias ) , and for the MWU test 51 were significant with W2S bias ( one significant with S2W bias ) . The union of the significant W2S simulations on the two tests comprised 169 cases while the intersection comprised 12 cases . Compared to the simulations , the 49 HAR regions had significantly more than the expected number of MWU W2S cases ( p = 0 . 003 for binomial probability of at least 11/49 cases using the simulation rate of 51/499 ) , but the MK test does not ( p = 0 . 77 binomially comparing 10/49 to 130/499 ) . On the other hand , the small ( one case ) intersection of MWU and MK tests in the HAR regions is not unexpected based on the simulations . That is , using the fraction 12/499 ( = 0 . 024 ) of the MWU and MK intersection in the simulations as the expected rate , the small fraction 1/49 ( = 0 . 020 ) in the HAR regions is not statistically significant using either a binomial ( p = 0 . 67 ) or Poisson ( p = 0 . 67 ) test . Finally , for neither the simulations ( Fisher's Exact test p = 0 . 74 ) nor the HAR regions ( p = 0 . 42 ) is there a significantly greater correlation between the MWU and MK results than expected by chance . Together , these analyses indicate that the MWU and MK results for the 49 HAR regions are consistent with a model of GC-biased evolution in terms of the overlap between the tests , although the number of MWU cases is enriched compared to the simulation model . For each of the studied regions , we used the SFS to calculate two population genetic statistics that can sometimes indicate positive selection: Tajima's D [42] , which is based on the folded SFS , and Fay and Wu's H [43] . Neither of these statistics exceeded the value of in any region ( Supplementary Table 1 in Text S1 ) . We next compared the distributions of these two statistics in the 49 harseq regions and the 13 ctlreg regions , to those for the same population ( YRI ) in 104 genic regions resequenced by Seattle SNPs ( see Materials and Methods ) . We found the ctlreg regions to be indistinguishable from the Seattle SNPs for these statistics , while the harseq regions were only mildly more negative for Tajima's D ( Wilcoxon rank sum p = 0 . 08 ) and not significantly different for H ( Supplementary Figure 1 in Text S1 ) . These results strongly suggest that the site frequency spectrum in harseq regions is indistinguishable from that found in either our control regions ( ctlreg ) , or in the Seattle SNPs data set . Thus we have no reason to believe that harseqs represent some kind of genomic outlier with respect to recent selective events . Examination of these statistics calculated separately for the W2S and S2W segregating sites ( Supplementary Figure 7 in Text S1 ) shows that the W2S subset in the harseq regions has a significantly more negative value of H than in the Seattle SNPs , which is consistent with the shift to higher derived allele frequencies for this subset noted above using the MWU test . To test for evidence of a selective sweep , we analyzed the spatial variation of derived allele frequencies at the segregating sites from our 22 chromosomal samples in each of the target 40kb regions using the SweepFinder program . This software determines a composite likelihood ratio ( CLR ) statistic comparing the hypothesis of a complete selective sweep at the location to the null hypothesis of no sweep using Test 2 from [28] . We tested along a grid of 1000 points in each target region ( see Materials and Methods ) . This test has been shown to be robust to demographic deviations from the standard neutral model in its ability to use an arbitrary background site frequency spectrum [29] . We tested with two such backgrounds: the first from the pooled set of all the data in the 49 harseq regions plus 13 ctlreg regions , the second from the same population ( YRI ) as our samples but with frequencies taken from the Seattle SNPs resequencing data [33] for a large set ( 104 ) of genic regions . It should be noted that using the SFS from our data as the background to define the neutral model should be particularly conservative in that we are testing any given region for deviations from that neutral model . To determine the significance of the maximum CLR values , we performed coalescent simulations of each target region and ran the SweepFinder program on each simulated set of segregating sites ( see Materials and Methods ) . We report as a p-value the fraction of simulations of each target that had a CLR greater than or equal to the actual maximum CLR for that target ( Supplementary Table 2 in Text S1 ) The harseq regions with the most significant five SweepFinder p-values are listed in Table 3 . These are nearly all at the 95% confidence level for either of the two background distributions used , but we note that none are individually significant after a conservative Bonferroni correction , given the 49 harseq regions that were tested . Since these may nevertheless harbor mutations that were selected for in the human lineage , here we briefly note some of their characteristics that can be seen in tracks from the UC Santa Cruz Genome Browser ( Supplementary Figure 4 in Text S1 ) . Unlike the other four SweepFinder hits , which all contain introns or exons of coding genes , harseq25 is in a gene “desert” . The nearest known gene , approximately 1Mb away on chromosome 4 , is ODZ3 , which is a transmembrane signaling protein most highly expressed in brain . Note that harseq25 also has a significant result on the MWU test discussed above . Evidence for a sweep in the harseq9 region is intriguing because it encompasses the 42-codon long , second exon of the PTPRT gene , a phosphatase with possible roles in the central nervous system . However , the human amino acid sequence of this exon matches the other primates chimp , gorilla , and orangutan , except where chimp has an obviously non-ancestral ThrAla substitution . The human sequence does have a single GA substitution near the 3′ splice site just upstream of this exon , but it falls in a position between the polypyrimidine ( Py ) tract and the AG acceptor site , for which the consensus sequence across many splice sites is evenly divided among the 4 nucleotides . The location of harseq11 on chromosome X places its evidence for a sweep in the first intron of the 2 . 4Mb long dystrophin gene DMD . The evidence for a sweep in harseq16 is offset to one end of its region , about 20kb from an apparent pseudogene comprising a single coding exon with a 270-codon open reading frame ( ORF ) that is probably derived from the Poly-A binding protein PABPC1 . The harseq24 region encompasses the second through fourth exons of the SKAP2 gene with the strongest evidence for a sweep about 10kb from the closest exon , but closer to a LINE transposable element that is present also in chimp , orangutan , and rhesus macaque ( but lost in gorilla ) . Although the above evidence for selective sweeps is not statistically significant , and none of it seems to point directly to a mutation in a core HAR element based on the position of the SweepFinder peak CLR values , it is important to note that while having the advantage of robustness to demography and recombination rate , our tests would not likely have power to detect sweeps that occurred beyond the last 200 , 000 years [29] . Under an assumption that substitutions in the HAR elements occurred uniformly over the last 5 million years and that most of these substitutions were adaptive , we estimate ( see Materials and Methods ) that we would be able to detect fewer than 8 with our tests . Therefore this negative result should not be interpreted as ruling out a role for adaptive evolution in the HARs .
In the era of comparative genomics , strong signals of conservation across multiple species serve as signposts that can indicate regions where evolutionary forces may be preserving functional elements that are subject to purifying selection ( e . g . [6] ) . By contrast , signals of positive selection pointing to adaptive changes in one lineage are harder to find , often employing sets of polymorphic sequences from multiple individuals of the same species . We exploited the two recently developed techniques of genomic enrichment and high throughput sequencing to characterize the polymorphism in a single human population across 40kb neighborhoods of the 49 HARs ( harseq regions ) . We investigated the harseq regions because the HARs were defined based on a presumption that the human lineage specific fixed differences therein might have arisen due to adaptive evolutionary forces . On the other hand , it has been emphasized by some that the presumably evolutionarily neutral mechanism of BGC can influence the frequency spectra at polymorphic positions , or cause fixation of alleles in a way that partially mimics the action of adaptive evolution . Indeed , fixation bias was noted in connection with the limited set of human specific alleles for some of the HARs when they were first described [7] . With the extensive novel polymorphism in our samples , we were able to carefully characterize fixation bias — both historical and ongoing — in the harseq regions and to conduct tests for recent selective sweeps across these regions . Our deep resequencing data is noteworthy because it eliminates issues of SNP ascertainment bias that could have skewed previous investigations of polymorphism near HARs . We applied several established population genetic tests , as well as an application of the MWU test , to identify differences in the fixation patterns of W2S and S2W mutations . Consistent with published reports [7] , [9] , [35] , we find evidence of historical W2S fixation bias in harseq regions . Using a MK test , we compared the proportion of W2S mutations among already fixed substitutions on the human or chimp lineage to that among the still segregating sites in our samples . We found that 11 of our 49 regions show statistically significant evidence of historical bias in allele fixation , with all but one favoring W2S fixation . These results strengthen and expand previous findings by identifying signals for W2S bias in much larger regions flanking the core HAR regions in an ascertainment-free population sample . This study goes beyond previous approaches by also looking at ongoing W2S fixation bias in the segregating site frequency spectrum . We performed a MWU test using only sites that are still segregating in the human population , separating out W2S from S2W mutations . This second test is designed to detect a phenomenon of bias that is currently driving W2S mutations to higher frequency in the population than S2W mutations . We found statistically significant evidence for this bias ( and none in the opposite direction ) in the regions flanking 11 of 49 HARs . For both of our tests , we showed that the core HAR element is generally not the main source of the signal that we detected , since the signal usually remains strong even when we mask out the central 1kb or even 5kb of the region . This is not consistent with BGC due to a recombination hot spot that has remained in the same location for millions of years , because the length scale of the effect of BGC is set by the length of the heteroduplex tract formed during recombination that needs to be repaired , which is thought to be 500bp ( e . g . [44]–[46] ) . However , it is consistent with a model in which the location of recombination hotspots drift fairly rapidly over evolutionary time scales , but may be denser in some regions [41] , [47]–[50] . It is noteworthy that there was little overlap in the regions identified by these two tests , one for older W2S fixations and the other for present day forces toward fixation , with a total of 20 found in one or the other . Although this is consistent with the hypothesis that the regional focus of BGC , which may be recombination hot spots , drifts significantly on a time scale of many hundreds of thousands or millions of years , we also found from simulations of GC-biased evolution over these time scales that the relatively minimal overlap between the tests is not unexpected . Another explanation for W2S fixation bias near HARs is selection for increased GC-content or individual fitness-improving GC alleles . To investigate these hypotheses and to attempt to disentangle the possible roles of BGC and positive selection in shaping the HARs , we applied a recently developed powerful method for detecting selective sweeps . Selection was previously investigated in much larger ( 500kb ) regions using more sparse polymorphic loci [29] . That study found 101 regions with strong evidence for a selective sweep within 100kb of a known gene . Here , we found only 5 possible candidates for such sweeps among our 49 target regions ( and none that were significant after correction for multiple hypothesis testing ) . Three of these candidates overlap regions with significant evidence of historical ( 2 ) or ongoing ( 1 ) W2S bias . As we are dealing with a lineage-specific evolutionary period of about 5 million years , and these tests can only see back a few hundred thousand years , it is quite possible that the original signal for selective sweeps in these regions has already decayed beyond our ability to recognize it in human population genetic data . That is , the lack of evidence for recent sweeps does not rule out the possibility that some of the excess substitutions in HARs were fixed by older selection . Similarly , the evidence for GC-biased evolution based on current population genetic data may not fully reflect patterns of polymorphism in the past . Consistent with the idea that HAR regions may have experienced positive selection too long ago to be detected with population genetic methods , very few positively selected regions in the human lineage have been identified to date , despite the existence of numerous public databases . Selective sweeps that have been identified have typically been the product of very recent events in human history , such as dairy farming affecting the lactase gene [51] or climate differences influencing a salt sensitivity variant [52] . Such environmental or cultural changes result in differences in the genetic makeup of disparate human populations , and such differences can be exploited to find evidence of recent , possibly still ongoing , selective sweeps . An alternative hypothesis that deserves consideration is that HARs may have an unusually high level of recent substitution due to a recent relaxation in purifying selection along the human lineage ( e . g . [53] ) . Using previously described methods [7] , we compared estimates of the rates of substitution in the 49 HAR elements to the neutral rate . We find that the human substitution rate exceeds the expected neutral rate in all 49 HARs , while this is true for the chimp substitution rate in only 10 HARs . Furthermore , in 33 HARs the human substitution rate significantly exceeds the neutral rate ( Poisson p-value ) while none of the chimp substitution rates significantly exceed the neutral rate . This evidence argues against the hypothesis that these HAR elements are the product of relaxed selection . We have focused in our study on 40kb neighborhoods of 49 HAR elements ( and 13 similar control regions ) because of their intrinsic interest but also because the scope of our study was appropriate to the state of the art of recently emerged enrichment and sequencing technologies . As larger data sets become available we will be able to apply our analysis on a genome-wide scale . Such analysis should give us insights into the properties associated with genomic regions that display this ongoing W2S fixation bias and their potential biological consequences . Despite the evidence that the unusually high level of recent substitution in the more extreme HAR elements , such as HAR1 and HAR2 , could be due to the process of BGC , there is ample evidence that these genomic elements remain functional , and thus the effect of BGC was to mutationally stress but not destroy these elements . HAR1 shows a very strong pattern of compensatory substitutions within its RNA helix structures , indicating a selective force to maintain these helix structures . The W2S substitutions all strengthen the RNA helices of HAR1 , and in one case , a substitution appears to extend one of them . Human HAR1 and HAR2 both show evidence of specific function , the former by its highly specific expression pattern during neurodevelopment and the latter by its ability to enhance gene expression during limb development . Whether the human-specific evolutionary changes to these elements reflect a process that was essentially like swimming upstream against an onslaught of non-selective BGC just to keep in place on the fitness landscape , or whether the mutational stress pushed these elements into a configuration that enabled some positive selection for higher fitness in humans , remains to be seen .
Genomic DNA for our samples was obtained from the NHGRI Sample Repository for Human Genetic Research distributed by the Coriell Institute for Medical Research [Camden NJ] [54] . All of the 11 samples were chosen from the Yoruba from Ibidan Nigeria ( YRI ) HapMap population . In particular , the samples were chosen as a subset of the Seattle SNPs P2 panel [33] . The Seattle SNPs PGA-VDR ( and Coriell repository numbers were ) : DY01 ( NA18502 ) , DY03 ( NA19223 ) , DY04 ( NA19201 ) , DY17 ( NA19143 ) , DY18 ( NA18517 ) , DY19 ( NA18856 ) , DY20 ( NA19239 ) , DY21 ( NA18871 ) , DY22 ( NA19209 ) , DY23 ( NA19152 ) , DY24 ( NA19210 ) . Sample preparation as described below was similar for all samples , except that prior to processing , the DY01 , DY03 , DY04 samples were subject to whole genome amplification ( WGA ) using the Repli-G Kit ( Qiagen N . V , The Netherlands ) according to manufacturer's specifications . It was found that WGA caused a loss of coverage in some isolated target regions , most notably in the 28kb section of the harseq1 region with coordinates hg18:chr20:61 , 183 , 966–61 , 212 , 244 , except for the core HAR1 element at chr20:61 , 203 , 919–61 , 204 , 081 . The primary target regions consisted of 20kb extensions in both directions from the 49 most statistically significant Human Accelerated Regions ( HARs ) identified as having a 5% false discovery rate [3] . Additional 40kb control regions were chosen in neighborhoods of 13 of the set of 34 , 498 vertebrate conserved elements that had extremely low LRT scores in the test used to define the HARs . The enrichment arrays were obtained from Nimblegen Systems ( Madison WI ) who designed the probes on their 385K array based on our specifications of the coordinates of the 62 target genomic regions ( Supplementary Table 1 in Text S1 ) . Probes were chosen to tile the target regions from both DNA strands . The design process avoided probes in highly repetitive sequences as described previously [19] , [21] . The fraction of bases in the target regions covered by the probes ranged from 98% ( harseq12 ) to 65% ( harseq4 ) with a median of 88% . Details of probed bases are available upon request . The library preparation of the samples for SOLiD ( Applied Biosystems , Foster City , CA ) sequencing generally followed the manufacturer's protocols for barcoded SOLiD System 2 . 0 Fragment Library Preparation ( samples DY01 , DY03 , DY04 ) and SOLiD System 3 . 0 Barcoded Fragment Library Preparation ( remaining samples ) , with changes as necessary for enrichment on the Nimblegen arrays as noted in the following: Samples were sheared to approximately 100bp using the Covaris S2 System Program B ( Covaris Inc , Woburn , MA ) . End repair was performed with End-It DNA End-Repair Kit ( Epicentre Biotechnologies , Madison , WI ) per manufacturer's instructions . Single stranded oligos for the P1 and P2-barcoded SOLiD adaptors were ordered from Invitrogen Corporation and annealed per the SOLiD protocol to form double stranded adaptors , which were ligated to the end-repaired DNA fragments using the Quick Ligation Kit ( New England BioLabs , Ipswich , MA ) per manufacturer's directions , leaving a nick at the 3′ end of each genomic DNA strand where the 5′ end of the adaptor was not phosphorylated . Samples DY01 , DY03 , DY04 were size selected to 150250bp from a 6% polyacrylamide gel and purified via ethanol precipitation . This was followed by nick translation and ligation mediated PCR ( LMPCR ) amplification ( 6 cycles ) in a combined reaction per the SOLiD protocols using Takara ExTaq polymerase ( Takara Bio , Madison , WI ) . After dividing into 10 aliquots , an additional 10 cycles of ExTaq LMPCR amplification were performed on these samples in preparation for array hybridization . Samples DY17–DY20 were first nick translated without amplification using Pfu polymerase ( Stratagene , La Jolla , CA ) . Samples DY21–DY24 were nick translated and ExTaq LMPCR amplified ( 6 cycles ) . Quantitation using a DNA 2100 BioAnalyzer ( Agilent Technologies , Waldbronn , Germany ) showed that the PCR associated with nick translation prior to size selection mainly served to amplify the nick translated adaptors . After the nick translation and any initial LMPCR , all of samples DY17–DY24 were size selected on an E-Gel SizeSelect 2% agarose gel ( Invitrogen ) per manufacturer's instructions . These size selected samples were then ExTaq LMPCR amplified ( 6 , 9 or 10 cycles ) in preparation for array hybridization . Array hybridization to the Nimblegen arrays was performed on the Nimblegen Hybridization System 4 station under Mix Mode “B” for 64 to 70 hours using the Nimblegen Sequence Capture Kit per manufacturer's instructions . Prior to hybridization , samples DY21–24 were pooled to a total of 5 . 7g . All the other samples were hybridized individually , in amounts ranging from 1 . 9g to 8 . 0g per sample . For competitive hybridization to probes on the array that might nonselectively bind repetitive DNA , Human Cot-1 DNA ( Invitrogen ) that had been Covaris sheared to approximately 100bp was added to the hybridization mix in a 5∶1 ratio by weight . Additionally , to block the adaptor ends of the denatured , single stranded DNA fragments from binding to each other , a 10∶1 molar excess of adaptor oligos ( P2 with an unused barcode sequence and P1 ) was also added . At the completion of hybridization , the slide was washed with the Nimblegen Sequence Capture Wash and Elution kit per manufacturer's directions , and the enriched DNA was eluted with 350L of C purified water using an affixed SA200 SecureSeal Hybridization Chamber ( Grace Bio-Labs , Bend OR ) . A secondary elution with an additional 350L was also taken . Quantitative real-time PCR ( qPCR ) was performed on the eluted material to determine the rough fraction of target DNA and to compare the primary and secondary elutions . For this purpose qPCR amplicons within the target were compared to amplicons not in the target regions . It was generally found that the primary elution captured more than 95% of the target DNA ( data not shown ) . By normalizing to pre-enrichment material , and taking into account the fact that the 2 . 1Mbp target region comprised approximately 0 . 1% of the entire human genome , it was estimated that more than 35% of the eluted material fell in the target regions ( data not shown ) . The eluted material was ExTaq LMPCR amplified ( samples DY01 for 19 cycles , samples DY03–04 for 15 cycles , pooled samples DY21–24 for 10 cycles , samples DY17–20 for 12 cycles , ) in preparation for the emulsion PCR step of SOLiD sequencing that was performed in the UC Santa Cruz Genome Sequencing Center . Samples DY01 , DY03 , DY04 were processed with the SOLiD Version 2 system , producing 35 bases of sequence information for each read . The remaining samples were processed with the SOLiD Version 3 system , producing 50 bases of sequencing information for each read . The 35mer ( DY01 , DY03 , DY04 ) or 50mer ( remaining samples ) sequencing reads were mapped to the whole human genome using the bwa program [55] which generates mappings and associated quality scores in the sam format [56] that can be processed with the samtools suite . The bwa program is aware of the colorspace nature of the SOLiD sequencing reads , and uses a dynamic programming algorithm to infer the best nucleotide sequence for the read [55] . All reads were also mapped to the DNA of the Epstein-Barr Virus , which was used to transform the Coriell cell lines from which the supplied genomic DNA was extracted . For the female samples , the Y chromosome was excluded from the mapping . For the male samples , the pseudo-autosomal region of the Y chromosome was excluded from the mapping . The set of mappings for each sample was then filtered to the regions covered by the probes on the Nimblegen enrichment array described above . To eliminate spurious pileups caused by overamplification of particular molecules in the library preparation process , the mapped reads were further filtered to select at most 4 reads from each strand at a given genomic starting position . Where there were more than 4 , the 4 with the highest total read quality ( not the best mapping quality , which would bias against reads containing non-reference alleles ) from the SOLiD instrument were selected . Between 40% and 60% of the reads for a given sample were successfully mapped , and of those reads , between 33% and 48% mapped to bases covered by the probes on the enrichment array ( Supplementary Table 3 in Text S1 ) . For samples DY01 , DY03 , DY04 about 50% of the latter reads were lost in the “maximum 4 per strand” pileup elimination step , while only 11% to 23% were lost in this step in the remaining samples ( Supplementary Table 3 in Text S1 ) . This was likely due to the difference in LMPCR cycles used for the different samples as noted above . To determine the coverage at each position in the target region and the consensus genotypes for each sample , the command “samtools pileup -v” was used with default parameters for its consensus calling model . Possible confounding of the genotypes due to contamination by paralogous sequences was avoided in two ways . First , as noted , only genotypes at positions delimited by the Nimblegen probes were used in the analysis and these probes were designed to avoid repetitive sequences . Second , the bwa mapping algorithm assigns low mapping quality to reads that are not genome-wide unique , and the samtools consensus caller requires high mapping quality . To filter SNPs from among the not homozygous reference genotypes , “samtools . pl varFilter” was run with default parameters , except that the maximum read depth was set to 425 , because with up to 4 reads of length 50 on each strand , it was possible to get coverage of 400 . This command filters out potential SNPs when more than 2 fall within a 10bp window , on the grounds that there might be an insertion/deletion event rather than separate SNPs , and also filters out reads with RMS mapping quality value less than 25 . A similar quality filter was applied to the genotype calls that were homozygous reference . Because of stochastic variation in the composition of reads from the two chromosomes of each diploid individual , low coverage might cause an erroneous homozygous call in a true heterozygote . Therefore a further filter restricted the subsequent analysis to the SNP or homozygous reference calls made for a sample only at positions for which the coverage was 35 or greater . For each target region , the count of the union of such positions across all samples is listed in Supplementary Table 1 in Text S1 . As shown in Supplementary Figure 5 in Text S1 , the vast majority of the segregating sites that remain after the application of our 35× coverage filter are in Hardy-Weinberg Equilibrium , with only 0 . 3% having a p-value less than 0 . 05 . For purposes of all subsequent analysis , an ancestral allele at each position in the target regions was determined from the Enredo-Pecan-Ortheus ( EPO ) pipeline [57] , [58] as published on the 1000 Genomes website [59] . This pipeline determines the common ancestor of human and chimp at a locus by considering alignments of the human , chimp , orangutan , and rhesus macaque genomes . From the sets of filtered genotype calls in the 11 diploid samples as described above all the segregating sites were selected . A set of filters was applied to this list to produce the final set of segregating site derived ( i . e . non-ancestral ) allele frequencies ( DAFs ) for all downstream analyses . To avoid skewing the DAFs towards higher frequencies , segregating sites with less than 8 chromosomal samples were eliminated . Also eliminated were any positions with more than 2 alleles among the reference , ancestral , or sample alleles , or where the ancestral allele was not determined by the EPO pipeline . Lowercase values of the EPO ancestral allele , which result from various cases without complete evidence in all species were not eliminated . The SweepFinder program [28] was applied to the allele frequencies for the final list of segregating sites to determine the composite likelihood ratio ( CLR ) of a selective sweep at each one of a grid of 1000 positions across each target region . The model used in this program requires a background derived allele frequency spectrum . Two such backgrounds were used . First , all of the DAFs from all filtered segregating sites in our sample were aggregated and used as input to the command “SweepFinder -f” , which accounts for missing data using a Broyden-Fletcher-Goldfarb-Shanno ( BFGS ) algorithm . We refer to this as the “harseq” background . A second , presumably more neutral background was obtained from the African-Derived ( AD ) YRI subset of 24 individuals in the Seattle SNPs P2 panel . The DAFs for all segregating sites in all 104 genes resequenced for this panel by Seattle SNPs [33] were included . As for the harseq background , the “seasnp” background was obtained with the command “SweepFinder -f” applied to these DAFs . The resulting “seasnp” frequency spectrum ran from 1 to 47 and was reduced to a spectrum running from 1 to 21 , as needed by SweepFinder with our data , by hypergeometric weighting the relevant components of the input allele frequencies at each target allele frequency . ( 1 ) Dividing by the sum of the in Eqn 1 produces a valid frequency spectrum that sums to 1 . A similar hypergeometric weighting was also required to reduce the spectrum to a range from 1 to 19 for the harseq1 , 2 and ctlreg60 regions . In the latter regions missing data reduced the maximum number of samples at the segregating sites to 20 . P-values for our SweepFinder results were obtained via coalescent simulation conditional on the observed number of segregating sites in focal region , the observed coverage of this region , and the estimated recombination rate for a given region according to the pedigree data of [36] assuming a human effective population size of . The second point is important here in that our resequencing of both the harseq regions and the control regions was not perfectly complete , but instead was partial owing to an inability to design proper probes for our Nimblegen enrichment procedure ( see above ) in certain genomic segments . For each region examined we performed coalescent simulations under the standard neutral model which has been shown to be conservative for the SweepFinder procedure [29] . From the set of fixed differences between human and chimp in the 49 core HAR elements we use the EPO determined ancestral allele ( see above ) to count 206 as the total number of human lineage specific substitutions . Since a small number of substitutions are expected to occur by chance even in constrained elements , we used the number of substitutions on the chimp lineage as an estimate of the minimum number of non-adaptive substitutions in each HAR . These total 16 for all 49 HARs . So , we approximate that at most 190 ( = 206-16 ) substitutions were adaptive in humans . In reality some of the excess nucleotide changes for a given HAR were probably segregating at the same time on the same haplotype . So , 190 is most likely an overestimate of the number of adaptive events in HARs . But suppose there were indeed 190 separate adaptive substitutions and that these occurred uniformly over the last 5 million years . Further assume that any sweep from the last 200 , 000 years could be detected by SweepFinder . Then , 4% of the 190 adaptive substitutions ( i . e . , 7 . 6 sweeps ) should be in the detectable time frame . Since the number 190 and the percentage 4% are both upper bounds , we conclude that at most 8 and probably much fewer than 8 sweeps would be detectable by our SweepFinder analysis even if all 49 HARs were shaped by adaptive evolution . Our finding of no significant sweeps after Bonferroni correction and 5 significant before correction is therefore consistent with expectations . To determine if mutations from an ancestral weak ( A or T ) basepair to a strong ( G or C ) basepair ( W2S mutations ) are more likely to spread in the population represented by our samples , we compared W2S segregating sites to S2W segregating sites for each target region and for the aggregate set of segregating sites . We performed a Mann-Whitney U ( MWU ) test for a difference between the W2S and S2W derived allele frequency spectra . The test was performed in the R language with the command “wilcox . test ( paired = FALSE , alternative = two . sided ) ” . The resulting “location” parameter was normalized to 22 samples and is positive if W2S mutations are segregating at higher frequencies than S2W . The resulting p-values are in Supplementary Table 2 in Text S1 . To determine if relatively more W2S mutations fixed along the human or chimp lineages than are segregating in the human population represented by our samples , we first determined the high mapping quality chimp reference bases that differ from the human reference using reciprocal best alignments of the chimp and human genomes [37] . This set was then restricted to the positions in our target regions for which we had a genotype call for at least one sample with read depth of coverage of 35 or greater as discussed above . From this set of fixed differences we removed any for which the EPO ancestral allele was not determined as discussed above , or for which we had a segregating site , or which appeared in dbSNP release 129 [38] . The remaining fixed differences as well as the segregating sites were divided into W2S or S2W ( or other ) . A McDonald Kreitman-like ( MK ) test on the resulting 2×2 contingency table was performed in the R language with the command “fisher . test ( alternative = two . sided ) ” . The resulting p-values are in Supplementary Table 2 in Text S1 . For the significant cases it was easy to determine from the data in the contingency table if the fixed differences favored S2W mutations relatively more than the segregating sites ( column “S2W” in Table 2 ) . For the target regions with significant p-values on either the MWU or MK tests , we tested whether the significance was due to a restricted locus within the region , by removing all segregating sites and fixed differences under a mask of a given size at a given position within the region and rerunning the test with the remaining data ( Table 1 and Table 2 ) . We downloaded the data for the 104 genes resequenced with the Seattle SNPs P2 panel . The genotypes for our 11 YRI samples ( which are a subset of the P2 panel ) and the coordinates for the genotyped segregating sites were obtained from the global “prettybase” file mapped to UCSC hg18 coordinates . The total set of positions genotyped was obtained from the individual gene “genbank” files by excluding the features defined as “Region not scanned for variation” and aligning the remaining regions to the hg18 coordinates of the full extent of the genic region sequenced specified in the associated “ucscDataFile” . Given these coordinates and the genotypes at the segregating sites , the same techniques as described above for our resequencing data was applied to derive ancestral alleles and human/chimp fixed differences , and to perform the MWU and MK tests . Our data was derived from ( probed ) regions of a relatively tight size distribution ( Supplementary Table 1 in Text S1 ) . By contrast , the sizes of Seattle SNPs variation-mapped genic regions varied widely . Some were rather small and contained few segregating sites . Therefore , we included only genic regions with a minimum of 10kb variation-mapped and a minimum of 40 segregating sites . Additionally , a small number of the genic regions were excluded because of data missing from the “prettybase” file or because there were no associated high quality reciprocal best human chimp differences as described above , possibly because of paralogous genes in one or the other lineage . The remaining set of results for the MWU and MK tests on 62 genic regions ( Supplementary Table 4 in Text S1 ) were used for comparison to our 49 harseq regions . To determine if the p-values for the MWU and MK tests were accurate , we also conducted the tests on sets of simulated data under a neutral model . For the MWU test , we performed coalescent simulations using Hudson's ms program [60] . For each simulation we generated 22 samples at 85 segregating sites ( the average number of W2S plus S2W segregating sites in the 49 harseq target regions ) and then randomly assigned the sites as either W2S or S2W in Bernoulli trials using the W2S∶S2W ratio from the 49 harseq regions of 2057∶2114 . After calculating the MWU test p-value for each simulation , the fraction of simulations with p-value less than a given value was computed , as well as the subset of that fraction in which the W2S spectrum was offset towards higher derived allele frequencies ( Supplementary Figure 2 in Text S1 ) . For the MK test , for each simulation we separately derived a set of human-chimp fixed differences and a set of segregating sites . The fixed differences were derived using the phyloBoot program from the PHAST package [61] . We used a phylogenetic model and substitution rate matrix derived from 4-fold degenerate amino-acid coding synonymous sites across the genome as an unbiased neutral model . The equilibrium GC-content of this model was adjusted to reflect the genome-wide average GC-content . From the primate sequences so generated , we extracted positions containing a human-chimp difference that could also be unambiguously assigned an ancestral allele based on the macaque allele at that position . Each simulation used 335 such sites , ( the average number of fixed differences in the 49 harseq target regions ) which were divided based on whether they were W2S or S2W ( or other ) . The segregating sites for each simulation were derived from 101 ( the average total number of segregating sites in the 49 harseq target regions ) Bernoulli trials , randomly dividing them as W2S or S2W ( or other ) according the corresponding ratios in the fixed differences from all of the phyloBoot simulations . After calculating the MK test p-value for each simulation , the fraction of simulations with p-value less than a given value was computed , as well as the subset of that fraction for which the ratio of W2S∶S2W was higher for the simulated fixed differences than for the simulated segregating sites ( Supplementary Figure 3 in Text S1 ) . Simulations of GC-biased evolution due to BGC were generated using a forward time Wright-Fisher model of a population . Simulations were run at a population size of 10 , 000 , which is approximately comparable to the long term human effective population size . Details of the simulation method can be found in [62] and references therein . Briefly , we model BGC as a selection process in which each W2S ( S2W ) mutation adds ( subtracts ) some normal deviate fitness value to the haplotype on which it is found . This model is approximately equal to the normal shift model [63] if we were only to consider the W2S subset of mutations . Simulations were run for 40 * N generations as a burnin period to reach stationarity , at which point we modeled a vicariance event representing the human chimp divergence . After the population split we ran the two populations for 6 . 5 units of 4N generations , to approximate the divergence time between humans and chimps . We assume the strength of BGC acting was 4NB = 1 . 3 as recently estimated from human data [10] , [64] . We also assumed a ratio of 4NB∶4Nu of 1 . The MWU and MK tests were performed as above using a single sample from the chimp lineage and 50 samples from the human lineage . Association between MWU and MK tests on simulated ( and HAR region ) data was assessed using Fisher's Exact Test on the 2×2 contingency table defined by the counts of significant or not significant tests: {MWU , notMWU}×{MK , notMK} . The numbers of significant tests by either or both MWU and MK were compared between the simulated and HAR region data using binomial and Poisson tests . | The search for functional regions in the human genome , beyond the protein-coding portion , often relies on signals of conservation across species . The Human Accelerated Regions ( HARs ) are strongly conserved elements , ranging in size from 100–400 bp , that show an unexpected number of human-specific changes . This pattern suggests that HARs may be functional elements that have significantly changed during human evolution . To analyze the evolutionary forces that led these changes , we studied 40 kb neighborhoods of the top 49 HARs . We took advantage of recently developed DNA sequencing technology , coupled with methods to isolate genomic DNA for our target regions only , to determine the genotypes in 22 chromosomal samples . This polymorphism data showed no significant evidence for adaptive selective sweeps in HAR regions . By contrast , we found strong evidence for a nucleotide bias in the fixation of mutations from A or T to G or C basepairs . Our work reveals that this bias in the HAR neighborhoods is not just an historic phenomenon , but is ongoing in the present day human population . This finding adds credence to the possibility that non-selective forces , such as biased gene conversion , could have contributed to the evolution of several of these regions . | [
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| 2010 | GC-Biased Evolution Near Human Accelerated Regions |
During online speech processing , our brain tracks the acoustic fluctuations in speech at different timescales . Previous research has focused on generic timescales ( for example , delta or theta bands ) that are assumed to map onto linguistic features such as prosody or syllables . However , given the high intersubject variability in speaking patterns , such a generic association between the timescales of brain activity and speech properties can be ambiguous . Here , we analyse speech tracking in source-localised magnetoencephalographic data by directly focusing on timescales extracted from statistical regularities in our speech material . This revealed widespread significant tracking at the timescales of phrases ( 0 . 6–1 . 3 Hz ) , words ( 1 . 8–3 Hz ) , syllables ( 2 . 8–4 . 8 Hz ) , and phonemes ( 8–12 . 4 Hz ) . Importantly , when examining its perceptual relevance , we found stronger tracking for correctly comprehended trials in the left premotor ( PM ) cortex at the phrasal scale as well as in left middle temporal cortex at the word scale . Control analyses using generic bands confirmed that these effects were specific to the speech regularities in our stimuli . Furthermore , we found that the phase at the phrasal timescale coupled to power at beta frequency ( 13–30 Hz ) in motor areas . This cross-frequency coupling presumably reflects top-down temporal prediction in ongoing speech perception . Together , our results reveal specific functional and perceptually relevant roles of distinct tracking and cross-frequency processes along the auditory–motor pathway .
Participants listened to single sentences and indicated after each trial which ( out of 4 ) target words occurred in the sentence . Participants reported the correct target word , on average , in 69 . 7 ± 7 . 1% ( mean ± SD ) of trials , with chance level being 25% . Performance of individual participants ranged from 56 . 1% to 81 . 1% , allowing a comparison between correct and incorrect trials for each participant . The MI between the source-localised , Hilbert-transformed MEG time series and the Hilbert-transformed speech envelope was computed within 4 frequency bands . These reflected the rates of phrases ( 0 . 6–1 . 3 Hz ) , words ( 1 . 8–3 Hz ) , syllables ( 2 . 8–4 . 8 Hz ) , and phonemes ( 8–12 . 4 Hz ) in the stimulus corpus ( for an example sentence , see Fig 1 ) . The boundaries of each band were defined based on the slowest and fastest event per linguistic category across sentences ( see Materials and methods ) . When compared to surrogate data , speech MI was significant in all analysed frequency bands ( Fig 2A; phrases: Tsum ( 19 ) = 32 , 262 . 9 , p < . 001; words: Tsum ( 19 ) = 22 , 2243 . 8 , p < . 001; syllables: Tsum ( 19 ) = 13 , 428 . 6 , p < . 001; phonemes: Tsum ( 19 ) = 1 , 294 . 0 , p = . 018 ) . As in previous studies , MI was strongest in early auditory areas [2 , 22 , 30] and decreased with increasing frequency [6 , 22] . Tracking of phrases , words , and syllables was reflected in a bilateral cluster , whereas phoneme tracking was only significant in the right hemisphere . These results confirm the previously reported existence of speech encoding in rhythmic brain activity versus a null hypothesis of no encoding but do not speak on the perceptual relevance . To localise cortical regions where entrainment was functionally relevant for comprehension , we statistically compared MI between correct and incorrect trials within each band ( hereafter called ‘perceptually relevant’ ) . This yielded significant left-hemispheric clusters in 2 frequency bands ( Fig 2B ) . For the phrasal timescale ( 0 . 6–1 . 3 Hz ) , MI was larger for correctly versus incorrectly comprehended trials in a cluster comprising left pre- and postcentral regions , supramarginal gyrus , and Heschl gyrus ( HG; Tsum ( 19 ) = 568 . 00 , pcluster = . 045; 205 grid points ) . The effect peaked in the left premotor ( PM ) cortex ( left Brodmann area [BA] 6 ) . For the word timescale ( 1 . 8–3 Hz ) , MI was larger in a cluster comprising left superior , middle , and inferior temporal gyrus as well as supramarginal gyrus and HG ( Tsum ( 19 ) = 739 . 59 , pcluster = . 018; 263 grid points ) . The effect peaked in the left middle temporal gyrus ( MTG; left BA 21 ) . There was a small overlap of the clusters for phrasal and word timescales , peaking in the left HG ( left BA 41 , see Fig 3A ) . We performed several further analyses to determine whether these effects were specific to those timescales extracted from the stimulus corpus . First , we performed posthoc t tests at the peak grid points of each cluster to see whether phrasal and word effects were indeed significant only for the respective timescales ( Fig 3B ) . As expected , MI differed between correct and incorrect trials at the phrasal timescale in left PM cortex and HG ( t ( 19 ) = 4 . 90 , pFDR < . 001 and t ( 19 ) = 2 . 53 , pFDR = . 031 , respectively ) . Likewise , MI differed at the word timescale in the left MTG and HG ( t ( 19 ) = 5 . 22 , pFDR < . 001 and t ( 19 ) = 3 . 48 , pFDR = . 005 , respectively ) . MI neither differed at the phrasal scale in MTG ( t ( 19 ) = −1 . 78 , pFDR = . 11 ) nor at the word scale in PM cortex ( t ( 19 ) = −0 . 57 , pFDR = . 58 ) . We also compared correct and incorrect trials at the same peak grid points for syllable and phoneme timescales , although the whole-brain analysis did not indicate that effects were perceptually relevant for the task at these timescales . This was to make sure that effects had not been overlooked due to corrections for multiple comparisons . None of the comparisons were significant ( all pFDR > . 56 , see S1 Fig ) , indicating that none of the peak grid points in PM , HG , or MTG showed perceptual relevance at the faster scales of syllables or phonemes . Second , we compared brain-wide MI values between correctly and incorrectly comprehended trials in 7 generic , 2 Hz–wide frequency bands ( from 0–8 Hz , in 1-Hz steps ) to confirm that the above intelligibility-related effects were indeed specific to frequency bands matched to the specific temporal structure of the speech material . This is an important contrast because most previous studies used generic bands with a predefined fixed frequency spacing . Perceptually relevant effects were found in only 2 bands ( S2A Fig ) . For the 1–3 Hz band , which largely overlaps with the word scale , MI was larger for comprehended than uncomprehended trials in a cluster centred around auditory cortex ( Tsum ( 19 ) = 1 , 078 . 85 , pcluster = . 030 ) , confirming the relevance of auditory regions for word-level encoding . For the 2–4 Hz band , which spans the scale of words and syllables , MI was only marginally enhanced for comprehended sentences in a cluster covering middle and inferior temporal cortex ( Tsum ( 19 ) = 751 . 93 , pcluster = . 046 ) . Third , using posthoc statistics , we also verified that the MI at the previously identified peak grid points ( see Fig 3A for peaks ) differed between correct and incorrect trials only at those timescales that matched the stimulus-specific bands ( S2B Fig ) . The motor cortex was not found to be perceptually relevant in any of the probed generic bands , which suggests that exact frequency boundaries are necessary to detect phrase tracking in motor areas . Rhythmic brain activity represents neuronal excitability changes [32 , 33] . In auditory areas , this mechanism has been suggested to reflect a segmentation of the incoming sensory stream [34 , 35] . But what is the role of slow excitability changes in the motor system ? The motor system plays a role in the temporal prediction of rhythms and beats [36 , 37 , 38 , 39] . Previous studies have suggested that these predictions rely on the coupling of delta phase to rhythmic activity in the beta band [36 , 40 , 41] . We therefore hypothesised that speech entrainment at the phrasal scale—and its perceptual relevance—is directly linked to phase-amplitude coupling ( PAC ) with motor cortical beta activity . Indeed , we found that the coupling of the phase of phrasal-scale activity ( 0 . 6–1 . 3 Hz ) to beta power ( 13–30 Hz ) was significantly stronger for correctly versus incorrectly comprehended trials in our motor cluster ( t ( 19 ) = 2 . 96 , pFDR = . 032; Fig 3C ) . In contrast , there was no such cross-frequency coupling for the phase of word-scale activity relative to beta power ( t ( 19 ) = 1 . 14 , pFDR = . 356 ) or of phrasal phase to either alpha ( t ( 19 ) = 1 . 38 , pFDR = . 356 ) or theta power ( t ( 19 ) = −0 . 38 , pFDR = . 708 ) . To confirm that the PAC between phrasal phase and beta power is confined to motor regions , we performed a further whole-brain analysis , comparing PAC between correct and incorrect trials . This analysis yielded 1 cluster in which PAC was larger for comprehended than uncomprehended trials ( Tsum = 203 . 94 , pcluster = . 030 , one-sided; Fig 3D ) . The cluster included left pre- and postcentral regions . Therefore , perceptually relevant PAC was confined to the left motor system , overlapping with the speech tracking effect in left motor areas . The sentence structure in the present study was relatively rigid and predictable , which could have emphasised effects at the phrasal timescale . We therefore tested the presence of the phrasal tracking effect and the PAC between phrasal phase and beta power in the motor cortex in an additional dataset in which the sentence and phrase structure were more variable . Here , participants listened to a natural 7-min narration while their MEG was recorded [2 , 42] . The phrasal rate of the narration ranged between 0 . 1 Hz and 1 . 5 Hz . We specifically tested phrase tracking and PAC in the motor cluster , defined by results in the main dataset above , by contrasting the actual MI with surrogate data . Phrase tracking was larger in actual data ( t ( 22 ) = 6 . 22 , pFDR < . 001 ) , as was PAC between phrasal phase and beta power ( t ( 22 ) = 4 . 52 , pFDR < . 001; Fig 4 ) . These results suggest that the found mechanisms in the present study also exist in an unrelated dataset with highly variable sentence structure .
The motor system plays a causal role in speech perception [43 , 44 , 45] . Previous studies have attributed functions for simulating speech production [46 , 47] or sensorimotor speech processing [48] to the motor system . Furthermore , the PM cortex and the motor system generally have been associated with generating temporal predictions [49 , 50 , 51 , 52 , 53] and the processing of rhythms and beats [37 , 38 , 39] . In the present study , we increase the knowledge about its role for natural speech processing by uncovering two specific neural mechanisms . The first mechanism is a perceptually relevant speech tracking specifically at the phrasal timescale , peaking in the left PM cortex . Notably , the timing of phrasal elements in the used stimulus corpus was relatively predictable because all sentences followed the same structure . The phrasal structure was also defined by prominent pauses between phrasal elements ( evident in the clear peak in the frequency spectrum , S3A Fig ) . On the other hand , words ( and therefore syllables and phonemes ) were not semantically—or temporally—predictable due to the recombination of words across sentences . The motor system likely exploited the temporally predictive phrasal information for parsing and segmenting the sentences , thus facilitating comprehension by providing a temporal prediction of when the relevant target word was likely to occur . Our results therefore suggest that perceptually relevant speech entrainment emerges not only at the time-scale of the directly task-relevant feature ( here words ) but also at those time-scales that can be exploited to better detect or encode this feature . We confirmed this motor mechanism in a second dataset , which featured a less stereotyped phrasal structure . Yet it is possible that this mechanism is not specific to the phrasal structure per se . Instead , it could be that the motor system would exploit any temporal regularities [38] , regardless of their linguistic or metalinguistic relevance . Future research is required to directly compare acoustic and linguistic regularities and their relevance for speech tracking . It has been suggested that delta entrainment to speech in the left hemisphere reflects a motor-driven top-down modulation [42 , 54] . These top-down modulations have been associated with beta oscillations [50 , 55 , 56 , 57] , which are prevalent in the motor system [58] . Beta power in the motor system has also been related to speech comprehension [57] . The finding that the temporal prediction of tone sequences is mediated by prestimulus delta–beta coupled oscillations further supports this hypothesis ( [36] , see also [40 , 41] ) . Here , we show—to our knowledge , for the first time—that such a cross-frequency mechanism also operates during the encoding and perception of continuous speech . This coupling is ( i ) specific to phrasal delta phase ( 0 . 6–1 . 3 Hz ) and beta power ( 13–30 Hz ) and ( ii ) only perceptually relevant in left motor areas . Furthermore , in an additional dataset , we show that this phrasal delta–beta coupling is also present during the processing of a natural , spontaneous narration . Based on the above-mentioned findings [36 , 40 , 41] , we speculate that this cross-frequency motor coupling reflects top-down temporal prediction , which is relevant both for the perception of simple sounds [36] and speech . Speech tracking at the word scale was perceptually relevant across the entire midtemporal gyrus , peaking in MTG and including superior and inferior temporal gyrus as well as inferior supramarginal gyrus . Previous MEG studies that have localised tracking processes typically show that this peaks in early auditory areas independent of the frequency band ( for example , when contrasted with the null hypothesis of no speech encoding ) [2 , 22 , 30] . Only by using a direct comprehension measure can we show that perceptually relevant word segmentation peaks in the left MTG . The MTG is associated with lexical semantic processes [59 , 60] and is one endpoint of the ventral auditory pathway , mapping sound to meaning [61] . It is plausible that stronger speech tracking , and therefore better word-scale segmentation in these regions , is directly linked to comprehension performance . The result that the effect at the word scale extends dorsally to supramarginal gyrus seems to contradict models of a ventral focus of word comprehension . However , it is consistent with the notion of a dorsal lexicon , thought to store articulatorily organised word form representations [62] . An analysis of 2 Hz–wide generic bands showed that ( i ) the activity in the motor system was not predictive for comprehension in any generic band; ( ii ) the 1–3 Hz band , which largely overlaps with the word scale , yielded a similar pattern as the word-specific timescale; and ( iii ) the 2–4 Hz band also overlapped with the effect at the word timescale , albeit only minimally significantly ( S2 Fig ) . These results suggest that perceptually relevant speech tracking in the motor system is specific to the phrasal timescale in the stimulus material . In temporal regions , perceptually relevant tracking was found in the delta band ( above 1 Hz and below 4 Hz ) , independent of the specific boundaries of the used bands ( although 0–2 Hz did not yield a significant effect ) . This suggests that speech tracking in temporal areas emerges at more widespread timescales , perhaps because word length is more variable than phrasal length in the present stimulus material . Analyses of the coefficient of variation ( cv ) supported this interpretation: when compared with phrases ( cv = 0 . 27 ) , words varied in length almost twice as much ( cv = 0 . 48 ) . We chose to base the timescales on linguistic categories of phrases , words , syllables , and phonemes . This is the most pragmatic approach because the language system ultimately has to parse the speech stream into these segments . However , one could argue that these linguistic categories overlap with other metalinguistic elements that also follow temporal modulations below 4 Hz such as prosodic features [16] . The most relevant prosodic features for speech segmentation are pauses , stress , and intonation [14 , 63] . The phrases in the stimulus material are defined by pauses , and therefore phrasal timescale and timing of pauses can be considered one and the same . The interaction between linguistic categories and lexical stress is more complex . If we consider every third syllable as stressed [64] , one can derive a ‘stress timescale’ of 0 . 9 to 1 . 6 Hz , which partly overlaps with the phrasal timescale ( 0 . 6–1 . 3 Hz ) . The role of stress is manifold in speech ( disambiguation of phonemically identical words , highlighting the meaning of words , metrical stress ) , and we cannot rule out that stressed syllables are reflected in neural activity . However , the segmentation into phrases does not typically have stressed syllables as boundaries because this would often yield nonsense phrases . Therefore , although stress is important and useful in speech comprehension , focusing on the phrasal timescale ( as opposed to the ‘stress timescale’ ) is a direct way to address phrase segmentation . Fluctuations in pitch , or intonation , also occur in the delta band ( see S3B Fig for spectral analysis of pitch , or its acoustic correlate the fundamental frequency ) . Pitch fluctuations can signal phrasal boundaries [65] , and an overlap with the phrasal timescale is therefore not surprising . Because the auditory system is able to track pitch fluctuations [9] and fundamental frequency and intensity are related , we cannot completely disentangle pitch tracking from envelope tracking . But language comprehension requires the grouping of words into phrases [15] , and focusing the analysis on the phrasal timescale is the most direct way of analysing phrasal processing . Future research needs to address the question of how much phrasal segmentation relies on the acoustic envelope , pitch fluctuations , or both . Taken together , linguistic and metalinguistic events in natural speech have a tendency to co-occur [66] , and their interaction is complex . However , for natural speech processing , the division into linguistic categories , as done in the present study , seems the most pragmatic and ecologically valid solution to gain specificity about speech comprehension effects . Finally , in the present study , the average speech rate was approximately 130 words per minute . In two other studies that reported speech rate , it was considerably higher , at approximately 160 words per minute [22] and approximately 210 words per minute [25] . The rate of syllables is typically associated with frequencies between 4 and 8 Hz [3 , 67] . In the present study , it was 2 . 8 to 4 . 8 Hz , and in another study , it was even lower at 2 to 4 Hz [4] . These differences demonstrate that , even in experimental contexts , speech rates can deviate from the assumed standard . Furthermore , a recent study has shown that the auditory system is not limited by traditionally imposed frequency bands [57] . It therefore is highly beneficial to calculate stimulus-specific speech regularities for speech tracking analyses instead of applying generic frequency bands ( cf . [68] for visual modality ) . Our results regarding overall speech tracking ( compared with chance ) replicate previous reports of widespread speech-to-brain entrainment at multiple timescales [2 , 22] . However , in our data , only speech tracking in specific bands within the delta frequency range differed between trials with correct and incorrect comprehension and was therefore likely relevant for the perceptual outcome . One interpretation of why only the slow timescales were directly perceptually relevant is that the comprehension task focused on words , thus stressing the word timescale . Furthermore , the stereotyped phrasal structure of the sentence provided a temporal structure on which the emergence of the target word could be expected . Therefore , participants may have relied on the encoding of the phrasal structure to exploit its regularity , thereby stressing the phrasal timescale . However , it has been suggested that speech tracking in the delta and theta bands index different functional roles for speech perception [69] , such that theta tracking reflects the analysis of acoustic features and delta tracking reflects linguistic representations . In line with the this is the notion that only speech-to-brain entrainment in the delta band reflects active speech-specific processing , as opposed to a passive , low-level synchronisation to acoustic properties at other timescales [30] . Therefore , these and our findings tentatively support the conclusion that only speech tracking in the delta band might indicate a speech-specific , perceptually relevant process during continuous speech processing . Recent findings also highlight the distinction between widely distributed versus focal ( but perceptually relevant ) auditory encoding [19 , 20] that could contribute to this pattern of results . In these accounts , perceptual choices are determined by the efficient readout of a restricted neural area , whereas widespread neural activity represents collateral processes of sensory processing . Therefore , such distributed processes could also explain the widespread speech tracking at all timescales we found when compared to chance level . In the present data , speech tracking at the syllabic and phonetic scales did not differ between trials with correct and incorrect comprehension . But for the participants to comprehend target words correctly , at least some syllables must have been encoded phonetically . It could be that the use of a noisy background prevented the robust encoding of individual syllables or phonemes , thus reducing the tracking at these timescales or reducing the statistical power in detecting between-trial differences . Furthermore , as mentioned above , the use of a word-related task could have highlighted effects at the word level and obscured effects at faster timescales . Additional work is required to understand whether speech tracking at the syllabic and phonetic timescales is indeed a robust marker of the actual neural encoding of these features or whether only speech tracking at timescales below the syllabic rate directly indexes functionally and perceptually relevant processes . Furthermore , the left-lateralised perceptually relevant speech tracking at slow timescales stands in contrast with bilateral overall speech tracking at these scales ( Fig 2 ) . This supports the notion that ‘early’ acoustic processes are bilateral , whereas ‘higher-order’ speech comprehension is left-lateralised [70] .
All participants provided written informed consent prior to testing and received monetary compensation of £10 per h . The experiment was approved by a local ethics committee ( College of Science and Engineering , University of Glasgow , application number 300140078 ) and conducted in compliance with the Declaration of Helsinki . Following previous sample sizes of MEG studies that used MI to study speech tracking [2 , 22] , as well as previous recommendations [71 , 72 , 73] , 20 healthy , native British participants took part in the study ( 9 female , age 23 . 6 ± 5 . 8 years [mean ± SD] , age range: 18 to 39 years ) . All participants were right-handed [Edinburgh Handedness Inventory; 74] , had normal hearing [Quick Hearing Check; 75] , and normal or corrected-to-normal vision . Furthermore , participants had no self-reported current or previous neurological or language disorders . MEG was recorded with a 248-magnetometer , whole-head MEG system ( MAGNES 3600 WH , 4-D Neuroimaging , San Diego , CA ) at a sampling rate of 1 KHz . Head positions were measured at the beginning and end of each run , using 5 coils placed on the participants’ heads . Coil positions were codigitised with head shape ( FASTRAK , Polhemus Inc . , Colchester , VT ) . Participants sat upright and fixated at a fixation point projected centrally on screen with a DLP projector . Sounds were transmitted binaurally through plastic earpieces , and 3 . 7 m–long plastic tubes connected to a sound pressure transducer . Stimulus presentation was controlled with Psychophysics toolbox [76] for MATLAB ( The MathWorks , Inc . , Natick , MA ) . The stimulus material consisted of 2 structurally equivalent sets of 90 sentences ( 180 unique sentences in total ) that were spoken by a trained , male , native British actor . The speaker was instructed to speak clearly and naturally . Sentences were constructed to be meaningful but unpredictable . Each sentence consisted of the same basic elements and therefore had the same structure . For example , the sentence ‘Did you notice , on Sunday night , Graham offered ten fantastic books’ consists of a ‘filler’ phrase ( ‘Did you notice’ ) , a time phrase ( ‘on Sunday night’ ) , a name , a verb , a numeral , an adjective , and a noun . There were 18 possible names , verbs , numerals , adjectives , and nouns that were each repeated 10 times . Sentence elements were randomly combined within a set of 90 sentences . To measure comprehension , a target word was included that was either the adjective in 1 set of sentences ( ‘fantastic’ in the above example or ‘beautiful’ in Fig 1 ) or the number in the other set ( for example , ‘twenty-one’ ) . The duration of sentences ranged from 4 . 2 s to 6 . 5 s ( 5 . 4 ± 0 . 4 s [mean ± SD] ) . Sentences were presented at a sampling rate of 22 , 050 Hz . During the experiment , speech stimuli were embedded in noise . The noise consisted of ecologically valid environmental sounds ( traffic , car horns , people talking ) , combined into a mixture of 50 different background noises . The individual noise level for each participant was determined with a staircase procedure that was designed to yield a performance of around 70% correct . For the staircase procedure , only the 18 possible target words were used instead of whole sentences . Participants were presented with single target words embedded in noise and subsequently saw 2 alternatives on screen . They indicated by button press which word they had heard . Depending on whether their choice was correct or incorrect , the noise level was increased or decreased ( one-up-three-down procedure ) until a reliable level was reached . The average signal-to-noise ratio across participants was approximately −6 dB . The 180 sentences were presented in 4 blocks with 45 sentences each . In each block , participants either indicated the comprehended adjective or the comprehended number , resulting in 2 ‘adjective blocks’ and 2 ‘number blocks’ . The order of sentences and blocks was randomised for each participant . The first trial of each block was a ‘dummy’ trial that was discarded for subsequent analysis; this trial was repeated at the end of the block . After each sentence , participants were prompted with 4 target words ( either adjectives or numbers ) on the screen . They then had to indicate which one they heard by pressing 1 of 4 buttons on a button box . After 2 s , the next trial started automatically . Each block lasted approximately 10 min , and participants could rest in between blocks . The session , including instructions , questionnaires , preparation , staircase procedure , and 4 blocks , took approximately 3 to 3 . 5 hours . For each sentence , we computed the wideband speech envelope at a sampling rate of 150 Hz following procedures of previous studies [2 , 6 , 12 , 77] . Acoustic waveforms were first filtered into 8 frequency bands ( between 100 and 8 , 000 Hz; third-order Butterworth filter; forward and reverse ) that were equidistant on the cochlear frequency map [77] . From these 8 individual bands , the wideband speech envelope was extracted by averaging the magnitude of the Hilbert transformed signals from each band . To define the timescales on which to probe speech encoding , we evaluated the rates of phrases , words , syllables , and phonemes in the stimulus material . For this , the duration between onsets of linguistic categories ( i . e . , phrases , words , and phonemes ) was calculated . The exact onset timing was extracted from the speech signals using Penn Phonetics Lab Forced Aligner ( P2FA; [78] ) . Phrases were defined as the first 2 clauses in each sentence ( for example , ‘I have heard’ and ‘on Tuesday night’ ) . These phrases had distinct pauses ( see Fig 1 for an example sentence ) that determined the rhythm of the sentence ( also visible in the frequency spectrum S3A Fig ) . The syllable rate is generally difficult to assess [18 , 79] . Here , we chose to count the actually produced syllables for each sentence . Finally , timescales were converted to frequencies , and the specific frequency bands for each category were then defined as the minimum and maximum frequencies across all 180 sentences . This led to the following bands: 0 . 6–1 . 3 Hz ( phrases ) , 1 . 8–3 . 0 Hz ( words ) , 2 . 8–4 . 8 Hz ( syllables ) , and 8–12 . 4 Hz ( phonemes ) . Mean and standard deviations for linguistic categories were as follows: 1 . 0 ± 0 . 1 Hz for phrases , 2 . 4 ± 0 . 3 Hz for words , 3 . 8 ± 0 . 4 Hz for syllables , and 10 . 4 ± 0 . 8 Hz for phonemes . Furthermore , the fundamental frequency for each sentence was extracted using Praat [80] . This was used to determine the frequency spectrum of the pitch fluctuations ( see S3B Fig ) . Preprocessing of MEG data was carried out in MATLAB ( The MathWorks , Inc . , Natick , MA ) using the Fieldtrip toolbox [81] . The 4 experimental blocks were preprocessed separately . Single trials were extracted from continuous data starting 2 s before sound onset and until 10 s after sound onset . MEG data were denoised using the reference signal . Known faulty channels ( N = 7 ) were removed before further preprocessing . Trials with SQUID jumps ( 3 . 5% of trials ) were detected and removed using Fieldtrip procedures with a cutoff z-value of 30 . Before further artifact rejection , data were filtered between 0 . 2 and 150 Hz ( fourth-order Butterworth filters , forward and reverse ) and down-sampled to 300 Hz . Data were visually inspected to find noisy channels ( 4 . 37 ± 3 . 38 on average across blocks and participants ) and trials ( 0 . 66 ± 1 . 03 on average across blocks and participants ) . Finally , heart and eye-movement artifacts were removed by performing an independent component analysis with 30 principal components . Data were further down-sampled to 150 Hz to match the sampling rate of the speech signal . Source localisation was performed using Fieldtrip , SPM8 , and the Freesurfer toolbox . We acquired T1-weighted structural magnetic resonance images ( MRIs ) for each participant . These were coregistered to the MEG coordinate system using a semiautomatic procedure [2 , 6] . MRIs were then segmented and linearly normalised to a template brain ( MNI space ) . A volume conduction model was constructed using a single-shell model [82] . We projected sensor-level waveforms into source space using frequency-specific linear constraint minimum variance ( LCMV ) beamformers [83] with a regularisation parameter of 7% and optimal dipole orientation ( singular value decomposition method ) . Grid points had a spacing of 6 mm , resulting in 12 , 337 points covering the whole brain . We quantified the statistical dependency between the speech envelope and the source-localised MEG data using MI [2 , 6 , 34 , 84] . The speech envelopes , as well as MEG data , were filtered in the 4 frequency bands reflecting the rates of each linguistic category using third-order ( for delta and theta bands ) forward and reverse Butterworth filters . Within these bands , we computed the Hilbert transform and used real and imaginary parts for further analysis . Both parts were normalised separately and combined as a two-dimensional variable for the MI calculation [84] . To take into account the stimulus–brain lag , we computed MI at 5 different lags ( from 60 to 140 ms in 20-ms steps ) and summed the MI values across lags . This procedure prevents spurious results that can occur when using a single lag . First , we calculated the overall MI for each source grid point . For a robust computation of MI values , we concatenated MEG and speech data from all trials . The resulting MI values were compared with surrogate data to determine their statistical significance . Surrogate data were created by randomly shuffling trials 50 times and averaging surrogate MI values across iterations . This repetition was necessary because all sentences followed the same structure and their envelope was often comparable , especially when filtered at low frequencies . We used a dependent t test for statistical comparison for each grid point and corrected for multiple comparisons with cluster-based permutation . Specifically , we used Monte-Carlo randomisation with 1 , 000 permutations and a critical t value of 2 . 1 , which represents the critical value of the Student t distribution for 20 participants and a two-tailed probability of p = . 05 . The significance level for accepting clusters was 5% . We report summed t values ( Tsum ) as indicator of effect size . For the analysis of perceptual relevance , we compared MI between trials in which participants responded correctly and incorrectly . Because the number of trials differed between these samples ( on average , approximately 70% correct and 30% incorrect ) , we performed the calculations based on 80% of the minimally available number of trials . This way , the number of compared correct and incorrect trials was equal . However , because this included only a small part of all available trials , we repeated the analysis 20 times with a random selection of trials to yield representative values . The resulting MI values were averaged . Again , trials were concatenated to yield robust MI values . MI values between correct and incorrect trials were compared using the same method and parameters as for the comparison between overall MI and surrogate MI . To examine the specificity of the effects , we compared MI between correct and incorrect trials for all peak grid points in both frequency bands ( i . e . , phrasal and word timescales ) . Peak grid points were those with the largest t values in each cluster and the largest summed t values for the overlap of grid points . This led to 12 comparisons ( 3 peak grid points × 4 frequency bands ) . MI values were compared using dependent sample t tests , corrected for multiple comparisons using the FDR method [85] . To examine the hypothesis that beta power is coupled with delta phase in the motor cluster and that this is perceptually relevant , we quantified PAC using the MI between beta power and delta phase . Phase and power were derived from Hilbert-transformed time series and filtered in the phrasal ( 0 . 6–1 . 3 Hz ) and beta band ( 13–30 Hz ) . Phase was expressed as a unit magnitude complex number . To get an equal number of trials for correct and incorrect trials , we again took 80% of trials of the smaller sample , concatenated trials , and repeated the calculation 50 times . This was done for all grid points within the motor cluster ( N = 205 ) and then averaged across grid points and iterations . PAC was compared between correct and incorrect trials across participants using a dependent sample t test . We performed 3 control analyses within the motor cluster to address the frequency specificity of the effect . First , we analysed PAC between phrasal phase ( 0 . 6–1 . 3 Hz ) and alpha power ( 8–12 Hz ) as well as theta power ( 4–8 Hz ) . Second , we analysed PAC between the word phase ( 1 . 8–3 Hz ) and beta power . All p-values were corrected for multiple comparisons using the FDR method [85] . To address the spatial specificity of the delta–beta PAC , we also performed a whole-brain analysis . Based on the results in the motor cluster , we hypothesised that PAC should be larger in correct than incorrect trials . PAC between phrasal delta phase ( 0 . 6–1 . 3 Hz ) and beta power ( 13–30 Hz ) was compared between correct and incorrect trials , again equalling sample sizes by using 80% of the minimally available number of trials and repeating the analysis 20 times . PAC MI was averaged across all iterations and then compared between correct and incorrect trials across participants using a dependent sample t test for each grid point . To correct for multiple comparisons , we used the same parameters for cluster correction as in all previous analyses except that the significance level to choose significant clusters was one-sided , due to the clear hypothesis . We analysed an additional and previously published dataset to confirm the present effects in the motor cortex . For this , we used data from 23 participants [2 , 42] who passively listened to a 7-min continuous natural narration . Preprocessing and analysis were identical to the procedures of the main data . We compared ( i ) phrasal tracking and ( ii ) PAC between phrasal phase and beta power with surrogate data in the motor cortex . The phrasal rate of the speech stimulus was 0 . 5 ± 0 . 26 Hz ( mean ± SD ) and ranged between 0 . 1 Hz and 1 . 5 Hz . Surrogate data were created by reversing the time series for speech and computing MI between forward brain time series and reversed speech time series . This represents values that would be expected by chance [2] . Values were computed for all grid points in the motor cluster and then spatially averaged . Actual MI values and surrogate data were compared using a dependent t test , and p-values for both tests were FDR corrected . Data were deposited in the Dryad repository ( https://doi . org/10 . 5061/dryad . 1qq7050 ) [31] . | How we comprehend speech—and how the brain encodes information from a continuous speech stream—is of interest for neuroscience , linguistics , and research on language disorders . Previous work that examined dynamic brain activity has addressed the issue of comprehension only indirectly , by contrasting intelligible speech with unintelligible speech or baseline activity . Recent work , however , suggests that brain areas can show similar stimulus-driven activity but differently contribute to perception or comprehension . To directly address the perceptual relevance of dynamic brain activity for speech encoding , we used a straightforward , single-trial comprehension measure . Furthermore , previous work has been vague regarding the analysed timescales . We therefore base our analysis directly on the timescales of phrases , words , syllables , and phonemes of our speech stimuli . By incorporating these two conceptual innovations , we demonstrate that different areas of the brain track acoustic information at the time-scales of words and phrases . Moreover , our results suggest that the motor cortex uses a cross-frequency coupling mechanism to predict the timing of phrases in ongoing speech . Our findings suggest spatially and temporally distinct brain mechanisms that directly shape our comprehension . | [
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| 2018 | Perceptually relevant speech tracking in auditory and motor cortex reflects distinct linguistic features |
Membrane-protein design is an exciting and increasingly successful research area which has led to landmarks including the design of stable and accurate membrane-integral proteins based on coiled-coil motifs . Design of topologically more complex proteins , such as most receptors , channels , and transporters , however , demands an energy function that balances contributions from intra-protein contacts and protein-membrane interactions . Recent advances in water-soluble all-atom energy functions have increased the accuracy in structure-prediction benchmarks . The plasma membrane , however , imposes different physical constraints on protein solvation . To understand these constraints , we recently developed a high-throughput experimental screen , called dsTβL , and inferred apparent insertion energies for each amino acid at dozens of positions across the bacterial plasma membrane . Here , we express these profiles as lipophilicity energy terms in Rosetta and demonstrate that the new energy function outperforms previous ones in modelling and design benchmarks . Rosetta ab initio simulations starting from an extended chain recapitulate two-thirds of the experimentally determined structures of membrane-spanning homo-oligomers with <2 . 5Å root-mean-square deviation within the top-predicted five models ( available online: http://tmhop . weizmann . ac . il ) . Furthermore , in two sequence-design benchmarks , the energy function improves discrimination of stabilizing point mutations and recapitulates natural membrane-protein sequences of known structure , thereby recommending this new energy function for membrane-protein modelling and design .
Membrane proteins have essential biological roles as receptors , channels , and transporters . Over the past decade , significant progress has been made in the design of membrane proteins , including the first design of membrane-integral inhibitors [1] , a transporter [2 , 3] , and a de novo designed structure based on coiled-coil motifs [4] . Despite this exciting progress , however , modelling , design , and engineering of membrane proteins lag far behind those of soluble proteins [5] . This lag is due , in part , to the relatively small number of high-resolution membrane-protein structures [6] and is exacerbated by these proteins’ typically large size . Clearly , however , the heterogeneity of a membrane protein’s environment , comprising water , lipid , and polar headgroups , is the most significant complication [7] . Therefore , modelling solvation is a fundamental problem that impacts all membrane-protein structure prediction and design . Current energy functions used in modelling and design incorporate simplified solvation models [8] . For instance , RosettaMP uses information inferred from water-to-cyclohexane partitioning [9] as a proxy for amino acid solvation in the plasma membrane [10 , 11] . Due to these simplifications , expert analysis has been a prerequisite for accurate membrane-protein modelling and design [12 , 13] . Automating modelling and design processes and extending them to complex membrane proteins will likely require an accurate energy function that correctly balances intra-protein interactions , membrane solvation and water solvation [14–16] . To understand the contributions to membrane-protein solvation , we recently established a high-throughput experimental screen , called deep sequencing TOXCAT-β-lactamase ( dsTβL ) , which quantified apparent amino acid transfer energies from the cytosol to the E . coli plasma membrane [17] . From the resulting data , we inferred apparent position-specific insertion profiles for each amino acid relative to alanine , reconciling previously conflicting lines of evidence [18] . Foremost , the lipophilicity inferred for hydrophobic residues , such as Leu , Ile , and Phe , was greater than previously measured in some membrane mimics , including the water-to-cyclohexane transfer energies that are the basis for membrane solvation in Rosetta [9 , 11 , 16] ( approximately 2 kcal/mol according to dsTβL compared to ½ kcal/mol ) , and in line with theoretical considerations [19 , 20] . Second , the profiles exhibited a strong 2 kcal/mol preference for Arg and Lys in the intracellular side of the plasma membrane compared to the extracellular side . While this preference , known as the “positive-inside” rule , was revealed based on sequence analysis 30 years ago [21–23] , the dsTβL assay was the first to indicate a large energy gap favouring positively charged residues in the intracellular relative to the extracellular membrane leaflet . The accuracy and generality of the dsTβL apparent transfer energies were partly verified by demonstrating that they correctly predicted the locations and orientations of membrane spans directly from sequence even in several large and complex eukaryotic transporters [24] . Taken together , these results provided reassurance that the dsTβL apparent insertion energies correctly balanced essential aspects of membrane-protein solvation . As the next step towards accurate all-atom membrane-protein modelling and design , we develop a new lipophilicity-based energy term based on the dsTβL amino acid-specific insertion profiles and integrate this energy term in the Rosetta centroid-level and all-atom potentials . We furthermore develop a strategy to enhance conformational sampling of membrane-spanning helical segments and of helix-tilt angles observed in naturally occurring membrane proteins . Encouragingly , the new energy function outperforms previous ones in three benchmarks essential to modelling and design: atomistic ab initio structure prediction starting from completely extended chains of single-spanning membrane homo-oligomers of known structure , prediction of mutational effects on stability , and sequence recovery in combinatorial sequence design . An automated web server for structure prediction in transmembrane homooligomeric proteins ( TMHOP ) is available at ( http://tmhop . weizmann . ac . il ) and may enable modelling of the membrane-spanning domains of receptors . We conclude that the combination of lipophilicity and energetics developed for soluble proteins provides a basis for accurate structure prediction and design of membrane proteins .
The recent all-atom energy function in Rosetta , ref2015 , is dominated by physics-based terms , including van der Waals packing , hydrogen bonding , electrostatics and water solvation [25] . This energy function was parameterized on a large set of crystallographic structures and experimental data of water-soluble proteins and was shown to outperform previous energy functions in several structure-prediction benchmarks . For membrane-protein modelling and design , however , the ref2015 solvation potential is relevant only to the water-embedded regions of the protein; a different potential is required to model the energetics of amino acids near and within different regions of the plasma membrane . Accordingly , we sought to replace the ref2015 solvation model with one that encodes a gradual transition from the default water-solvation that evaluates regions distant from the plasma membrane and the dsTβL insertion profiles near and within the plasma membrane . The dsTβL profiles were inferred from an experimental mutation analysis of a monomeric membrane span into which each of the 20 amino acids were individually introduced at each position [17]; the profiles were then normalized to express the apparent transfer energy for each amino acid at each position relative to a theoretical poly-Ala membrane span , yielding apparent ΔΔGAla—>mut at each position across the plasma membrane ( Fig 1 ) . As a first step to encoding these energy profiles in Rosetta , we smoothed these profiles and symmetrised them with respect to the presumed membrane midplane , except the profiles for Arg , His , and Lys , for which the “positive-inside” rule applies ( S1 Fig ) . Next , we implemented an iterative strategy to encode the dsTβL energetics in a modified ref2015 all-atom energy function which we called ref2015_memb . To enable efficient conformational search as required in ab initio structure prediction and de novo design , we also encoded this energetics in the centroid-level energy function [26] . As a reference state in both all-atom and centroid-level modelling , we generated an ideal poly-Ala α helix and placed it perpendicular to the membrane plane . At each position along the helix ( including the aqueous and membrane phases ) , we introduced each of the 19 point mutations , relaxed the models using the all-atom or centroid-level energy function , and computed the energy difference due to each single-point mutation ΔΔGAla—>mut . In the first iteration of these calculations , the unmodified ref2015 or centroid-level energy functions were used , resulting , as expected , in large deviations from the apparent energies observed in the dsTβL profiles ( red lines in Fig 1 ) . We then added a new context-dependent 1-body energy term , called MPResidueLipophilicity , which encoded the difference between the computed and dsTβL energies for each mutation at each position , ΔΔΔGAla—>mut . We iterated mutation , relaxation , energy calculations , and MPResidueLipophilicity updates for each of the mutations at each position up to ten times , noting that the computed energies converged with the trends observed in the experiment ( blue and red lines in Fig 1 , respectively ) . Scripts for calibrating the all-atom and centroid energy functions are available at github . com/Fleishman-Lab/membrane_protein_energy_function to enable adapting future improvements of the Rosetta energy functions to encode the dsTβL energetics . The dsTβL apparent energy profiles were inferred from a monomeric segment [17] . Consequently , the profiles express the lipophilicity of each amino acid relative to Ala across the membrane when that amino acid is maximally solvent-exposed . To account for amino acid burial in multispan or oligomeric membrane proteins , we derived a continuous , differentiable and easily computable weighting term that expresses the extent of a residue’s burial in other protein segments . For any amino acid , this weighting term is based on the number of heavy-atom neighbours within 6 and 12 Å distance of the amino acid’s Cβ atom ( Eq 1 ) resulting in a weight that expresses the extent to which a residue is buried in other protein segments or exposed to solvent ( 0 to 1 , respectively ) . Water-embedded and completely buried positions are treated with the ref2015 solvation energy; fully membrane-exposed positions are treated with the MPResidueLipophilicity energy , and positions of intermediate exposure are treated with a linearly weighted sum of the two terms . In summary , the actual contribution from solvation of an amino acid is a function of its exposure to the membrane and depends on the amino acid’s lipophilicity according to the dsTβL apparent energy and the position’s location relative to the membrane midplane . Note that this energy term averages lipophilicity contributions in the plasma membrane and does not express atomic contributions to solvation that are likely to be important in calculating membrane-protein energetics in different types of biological membranes [11 , 27] , in non-helical membrane-exposed segments , or surrounding water-filled cavities [28] . The dsTβL assay reports on residue-specific insertion into the plasma membrane . Ab initio modelling and de novo design , however , also require a potential that addresses the protein backbone solvation . Although the low-dielectric environment in the core of the membrane enforces a strong tendency for forming canonical α helices [7] , deviations from canonical α helicity can make important contributions to membrane-protein structure and function [29] . Therefore , we encoded an energy term , called MPHelicality , that allows sampling backbone dihedral angles and penalises deviations from α helicity ( Eq 5 ) . MPHelicality enforces strong constraints on the dihedral angles in the lipid-exposed surfaces at the core of the membrane and is attenuated in regions that are buried in other protein segments and in the extra-membrane environment ( using the same weighting as for lipophilicity , Eq 1 ) ; this term thus allows significant deviations from α helicity only in buried or water-embedded regions . In preliminary ab initio calculations starting from a fully extended chain , we noticed that conformational sampling significantly favoured large helical tilt angles relative to the membrane normal ( θ in Fig 2 ) . By contrast , 50% of naturally observed membrane spans exhibit small tilt angles in the range 15–30° . The skew in conformational sampling towards large tilt angles is expected from previous theoretical investigations according to which the distribution of helix-tilt angles in random sampling is proportional to sin ( θ ) , substantially preferring large angles compared to the distribution observed in natural membrane proteins [30] . To eliminate this skew in conformational sampling , we introduced another energy term , called MPSpanAngle ( Eq 4 and Fig 2 ) , that strongly penalised large tilt angles , guiding ab initio sampling to tilt angles observed in natural proteins . In summary , ref2015_memb encodes three new energy terms relative to the soluble energy function ref2015: ( 1 ) a lipophilicity term based on amino acid type , membrane-depth , and burial; ( 2 ) a penalty on deviations from α helicity in backbone-dihedral angles; and ( 3 ) a penalty on the sampling of large tilt angles with respect to the membrane-normal ( S1 Table ) . In the calculations reported below , the penalties on deviations from α helicity and helix-tilt angles are implemented in all centroid-level ab initio structure prediction simulations; all-atom calculations use the ref2015 energy modified with the lipophilicity term ( ref2015_memb ) . Previous structure-prediction benchmarks started from canonical α helices or from monomers obtained from experimental structures of homodimers and used the bound-structures in grid search or rigid-body docking [11 , 16 , 31–34] . Additionally , structure-prediction studies used experimental constraints , conservation analysis or correlated-mutation analysis to predict residue contacts in order to constrain conformational sampling [12 , 13 , 35–40] . Several automated predictors dedicated to single-span dimers used shape complementarity [41 , 42] , sequence-packing motifs [43] or comparative modelling [44] , but to the best of our knowledge , ab initio modelling calculations , starting from a fully extended chain , have not been described . Given that deviations from canonical α helicity make important contributions to membrane-protein structure and function [29] , we decided to apply a more stringent test using ab initio modelling , sampling all symmetric backbone , sidechain , and rigid-body degrees of freedom . To test ab initio modelling using the new energy function , we applied the fold and dock protocol [45] , which has been successfully applied in a variety of soluble-protein structure prediction and design studies [46–49] . Briefly , fold and dock starts from an extended chain and conducts several hundred iterations of symmetric centroid-level backbone-fragment insertion and relaxation moves . It then applies symmetric all-atom refinement including all dihedral sidechain and backbone degrees of freedom ( S1 Movie ) . To generate an energy landscape , we ran 5 , 000 independent trajectories ( 50 , 000 for high-order oligomers ) for every 19 and 21 residue subsequence of each homooligomer , filtered the resulting models according to energy and structure parameters ( Methods ) , and isolated the lowest-energy 10% of the models . Models were then clustered according to their energies and conformations , and five cluster representatives were compared to the experimental structures ( Figs 3 and 4 , Table 1 ) . For comparison , we applied the described methodology using ref2015_memb , ref2015 and the current membrane-protein energy function in Rosetta , RosettaMP [11] . The Protein Data Bank ( PDB ) contains 17 nonredundant ( sequence identity <80% ) NMR and X-ray crystallographic structures ( adopted from Lomize et al . [44] ) of natural single-span homodimers , two tetramers and one pentameric structure . Of the 20 cases in the benchmark , fold-and-dock simulations using ref2015_memb predicted near-native ( <2 . 5 Å root-mean-square deviation [RMSD] ) low-energy models for 14 homooligomers compared to nine using RosettaMP; of the 14 oligomers accurately predicted by ref2015_memb , the soluble energy function ref2015 also resulted in nine correct predictions . Prediction success rate using ref2015_memb was somewhat higher for right-handed relative to left-handed homodimers ( 80 and 50% , respectively; S2 Table ) , reflecting the tendency of right-handed homodimers to be more tightly packed [31] , and in 11 cases , a near-native prediction was found among the top 3 lowest-energy predicted models ( Fig 3 ) . Of the three high-order oligomers tested , ref2015_memb successfully recapitulated the structures of the M2 tetramer and phospholamban pentamer . The PREDDIMER [42] and TMDIM [43] structure-prediction web servers , which do not use ab initio modelling , found models at <2 . 5 Å RMSD for nine and eight of the 17 homodimers , respectively . Thus , ab initio calculations using ref2015_memb accurately predict structures in two-thirds of the homooligomers in our benchmark , including high-order oligomers that cannot be predicted by other automated methods . Given the high success rate of the ab initio calculations , we developed a web-accessible server for predicting the structures of membrane-spanning homo-oligomers such as are observed in receptor tyrosine kinases and other membrane proteins ( http://tmhop . weizmann . ac . il ) . The successfully predicted homooligomers exhibit different structural packing motifs . The majority of the homodimer interfaces are mediated by the ubiquitous Gly-xxx-Gly motif [50] , in which two small amino acids separated by three positions on the primary sequence enable close packing between the helices . There is uncertainty whether these motifs additionally form stabilising Cα hydrogen bonds [51 , 52] . Our structure-prediction analysis cannot resolve this uncertainty; note , however , that the new energy function ref2015_memb does not encode terms for Cα hydrogen bonds and yet recapitulates a large fraction of the homodimer structures ( Figs 3 and 4 , and Table 1 ) . The underlying reason for successful prediction is that the dsTβL energetics encodes a strong penalty on exposing Gly residues to the lipid bilayer ( approximately 2 kcal/mol/Gly at the membrane mid-plane; Fig 1 ) , driving the burial of Gly amino acids within the homodimer interface ( i . e . , “solvophobicity” ) . Thus , lipophilicity and interfacial residue packing are sufficient for accurate structure prediction in a large fraction of the targets we examined . In several single-spanning membrane receptors , conformational change in the membrane domain is thought to underlie receptor activation . For instance , past modelling of the ErbB2 membrane domain suggested two non-overlapping interaction sites involving two small-xxx-small motifs within the membrane domain and a molecular switching mechanism that underlies receptor activation [54] . The only experimental structure for ErbB2 involves the N-terminal small-xxx-small motif [55] , which is recapitulated by the second predicted cluster ( Fig 5A ) , whereas in the fourth predicted cluster , dimerisation is mediated via the C-terminal motif ( Fig 5B ) , suggesting that in some cases , TMHOP may provide structural hypotheses for alternative binding modes for receptor homooligomeric domains . Using the dsTβL assay , we also examined the effects of dozens of point mutations in glycophorin A on apparent association energy ( ΔΔGbinding ) in the bacterial plasma membrane [17] . As a stringent test of the new energy function , we conducted fold-and-dock calculations using both ref2015_memb and RosettaMP starting from the sequences of each of the point mutants . To reduce uncertainty in interpreting the experimental results , we focused on 32 mutations that exhibited large apparent energy changes in the experiment ( |ΔΔGbinding| ≥ 2 kcal/mol ) and compared the median computed ΔΔGbinding of the lowest-energy models to the experimental observation ( Fig 6 , S3 Table ) . ref2015_memb outperformed RosettaMP , correctly assigning 81% of mutations as stabilizing or destabilizing compared to 66% for RosettaMP . The six false-positive predictions using ref2015_memb are due to mutations at position Gly86 , which is exposed to the membrane , explaining why our simulations predict these mutations to be neutral or favourable . Note that as observed in studies of mutational effects on stability in soluble proteins , the correlation coefficient between computed and observed values is low ( Pearson r2 = 0 . 21 and 0 . 02 for ref2015_memb and RosettaMP , respectively ) [56–59] . Such low correlation coefficients provide an impetus for improving the energy function; however , as we previously demonstrated , discriminating stabilizing from destabilizing mutations is sufficient to enable the design of accurate , stable , and functionally efficient proteins [59–64] . We next tested sequence-recovery rates using combinatorial sequence optimisation based on ref2015 , ref2015_memb , and RosettaMP in a benchmark of 20 non-redundant structures ( <80% sequence identity ) ranging in size from 124–765 amino acids [65] . ref2015_memb outperformed the other energy functions , exhibiting 83% sequence recovery , on average , when each design was compared to the target’s natural homologs ( Table 2 ) . To our surprise , the soluble energy function ref2015 outperformed RosettaMP in this test and was almost as successful as ref2015_memb ( 78% overall success ) , implying that the packing and electrostatic models of ref2015 [25] enabled at least some of the improvement observed in sequence recovery by ref2015_memb ( see S1 Table for a comparison of the energy functions ) . High sequence recovery in both buried and exposed positions implies that ref2015_memb may be applied effectively to design large and complex membrane proteins .
An accurate energy function is a prerequisite for automated modelling and design , and solvation makes a critical contribution to protein structure and function . The recent dsTβL apparent energies of insertion into the plasma membrane [17] enabled us to derive an empirical lipophilicity-based energy function for Rosetta . The results demonstrate that ref2015_memb outperforms RosettaMP in three benchmarks that are important for structure prediction and design . As ref2015_memb is based on the current state-of-the-art water-soluble Rosetta energy function , prediction accuracy is high for ref2015_memb both in soluble regions and in the core of the membrane domain . Thus , the lipophilicity preferences inferred from the dsTβL energetics together with the residue packing calculations in Rosetta enable accurate modelling in several ab initio prediction cases . The current energy function and the fold and dock procedure accurately model homooligomeric interactions in the membrane and the effects of point mutations , suggesting that they may enable the accurate design of homooligomeric single-span receptor-like transmembrane domains . The high accuracy models generated by the TMHOP method also suggest that laborious and often failed experiments to determine the structures of homooligomeric receptor membrane domains may be circumvented through ab initio modelling . Nevertheless , certain important attributes of membrane-protein energetics are not yet addressed by ref2015_memb; for instance , atomic-level solvation and the impact on electrostatic interactions due to changes in the dielectric constant in various parts of the membrane are currently not treated [16 , 28] and warrant further research . Furthermore , the dsTβL profiles are based on measurements conducted on α-helical proteins in E . coli inner membranes . Ref2015_memb may , therefore , not perform as well in outer-membrane proteins or in proteins residing in membranes with a substantially different lipid composition . The benchmark reported here provides a basis on which improvements in the energy function can be verified . Furthermore , structure prediction in heterooligomers is important for understanding receptor cross-activation and for the design of membrane inhibitors [1] . In preliminary calculations , however , we found that fold and dock simulations of heterooligomeric systems fail to converge due to the much larger conformation space open to a non-symmetric system . A potentially exciting extension of the current work is to use the information on preferred crossing angles between membrane helices to constrain conformational sampling in heterooligomers [66 , 67] . We recently showed that evolution-guided atomistic design calculations , which use phylogenetic analysis to guide the design processes [68] , enabled the automated , accurate and effective design of large and topologically complex soluble proteins . Designed proteins exhibited atomic accuracy , high expression levels , stability [59 , 60 , 69] , binding affinity , specificity [64] , and catalytic efficiency [62 , 63] . Membrane proteins are typically large and challenging targets for conventional protein-engineering and design methods . Looking ahead , we anticipate that evolution-guided atomistic design using ref2015_memb may enable reliable design in this important but often formidable class of proteins .
All code is available in the Rosetta release at www . rosettacommons . org ( git version: b210d6d5a0c21208f4f874f62b2909f926379c0f ) . Command lines and RosettaScripts [70] are available in the supplement . The original dsTβL insertion profiles [17] were modified to generate smooth and symmetric functions [24] . The polar and charged residues Asp , Glu , Gln and Asn , which exhibited few counts in the deep sequencing analysis , were averaged such that the insertion energy at the membrane core ( -10 to 10 Å; negative values correspond to the inner membrane leaflet and positive values to the outer leaflet ) was applied uniformly to the entire membrane span . The profile for His was capped at the maximal value observed in the experiment ( 2 . 3 kcal/mol ) between 0Å ( membrane midplane ) and 20 Å . The dsTβL profile for Cys is unusually asymmetric . Cys residues are rare in membrane proteins [71] and are likely to have similar polarity to Ser . We , therefore , applied the profile measured for Ser to Cys . To convert the values from the dsTβL insertion profiles to Rosetta energy units ( R . e . u . ) they were multiplied by 2 . 94 following the interpolation reported in ref . [25] . The dsTβL profiles spanned 27 positions , and we correspondingly translated them to span -20 to +20 Å relative to the membrane midplane . The context-dependent , one-body energy term MPResidueLipophilicity was implemented to encode the dsTβL insertion profiles in ref2015 . Starting from an ideal poly Ala α helix embedded perpendicular to a virtual membrane , every position was mutated to all 19 identities , relaxed , and the energy difference between the ref2015 energy and the dsTβL energy was implemented in MPResidueLipophilicity . This process was repeated ten times to reach convergence , and the resulting energy profiles were fitted by a cubic spline [72] , generating continuous , differentiable functions for all 19 amino acids relative to Ala , which was assumed to be 0 throughout the membrane . The splines were recorded in the Rosetta database and are loaded at runtime . Insertion -profile adjustments were done using a python3 script available at github . com/Fleishman-Lab/membrane_protein_energy_function . The number of protein atoms within 6 and 12 Å of each amino acid’s Cβ atom is computed and transformed to a burial score ( Eq 1 ) . We used sigmoid functions which range from 0 to 1 , corresponding to completely buried and completely lipid-exposed , respectively . Where N is the number of heavy atoms and S and O determine the slope and offset of the sigmoids and are different for all-atom and centroid calculations . Each parameter has different thresholds at 6 or 12 Å . For all-atom calculations , S = 0 . 15 and 0 . 5 and O = 20 and 475 , for 6 and 12 Å radii , respectively . For centroid-level calculations , S = 0 . 15 and 5 and O = 20 and 220 for 6 and 12Å radii , respectively . For each amino acid , the product of the 6 and 12Å sigmoid functions is taken , producing a continuous , differentiable function that transitions from buried to exposed states . These parameters were determined by visualising the burial scores of all amino acids in several polytopic membrane proteins of known structure . All membrane-spanning helices reported in the PDBTM [73] dataset ( version 20170210 ) were analyzed for their tilt angles with respect to the membrane normal . A second-degree polynomial was fitted to this distribution using scikit-learn [74] . As Bowie noted , the expected distribution function of helix-tilt angles is sin ( Θ ) [30] . We , therefore , used a partition function to convert the expected distribution ( sin ( Θ ) ) and observed one ( Eq 2 ) to energy functions , finally subtracting the expected energy from the observed one to derive the helix-tilt penalty function: penalty=− ( ln ( −2 . 36×10−4×θ2+0 . 01095×θ+0 . 0202 ) −ln ( sin ( θ ) ) ) ( 3 ) Where θ is given in degrees . In order to simplify runtime calculations , we approximated Eq 3 using a third-degree polynomial ( using scikit-learn ) ( Fig 2 ) . The MPHelicality energy term penalizes the energy of every position that exhibits ϕ-ψ torsion angles significantly different from ideal α helices . A paraboloid function was manually calibrated to express a penalty for any given ( ϕ , ψ ) . The paraboloid centre , for which the penalty is 0 , was set to the centre of the helical region according to the Ramachandran plot ( ϕ = 60° , ψ = 45° ) [75] . The paraboloid curvature was set to 25 , such that the penalty is low throughout the ϕ-ψ torsion angles space observed for α helices [75] . As segments buried against the protein should not be penalized to the same extent as those completely exposed to the membrane , the burial approximation of Eq 1 is used to weight MPHelicality . Moreover , as the protein extends outside of the membrane , the penalty is attenuated with a function that follows the trend observed for the hydrophobic residues , Leu , Ile , and Phe ( see Fig 1A ) . In effect , the MPHelicality term favours α helicity in lipid-exposed surfaces in the core of the membrane , thereby enforcing some of the electrostatic and solvophobic effects that are essential for correctly modelling the backbone but are not expressed in the residue-specific dsTβL energy profiles . Where ϕ and ψ are given in degrees , z is the distance from the membrane midplane of residue i , and burial is calculated as in Eq 1 . 17 structures of single-span homodimers , two homotetramers and one pentamer were selected from the PDB ( S2 Table ) . For each structure , a 20–30 residue segment comprising the membrane-spanning domain was manually chosen . A sliding window then extracted all 19 or 21 residue subsequences . For each subsequence , three and nine residue backbone fragments were generated using the Rosetta fragment picker application [76] . The fold-and-dock protocol [45] was used to compute 5000 models ( 50 , 000 models for tetramers and the phospholamban pentamer ) , and the lowest-energy 10% of the models were subsequently filtered using structure and energy-based filters ( solvent accessible surface area >500 Å; shape complementarity [77] Sc>0 . 5; ΔΔGbinding <-5 R . e . u . ; rotameric binding strain [78] < 4 R . e . u . ; helicality <0 . 1 R . e . u . ( computed using Eq 5 ) ; and closest distance between the interacting helices < 9 Å , as calculated by the filter HelixHelixAngle ) . For each target , the filtered models from all subsequences were then pooled together and clustered using a score-wise clustering algorithm . This is an iterative process , where each iteration calculates the RMSD of all unclustered models to the best-energy model , and removes the ones closer than 4 Å . RMSD to NMR structures were calculated with respect to the first model in the PDB entry . Glycophorin A mutants that exhibited |ΔΔGbinding| > 2 kcal/mol according to the dsTβL study [17] were modelled using the same fold-and-dock protocol described for the structure prediction of homodimers . To reduce computational load , we used a single sequence ( 73-ITLIIFGVMAGVIGTILLI-91 ) , and the median of computed ΔΔGbinding for the top models was reported ( models were filtered using structure and energy based filters; solvent accessible surface area > 600 Å , packing > 0 . 4 , shape complementarity > 0 . 5 , ΔΔGbinding < -10 R . e . u . , binding strain < 4 R . e . u . MPHelicality < 0 . 1 , minimal atomic distance between helices < 4 . 5 Å and minimal distance between helix vectors < 8 Å . Of these models , only the top 10% scoring models were used ) . 20 structures of polytopic membrane-spanning proteins were taken from ref . [65] , 11 of which were symmetric complexes . All were refined ( eliminating sidechain conformation information before refinement ) , and for each protein , 100 designs were computed using combinatorial sequence design followed by sidechain and backbone minimization , and the lowest-energy 10 designs were checked for the fraction of mutations relative to the target protein . For each target protein , a multiple-sequence alignment was prepared: homologous sequences were automatically collected using BLASTP [79] on the nonredundant sequence database [80] with a maximal number of targets set to 3 , 000 and an e-value ≤ 10−4 . All sequences were clustered using CD-hit [81] with a 90% sequence identity threshold . Sequences were then aligned using MUSCLE [82] with default parameters . A position-specific scoring matrix ( PSSM ) was calculated using PSI-BLAST [83] . In the sequence-recovery benchmark , where homologous sequences are considered , the substitution of a given position to an identity with a PSSM score ≥ 0 is considered a match . | Membrane proteins comprise a third of the genome and have essential roles as intermediaries between the cell and its environment . Despite exciting recent progress in membrane-protein modelling and design , however , these fields lag far behind advances in soluble proteins , chiefly because of inaccurate modelling of the membrane environment . Recently , our lab developed an assay , called dsTβL , that used high-throughput experimental screening to infer the energetics of each amino acid across the bacterial plasma membrane . Here , we encode the dsTβL energetics in the Rosetta software suite for biomolecular modelling and design and subject the energy function to three structure prediction and design benchmarks . The new energy function consistently outperforms the previous Rosetta membrane energy function . Additionally , ab initio structure prediction of homooligomeric membrane proteins results in accurate predictions in ⅔ of the examples in our benchmark . Therefore , we present a web server , called TMHOP , to compute the structures of single-pass homooligomeric membrane proteins directly from sequence . The results suggest that the automated design of large and complex membrane proteins is within reach . | [
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| 2019 | A lipophilicity-based energy function for membrane-protein modelling and design |
With the antibiotic development pipeline running dry , many fear that we might soon run out of treatment options . High-density infections are particularly difficult to treat due to their adaptive multidrug-resistance and currently there are no therapies that adequately address this important issue . Here , a large-scale in vivo study was performed to enhance the activity of antibiotics to treat high-density infections caused by multidrug-resistant Gram-positive and Gram-negative bacteria . It was shown that synthetic peptides can be used in conjunction with the antibiotics ciprofloxacin , meropenem , erythromycin , gentamicin , and vancomycin to improve the treatment outcome of murine cutaneous abscesses caused by clinical hard-to-treat pathogens including all ESKAPE ( Enterococcus faecium , Staphylococcus aureus , Klebsiella pneumoniae , Acinetobacter baumannii , Pseudomonas aeruginosa , Enterobacter cloacae ) pathogens and Escherichia coli . Promisingly , combination treatment often showed synergistic effects that significantly reduced abscess sizes and/or improved clearance of bacterial isolates from the infection site , regardless of the antibiotic mode of action . In vitro data suggest that the mechanisms of peptide action in vivo include enhancement of antibiotic penetration and potential disruption of the stringent stress response .
ESKAPE pathogens ( E . faecium , S . aureus , K . pneumoniae , A . baumannii , P . aeruginosa , E . cloacae ) are recognized to be responsible for the majority of difficult-to-treat community-acquired , healthcare-associated , and nosocomial infections [1] . Multidrug-resistant bacteria represent major therapeutic challenges and pose a great threat to human health [2] . The increasing resistance to available antibiotics dampens treatment possibilities and there is a serious lack of adequate treatment options . Less discussed but of even greater concern are infections associated with high bacterial densities ( >107 CFU/ml bacteria ) especially biofilm and/or abscess infections . High bacterial densities lead to elevated MICs to multiple antibiotics [3] and are extremely difficult to treat with antibiotics [4] . In this context , skin and soft tissue infections ( SSTIs ) are an emerging problem , a significant burden in health care facilities , and responsible for increased antibiotic administration [5] . SSTIs such as abscesses form fluid , pus-filled pockets infiltrated by bacteria and immune cells [6] , and are often highly resistant to antibiotic treatment . Indeed , abscesses are the most common indication for frequent ( 6–12 h ) , high-dose ( up to 1 g/kg ) and long term ( >5 d ) [7] intravenous ( IV ) broad-spectrum antibiotic administration [5] . SSTIs have been traditionally thought to be largely caused by S . aureus and Streptococcus pyogenes but recent findings show that other microbes are very prevalent [8 , 9] . Indeed , the SENTRY antimicrobial surveillance program ( North America ) [10] reported that the major pathogens isolated from SSTIs now include 10 . 8% P . aeruginosa , 8 . 2% Enterococcus sp . , 7 . 0% E . coli , 5 . 8% Enterobacter sp . , and 5 . 1% Klebsiella sp . , as well as 45 . 9% S . aureus . Moreover , recently A . baumannii is increasingly recognized as an emerging cause of nosocomial infections and important cause of severe , life-threatening soft tissue infections [11] . High bacterial numbers of greater than 108 CFU/ml isolated bacteria are present in soft-tissue and peritoneal infections [12] , highlighting the importance of investigating high-bacterial density infections . However , standard in vitro susceptibility tests employ modest bacterial concentrations of 2–5 x 105 per ml which critically underestimates the strong impact on antibiotic susceptibility of the high concentrations of bacteria in such infections [12] . Thus , it remains a major challenge to translate in vitro findings into in vivo efficacy and compounds that show excellent in vitro activity ( e . g . , low MIC in defined medium ) , often work poorly when tested under in vivo conditions .
To investigate abscess infections caused by the ESKAPE pathogens and E . coli , we extrapolated from our previously-developed cutaneous mouse infection model [4] , prioritizing the study of resistant , recalcitrant host-adapted pathogens rather than commonly used laboratory strains . We identified clinical isolates that were able to cause chronic skin abscesses on the backs of CD-1 female mice after injection of a high bacterial dose ( ≥ 107 bacteria ) ; each of these strains persisted throughout the course of a three day experiment and did not cause mortality in mice . MIC assays revealed that these strains had generally low antibiotic susceptibility and were resistant to antibiotics from at least three different classes ( Table 1 , S1 Table ) . Plasmid-encoded bioluminescently-tagged isolates were created to enable visualization and monitoring of the progress of disease using non-invasive techniques , and to provide evidence that the skin infection contained metabolically active bacteria; this enabled us to follow the infection for all strains ( Fig 1 ) . To optimize the treatment strategy , antibiotics were chosen based on their moderate in vitro MIC values ( 0 . 02 to 15 . 6 μg/ml ) and empirically tested in vivo to determine an appropriate concentration that reduces abscess sizes and/or CFU just enough to observe synergy of the peptides and antibiotics . Since the drug concentrations after IV injection might be affected by various factors including blood perfusion , penetration into tissues and/or binding to plasma proteins or dermal components [13] , we directly injected antibiotics into the infected tissue . This allowed us to overcome the distinct pharmacokinetics of antibiotics used in humans as opposed to mice when applied intravenously , as well as the amounts delivered and time for penetration ( S1 Table ) to the target site [13 , 14] . For each antibiotic directly injected into the infected abscess tissue , an amount that would provide a total body concentration greater than the effective antibiotic dose was chosen . Meropenem was used to treat A . baumannii and K . pneumoniae infections at concentrations of 6 and 10 mg/kg , respectively . The MIC for meropenem against K . pneumoniae was very low ( 0 . 1 μg/ml ) , while the MIC against A . baumannii was quite high ( 15 . 6 μg/ml ) . Based on EUCAST clinical breakpoint information , K . pneumoniae KPLN649 is resistant to meropenem while A . baumannii Ab5075 is sensitive ( S1 Table ) . However intriguingly , treatment of an A . baumannii infection reduced bacterial cell numbers by 111-fold , while there was only a 2 . 7-fold clearance of K . pneumoniae . Indeed , K . pneumoniae showed the highest recalcitrance towards all tested antibiotic treatments in this skin infection model and high concentrations of azithromycin ( 500 mg/kg ) or colistin ( 3 mg/kg ) had no anti-infective activity . Similarly , although the MICs of ciprofloxacin against P . aeruginosa ( 3 . 1 μg/ml ) and K . pneumoniae ( 6 . 3 μg/ml ) were quite similar , and both strains resistant to ciprofloxacin based on EUCAST ( S1 Table ) , as little as 0 . 4 mg/kg ciprofloxacin were required to reduce the P . aeruginosa load by 15-fold , while a 75-fold greater dosage of 30 mg/kg reduced the K . pneumoniae bacterial burden by only 2-fold . Similarly , the E . coli and E . cloacae strains were sensitive towards ciprofloxacin ( S1 Table ) , with MICs 0 . 02 and 0 . 04 μg/ml , respectively; however , in the mouse abscess model 4 mg/kg was required to reduce E . coli cells by 5 . 8-fold while only 0 . 006 mg/kg was required to reduce E . cloacae by 2 . 8-fold ( Fig 2A–2E ) ( S2 Table ) . These important observations highlight that antibiotic mono-therapies as well as high antibiotic dosages are often ineffective when bacteria form high density infections . Thus in vitro MICs are useful indicators of potential , but are not always predictive of in vivo efficacy especially when adaptive resistance occurs . The situation was even more difficult for strains that were resistant towards several classes of antibiotics , e . g . an E . faecium patient isolate that showed extensive drug resistance towards all tested antibiotics ( Table 1 ) . In this case the use of gentamicin for in vivo treatment at a high dosage ( 16 mg/kg ) led to a reduction of the bacterial burden by about 4-fold ( Fig 2F , S2 Table ) . Conversely , a methicillin resistant S . aureus infection was somewhat treatable , although sensitive to the used antibiotics based on EUCAST ( S1 Table ) , with clindamycin ( 0 . 01 mg/kg ) and vancomycin ( 0 . 15 mg/kg ) , both of which visually reduced abscess sizes , but had no impact on bacterial clearance ( Fig 2G , S2 Table ) . Host defense peptides ( HDPs ) are small cationic amino acids groups produced by the body as a defense mechanism and are key components of immunity [15] , while short synthetic derivatives show promise as broad spectrum anti-infectives that protect in various animal models [16] . A discrete subset of these peptides is effective against a broad spectrum of bacterial biofilms [17] . The mechanism of action of such peptides , e . g . 1018 and DJK-5 , has been linked to the disruption of the stringent stress response [17 , 18] . The bacterial stringent response is a highly conserved ( present in Gram-positive and Gram-negative bacteria ) response to various stresses that impacts virulence and antibiotic susceptibility . Their unique ability to disrupt this stress response enables such peptides to show activity against stringent response controlled abscess infections , where biofilm phenotypes have also been suggested to play a crucial role [17 , 19 , 20] . In this context , although our peptides showed high MICs in vitro ( Table 1 ) , we previously showed that they can reduce abscess sizes as well as have modest effects in reducing bacterial numbers when S . aureus or P . aeruginosa infections were treated [18 , 20] . Here we hypothesized that they would convert bacteria into a more relaxed ( unstressed ) state that would render them more susceptible to antibiotic treatment . In this regard , peptide DJK-5 at just 3 mg/kg reduced bacterial loads of P . aeruginosa ( 4 . 6-fold ) , E . faecium ( 22-fold ) , K . pneumoniae ( 4 . 0-fold ) , A . baumannii ( 9 . 9-fold ) , and E . coli ( 2 . 2-fold ) infections ( Fig 2A–2C and 2E , S2 Table ) . Other peptides , namely 1002 , 1018 , and HHC-10 at concentrations of 10 mg/kg had no significant impact on the bacterial burden but showed promise in visually reducing abscess sizes for P . aeruginosa , E . faecium , K . pneumoniae , E . coli , and E . cloacae infections ( Fig 2A , 2C–2F ) . Antibiotic combination therapy is frequently used as a possible method of outmaneuvering recalcitrant bacterial pathogens [21] but its application remains controversial and debated , in part due to the increased risk of toxicity , organ damage , and the selection and emergence of resistant strains [22] . Unfortunately , although in vitro assessments of synergy employing checkerboard titration have been used to justify combination therapy , these are rarely followed up with animal model infection studies . Testing synthetic peptides in combination with antibiotics in vivo showed the ability to enhance the treatment outcome of multidrug resistant bacterial infections . In vivo synergy was defined as demonstrating an effect that was significantly more pronounced in the combination than the sum of the effects of each agent alone using saline-treated animals as a negative control [23 , 24] . By this definition , combination therapies applied with DJK-5 and antibiotics significantly worked against P . aeruginosa ( reduction of bacterial load by 245-fold in combination with ciprofloxacin ) , E . faecium ( 265-fold with gentamicin ) , K . pneumoniae ( 91-fold with meropenem ) , A . baumannii ( 1325-fold with erythromycin and 2006-fold with meropenem ) , and S . aureus ( 11-fold with vancomycin ) when comparing bacterial burdens to the saline control . Indeed DJK-5 worked synergistically and showed an average overall improvement of 14-fold compared to the sum of the individual single treatments . The highest synergistic activity occurred when DJK-5 was combined with meropenem or erythromycin against A . baumannii , reducing the bacterial load by 65-fold and 33-fold , respectively , in combination treatment compared to the sum of individual treatments ( Fig 2B; S2 Table ) . For E . coli , DJK-5 in combination with ciprofloxacin showed a 10 . 3-fold reduction in bacterial numbers over the saline control , which was still a 2 . 9-fold improvement over the summed monotherapies ( Fig 2E ) . Analogous data was also obtained with other peptides; 1018 in combination with ciprofloxacin showed about 186-fold , 6 . 9-fold , 5 . 4-fold , and 8-fold reduction in bacterial burdens against P . aeruginosa , K . pneumoniae , E . cloacae , and E . coli over the saline control ( Fig 2A and 2C–2E ) and additionally was synergistic in reducing P . aeruginosa and K . pneumoniae numbers from the abscess tissue by 30- and 6 . 4-fold , respectively compared to the summed monotherapies ( Fig 2A and 2C ) . The combination of HHC-10 with ciprofloxacin against E . cloacae reduced the bacterial load by about 36-fold compared to the saline control and showed synergistic effects over the summed monotherapies ( 13-fold enhanced activity ) ( Fig 2D ) . Gentamicin in combination with peptide 1002 against E . faecium showed an 18-fold reduction in comparison to the control , and a synergistic effect over the sums of the single administrations ( 7 . 1-fold increased reduction in bacterial burden ) ( Fig 2F ) . Thus , although peptides 1002 , 1018 , and HHC-10 appeared to be less active in combination treatments , possibly due to their being composed of L-amino acids that makes them susceptible to host proteases , they were also shown to work synergistically under in vivo conditions . Although genetically-determined antibiotic resistance has been well publicized as a major issue in human health , the current inability to deal with phenotypic multi-drug resistance ( e . g . engendered due to growth conditions , especially biofilms ) , and in particular high-density infections , has not been well addressed . Here , we examined peptides as a possible adjuvant to antibiotic therapy for treating high-density , recalcitrant bacterial abscess infections caused by the most intractable bacterial species . Our strategy to combine conventional antibiotics with synthetic peptides offers a novel therapeutic approach to effectively treat high density infections in that a range of combination therapies were able to reduce the bacterial burden of problematic clinical isolates in our subcutaneous infection model . At least part of the effect of peptides on antibiotic action was likely due to the ability of peptides to elicit degradation of the stringent stress response intracellular signaling molecule ppGpp [17 , 25] , which has been tied to resistance induction [26 , 27] , and the development of energy-starved persisters [28 , 29] . To further elucidate how the peptides acted , we performed checkerboard titration experiments to determine interactions between the antibiofilm peptides 1018 and DJK-5 and the antibiotic ciprofloxacin against P . aeruginosa LESB58 . Since antibiofilm peptides exert their activities under stringent stress conditions [17 , 25] , such as encountered in biofilms or abscesses , the stringent response ( ppGpp ) inducing agent serine hydroxamate ( SHX ) as well as a ppGpp-overproducing strain ( LESB58 containing the overexpressed cloned relA gene ) were used ( S3 Table ) . As expected , under planktonic growth conditions there was no effect of the combined treatment . However , for the two ppGpp-overexpressing situations , combining the two agents lowered the effective concentrations of each agent to below the MIC , but this was prevented in a stringent response ΔrelAΔspoT double mutant . The insertion of the cloned relA gene into the double mutant enabled effective ciprofloxacin-peptide combinations . In this context , the mutant lacking the stringent response genes , was 2-fold more susceptible towards ciprofloxacin while the mutant complemented with the ppGpp synthetase , RelA , had a 4-fold higher MIC to ciprofloxacin ( S3 Table ) . Stress related responses could provide another mechanism of synergy in addition to peptide-mediated breach of the Gram-negative permeability barrier . These in vitro findings provide a plausible mechanism that may also be occurring in vivo . Most available antibiotics target intracellular processes and therefore must penetrate the bacterial cell envelope , which is particularly challenging in Gram-negative bacteria due to their formidable outer membrane . To further investigate the molecular basis of the synergistic and additive effects of the combinatorial treatment , we performed outer membrane permeability assays , observing the ability of peptides to enhance the uptake of the normally impermeable hydrophobic fluorophore N-phenyl-1-naphtylamine ( NPN ) . Colistin , a cationic lipopeptide antibiotic that is known to permeabilize the bacterial outer membrane [30] , served as a positive control , while meropenem , gentamicin , or vancomycin were used as negative controls . Except for 1002 , each peptide was able to interact with and permeabilize the outer membrane of Gram-negative bacteria at their corresponding MICs ( Table 2 ) and/or 10xMICs ( S4 Table ) . The effect of the peptides on Gram-positive bacteria was almost undetectable since NPN has almost unimpeded access to Gram-positive bacteria . The peptides used here worked with a variety of antibiotics . However , future work should explore further possible combinations to find those most optimal . Other possible mechanisms contributing to synergy should be investigated including modulating the host innate immune/inflammatory responses ( increasing protective responses while dampening inflammation ) and yet-to-be-identified downstream processes associated with the blockage of the stringent response . The insights from our study could help physicians to understand bacterial infections in skin and soft tissues , and aid in management and development of improved treatment strategies . Ultimately , we have provided evidence that our peptides , especially DJK-5 , showed superior effects when paired with antibiotics .
Bacterial strains used in this study were E . faecium #1–1 ( BEI resources , NR-31903 ) , K . pneumonia KPLN649 [31] , A . baumannii Ab5075 [32] , P . aeruginosa LESB58 [33] , E . cloacae 218R1 [34] , E . coli E38 ( Serotype O78:H- ) ( BEI resources , NR-17717 ) , and S . aureus LAC USA300 [35] ( S5 Table ) . All organisms were cultured at 37°C in double Yeast Tryptone ( dYT ) . Liquid cultures were grown at 37°C with shaking at 250 rpm . Cultures harboring individual plasmids were supplemented with 15 μg/ml gentamicin ( Gm ) , 100 μg/ml ampicillin ( Ap ) , 10 μg/ml tetracycline ( Tc ) , 25 μg/ml chloramphenicol ( Cm ) , 100 μg/ml spectinomycin ( Sp ) for E . coli , 500 μg/ml Gm for P . aeruginosa , 100 μg/ml Cm for K . pneumoniae , 25 μg/ml Tc for A . baumannii , 25 μg/ml Gm for E . cloacae , and 400 μg/ml Sp for E . faecium . Bacterial growth was monitored using a spectrophotometer at an optical density of 600 nm ( OD600 ) . The 5 . 8-kb luxCDABE operon was PCR amplified from pUCP . lux using the primer lux-fd ( CCGCAAATGGATGGCAAATA ) and transcriptional terminator t0-containing primer lux-rv-t0 ( TGGACTCACAAAGAAAAAACGCCCGGTGTGCAAGACCGAGCGTTCTGAACAATCAACTATCAAACGCTTCGG ) . The resulting PCR fragment was cloned into the pCR-BluntII-TOPO vector and successful transformants isolated based on their expression of luminescence . A broad-host-range cloning vector , pBBR1MCS [36] , with various antibiotic resistance markers to constitutively express luminescence genes from the lac promoter was utilized by transferring the complete luxCDABE-t0 fragment into pBBR1MCS-1 , pBBR1MCS-3 , or pBBR1MCS-5 , via KpnI / PstI restriction sites ( S6 Table ) . Successful transformants were selected based on luminescence expression and further verified by restriction digestion . P . aeruginosa LESB58 and A . baumannii Ab5075 were made electrocompetent with 300 mM sucrose . E . cloacae 218R1 , K . pneumonia KPLN649 , E . faecium #1–1 , and E . coli E38 were made electrocompetent with ice-cold water . Briefly , individual strains except E . faecium were scraped from an overnight grown agar plate and washed with either sucrose or water . E . faecium was scraped from a plate and grown overnight in dYT supplemented with 3% glycine . Electroporation conditions were 2 . 5 kV , 25 μF , 200 Ω . Plasmid pUCP . lux was transformed into P . aeruginosa , pBBR1 . lux into K . pneumoniae , pBBR3 . lux into A . baumannii and E . coli , pBBR5 . lux into E . cloacae , and plasmid pSL101-P16S , which has a broad-host range replicon for Gram-positive bacteria , into E . faecium . Successful transformants were checked for luminescence expression and plasmid stability further verified by re-streaking single colonies on agar plates without antibiotic selection for 4 days , which did not lead to the loss of luminescence signals . A 2447-bp fragment containing the relA gene including a 100-bp upstream promoter region was PCR amplified from P . aeruginosa LESB58 genomic DNA using the primers relA_oe_fwd-Spe ( CATACTAGTGGGTATCTCGGGTCTTCAG ) and relA_oe_rev-Apa ( TCAGGGCCCGCTAGGATGCCTGCGTAATC ) . The resulting PCR fragment was cloned into pBBR1MCS-5 [36] via SpeI and ApaI restriction sites and sent for sequencing before transformation into P . aeruginosa LESB58 . Peptides HHC-10 ( KRWWKWIRW-NH2 ) [37] , 1002 ( VQRWLIVWRIRK-NH2 ) [38] , 1018 ( VRLIVAVRIWRR-NH2 ) [39] and the D-enantiomer DJK-5 ( VQWRAIRVRVIR-NH2 ) [25] were synthesized by CPC Scientific using solid-phase 9-flurenylmethoxy carbonyl ( Fmoc ) chemistry and purified to >95% purity using reverse-phase high-performance liquid chromatography ( HPLC ) . The lyophilized peptides were resuspended in endotoxin-free water . The antibiotics gentamicin , ciprofloxacin , meropenem , erythromycin , clindamycin , vancomycin , azithromycin , and colistin were purchased from Sigma-Aldrich at a United States Pharmacopeia ( USP ) Reference Standard grade . Erythromycin and azithromycin were initially dissolved in 70% ethanol , while all other antibiotics were dissolved in endotoxin-free water ( E-Toxate , Sigma-Aldrich ) . Antibiotics and peptides were further diluted into saline ( Sigma-Aldrich ) for in vivo application . The MICs of drugs for all clinical isolates were determined by using the broth microdilution assay [40] in 96-well plates using Mueller-Hinton broth ( MHB; Difco ) . All tests were performed in at least triplicate following the Clinical and Laboratory Standards Institute recommendations . Bacterial growth ( 37°C ) was examined by visual inspection after 16 h to 48 h of incubation . The MIC was defined as the lowest concentration of a compound that completely prevented visible cell growth . P . aeruginosa LESB58 , LESB58 . relA , LESB58 . ΔrelA/ΔspoT , and LESB58 . ΔrelA/ΔspoT ( complement with plasmid pBBR5 . relA ) were adjusted to an OD600 of 0 . 001 and grown in a 96-well plate in MHB for 24 h at 37°C under static conditions . To chemically induce stringent conditions , 500 μM serine hydroxamate ( SHX; Sigma-Aldrich ) was added to the growth medium . The minimum fractional inhibitory concentration for each compound was visually determined in wells that showed 100% growth inhibition . Animal experiments were performed in accordance with The Canadian Council on Animal Care ( CCAC ) guidelines and were approved by the University of British Columbia Animal Care Committee ( certificate number A14-0363 ) . Mice used in this study were female outbred CD-1 . All animals were purchased from Charles River Laboratories ( Wilmington , MA ) , were 7 weeks of age , and weighed about 25 ± 3 g at the time of the experiments . 1 to 3% isoflurane was used to anesthetize the mice . Mice were euthanized with carbon dioxide . The abscess infection model was performed as described earlier [4] . All microorganisms used in this infection model were grown to an OD600 of 1 . 0 in dYT broth . Prior to injection , bacterial cells were washed twice with sterile PBS and resuspended to the following ( strain-dependent ) concentrations to produce reproducible abscesses and bacterial counts: P . aeruginosa LESB58 , 5 × 107 CFU; A . baumannii Ab5075 , 1 × 109 CFU; K . pneumoniae KPLN49 , 1 × 109 CFU; E . faecium #1–1 , 1 × 109 CFU; E . cloacae 218R1 , 2 . 5 × 108 CFU; E . coli E38 , 1 × 108 CFU; and S . aureus LAC , 5 × 107 CFU/ml ( S5 Table ) . A 50 μl bacterial suspension was injected into the right side of the dorsum . All utilized peptides and antibiotics were tested for skin toxicity prior to efficacy testing . Treatment was applied directly into the subcutaneous space into the infected area ( 100 μl ) at 1 h post infection . The progression of the disease/infection was monitored daily and abscesses ( visible swollen , inflamed lumps ) were measured on day three using a caliper . Skin abscesses were excised ( including all accumulated pus ) , homogenized in sterile PBS using a Mini-Beadbeater-96 ( Biospec products ) for 5 min and bacterial counts determined by serial dilution . Experiments were performed at least 3 times independently with 2 to 4 animals per group . To follow disease progress in real-time bioluminescently labelled strains were used ( S5 Table ) . Bioluminescence images were acquired ( auto exposure , medium binning ) at different times after the initiation of infection by using the IVIS Lumina system ( Perkin Elmer , Waltham MA ) and analyzed using Living Image software . The induction of increases in the outer membrane permeability caused by antibiotics or peptides was evaluated using the fluorescence dye N-phenyl-1-naphthylamine ( NPN; Sigma-Aldrich ) , based on the protocol of Loh et al [41] . Briefly , microtitre plates were prepared with 100 μl Hepes buffer ( 5 mM , pH 7 . 2 ) ( control ) or buffer supplemented with 0 . 5 mM NPN with or without peptides/antibiotics at a concentration equivalent to the MIC and 10-fold the MIC of individual bacterial strains . Bacterial strains were grown overnight on dYT agar plates , scraped from the plate , resuspended in buffer and adjusted to an OD600 of 1 . 0 . One hundred μl of the cell suspension was then added to each well of the microtitre plate and the fluorescence immediately measured at an excitation wavelength of 350 nm and emission wavelength of 420 nm in a Synergy H1 microplate reader ( BioTek ) . The values obtained from the cell suspension without test compounds were subtracted from the value for the suspension with test substrates to express the relative fluorescence units . All obtained data points were divided by 100 for presentation . Statistical evaluations were performed using GraphPad Prism 7 . 0 ( GraphPad Software , La Jolla , CA , USA ) . P-values were calculated using one-way ANOVA , Kruskal-Wallis multiple-comparison test . Data was considered significant when p-values were below 0 . 05 , 0 . 01 or 0 . 001 as indicated . | There has been enormous publicity about the inexorable rise of resistance and the dearth of new therapies . However less attention has been placed on adaptively multidrug-resistant high density bacterial infections for which antibiotics are highly used but no effective therapies currently exist . Here we have provided new hope for this previously intractable class of infections as typified by abscess infections that are responsible for 3 . 2 million annual emergency room visits in the US alone . We show how to enhance the activity of antibiotics to treat multidrug-resistant Gram-positive and Gram-negative bacteria , using peptides that target the bacterial stress response , persister-based resistance and the outer membrane permeability barrier . In particular we have employed a new bacterial subcutaneous abscess mouse model to demonstrate that: ( a ) 7 of the society’s most recalcitrant pathogens formed cutaneous abscesses and even when antibiotics were directly delivered into abscess tissues , they showed poor efficacy; ( b ) By combining antibiotics with the local administration of anti-biofilm peptides that target cellular ( stringent ) stress responses , we could pharmacologically treat the infection and reduce the severity of cutaneous abscesses; ( c ) This synergy was due to increased outer membrane permeability as well as the disruption of the conserved stringent stress response that controls virulence and antibiotic resistance , particularly due to so-called persisters . These peptides have therefore the potential to broaden our limited antibiotic arsenal for a group of extremely difficult to treat infections . | [
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| 2018 | Synergy between conventional antibiotics and anti-biofilm peptides in a murine, sub-cutaneous abscess model caused by recalcitrant ESKAPE pathogens |
Impairment of the intestinal barrier and subsequent microbial translocation ( MT ) may be involved in chronic immune activation , which plays a central role in HIV pathogenesis . Th17 cells are critical to prevent MT . The aim of the study was to investigate , in patients with primary HIV infection ( PHI ) , the early relationship between the Th17/Treg ratio , monocyte activation and MT and their impact on the T-cell activation set point , which is known to predict disease progression . 27 patients with early PHI were included in a prospective longitudinal study and followed-up for 6 months . At baseline , the Th17/Treg ratio strongly negatively correlated with the proportion of activated CD8 T cells expressing CD38/HLA-DR or Ki-67 . Also , the Th17/Treg ratio was negatively related to viral load and plasma levels of sCD14 and IL-1RA , two markers of monocyte activation . In untreated patients , the Th17/Treg ratio at baseline negatively correlated with CD8 T-cell activation at month 6 defining the T-cell activation set point ( % HLA-DR+CD38+ and %Ki-67+ ) . Soluble CD14 and IL-1RA plasma levels also predicted the T-cell activation set point . Levels of I-FABP , a marker of mucosal damages , were similar to healthy controls at baseline but increased at month 6 . No decrease in anti-endotoxin core antibody ( EndoCAb ) and no peptidoglycan were detected during PHI . In addition , 16S rDNA was only detected at low levels in 2 out 27 patients at baseline and in one additional patient at M6 . Altogether , data support the hypothesis that T-cell and monocyte activation in PHI are not primarily driven by systemic MT but rather by viral replication . Moreover , the “innate immune set point” defined by the early levels of sCD14 and IL-1RA might be powerful early surrogate markers for disease progression and should be considered for use in clinical practice .
High levels of immune activation occur early in primary HIV infection ( PHI ) and the CD8 T-cell activation set point ( i . e . the steady state level of activation following PHI ) is a strong predictor of subsequent CD4 T-cell loss independently of viral load [1] . Generalized immune activation is known to be a major contributor to HIV-1 pathogenesis [2] . Although immune activation is dramatically reduced by antiretroviral treatment , residual immune activation remains in virally suppressed ART-treated patients and is associated with poor immune reconstitution [3] and increased morbidity/mortality in treated patients [4] . Impairment of the intestinal barrier and subsequent microbial translocation might be one of the main causes of chronic T-cell activation [5] , together with innate and adaptive immune responses , stimulation by HIV viral proteins and reactivation of other viruses ( e . g . cytomegalovirus , hepatitis viruses ) ( reviewed in Appay and Sauce [6] ) . Microbial translocation leads to the release of bacterial products such as lipopolysaccharide ( LPS ) which induce monocyte activation , as demonstrated in vitro and in different clinical situations including sepsis [7] , [8] . LPS levels were shown to be elevated in chronic HIV infection – but not significantly during PHI – and to correlate with T-cell activation [9] . In viremic chronic HIV-infected patients , the spontaneous production of IL-1 by circulating monocytes suggested that these cells were activated in vivo [10] . To which extent monocyte activation was caused by the virus and/or by microbial translocation remained unclear . More recently , plasma levels of the monocyte activation marker , soluble CD14 ( sCD14 ) were found to correlate with LPS amounts [9] , [11] . Furthermore , sCD14 levels were shown to predict mortality in chronically HIV-infected patients [12] as well as in other contexts ( e . g . hemodialysis patients ) [13] . Whether systemic microbial translocation occurs and causes immune activation during primary HIV infection has been suggested but not clearly demonstrated . How much immune activation is caused by microbial translocation and when does microbial translocation begin during HIV infection remain outstanding questions [14] . Th17 cells might be crucial in the maintenance of the intestinal mucosal barrier integrity and in the control of microbial translocation [15] . These cells were reported to be depleted in advanced HIV or SIV disease , but preserved in patients with slow disease progression , including elite controllers [16] . Indeed , reduced Th17 cell frequency has also been found in patients with high viral load [17] or low CD4 T cell count [18] . Although exerting mostly opposing functions , Th17 and Treg cells are two closely related CD4 T-cell subsets sharing reciprocal maturation pathways [19] . There is an active balance between the development of either Tregs or Th17 cells and even plasticity between the two subsets [20] . The imbalance between Th17 and Tregs has been involved in different settings including autoimmune diseases and cancer [21] , [22] . In HIV infection , Tregs might expand following immune activation; however , the increase in Treg frequency was mostly reported as inadequate resulting in a failure to dampen high generalized immune activation in viremic patients [23] . A loss in Th17 to Treg balance has been found in pathogenic SIV infection [24] . Moreover , the Th17/Treg ratio was shown to be lower in progressors compared to elite controllers and was reported to be inversely related to systemic T cell activation in rectosigmoid biopsies from chronically infected patients [18] . In order to decipher the respective role of viral replication and microbial translocation on the establishment of the T-cell activation set point , we investigated , in patients with acute HIV infection , the early relationship between the Th17/Treg balance , monocyte activation and systemic microbial translocation and their impact on the T-cell activation set point , known to predict the rate of CD4 T-cell decline .
Twenty-seven patients diagnosed early during PHI ( median of estimated time post-infection: 42 days ) were prospectively enrolled in the study between June 2009 and December 2011 . Patients' clinical characteristics at baseline and at month 6 ( M6 ) of follow-up are depicted in Table 1 . A subgroup of these patients have been previously described [25] . Thirteen patients remained untreated during the study period . Ten patients were treated with cART just after baseline sampling; two patients were treated between M3 and M6 . Two patients were lost to follow-up . T-cell activation levels were determined by the proportion of cells that expressed the CD38 , HLA-DR and/or Ki-67 activation markers , as illustrated in Figure S1 . ART-treated and untreated patients did not differ for CD4 and CD8 T-cell activation at baseline ( Figure 1A ) . In untreated patients , the proportion of double positive HLA-DR/CD38 CD8 T cells and of Ki-67-expressing CD8 T cells significantly decreased between baseline and M6 ( p = 0 . 0002 and p = 0 . 0005 respectively ) ( Figure 1B and 1C ) although T-cell activation levels remained higher than in ART-treated patients ( Figure 1D ) . Interestingly , levels of double positive HLA-DR/CD38 CD8 T cells in those patients , initiating ART early at the time of primary infection were similar to that measured in healthy seronegative controls ( Figure 1D ) . CD8 T-cell activation remained stable between M3 and M6 , indicating that the immune activation set point was reached by month 6 of follow-up . CD4 T-cell counts did not change significantly during the 6 months of follow-up ( Figure 1E ) . Median HIV-RNA plasma levels did not significantly decrease during the study period ( Figure 1F ) . A decrease >0 . 5 log10/mL was observed in 5 untreated patients whereas in most untreated patients ( 8/13 ) , viral set point was reached before inclusion in the study . At baseline , we found a strong positive correlation between plasma viral load and the frequency of CD8 T cells co-expressing CD38 and HLA-DR ( r = 0 . 66 , p = 0 . 0002 ) or expressing Ki-67 ( r = 0 . 77 , p<0 . 0001 ) ( data not shown ) . At baseline , a median ( IQR ) of 3 . 7% ( 2 . 4–4 . 7 ) of isolated CD4+ T cells produced IL-17 following stimulation with a combination of PMA and ionomycine . At month 6 , there was a trend to a decrease in Th17 cell frequencies ( median ( IQR ) of 2 . 38% ( 1 . 9–3 . 6 ) when considering all patients treated or untreated ( p = 0 . 08 ) , the two groups showing similar Th17 levels at M6 ( Figure S2 ) ) . As illustrated in Figure 2A , most Th17 cells expressed the chemokine receptor CCR6 . A median of 31 . 5% of Th17 cells expressed CCR4 . In contrast , CXCR3 was less frequently expressed on Th17 cells compared to IL-2 and IFN-γ-producing Th1 cells ( median: 28 . 5% vs 65 . 8% ) . The proportion of CCR6-expressing T cells was directly correlated to the frequency of Th17 cells ( r = 0 . 50 , p = 0 . 007 ) ( Figure 2B ) . As CCR6+ CD4 T cells were reported to be highly permissive to HIV infection [26] , we analyzed the impact of HIV replication in vivo on the expression of CCR6 by Th17 cells . Less Th17 cells expressed CCR6 in patients with high viral load ( i . e . above median of 5 . 65 log copies/mL ) compared to patients with low viral load ( i . e . below median ) ( p = 0 . 008 ) whereas Th17 cells expressed similar levels of CCR4 and CXCR3 in both groups ( Figure 2C ) . Accordingly , CCR6 expression on Th17 cells was negatively correlated to plasma HIV-RNA levels ( r = −0 . 54 , p = 0 . 003 ) ( Figure 2D ) . The expression of CCR6 on Th17 cells was unchanged between baseline and month 6 . However , CCR6/CXCR3 co-expression on Th17 cells decreased at month 6 ( p = 0 . 03 ) while CCR6/CCR4 co-expression increased ( p = 0 . 003 ) ( Figure 2E ) . We put forward the hypothesis that Th17 cells and/or the balance between Th17 and Tregs could impact the level of immune activation in early PHI . Therefore , we investigated the relationship between ex vivo CD4 and CD8 T-cell activation levels and the proportion of IL-17-expressing cells . Tregs were defined as CD4+CD25+CD127lowFoxP3+ T cells . We found a strong negative relationship between the proportion of Th17 cells and the level of CD8 T cells that co-expressed CD38 and HLA-DR at baseline ( r = −0 . 54 , p = 0 . 004 ) as well as with the proportion of Ki-67-expressing CD8 T cells ( r = −0 . 63 , p = 0 . 0004 ) ( Figure 3A ) . CD4 T cell activation as measured by HLA-DR expression on CD4 T cells was also negatively correlated with the percentage of Th17 cells ( r = −0 . 52 , p = 0 . 006 ) ( data not shown ) . In addition , the proportion of Th17 cells negatively correlated at baseline with HIV-RNA plasma levels ( r = −0 . 46 , p = 0 . 015 ) ( Figure 3A ) and with HIV-DNA in PBMCs ( r = −0 . 49 , p = 0 . 021 ) ( data not shown ) . We previously reported that Treg cell frequency was not correlated with CD4 or CD8 T-cell activation [25] . Considering the loss in Th17 to Treg balance reported in pathogenic SIV infection [24] , we assessed the relationship between CD8 T-cell activation and the Th17/Treg ratio and found results similar to those observed with Th17 levels ( Figure 3B ) . Of note , Th1 responses as defined by IL-2 or IFN-γ expressing CD4 T cells did not correlate with immune activation or plasma viral load ( data not shown ) . We also assessed Tregs by measuring TGF-β and/or IL-10-producing Foxp3+ cells among isolated CD4+CD25+ T cells following PMA/Ionomycin stimulation . The Th17 to cytokine-expressing Treg ratio also negatively correlated with both HIV-RNA plasma levels and HIV-DNA levels in PBMCs ( Figure 3C ) . Lower Th17 cells being associated with higher T-cell activation , one may hypothesize that T-cell activation results from microbial translocation through monocyte activation . Thus , we focused on the relationship between the Th17/Treg balance and soluble markers of monocyte activation . Plasma levels of sCD14 and IL-1RA did not significantly change between baseline and M6 and were similar in untreated and treated patients ( Figure S3 ) . At baseline , sCD14 plasma levels negatively correlated with the Th17/Treg ratio ( r = −0 . 56 , p = 0 . 002 ) , and was positively related to CD8 T-cell activation ( r = 0 . 55 , p = 0 . 004 ) ( Figure 4A ) but not to CD4 T-cell activation , as assessed by the proportion of HLA-DR expressing CD4 T cells ( data not shown ) . In addition to sCD14 , which binds LPS but is also an acute phase protein , we investigated IL-1RA as a cytokine antagonist secreted by activated monocytes in concert with the pro-inflammatory cytokine IL-1 [27] . We found a strong negative relationship between plasma levels of IL-1RA and the Th17/Treg ratio ( r = −0 . 55 , p = 0 . 003 ) . IL-1RA was positively associated with CD38+HLA-DR+ ( r = 0 . 61 , p = 0 . 0009 ) and Ki-67+ CD8 T cells ( r = 0 . 61 , p = 0 . 0008 ) . IL-1RA was also found to be positively related to HLA-DR+ CD4 T cells ( r = 0 . 46 , p = 0 . 015 ) ( Figure 4B ) . Of note , MIP-1α , highly expressed in LPS-stimulated monocytes , was below limit of detection ( 10 pg/mL ) in 24 of the 27 patients . Considering the lack of direct demonstration of MT in acute HIV infection , we were interested to further investigate evidence of mucosal damage and gauged the levels of microbial translocation in the study population . Fatty acid-binding proteins ( FABPs ) are plasmatic markers of tissue injuries . Intestinal-FABP ( I-FABP ) can be detected in plasma after leaking out of damaged enterocytes from the small intestine [28] , [29] . At baseline , levels of I-FABP were similar in patients and healthy donors . However , we found a significant increase in I-FABP levels from baseline to M6 ( p = 0 . 0001 when considering all treated and untreated patients ) ( Figure 5A ) . Of note , I-FABP also increased in treated patients at month 6 ( p = 0 . 0005 ) . No relationship was found between Th17 frequency and I-FABP levels ( data not shown ) . The hypothesis of the occurrence of MT in patients with acute/early HIV infection was suggested by a decrease in anti-LPS antibodies ( EndoCAb ) , rather than the presence of LPS in plasma [9] . In the present study involving patients with early primary HIV infection , baseline EndoCAb levels did not differ from those of healthy donors ( Figure 5B ) . In addition , EndoCAb levels did not change between baseline and M6 and did not correlate with T-cell activation ( Figure 5B and data not shown ) . Apart from LPS , peptidoglycan ( PGN ) , another microbial component , could be found in plasma following microbial translocation . In contrast to patients with chronic infection , in whom we detected plasma pepditoglycan ( in 4 of 7 tested patients ) , we found no detectable peptidoglycan in any of the 27 PHI patients ( Figure 5C ) . To avoid misinterpretation due to the SLP test sensitivity , we also quantified the 16S ribosomal DNA by quantitative PCR in all plasma samples at baseline and M6 . Bacterial rDNA was only detected in 2 out of 27 patients at baseline and in one additional patient at M6 . Moreover , plasma levels of 16S rDNA in these three patients were low ( <400 copies/µL at month 6 ) compared to the levels detected in 20 patients with chronic untreated HIV infection ( median: 2 . 285 copies/µL ) and in 10 patients with sepsis and blood cultures positive for Staphylococcus aureus ( median: 89 . 100 copies/µL ) ( Figure 5D ) . Finally , we assessed the impact of the Th17 to Treg balance at baseline on the T-cell activation set point after 6 months of follow-up in untreated patients . The Th17/Treg ratio at baseline negatively correlated with the proportion of CD38+HLA-DR+ CD8 T cells at month 6 ( r = −0 . 63 , p = 0 . 020 ) as well as with Ki-67-expressing CD8 T cells ( r = −0 . 83 , p = 0 . 0005 ) ( Figure 6A ) . We also investigated whether soluble markers of monocyte activation , shown to correlate with the Th17/Treg ratio , can also predict the T-cell activation set point . Levels of sCD14 at baseline correlated with the proportion of CD8 T-cells co-expressing CD38 and HLA-DR at month 6 ( r = 0 . 61 , p = 0 . 026 ) and with Ki-67 expression in CD8 T-cells ( r = 0 . 57 , p = 0 . 041 ) ( Figure 6B ) . Strikingly , baseline levels of IL-1RA were strongly correlated with CD38+HLA-DR+ CD8 T cells at month 6 ( r = 0 . 72 , p = 0 . 005 ) as well as with Ki-67+ CD8 T cells ( r = 0 . 80 , p = 0 . 001 ) ( Figure 6C ) .
The present study shows that the Th17/Treg ratio strongly correlates with the level of generalized T-cell activation in acute HIV infection . The loss of Th17 to Treg balance was found to be associated with elevated plasma levels of monocyte/macrophage activation soluble markers . Here , we show for the first time that IL-1RA plasma levels are associated with T-cell activation and that the early level of IL-1RA is a strong predictor of the T-cell activation set point . In addition , data indicate that , in acute HIV infection , immune activation is closely dependent on viral replication and not on systemic microbial translocation that occurs later in the natural history of infection . Th17 cells , involved in the maintenance of the intestinal mucosal barrier integrity and in the defence against microbial infections were reported to be depleted in advanced HIV disease [15] , [17] . Few data are available regarding Th17 cells in primary HIV infection . Th17 cells were shown to decrease early following SIV infection of pigtail macaques [24] . In the present study , we enrolled patients within approximately 40 days following estimated time of infection . The frequency of Th17 cells did not significantly change between baseline and month 6 . In humans , TH17 cells express the chemokine receptors CCR6 and CCR4 whereas Th1 cells mainly express CXCR3 [30] . We found that the great majority of IL-17+ cells expressed CCR6 . The expression of CCR4 and CXCR3 was heterogeneous among patients; cells that coexpressed CXCR3 and CCR6 were preferentially Th1/Th17 cells , which also produced IFN-γ ( data not shown ) . CCR6+ Th17 cells were demonstrated to be highly permissive to HIV infection and preferential targets for the virus [26] , [31] . We observed a slight but significant decrease in CCR6 expression on Th17 cells in the group of patients with high viral loads and a negative relationship between the proportion of Th17 cells that express CCR6 and the plasma HIV-RNA levels . These data could result from an early loss of CCR6+Th17 cells . Alternatively , activated cells could have downregulated CCR6 expression [32] . From baseline to month 6 , the proportion of CCR6+ Th17 cells did not change whereas Th17 cells co-expressing CCR6 and CXCR3 decreased while CCR6+/CCR4+ Th17 cells increased . CCR6+CXCR3+ cells were reported to express more frequently the HIV co-receptor CCR5 and the gut-homing integrin β7 compared to CCR6+CCR4+ cells [26] . Thus , a relative depletion of peripheral CCR6+CXCR3+ IL-17-secreting cells might be related to a preferential targeting of these cells , as well as their migration to the gut . By preventing microbial translocation , Th17 cells and/or the balance between Th17 and Tregs could impact the level of immune activation in early PHI , as suggested in pathogenic SIV infection [24] . Here , we show that , as in the macaque model , the Th17/Treg ratio at baseline negatively correlated with T-cell activation . In addition this ratio was also inversely related to plasma viral load . It could be postulated that a high Th17/Treg ratio might be associated with the control of microbial translocation . Thus , we measured sCD14 plasma levels as well as other monocyte activation markers including IL-1RA and MIP-1α . We investigated IL-1RA rather than IL-1 , that cannot be reliably measured in plasma due to a short half-life and/or to a rapid clearance from circulation [27] , [33] . The Th17/Treg ratio was negatively associated with the monocyte activation markers sCD14 and IL-1RA; MIP-1α was undetectable in most patients . Soluble CD14 is frequently measured as a surrogate marker of microbial translocation but increased levels of sCD14 reflect monocyte activation , whatever the stimulus . We thus assessed markers of mucosal integrity ( I-FABP ) and of microbial translocation including anti-LPS antibodies ( EndoCAB ) , peptidoglycan and 16S rDNA plasma levels . I-FABP levels have been reported to be higher in HIV-infected ART-treated patients as compared with healthy donors; I-FABP was also found to be associated with lower CD4 T cell counts [12] , [34] . In this study , I-FABP levels were increased at month 6 but not significantly at baseline , although statistical significance might have been reached with higher number of patients . The increase in I-FABP levels suggests that the loss of mucosal integrity appeared between baseline and month 6 in most patients . This is consistent with the decreased expression of genes involved in the regulation of epithelial barrier maintenance reported after 1–2 months in primary HIV infection [35] . However , we cannot exclude that impairment of the mucosal barrier occurred earlier before the detection of released I-FABP . Interestingly , early ART initiation did not prevent damages in the intestinal mucosa at month 6 as I-FABP also increased in patients receiving antiretroviral treatment during the study period . There was no correlation between the proportion of IL-17-secreting cells and I-FABP levels . However , the cytokine , produced by Th17 cells , crucial for the maintenance of normal barrier homeostasis and the prevention of dissemination of commensal bacteria is probably IL-22 , rather than IL-17 [36] , [37] . Accordingly , loss of IL-22+ lymphocytes was reported to be associated with mucosal damage in SIV infection [38] . The hypothesis of the occurrence of systemic microbial translocation in patients with acute/early HIV infection was indirectly suggested by a decrease in EndoCAB , since the increase in plasma LPS has been observed in patients with chronic infection but not with acute or “early chronic” infection [9] . It was suggested that LPS could not be detected in acute HIV infection because of naturally occurring EndoCAbs that bind to and clear translocated LPS from the circulation . In this study , EndoCAb levels were similar in patients and in healthy controls and remained unchanged at month 6 . In our study , patients were included in the early phase of acute HIV infection which could explain the discrepancy . Of note , it was shown that EndoCAb levels remained stable during HIV disease progression in Africa and that LPS and EndoCAb levels were not correlated [39] . To directly assess the presence of microbial products , we measured plasma levels of peptidoglycan , a major cell-wall component of both Gram-negative and Gram-positive bacteria as well as bacterial 16S rDNA . Unlike chronically infected patients , all plasma samples from patients with acute HIV infection were negative for peptidoglycans . Similar to the Limulus amebocyte lysate assay which was suspected to give inconsistent results and to underestimate microbial translocation in HIV/SIV infection [40] , the test used to detect peptidoglycans could fail to detect low levels of this microbial product in plasma samples . We thus also quantified bacterial 16S rDNA by a sensitive quantitative PCR method to detect conserved regions of bacterial DNA . We found no 16S rDNA in most patients with primary HIV infection , in contrast to patients with chronic HIV infection and with sepsis . Taken together , data indicate the lack of systemic microbial translocation in early PHI , although microbial products may be increased in the gut or liver and cleared before reaching peripheral circulation . Detection of variable levels of sCD14 in the absence of microbial translocation is consistent with previous studies suggesting that sCD14 might be independent of LPS levels , at least in some patients [39] , [41] . Our results strengthen the idea that sCD14 and LPS should not be indistinctly used to evaluate microbial translocation in HIV-infected patients . Data from the present study clearly demonstrate that immune activation in acute HIV infection does not result from systemic microbial translocation . The lack of evidence of microbial translocation at the time of acute infection strongly suggests that early immune activation mainly results from viral replication . This hypothesis is further supported by the absence of MIP-1α , a chemokine highly expressed by LPS-stimulated monocytes not correlated with viral replication [42] , [43] . HIV-1 drives monocyte/macrophages towards an inflammatory phenotype following infection and/or through gp120/CD4 interaction [44] , [45] . Toll-like receptor ( TLR ) -independent activation may result in an increased responsiveness of macrophages to TLR ligands [46] . HIV-1 single stranded RNA is recognized by the TLR8 on monocyte/macrophages [47] . A recent study showed that monocyte TNF-α responses following TLR8 stimulation were higher in HIV-infected individuals compared to healthy donors . Interestingly , the percentage of TNF-α -producing monocytes following TLR8 stimulation strongly positively correlated with HIV-1 RNA levels both in acute and chronic HIV-1 infection [48] . We showed that the baseline level of CD8 T-cell activation strongly correlated with sCD14 and IL-1RA as well as with the HIV-RNA plasma levels [25] . Generalized CD8 T-cell activation may result from HIV-induced activation of monocytes/macrophages and from other innate immune responses including the strong cytokine storm detected during the peak of viral replication [49] . Moreover , HIV may directly activate T cells , as suggested by the observation that , in HIV-infected patients , a high expression of TLR7 on purified CD8 T cells was associated with the up-regulation of activation markers following TLR7 stimulation [50] . Besides , HIV-specific CD8 T cells may stand for a substantial part of activated CD8 T cells since major HIV-driven oligoclonal expansions of TCR Vβ subsets of CD8 T cells was reported during acute HIV infection [51] . Moreover , in line with a direct role for the virus on T-cell activation , we found that early ART initiation , at the time of acute infection , decreased CD8 T cell activation at levels similar to that of healthy donors . Both CD8 T-cell activation and soluble markers of monocyte activation were found to be negatively associated with the Th17 to Treg balance . In addition , the Th17/Treg ratio itself negatively correlated with viral load including HIV-DNA in PBMCs . As discussed above , Th17 cells are one of the preferential targets of the virus [26] , [31] , which may account for the negative relationship observed between HIV-RNA plasma levels and peripheral Th17 cell frequency . On the other hand , HIV induces Treg cell expansion , through direct and indirect mechanisms ( reviewed in [52] ) . In the context of primary HIV infection , interferons , and also HIV itself may drive the production of the enzyme indoleamine 2 , 3-dioxygenase ( IDO ) and tryptophan ( Trp ) catabolism by macrophages and dendritic cells [18] , [53] , [54] . IDO-mediated metabolism leads to induction of Tregs and inhibition of Th17 differentiation through the accumulation of Trp catabolites [18] . Altogether , this might result in decreased Th17/Treg ratio in patients with high viral replication . We can thus hypothesize that the loss of the Th17 to Treg balance is a consequence of viral replication and immune activation in acute HIV infection . In addition , the alteration of the Th17 to Treg balance could result from the high levels of circulating IL-1RA secreted by activated monocytes . This hypothesis is supported by the demonstration that IL-1RA reduces the differentiation of Tregs into Th17 cells both in vitro [55] and in vivo in a model of IL-1RA-deficient mice [56] and in humans [57] . The loss of Th17 cells could facilitate microbial translocation and subsequent generalized T-cell activation only in the chronic phase of infection [18] , [58] ( Figure 7 ) . We put forward the hypothesis that HIV replication might reduce the Th17/Treg ratio directly and through activation of innate immune cells , so that early Th17/Treg ratio and the level of monocyte/macrophage activation may reflect the intensity of host responses and impact the T-cell activation set point , known to predict disease progression [1] . The level of monocyte activation and the Th17/Treg ratio at baseline were found to predict the CD8 T-cell activation set point . T-cell activation was shown to significantly decrease between baseline and month 6 , the immunologic set point being reached between 3 and 6 months of follow-up . In contrast , the levels of monocyte activation and the Th17/Treg ratio did not significantly vary during the first 6 months . Also , HIV-RNA levels did not significantly decrease throughout the follow-up indicating that the viral set point was already reached at the time of inclusion in the study in most patients . Altogether , this suggests that monocyte/macrophage activation paralleled viral replication and that both had already decreased at study baseline while the establishment of the T-cell activation set point was delayed beyond 3 months . These different kinetics of viral replication , monocyte and CD8 T-cell activation probably explain why the level of CD8 T-cell activation at baseline did not predict its own set point at month 6 ( data not shown ) . One may postulate that an “innate immune set point” ( i . e . the steady state level of monocyte/macrophage activation ) precedes and predicts the T-cell activation set point itself predictive of the rate of subsequent CD4 T-cell decline [1] . Of note , IL-1RA and sCD14 levels only decreased in the few patients with the highest levels at baseline ( Figure S3 ) . In conclusion , the early Th17 to Treg balance as well as sCD14 and IL-1RA levels – that may be indicative of an “innate immune set point” – predict the CD8 T-cell activation set point . Altogether , data support the hypothesis that T-cell activation in acute infection is primarily driven by the HIV-induced innate immune responses and not by systemic microbial translocation , which occurs later in HIV disease . Soluble CD14 and IL-1RA can be easily measured and should be considered for use in clinical practice as early surrogate markers for disease progression . This needs to be confirmed in larger prospective cohorts of patients with primary HIV infection .
Twenty-seven individuals with acute HIV infection were enrolled in a prospective study ( co-inclusion in the CO6-PRIMO ANRS cohort ) conducted in four clinical sites in Paris , France . Acute HIV infection was defined by a negative or weakly positive ELISA , and at least one of the following criteria: less than three bands on HIV Western Blot , a positive p24 antigenaemia or detectable plasma HIV-RNA . The estimated date of infection was calculated as 2 weeks before onset of symptoms for patients with symptomatic PHI ( 26/27 ) or 4 weeks before the first positive Western Blot . At baseline ( day 0 of enrollment ) , all patients were treatment-naive . Some of the patients started combination antiretroviral treatment ( cART ) during the follow-up , based on clinical symptoms , CD4 cell counts ( e . g . below 500/mm3 according to French recommendations ) and the decision of both physicians and patients . Patients who were treated during the study were receiving a combination of nucleoside analogues , a boosted protease inhibitor and raltegravir with or without maraviroc . Written informed consent was provided by study participants according to French ethical laws . The ethical committee of Ile de France II , approved the study . Blood from patients was collected at baseline , day 15 , month 1 ( M1 ) , month 3 ( M3 ) and month 6 ( M6 ) . Plasma samples were also collected from healthy volunteers ( n = 17 ) . Peripheral blood was collected in EDTA-containing tubes . Fresh peripheral blood mononuclear cells ( PBMCs ) were purified by density gradient centrifugation ( Isopaque-Ficoll ) within 2–4 hours after blood sampling . Freshly isolated cells were used the same day and plasma samples were frozen for subsequent use . After washings , cells were stained using multicolor panels and analyzed by flow-cytometry ( LSRII cytometer driven by the FACSDiva software , Becton Dickinson ) as described previously [25] . The following monoclonal antibodies ( mAbs ) conjugated to PE Texas Red ( ECD ) , peridinin chlorophyll protein–cyanin 5 . 5 ( PerCP–Cy5 . 5 ) , Alexa Fluor 488 ( AF488 ) , Alexa Fluor 647 ( AF647 ) , Alexa Fluor 700 ( AF700 ) , allophycocyanin ( APC ) , allophycocyanin–Hilite7 ( APC–H7 ) , phycoerythrin–cyanin 7 ( PE–Cy7 ) , phycoerythrin–cyanin 5 ( PE–Cy5 ) , fluorescein isothiocyanate ( FITC ) , and phycoerythrin ( PE ) and eFluor 450 ( eF450 ) were used at predetermined optimal concentrations: anti–CD3–ECD ( Beckman Coulter ) ; anti–CD4–PerCP–Cy5 . 5 , anti–CD4–APC–H7 , anti–CD8–AF488 , anti–CD25–APC , anti–HLA-DR–PerCP–Cy5 . 5 , anti–CD38–APC , anti–CCR4–PE–Cy7 , anti–CCR6–PE , anti–CXCR3– PE–Cy5 , anti–IL-17–AF647 , anti–IL-2–FITC and anti–IFN-γ–AF700 ( BD Biosciences ) ; anti–CD127–PE–Cy7 , anti–IL-10–eF450 , anti–FoxP3–APC and anti–FoxP3–AF700 ( eBiosciences ) ; anti–TGF-β–PE ( IQ Products ) and anti–Ki-67–FITC ( Dako ) . FcR Blocking Reagent ( Miltenyi Biotec ) was used to block unwanted binding of antibodies and increase the staining specificity of cell surface antigens . For intracellular staining of FoxP3 , Ki-67 , IL-10 , TGF-β , IL-2 , IFN-γ or IL-17 , cells were fixed and permeabilized using the “FoxP3 Staining Buffer Set” ( eBioscience ) according to the manufacturer's recommendations . Analyses were performed using FlowJo software ( TreeStar ) . CD4+ T-cell enrichment was performed prior to Ficoll-Hypaque density gradient centrifugation by incubating the blood with RosetteSep human CD4 T-cell enrichment antibody coktail ( Stem Cell Technologies ) according to the manufacturer's instructions . Subsequent enrichment of CD25+ cells was performed using EasySep human CD25 positive selection cocktail and the cell separator RoboSep ( Stem Cell Technologies ) . Fresh CD4+ T cells and the CD25-enriched fraction were stimulated with PMA ( 5 ng . mL−1 ) and ionomycin ( 1 µg . mL−1 ) at 37°C for 5 hours . After 2 hours of culture , brefeldin A ( 5 µg/mL ) ( Sigma-Aldrich ) was added . Intracellular cytokine staining was performed as described above . Commercially available enzyme-linked immunosorbent assay ( ELISA ) kits were used according to the manufacturers' recommendations for measuring concentrations of intestinal fatty acid binding protein ( I-FABP ) ( Hycult Biotech ) , soluble sCD14 ( sCD14 ) ( R&D Systems ) , IL-1 receptor antagonist ( IL-1RA ) ( eBioscience ) , Endotoxin Core IgM Antibody ( EndoCAb ) ( Hycult Biotech ) , MIP-1α ( Tebu-bio ) in plasma samples . For the measurement of sCD14 and EndoCAb , plasma samples were diluted 1∶1000 and 1∶100 ( v/v ) in the provided assay diluents , respectively . Results were analyzed using a five parameter-logistic ( 5PL ) function for fitting standard curves obtained from recombinant protein standards . The Silkworm Larvae Plasma ( SLP ) reagent set ( Wako Pure Chemical Industries ) was used to quantify peptidoglycans ( PGN ) in plasma samples [59] . Plasma were diluted at a 1∶10 ratio in sterile water and heated for 10 minutes at 80°C . Samples and an equal volume of reconstituted SLP reagent were mixed in a 96-well plate . The OD650 was measured after 1 hr incubation at 30°C . The amount of PGN was calculated using a standard curve obtained with digested PGN from S . aureus ( Wako Pure Chemical Industries ) serially diluted and heated in plasma from healthy donors that were previously tested as PGN free . DNA was extracted from 200 µL of plasma using the DNeasy Blood and Tissue Kit ( Qiagen ) , according to the manufacturer's instructions . A NanoDrop 2000 spectrophotometer ( Thermo Scientific ) was used to determine DNA concentrations . Bacterial 16S rDNA levels were measured by quantitative polymerase chain reaction ( PCR ) . A 20 µL amplification reaction consisted of 2 µL of 10× PCR buffer ( 100 mmol/L Tris-HCl , pH 8 . 3; and 500 mmol/L KCl [Invitrogen] ) , 3 . 5 mmol/L MgCl2 , 0 . 2 mmol/L deoxynucleoside triphosphate , 0 . 5 µmol/L forward and reverse primers , 0 . 75 U of Taq polymerase ( Invitrogen ) , and 5 µL of the template plasma DNA . Degenerate forward ( 8F: 5′-AGAGTTTGATYMTGGCTCAG ) and reverse ( 361R: 5′-CGYCCATTGBGBAADATTCC ) primers were used to amplify DNA templates encoding 16S rRNA . The DNA was amplified in triplicate , and mean values were calculated . A standard curve was created from serial dilutions of plasmid DNA containing known copy numbers of the template . The reaction conditions for amplification of DNA were 94°C for 5 min , followed by 45 cycles at 94°C for 10 s , 54°C for 45 s and at 72°C for 60 s . The assays were performed using a LightCycler 480 ( Roche ) . The experiment was performed twice and positive samples were tested a third time . Plasma HIV-RNA levels were determined on site , using the locally available technique with a detection limit of 20 copies/mL . The HIV DNA level in PBMCs was quantified in whole blood using the “Agence Nationale de Recherches sur le Sida et les Hépatites Virales” ( ANRS ) real-time PCR method ( Biocentric , Bandol , France ) , as previously described [60] . Results were expressed as the log10 number of HIV-1 DNA copies per 106 PBMCs ( threshold: 60 copies/106 PBMCs ) . Data were described by medians and interquartile ranges ( IQR ) for continuous variables . All patients at baseline and only untreated patients at M6 were considered for the analyses . Non parametric tests were used to avoid the impact of potential outlier values in a small study . Comparisons between groups were performed using the Mann-Whitney test . The Wilcoxon matched-pairs test was used to estimate the changes in the different variables throughout the follow-up . The Spearman's non parametric correlation was used to estimate the association of two continuous variables of interest . P-values below 0 . 05 were considered statistically significant . The UniProtKB ( http://www . uniprot . org/ ) accession numbers for the proteins discussed in this paper are IL-17 ( Q16552 , IL17_HUMAN ) ; IL-1RA ( P18510 , IL1RA_HUMAN ) ; Soluble form of CD14 ( P08571 , CD14_HUMAN ) ; MIP-1α ( P10147 , CCL3_HUMAN ) ; I-FABP ( P12104 , FABPI_HUMAN ) ; CCR4 ( P51679 , CCR4_HUMAN ) ; CCR6 ( P51684 , CCR6_HUMAN ) ; CXCR3 ( P49682 , CXCR3_HUMAN ) ; Ki-67 ( P46013 , KI67_HUMAN ) ; CD38 ( P28907 , CD38_HUMAN ) ; CD25 ( P01589 , IL2RA_HUMAN ) ; CD127 ( P16871 , IL7RA_HUMAN ) ; FoxP3 ( Q9BZS1 , FOXP3_HUMAN ) . | Generalized immune activation is pivotal in the pathogenesis of HIV disease . Impairment in the gut mucosal barrier allows the translocation of microbial flora from the gut towards the circulation . Translocated microbial products , together with HIV replication , contribute to chronic immune activation . Th17 cells are involved in epithelial barrier integrity and a loss of the balance between Th17 and regulatory T cells ( Tregs ) has been associated with disease progression . Early events occurring following infection are crucial for the subsequent disease progression . Thus , a high immune activation set point ( level of T-cell activation established at the end of acute infection ) is a marker of poor prognosis . Whether microbial translocation contributes to the immune activation set point remains an outstanding question . In our longitudinal prospective study of patients with acute infection , we investigated the early relationships between the Th17/Treg balance , monocyte activation and microbial translocation and their impact on the T-cell activation set point . We demonstrated that systemic microbial translocation does not occur at the time of acute infection . Moreover , we identified IL-1RA as a novel plasma biomarker predictive of the immune activation set point . This biomarker could be considered for use in clinical practice as a surrogate marker for disease progression . | [
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| 2013 | The Th17/Treg Ratio, IL-1RA and sCD14 Levels in Primary HIV Infection Predict the T-cell Activation Set Point in the Absence of Systemic Microbial Translocation |
The WHO has established the disability-adjusted life year ( DALY ) as a metric for measuring the burden of human disease and injury globally . However , most DALY estimates have been calculated as national totals . We mapped spatial variation in the burden of human African trypanosomiasis ( HAT ) in Uganda for the years 2000–2009 . This represents the first geographically delimited estimation of HAT disease burden at the sub-country scale . Disability-adjusted life-year ( DALY ) totals for HAT were estimated based on modelled age and mortality distributions , mapped using Geographic Information Systems ( GIS ) software , and summarised by parish and district . While the national total burden of HAT is low relative to other conditions , high-impact districts in Uganda had DALY rates comparable to the national burden rates for major infectious diseases . The calculated average national DALY rate for 2000–2009 was 486 . 3 DALYs/100 000 persons/year , whereas three districts afflicted by rhodesiense HAT in southeastern Uganda had burden rates above 5000 DALYs/100 000 persons/year , comparable to national GBD 2004 average burden rates for malaria and HIV/AIDS . These results provide updated and improved estimates of HAT burden across Uganda , taking into account sensitivity to under-reporting . Our results highlight the critical importance of spatial scale in disease burden analyses . National aggregations of disease burden have resulted in an implied bias against highly focal diseases for which geographically targeted interventions may be feasible and cost-effective . This has significant implications for the use of DALY estimates to prioritize disease interventions and inform cost-benefit analyses .
Developed by the World Health Organization ( WHO ) in the early 1990s as part of the creation of the Global Burden of Disease Study ( GBD ) , the Disability Adjusted Life Year ( DALY ) is a standardized metric that has been utilized widely in the two decades since to estimate the global and regional impact of diseases , set health care priorities , and assess the cost-effectiveness of targeted interventions . The publication of the first GBD study established the DALY as a standard metric for evaluating health outcomes and facilitating comparison between regions , diseases , and types of burden [1] , [2] . Comprehensive global burden assessments have been published regularly , providing a temporal lens on changing global health burden; the initial report in 1990 was followed by periodic publications throughout the past two decades , with the 2010 GBD study most recently published in late 2012 , and involving significant methodological changes compared to previous iterations . The DALY as a global health measure has not been without controversy , however . Of particular critique have been burden assessments for neglected tropical diseases ( NTDs ) , predominantly in sub-Saharan Africa ( SSA ) . In 2010 , GBD estimates placed the total burden of NTDs at about 108 . 7 million DALYs or 4 . 4% of the total global burden of disease and injury [3] . This represents a significant increase from the GBD estimate in 2002 of 20 million DALYs or 1 . 3% of the total global burden of disease and injury [4] . Other estimates have found that overall , NTDs account for up to 56 . 6 million DALYs globally , and 8 . 6–21 . 2 million DALYs in SSA [5] . These estimates are subject to high uncertainty , however , given a lack of adequate disease data for most NTDs in SSA [6] . The socio-economic impact of NTDs , encompassing various parasitic and bacterial infections affecting largely poor populations in tropical regions , has been poorly accounted for by DALY calculations [7] , [8] . Despite the value of GBD metrics , therefore , the utility of DALY calculations for NTDs remains problematic . This is particularly true for zoonotic NTDs which impose a dual burden through human illness as well as economic losses to livestock industries [9] . These limits in assessing human disease burden for NTDs have been acknowledged with the publication of the first WHO report on NTDs [10] . Human African trypanosomiasis ( HAT ) , or sleeping sickness , is a parasitic , zoonotic , vector-borne infection endemic to SSA . HAT causes non-specific febrile illness in the early stage , with progression to neuropsychiatric disturbances in the late stage , resulting in coma and death if untreated [11] . HAT primarily affects remote rural communities in SSA . The acute , rhodesiense form of the parasite is confined to eastern SSA , while the chronic , gambiense form occurs in western and central SSA [12] . HAT has a severe impact on affected communities as it causes not only social and economic losses due to human illness , but also economic losses from livestock morbidity and mortality caused by the zoonotic form of the infection [13] . Omission of these indirect impacts also have implications for prioritization of funding and cost-benefit estimates for intervention programs . In addition , control measures – for rhodesiense HAT in particular – need to account for the dual impact of human and livestock trypanosomosis prevalence [14] . The global incidence of HAT has been estimated at 17 500 cases annually , and 50 000–70 000 cases from 2000–2005 [15] . Current GBD estimates show a significant decrease in recent years , from 1 . 82 million DALYs attributed to HAT globally in 2000 , to 829 000 DALYs in 2005 and 560 000 DALYs in 2010 [3] . However , as a zoonotic NTD with highly focal distributions across the continent , HAT has been difficult to adequately estimate within the GBD framework . Furthermore , Simarro et al . [16] highlighted the risks of reduced HAT elimination efforts as a result of lowered national burden estimates , arguing that burden-driven decreases in funding for HAT surveillance and control could lead to disease re-emergence . Focal or epidemic-prone diseases present a particular challenge for burden calculation . Uncertainty exists in estimating the populations at risk , and the use of large geographic units for assessment poorly reflects spatial heterogeneity of incidence [4] , [5] . HAT is a focally concentrated disease , occurring in endemic foci within tsetse-infested zones of SSA , and gambiense HAT in particular shows significant spatial variation at the micro level [17] . Disease mapping and spatial analysis has thus long been a crucial component of HAT surveillance , prevention , and treatment [12] . Due to its focal nature , HAT imposes a particularly high burden on affected communities , yet ranks poorly in national or regional burden assessments [18] . The spatial scale for burden assessments is thus critical to comparative estimates of burden and assessment of intervention [19] . HAT incidence varies significantly in time as well as space . In Uganda , HAT incidence decreased over the 2000–2009 study period , from 948 cases of gambiense HAT in 2000 to 99 new cases in 2009 . Rhodesiense HAT showed a similar decrease , from 300 new cases in 2000 , peaking at 473 in 2005 , to 129 cases in 2009 . The specific incidence of these cases in areas of Uganda has shifted over time , for instance , with the movement of rhodesiense foci north into central Uganda , approaching the gambiense-endemic area [12] . Temporal variation in HAT must thus also be considered when examining the spatial distribution of the disease . With some 60–95% of HAT cases unreported [20]–[22] , and mortality 100% for untreated patients [23] , HAT burden – like many neglected tropical diseases – is inadequately represented within the country-based GBD framework . The GBD study assessments are based on nationally aggregated estimates . Efforts to estimate focal HAT burden have focused on the community level [24]–[26] . At the regional level , Odiit et al . [27] assessed the burden of rhodesiense HAT across Africa and in southeastern Uganda . While such studies provide valuable estimates of local burden in highly affected regions , no studies have yet estimated sub-national burden for an entire country . National level estimates are critical to inform broader policy-making and prioritization of interventions . Here we present the first study to assess spatially-disaggregated HAT burden at the national level . We focus on Uganda , the only country where both rhodesiense and gambiense HAT are endemic , and where concerns of parasite species convergence have raised international concern and emergency intervention [28]–[30] . The temporal variation in HAT incidence in Uganda shows an overall decrease from 2000–2009 . 948 new cases of gambiense HAT were reported in 2000 compared to 99 in 2009; similarly , 300 cases of rhodesiense HAT were reported in 2000 , peaking at 473 in 2005 , and decreasing to 129 in 2009 . Notwithstanding this decreasing trend , due to the significant spatial variation in incidence , specific areas of Uganda have seen HAT foci reduction , emergence , or resurgence over this time period [12] . The objectives of this study were to estimate the burden in DALYs of rhodesiense and gambiense HAT in Uganda for 2000–2009 on the sub-national scale , accounting for both reported and unreported cases , and to map the results by parish and by district to compare the spatial distribution of burden . Figure 1 shows the study area and districts affected by HAT incidence during the study period .
The disability-adjusted life year ( DALY ) is a summary health measure which incorporates both mortality and morbidity , the former in terms of years of life lost ( YLLs ) and the latter in years of life lived with disability ( YLDs ) . Premature mortality is evaluated based on the age-distribution of mortality attributable to each condition , using standard model life expectancies . For YLDs , a disability weighting between 0 and 1 , where 0 is equivalent to perfect health and 1 is equivalent to premature death , is assigned to a condition to quantify time lived with a disability based on its severity . When multiplied by incidence and duration of a particular disease or condition , this makes debilitation as a result of disease or injury comparable with mortality estimates . Thus , YLLs and YLDs are summed to estimate total DALYs . Burden is generally discounted to represent societal time preference and age-weighted to account for different social roles at different ages . Finally , DALYs are aggregated at the national level and presented as summed estimates for each disease or condition present in a particular country . [31] Prior to the GBD 2010 study , GBD DALY estimates used standard West Level 26 life tables with a female life expectancy at birth of 82 . 5 years , a time discount rate of 3% and an age-weighting function with parameters C = 0 . 1658 and β = 0 . 04 [31] . The recently published GBD 2010 study uses a new standard life table with a life expectancy at birth of 86 . 0 years , accounts for comorbidity in calculating YLDs , and does not discount DALYs over time or employ age weighting [32] . The revised GBD 2010 calculations estimated a total burden of 168 651 DALYs for HAT in Uganda in 2000 , 97 041 DALYs in 2005 , and 29 079 DALYs in 2010 [3] . Here , we use the pre-2010 GBD parameters and estimates as the GBD 2010 methods and results were not fully published at the time of this study . The 2004 GBD study estimated a total HAT incidence in SSA of 60 300 , combining separate estimates of rhodesiense and gambiense HAT incidence in which “completeness of reporting of cases was assumed to be around 33% for T . b . gambiense and 5% for T . b . rhodesiense” [22] . Published DALY calculations for HAT burden use a disability weighting of 0 . 191 for both rhodesiense and gambiense HAT , and do not distinguish between stages of disease [19] . Furthermore , the GBD 2004 study assumed durations of 5 years for gambiense HAT and 1 year for rhodesiense HAT . Lastly , the GBD 2004 assumed 85% of cases were untreated with 100% case fatality , and 15% treated with 5% case fatality . GBD calculations based on these parameters resulted in a total estimated burden of 59 423 DALYs for HAT in Uganda in 2004 [22] . This is notably less than the GBD 2010 estimate of 97 041 DALYs for 2005 , thus , estimates using the pre-2010 parameters may be considered conservative . Burden was calculated using the standard DALY formula as published in the Global Burden of Disease framework , with several important modifications ( Table 1 ) . We estimated burden using the DALY formula for an individual:DALYs were calculated using the standard discount rate of 3% ( r = 0 . 03 ) , with and without age-weighting ( C = 0 . 1658 and β = 0 . 04 ) . Since population-specific life tables are appropriate for regional burden studies , Uganda-specific life tables were used to provide a regionally specific estimate of burden due to premature mortality [19] , [33] . We acknowledge that this would bias our results towards lower burden estimates relative to GBD DALY totals , which use standard model life expectancies . However , given that Uganda life table values were significantly below model expectancies , their use ensured locally representative and conservative burden estimates . We additionally used HAT-specific parameters to provide for more accurate burden estimation . See Table 1 for a full summary of parameters used in DALY calculations . As proposed by Fèvre et al . [19] , these included parasite- and stage-specific disability weightings , durations , and mortality rates , in recognition that the rhodesiense and gambiense forms of HAT , as well as the early and late stages , have significantly different clinical manifestations and rates of disease progression [11] . In order to allow spatially disaggregated burden estimates , DALY burden was calculated for individual cases . The GBD study calculates burden based on national incidence totals; DALY estimation for HAT on a spatially disaggregated sub-national scale has not previously been calculated . Data for 2846 cases of rhodesiense HAT and 4026 cases of gambiense HAT in Uganda were obtained from the WHO Atlas of Human African Trypanosomiasis [12] . The Atlas has been used to estimate the population at risk for HAT in SSA [34] , [35] , and provides standardized and collated data for spatially and temporally explicit HAT cases across the continent . Atlas data was collected on historic case incidence for the years 2000–2009 . We note that the incidence of new cases of both gambiense and rhodesiense HAT declined over this time period [12] . Thus , our burden estimates reflect historic values and are not meant to portray the current situation of HAT in Uganda . DALY calculations were run 100 times , with the modelled case age and mortality randomly assigned in each iteration . Age data were not available , so age was randomly assigned based on an existing age distribution of HAT cases drawn from a sample of cases from southeastern Uganda [36] . Distribution fitting was conducted using STATA ( StatCorp version 11 ) . The stage-specific age distributions were modelled with a negative binomial function , and age was randomly assigned in the DALY model ( see Figures S1 and S2 ) . Uganda-specific life tables were obtained from the WHO [37] . Life expectancy values for both sexes for 2000 and 2009 were averaged and considered representative of the 10-year time period ( see Table S1 ) . This assumed minimal bias since life expectancy in Uganda increased fairly steadily over the study period [37] . Life expectancy also incorporated the sex ratio in rhodesiense HAT case incidence where 51 . 6% of cases were male [36] . Stage categorisation was available for the majority of reported HAT cases ( see Table 2 for a breakdown ) . Cases with no stage data available were considered as early-stage in the DALY calculations , so as to conservatively estimate burden . The haemolympathic early stage of HAT manifests in intermittent fever , headache , swollen lymph nodes , pruritus , skin lesions , and edema , among other nonspecific symptoms . Late-stage or meningoencephalitic HAT is characterised by invasion of the central nervous system , with symptoms including disruptions to the sleep cycle , neuropsychiatric , and endocrinal disorders [11] , [38] . Overall , clinical symptoms are similar for rhodesiense and gambiense HAT , though the duration and progress of symptoms differ markedly; rhodesiense progresses from early to late stage and death typically within weeks to months , while gambiense HAT may progress over many months to years , with a more chronic course of infection [11] , [39] , [40] . Disability quantification is thus stage-specific , with late-stage illness implying substantively higher disability than early-stage illness . YLDs were calculated for all cases , assuming that all fatal cases passed through a period of early- and late-stage disability before death . Disability weightings of 0 . 21 for early-stage illness and 0 . 81 for late-stage illness were used for rhodesiense HAT as per Fèvre et al . [25] . Stage-specific disability weightings have not been estimated for gambiense HAT . Weightings of 0 . 191 for early-stage illness and 0 . 81 for late-stage illness were thus used , based on estimates in the GBD study [22] and published estimates for rhodesiense HAT , respectively . These estimates reflect broadly similar clinical features for the two parasites despite different rates of progression , although some differences exist , with gambiense HAT generally being clinically milder and more variable [11] . The average duration of illness was estimated as 6 months for rhodesiense HAT , with 3 months of early stage and 3 months of late stage illness . This was based on various published estimates: 21 days for early-stage illness and 61 days for late-stage illness pre-admission [41] , a median survival time of 4 months in the early 1900s epidemic in Uganda [42] , 6 months average total duration [39] and 2–3 months late-stage duration in patients from Tanzania and Uganda [43] . The average duration for gambiense HAT was estimated to be 2 years for early-stage illness and 1 year for late-stage illness , based on estimates of 36 months early-stage and 12 months late-stage by Lutumba et al . [24] and 526 days early-stage and 500 days late-stage by Checchi et al . [40] . Burden calculation was done regardless of the point of diagnosis , in order to account retroactively for HAT impact prior to treatment . Cases were assumed to present at the beginning of early- or late-stage illness . Thus , YLD calculations included pre-diagnosis morbidity . Age-specific mortality was modelled using a quadratic function , with average mortality adjusted based on parasite type and disease stage ( see Figure S3 ) . Average mortality was estimated at 2 . 4% ( early stage ) and 8 . 1% ( late stage ) for rhodesiense HAT [36] . This is consistent with estimated case fatality rates of 8 . 4–9 . 3% for melarsoprol-treated late-stage rhodesiense HAT in Tanzania and Uganda [43] . Treatment-based case-fatality ratios ( CFRs ) have been estimated for late-stage gambiense HAT cases in the late 1990s and early 2000s [44] . Based on this , an average mortality rate of 3% was used for late- stage gambiense HAT . Most cases of gambiense HAT in northwestern Uganda prior to the mid-2000s were treated with melarsoprol , with a mortality rate of 5–5 . 9% [44] , [45] . However , Uganda changed its first-line treatment from melarsoprol to eflornithine ( 1 . 2% mortality ) treatment in 2002 [44] , [46] . Furthermore , the newer nifurtimox-eflornithine combination therapy was tested in various sites in Uganda during the study period [47] , and shows promise as a lower-mortality treatment regimen for late-stage gambiense HAT [16] , [48] . Thus , a reduced late-stage mortality rate of 3% was used as a conservative estimate . Negligible published data on early-stage mortality for gambiense HAT exist; a figure of 1% was used as an estimate since the mortality for early-stage gambiense illness is known to be low , as treatment-related complications due to pentamidine administration rarely occur [11] , [38] . These stage- and parasite- adjusted mortality distributions were used to assign the age-specific probability of death in each iteration of the DALY model . YLLs were calculated only for those cases that were randomly assigned as deaths , based on the age-specific mortality probability . We use the term ‘under-reporting’ herein to refer to the proportion of the estimated total number of cases which are not detected by active or passive screening . This lack of detection of HAT cases is known to be significant , yet remains poorly estimated . For rhodesiense HAT , a model of under-reporting based on the early to late stage ratio of presenting cases has estimated that approximately 40% of cases go unreported – and die – in Uganda [21] , [49] . For gambiense HAT , the level of under-reporting has not been directly estimated . Robays et al . [20] estimated the effectiveness of active case-finding and treatment in the DRC at less than 50% , and Lutumba et al . [24] subsequently used this work to estimate that 40% of gambiense HAT cases in a rural community of the DRC went undetected following one round of active screening . Burden estimates are limited by the need to properly quantify HAT under-reporting [19] , particularly as disease surveillance is lacking in many HAT-endemic areas [50] , [51] . Thus , while an under-reporting level of 40% was used when DALYs were estimated and mapped by parish , we conducted a sensitivity analysis , varying the reporting rate to account for the significant uncertainty regarding levels of HAT under-reporting . In the sensitivity analysis , the rate of under-reporting was varied in order to determine the effect on the magnitude and distribution of burden . For each reported case , the number of cases unreported was estimated based on various proportions of unreported cases: 0% , 20% , 40% for both parasites , as well as 67% ( gambiense ) and 95% ( rhodesiense ) based on the 2004 GBD study [22] . As described above , 40% was taken as a conservative estimate of the actual under-reporting rate when summing and mapping DALY totals by parish . All unreported rhodesiense HAT cases were assumed to be untreated and 100% fatal . Unreported gambiense HAT cases were assumed to be 50% fatal . The average duration of a gambiense HAT case is 3 years [40] , and cases of gambiense HAT which remain undetected following one active screening round could in practice be detected in subsequent active screening rounds or through passive detection [24] . Because of the long duration of illness and possibility of subsequent detection , the 50% mortality estimate for unreported gambiense HAT conservatively accounts for the likelihood that a significant proportion of undetected cases may not in fact lead to fatality . Thus , early- and late-stage YLDs were calculated for all unreported cases , and YLLs were calculated for all unreported rhodesiense and 50% of unreported gambiense cases . We assumed that all fatal unreported cases passed through the full disease duration before death . Case age for unreported cases was assigned independently of the reported case ages , using the late-stage age distributions and methods described above . Geographic location in latitude and longitude coordinates was available in the WHO dataset , with the exclusion of 78 rhodesiense cases and 23 gambiense cases which were missing latitude/longitude values . Furthermore , 8 rhodesiense HAT cases were identified as exported , as they represented patients who had migrated outside of HAT transmission zones . These were excluded to restrict the analysis to areas of active HAT transmission . These exclusions resulted in a total of 2760 rhodesiense and 4003 gambiense HAT cases which were mapped and analysed in ArcGIS ( ESRI version 10 ) . DALY totals for 2000–2009 were mapped and summarised by parish , the smallest administrative unit in Uganda . District population data were obtained from the 2002 Uganda census and projected to 2009 in the UN COD-FOD Registry [52] . Population values were averaged for the period 2002–2007 to give estimates representative of the study period . DALY totals were averaged over 2000–2009 , and divided by district population data to give values in terms of DALYs per 100 000 persons per year . This enabled comparison of the relative burden across districts with the national burden rates for HAT and other infectious diseases in Uganda , based on the DALY rates published in the 2004 GBD study [22] .
Table 2 summarises the total burden attributable to reported HAT cases in Uganda , summed over the period 2000–2009 . Results estimated a total of 4159 reported DALYs for rhodesiense HAT in southeastern Uganda from 2000–2009 ( an average of 1 . 46 DALYs per case ) . The majority ( 88% ) of the burden resulting from reported cases was due to years of life lost , with 12% of the burden due to years of life lived with disability . The total burden for gambiense HAT in northwestern Uganda was estimated at 5786 reported DALYs ( an average of 1 . 44 DALYs per case ) . In contrast to rhodesiense , the majority ( 67% ) of the burden of gambiense HAT resulted from years of life lived with disability . This is attributable to the longer duration and lower mortality rates of chronic gambiense HAT , whereas the acute , high-mortality rhodesiense form tends to progress more quickly and results in a greater burden due to mortality . As shown in Table 3 , model results are highly sensitive to reporting rate , particularly for rhodesiense HAT . When under-reporting was accounted for , unreported DALYs formed the bulk of the total burden . Total burden for unreported cases was high for both forms of HAT , with overall burden being higher for gambiense HAT . DALY totals for rhodesiense HAT for 2000–2009 ranged from less than 4200 DALYs with 0% of cases unreported , to approximately 54 000 DALYs with 40% of cases unreported and well over 1 million DALYs with 95% of cases unreported . For gambiense HAT , total DALYs ranged from less than 5800 DALYs with 0% under-reporting , to approximately 44 000 DALYs with 40% under-reporting and 123 000 DALYs with 67% under-reporting . The average annual DALY total when 40% of cases were assumed as unreported was 9 814 DALYs , resulting from an estimated average of 1 145 cases per year . This is significantly lower – approximately one-quarter – when compared to the existing GBD estimate of 59 423 DALYs . When GBD estimates of under-reporting were used , however , the annual average burden was more than double the GBD 2004 estimate at 136 287 DALYs , resulting from an estimated total incidence of 6 912 cases per year . Thus , the bulk of this burden resulted from unreported mortality of rhodesiense HAT . Spatial variation in burden was significant for both forms of HAT , with cases highly focally concentrated in a small number of parishes . This is shown by the parish-level burden map in Figure 2 . In the rhodesiense-endemic areas of central and southeastern Uganda , 413 parishes in total were affected by rhodesiense HAT . 62% of parishes had fewer than 5 reported DALYs , while 5 parishes ( 1 . 2% ) were afflicted by over 100 DALYs , and these 5 parishes accounted for 21 . 3% of the burden of reported cases . In northwestern Uganda , 189 parishes were affected by gambiense HAT . 25% of parishes had less than 5 reported DALYs , while 12 parishes ( 6 . 3% ) were afflicted by over 100 reported DALYs , accounting for 39 . 4% of the burden of reported cases . Similarly , when total burden was calculated with 40% under-reporting , 2 parishes in southeastern Uganda , Alwa and Kateta , accounted for over 2000 DALYs each , forming over 9% of the total burden . In northwestern Uganda , 2 parishes accounted for over 2000 DALYs each . A large portion of the burden of gambiense HAT was concentrated in a few severely afflicted parishes , whereas the burden of rhodesiense HAT was more geographically widespread across a greater number of parishes . Temporal variation in burden over the 2000–2009 period also showed an overall decreasing trend , reflective of the decrease in overall HAT incidence . These temporal trends varied by parish and district , as shown in Figure 3 and Table 4 . The most highly affected areas varied over time . For instance , in the district of Kaberamaido in central Uganda , foci of HAT burden did not occur until 2003 , showed a peak in the middle of the time period , and burden was decreasing as of the late 2000s . On the other hand , districts such as Iganga and Bugiri in southeastern Uganda showed a marked decrease in HAT burden over the same ten-year time period . Figure 4 shows that while 8 out of 25 affected districts ( 32% ) had a lower rate of HAT burden than the national GBD estimate of 212 DALYs/100 000 persons/year , 6 districts had a burden rate comparable to the national burden rate for tuberculosis , 3 districts had a burden rate comparable to malaria , and one district had a burden rate larger than the national average for HIV/AIDS . We emphasise that the annual average 2000–2009 burden values shown in Figure 3 reflect past incidence . Given the declining trend in both forms of HAT incidence in Uganda , current annual burden rates are likely to be lower than shown . These results must be further qualified , by the fact that district burden rates for tuberculosis , malaria , and HIV/AIDS , which have not been estimated , would likely exceed national averages in highly affected areas , some of which could overlap with HAT-endemic regions . The most highly afflicted districts , Kaberamaido and Soroti , were located in the northern area of the rhodesiense HAT transmission zone in central Uganda . Kaberamaido had a lower annual average DALY total ( 21 381 ) than Soroti ( 28 475 ) and Iganga ( 28 404 ) , but also represented a smaller district population . Dokolo and Iganga , also rhodesiense HAT-endemic districts in the southeast , had the 3rd and 4th highest DALY rates , respectively . Moyo was the gambiense HAT-endemic district with the highest DALY rate and 5th overall , with an annual average total of 5 048 DALYs and a DALY rate of 2 144 DALYs/100 000 persons/year . Notably , district DALY rates do not account for within-district spatial variation in burden . As discussed above and demonstrated in Figures 2 and 3 , burden varies significantly across parishes . Thus , parish-level per capita burden would be higher than district DALY rates in the most highly affected parishes .
HAT is not only spatially but also temporally focal , occurring in epidemics of high transmission and incidence [11] . We have included estimates of parish- and district-level burden sub-divided into total annual burden for periods of two to three years , but note that other results presented herein are estimated totals and average yearly values for 2000–2009 , which do not reflect the overall change in HAT incidence over time or the variation in incidence from year to year . In order to reflect the current burden situation in the affected districts of Uganda , updated spatial data on HAT incidence would be necessary . In addition , temporal variation in HAT would imply burden rates higher than those presented here since disease burden will be concentrated during epidemic years . Both spatial and temporal variation in HAT burden show the concentration of disease impact in those communities bearing the brunt of the burden of focal HAT epidemics . Accordingly , the potential benefits of targeted interventions in these areas will also be greater . . We have used case incidence data to map the burden of HAT in Uganda at a spatially specific sub-national scale . However , data on spatial variation in other model parameters , such as the age of incidence , mortality , or under-reporting rates ( discussed below ) , were not available . Hence , the spatial variation in burden identified reflects that of incidence . Nonetheless , this method to derive HAT burden values from incidence allows DALYs to be compared across parishes or districts , or with other conditions at varying spatial scales . Burden units enable this comparison , which would not be possible from examining disease incidence alone . Under-reporting of fatal cases has been noted previously as an important contributor to the DALY burden of HAT , but has not been quantified adequately in burden calculations [19] . When estimates of the proportion of unreported cases were included , there was a greater than 4-fold increase in burden with 20% under-reporting , and a nearly 10-fold increase in burden with 40% under-reporting . These increases were greater for rhodesiense HAT due to 100% mortality of unreported cases , with a greater than 5-fold and nearly 13-fold increase in burden with 20% and 40% under-reporting , respectively . Gambiense HAT showed more modest increases of 3 . 5- and 7 . 7-fold when 20% and 40% under-reporting was accounted for . Notably , these results are likely conservative given that Uganda's national sleeping sickness program shifted in 2005 from active to passive surveillance , resulting in reduced reporting [36] . Thus , average under-reporting rates for the study period of 2000–2009 may have been even higher than estimated . Under-reporting estimates have only been validated for rhodesiense HAT in southeastern Uganda , however , and may not be applicable for gambiense HAT in the northwest . Because of the milder and more varied clinical course of the natural progression of gambiense HAT , cases are typically detected through active case-finding . Though estimates of gambiense HAT under-reporting are lacking , surveillance activities have been shown to have poor sensitivity . Robays et al . [20] used a model to show that active case-finding and treatment failed to detect a significant portion of gambiense HAT cases , and Lutumba et al . [53] found that gambiense HAT screening failed to avoid more than 60% of deaths in the DRC . Broad under-reporting estimates also inadequately account for spatial variation in under-reporting related to the presence of HAT treatment centres . As discussed above , data on spatial variance for input parameters such as the under-reporting rate would strengthen the findings of this study . The proportion of early-stage patients detected was shown to be inversely related to distance from health unit by Odiit et al . [54] . However , it is difficult to define the causation between reported case incidence and the presence of treatment centres , as facilities are often deployed in response to sleeping sickness outbreaks . Efforts to quantify DALY burden due to HAT in Uganda remain constrained by a lack of robust local estimates of HAT under-reporting . The importance of improved and sustained disease surveillance efforts , notwithstanding recent reductions in reported HAT incidence , has been widely emphasized in order to promote control and elimination of HAT in endemic areas [16] , [30] , [55] , [56] . This study demonstrates their further importance in order to better account for the under-reported burden of HAT . Broad duration estimates were used for each stage and form of the disease . This could overestimate duration for patients diagnosed and treated early in the disease progression . However , significant delays ( median 7 months ) in patient presentation and diagnosis for gambiense HAT have been observed [57] , which underscores the improbability of early case detection and treatment . Significant variability in disease duration can exist , particularly in the case of gambiense HAT [58] , while varied disease severity and progression rates between foci have been observed for rhodesiense HAT in Uganda [59] . Furthermore , YLDs were calculated for all fatal as well as non-fatal cases , assuming all fatal and all unreported cases passed through the full early- and late-stage duration . This may have resulted in an overestimate of burden if cases died in the early stage or did not progress through the full late-stage duration . However , our duration estimates were based on multiple published estimates and were shorter than the 1-year ( rhodesiense ) and 5-year ( gambiense ) estimates used by the GBD 2004 study [22] , thus may be considered conservative . Mortality estimates for gambiense HAT are highly dependent on the treatment used . Information on treatment and mortality were not included in the dataset , which limited the accuracy of the gambiense mortality estimates . Furthermore , the treatment regimen traditionally used for late-stage gambiense HAT is melarsoprol , and a high incidence of melarsoprol treatment failures for gambiense HAT has been recorded in Uganda [60] . However , lower-mortality eflornithine and combination therapy treatments were introduced and tested in Uganda throughout the study period [44] , [46] , [47] . Thus , lack of data on treatment and shifts in treatment regimens led us to conservatively estimate gambiense HAT mortality based on published case-fatality ratios . In addition , 100% and 50% fatality of unreported cases was assumed for rhodesiense and gambiense HAT , respectively , with all unreported cases progressing through early and late stages . However , this would overestimate burden if cases spontaneously recovered or were trypanotolerant . Recent research has indicated the possibility of recovery for gambiense HAT in West Africa [61] but not rhodesiense HAT . However , previous research concluded that self-resolving and trypanotolerant gambiense HAT infections , if they do occur , are a small minority [23] . The possibility and frequency of non-fatal gambiense HAT occurrence remains unresolved , but seems unlikely to significantly impact HAT burden estimates . The GBD 2010 study was published in late 2012 , and presented revised methods for DALY calculations . See Text S1 for a full discussion of the differences in burden parameters and their potential effect on the results of this study . Notwithstanding these changes , we identify key priorities for estimation of burden parameterization for HAT: Limitations to burden parameterization are not unique to HAT . Parameters have been developed in varying levels of specificity for other diseases . For instance , GBD 2004 disability weightings for tuberculosis , malaria , and HIV/AIDS vary with age; weightings for malaria and HIV/AIDS also distinguish three separate disease sequelae and vary with treatment . Methods for estimating mortality rates vary across these three conditions , although none share the parasite- and treatment- specific issues related to HAT . While acknowledging that these varied assumptions underlie the DALY estimates used for broad comparison with other diseases , an in-depth evaluation of the GBD parameters for these diseases is beyond the scope of this study . It is critical that the public health research community move beyond the GBD framework's focus on nationally aggregated burden to consider spatial – and temporal – variation in burden . This is particularly relevant for neglected tropical diseases whose current burden values are already poorly estimated [5] , [7] . While national estimates remain a useful tool for systematic comparison of global trends , their use for prioritization of health intervention and funding has failed to consider the scale dependence of disease burden . While absolute DALY burden may be relatively low for focal diseases , the cost-benefit ratio of intervention measures targeted at small areas of high burden may be favourable compared to diseases with geographically dispersed burden . Sub-national burden evaluation for a range of conditions may allow quantitative comparison of the spatial variation in burden across diseases . This would also allow identification of those areas and populations suffering from highly focalized impacts and hence the greatest potential benefits of targeted interventions . This is especially imperative since we suspect HAT is not the only disease that shows a focalized burden , and further , multiple diseases may impact the same local populations and merit more general public health practices or combined interventions . Burden estimates are established as a valuable guide to directing scarce disease control resources , yet in many cases disease surveillance and health policy interventions operate at local levels . In compliment to the recent methodological revisions to the GBD study , it is thus imperative that the use of the DALY also be revised to validate and test its utility at differential spatial scales . | Since the 1990s the World Health Organisation has established the disability-adjusted life year ( DALY ) as a metric for the burden of human disease and injury . However , disease burden has primarily been estimated at the national scale , which does not account for sub-country variations in burden levels . We used the case of human African trypanosomiasis ( HAT ) , a highly focal NTD , in Uganda to calculate and map burden in DALYs . Our results show that HAT burden is highly sensitive to under-reporting estimates , and is particularly high in heavily affected parishes and districts of Uganda . Some districts in southeastern Uganda had HAT burden rates comparable to the national burden rates of major infectious diseases such as malaria and HIV/AIDS . Thus , the spatial scale of burden estimation is crucial , especially for focal diseases such as HAT , and national-level estimates may not reflect the level of impact in afflicted communities . We recommend sub-country burden estimation to identify key areas for prioritization of disease surveillance and targeted interventions . | [
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| 2014 | Incorporating Scale Dependence in Disease Burden Estimates: The Case of Human African Trypanosomiasis in Uganda |
This model-based design of experiments ( MBDOE ) method determines the input magnitudes of an experimental stimuli to apply and the associated measurements that should be taken to optimally constrain the uncertain dynamics of a biological system under study . The ideal global solution for this experiment design problem is generally computationally intractable because of parametric uncertainties in the mathematical model of the biological system . Others have addressed this issue by limiting the solution to a local estimate of the model parameters . Here we present an approach that is independent of the local parameter constraint . This approach is made computationally efficient and tractable by the use of: ( 1 ) sparse grid interpolation that approximates the biological system dynamics , ( 2 ) representative parameters that uniformly represent the data-consistent dynamical space , and ( 3 ) probability weights of the represented experimentally distinguishable dynamics . Our approach identifies data-consistent representative parameters using sparse grid interpolants , constructs the optimal input sequence from a greedy search , and defines the associated optimal measurements using a scenario tree . We explore the optimality of this MBDOE algorithm using a 3-dimensional Hes1 model and a 19-dimensional T-cell receptor model . The 19-dimensional T-cell model also demonstrates the MBDOE algorithm’s scalability to higher dimensions . In both cases , the dynamical uncertainty region that bounds the trajectories of the target system states were reduced by as much as 86% and 99% respectively after completing the designed experiments in silico . Our results suggest that for resolving dynamical uncertainty , the ability to design an input sequence paired with its associated measurements is particularly important when limited by the number of measurements .
Since experiments can be expensive and time consuming , it is important that they are planned to generate useful data . Traditional design of experiments is a well established field and has led to many advances in biology and medicine . The data obtained from strategically designed experiments has facilitated the creation of mathematical models that relate experimental stimuli to measurable outcomes . These models typically describe the system’s input-output relationship but fail to capture or encode knowledge of the system’s internal mechanisms and processes . Mechanistic and semi-mechanistic mathematical models encode the current understanding of the internal processes of the biological system even though many of these internal states or species are not directly measurable . These mechanistic models can be used to support optimal experiment design that considers the current knowledge of the system interactions and practical experimental constraints . In recent literature this type of experiment design has been referred to as model-based design of experiments ( MBDOE ) . MBDOE produces experiments meant to reduce some measure of uncertainty in the associated model while respecting cost , time and resource constraints . Most MBDOE strategies can be categorized by three types of objectives: ( 1 ) reducing model parameter uncertainty [1–7] , ( 2 ) discriminating among possible models [8–13] , and ( 3 ) reducing dynamical uncertainty [14–17] . This work advances current abilities to design experiments to resolve the trajectories of target states of a biological system model , thereby reducing its dynamical uncertainty . Many of the MBDOE strategies that support reduction of parameter uncertainty and model discrimination rely on linear approximations that are locally optimal to design an experiment by optimizing a criterion of the Fisher Information Matrix ( FIM ) [15 , 18–21] . Such techniques use the local sensitivities of parameters to design an optimal experiment which requires an initial estimate of the unknown parameters . Most biological system models are not well characterized , as data is limited and noisy , so initial estimates of the model parameters are inaccurate . Furthermore , biological models are typically nonlinear , so a poor initial parameter estimate is likely to result in a local minimum , which may result in sub-optimal experiment design . Alternatively , the Sigma Point method refines estimates of the characteristic values of the parameter statistics [22 , 23] . This method is computationally efficient and gives a better approximation of the covariance matrix when compared to the FIM-based and boot-strap methods . However , this method is not applicable when the biological system is not well characterized with existing experimental data . These local methods for MBDOE are limited to scenarios when initial estimates for the model parameters or their distributions are already fairly well known . To overcome the shortcomings of local MBDOE strategies , global approaches that consider the entire uncertain parameter range have been developed . Global optimization has been used to improve the FIM-based design [24] and local deterministic optimization of the likelihood with multi-start initial parameter estimates [25] have been previously employed . Some Bayesian MBDOE methods use Monte Carlo generated estimates of the parameter confidence intervals to design a global optimal experiment [16 , 26–30] . A comparison of the FIM-based and Bayesian-based MBDOE methods by Weber et al . [31] shows that Bayesian methods give more accurate and informative designs . However , all of these global methods are computationally prohibitive for large numbers of highly uncertain parameters and mathematical models that are computationally intensive . The limitation due to computation has been partially abrogated by methods that utilize surrogate models to approximate the mathematical model of the system . Herein , as in our previous work [14 , 15] , we use a sparse grid interpolation tool to approximate the biological model . To find the optimal design a search has to be performed over all of the experimental factors that define the feasible experimental perturbations and potential measurements . A complete exploration of the full design space over the uncertain parameter space for an optimal experiment design is impractical due to the combinatorial explosion of possible experimental factors evaluated for all possible model parameterizations . To solve the problem , most MBDOE strategies constrain the design space to a subset of the possible experiments . This reduces the design space by assuming a small predefined set of possible measurement points , measurable species and/or input levels [9 , 31–33] . Herein , we define the input to be the exogenous experimental factors , perturbations and/or stimuli that are applied to the biological system . Other designs have considered finding an optimal input sequence while the times and measurement species are defined a priori [2 , 10 , 24 , 34] . To consider both optimal measurements and an optimal input sequence , some strategies [16 , 35] evaluate the optimality of a pre-defined set of specified inputs and measurement points . All of these approaches may result in sub-optimal experiment designs if the best experiment is not among the specified experimental options . Ideally , experiment design determines the optimal solution within a feasible space defined by all the possible experimental measurements and inputs . Since different inputs can elicit dramatically different dynamics for some nonlinear systems , evaluating experiments for optimal inputs is not necessarily a trivial extension of the global techniques for identifying optimal measurements . Herein , we describe and evaluate a computationally efficient and tractable method for performing global MBDOE to define optimal experimental inputs and measurements to reduce the uncertainty in continuous nonlinear system dynamics . The method utilizes the sparse grid surrogate model technique first employed for global experimental designs presented in [14 , 15] , which selected only optimal measurements and not optimal inputs . We proposed an earlier version [36] of our input design MBDOE algorithm that used a combination of scenario trees and sparse grid interpolation to screen the input design space for optimal inputs that will generate diverse dynamics of the uncertain nonlinear system . This paper is an extension of that work . Herein we utilize a greedy search to determine the input magnitude to be applied using a method that is similar to that in [37] which determined near-optimal measurements in polynomial time . Our MBDOE algorithm is made even more computationally efficient and robust than our previous sparse grid based strategies due to the uniform sampling over the dynamical space via careful selection of representative parameters , the optimization criteria for optimal experiment design for a target system , and the repeated update of probability weights over the dynamical space using predicted data . Our new MBDOE method develops a strategic experiment design that yields highly informative data that can be used to constrain the system dynamics to dynamical uncertainty regions . In the methods section , we describe the experiment design problem and define the methods used to solve the problem as sequential optimizations to first select an optimal input sequence and , subsequently , specify the associated optimal measurement pairs ( species and time points ) . In the results section , we explore the optimality of the derived experiment design solution using a small 3-dimensional Hes1 oscillatory model and demonstrate the scalability of this MBDOE strategy with a 19-dimensional T-cell receptor model . The global applicability , support of hybrid experiment designs , and limitations of this MBDOE approach are discussed to conclude the paper .
This MBDOE algorithm is designed to resolve dynamical uncertainties associated with the biological system by reducing the predicted variance in the system dynamics . The system is described by nonlinear ordinary differential equations of the general form: x ˙ = f ( x , u , θ , θ ′ , t ) , x ( t 0 ) = x 0 ( 1 ) where f is a deterministic smooth function of x ∈ ℝ n x , the states of the system , θ ∈ ℝ n p , the known model parameters , θ ′ ∈ ℝ n p ′ , the unknown model parameters , and u ∈ ℝ n u the exogenous inputs . Using control vector parameterization ( CVP ) [38] , we represent u = [u ( τ1 ) , … , u ( τN ) ] ∈ ℝnu∗N , as vector-valued function of the system inputs that can change value at each discrete time point , τj , where j = 1 , … , N . There is uncertainty in the dynamics of the system which is generated by the uncertainty in the values of the unknown model parameters , θ′ ∈ Ω where Ω is a compact set . It is common in biological systems that not all states of this system are experimentally measurable . All feasible measurements for this system are modeled by: y = h ( x , u , θ , θ ′ , t ) , ( 2 ) where h is a smooth vector-valued function that relates to the system’s internal states described in Eq ( 1 ) and y ∈ ℝ n m . We abbreviate Eq ( 2 ) by the notation y ( u , θ′ , t ) . The user defines the number of measurements that they would like to have designed , K , as well as the set of possible times for measurement , 𝕋 , by either specifying discrete time points or defining a time resolution , δt , between TI and TF , the initial and final model simulation times , respectively . Our MBDOE algorithm designs experiments that reduce dynamical uncertainty in a target system . This target system is a subset of the system states termed , target states , xT , when stimulated by a target input , uT . This approach employs the concept of a target system also used to evaluate an alternative MBDOE strategy [16] . We consider dynamical uncertainty in the measurable states , y , and the target states , xT , generated by the uncertainty in the unknown parameters , θ′ . As a result of limited and noisy data , often biological system models will fit the data with a wide range of parameters values and associated dynamics . Therefore , for an optimal design , we need to consider all the possible data-consistent dynamical scenarios which are a function of the parameter space , Ω . The optimal experiment , D* ∈ 𝔻 , defines the optimal input stimulation and optimal measurement pairs that resolve the target state dynamics . This design is defined by a piecewise input sequence , u* ( Eq ( 3 ) ) , and associated measurements M* = { ( mk , tk ) :k = 1 , 2 , …K} ∈ 𝕄 , which defines K measurement pairs by the index of a measurement species mk and its corresponding measurement time tk . u * = { u 1 * τ 1 ≤ t ≤ τ 2 ⋮ ⋮ u j * τ j ≤ t ≤ τ j + 1 ⋮ ⋮ u N * τ N ≤ t ≤ T F ( 3 ) We choose D* as the experimental design that maximizes a measure of information gained from an experiment D * = argmin u ∈ ℝ n u * N , M ∈ 𝕄 γ ( u ) . ( 4 ) The target state dynamical uncertainty ( TDU ) , γ ( u ) , is quantified by the sum of the maximum variance in each target state that results when analyzed assuming the input sequence , u , has been applied and measurements , M , exist to constrain the uncertain parameter space: γ ( u ) = ∑ i max ( Var ( x T i ( t ) ) ∣ ( u , M ) ) ( 5 ) where the maximum variance of the ith target state , max ( Var ( xT i ( t ) ∣ ( u , M ) ) , is chosen across the simulated time t , from the initial , TI to the final time , TF . The ideal solution for the above optimization problem simultaneously solves for the optimal input vector , u* , together with the optimal measurements , M* . This is generally a computationally intractable problem due to the combinatorial explosion of all possible selections for the experiment design complicated by the expense of evaluating the design with the model for all possible uncertain parameter values . Our approach proposes a computationally efficient method to approximate both the optimal input vector and associated optimal measurement points by breaking the problem into smaller computationally feasible optimization problems . The first optimization problem solves for the input vector using a greedy method by minimizing the value of TDU at each iteration assuming a single best measurement is taken: u * = argmin u j ∈ ℝ n u γ ( u ) , j = 1 , ⋯ N . ( 6 ) The optimal input vector is then used in the second optimization to determine the multiple optimal measurements points . This combined solution of input and measurements approximates the optimal experiment design defined in Eq ( 4 ) . A flow-chart of the MBDOE algorithm is given in Fig 1 to display the sequence of events in determining the optimal experiment design: ( a ) identify representative parameters that maintain the diversity of simulated dynamics that fit the existing data , ( b ) determine the optimal input vector that minimizes TDU , and ( c ) specify associated multiple measurement pairs given an optimal input vector . The first step of this process screens the uncertain parameter space , Ω , to identify the space of acceptable parameters , ΩA . The acceptable parameters are those that support model simulations to fit the available data as: Ω A = { θ ′ ∈ Ω ∣ log 10 ( 1 + ∑ θ ′ ∈ Ω ( y ˜ i ( u , θ ′ , t ) - y ^ i ( u , t ) σ i ( t ) ) 2 ) ≤ T A } ( 7 ) where y ^ i ( u , t ) is the mean of the experimental data for the ith model output at the time point , t , collected with the applied input u , y ˜ i ( u , θ ′ , t ) is the corresponding model dynamics simulated with a parameter set , θ′ , and σi ( t ) is the standard deviation of the data . In this work , we approximate y ˜ i ( u , θ ′ , t ) using a sparse grid interpolation tool over the uncertain space Ω for computational efficiency . TA is the threshold for acceptability . This weighted least squares function has been used before to define acceptable parameters in [14 , 15] . The parameter space , Ω , is sampled using Latin Hypercube Sampling ( LHS ) . If the initial screen produces fewer acceptable parameters than NA , the desired number of acceptable parameter vectors that will support the experiment design , focused grids are created [15] to improve the resolution of the grid interpolant in the acceptable regions of the uncertain parameter space . LHS is also used to sample the focused grids to find more acceptable parameters until the cardinality ( ΩA ) ≥ NA . In this work , we define the dynamical uncertainty region to be the region spanned by the most extreme trajectories of the acceptable parameters . We propose solving the input vector using a greedy search algorithm . The input is discretized as in control vector parameterization ( CVP ) by the potential admissible input times 𝓣 = {τ1⋯τN} and by potential magnitude levels . The number of possible input magnitudes is determined by the inputs bound [umin , umax] and resolution , δu: N u = u m a x - u m i n δ u + 1 . ( 12 ) The algorithm for the greedy search method to select the optimal input vector is detailed in the flowchart in Fig 1 ( b ) . The input magnitude is initially set to a base input level , u1 = ub ( t ) ∀t ∈ [TI TF] , which is determined by the user . At each iteration , k , of the greedy search method , optimized input levels , u j * , for the admissible times , τj ∈ 𝓣k−1 , are selected from all Nu possible admissible input magnitudes as described in Algorithm 1 . We evaluate TDUj for each of the admissible input times τj ∈ 𝓣k−1 and associated u j * and select the input conditions , ( τ j * , u j * ) , that minimize the value of TDU according to Eq ( 6 ) as optimal . These optimal input conditions update the current input vector as follows: u k * = { u j * τ j * ≤ t ≤ τ ′ u k - 1 * Elsewhere ( 13 ) where τ′ is an input admissible time that has previously been optimized to update the vector , u*k−1 . We define 𝓢k−1 , a set of all previously optimized input admissible times such that τ ′ = m i n [ t ∈ 𝓢 k − 1 ∣ t > τ j * ] . When the input vector is updated with optimal input conditions , ( τ j * , u j * ) , the input time , τ j * is moved from the set 𝓣k−1 to the set 𝓢k−1 . This process continues iteratively , and the input vector is updated until 𝓣 is empty or no input magnitude change improves the value of TDUk−1 as shown in Fig 1 ( b ) . Algorithm 1 Algorithm for Selecting u * j Input: τj , umin , umax , δ u , 𝓢k−1 , 𝓣k−1 , u*k−1 Output: u j * and corresponding TDUj for p = 1:Nu do ( Note: Nu = number of possible input magnitudes ) 1 . Define u j p = u m i n + ( p − 1 ) δ u 2 . Simulate dynamics with u = up for τj ≤ t < τ′ where τ′ = min[t ∈ 𝓢k−1∣t > τj] 3 . Determine an associated measurement pair: mk ∈ nm and tk ∈ 𝕋 . See Eq ( 15 ) 4 . Update uncertain parameter probabilities given data for ( mk , tk ) . See Eq ( 16 ) 5 . Estimate variance of target states , xT , with target input , uT , using updated uncertain parameter probabilities 6 . Calculate TDUp . See Eq ( 5 ) end for p * =argmin p∈{1 , ⋯ , N u } TDUp . See Eq ( 6 ) u j * = u j p * TDUj = TDUp* Our design can identify multiple informative measurements given an optimal selected input vector , u* , as shown in Fig 1 ( c ) . We use an adaptation of the measurement scenario tree method [15] which makes multiple predictions of the data value from a given measurement point to allow the next informative point to be selected . In this work , we introduce the use of prior/posterior probability updates to minimize the bias due to outliers . The use of probabilities also enables the algorithm to work well with a smaller number of sampled uncertain parameters than previously , contributing to the computational efficiency . As in [15] , a measurement scenario tree is initialized by a node which defines the first optimal measurement point . At each node , we determine the optimal sampling time point according to Eq ( 15 ) to maximize the distinguishability metric for each measurable species . Our MBDOE algorithm selects the measurement pair that minimizes the TDU according to Eq ( 5 ) as the optimal measurement pair for that node . Subsequent measurements ( nodes ) are chosen by considering three different scenarios ( branches ) from the node . These scenarios arise by predicting three possible outcomes for the measurement pair: previously these were defined by the minimum , mean , and maximum predicted measurement values . Herein , the cumulative distribution function is computed from the priors to span from the minimum to maximum predicted data values for that measurement pair . The three predicted possible outcomes are the simulated values at the measurement pairs that correspond to the 10th , 50th , and 90th percentiles . Using each of these predicted possible outcomes to approximate the data , y ^ i ( u , t k ) , the posterior for each branch are updated for the θ′ ∈ ΩRA as in Eq ( 16 ) . Given the updated branch specific priors , the measurement pair specifying the next node in each branch is determined by the optimal measurement pair selected to minimize Eq ( 5 ) assuming the estimated posterior is the prior . This process is continued until the tree structure contains at least the specified number of unique measurements . The minimum number of levels in the tree is log3 ( K ) +1 where K is the user-specified desired number of measurements . Evaluating all possible experiments over the design space , 𝔻 , using model simulations for a large uncertain parameter space , Ω , is computationally prohibitive . The sparse grid tool offers an efficient way to approximate the system’s output dynamics using interpolating polynomials by sampling an uncertain space in a systematic way to create a surrogate model for the system . The grid approximation is then used to interpolate additional points on the uncertain space without the cost of directly simulating the model . The algorithm was coded in MATLAB ( 2013a ) . The Sparse Grid toolbox ( v5 . 1 . 1 ) was obtained from http://www . ians . uni-stuttgart . de/spinterp [40] and integrated with our model-based experiment design algorithm . We minimize the error due to inaccurate interpolants by specifying the termination conditions based on their depth or by the relative or absolute errors of 10% and 5 respectively . The max depth is set to 3 and 5 for the parameter screening and focused grids , respectively , and 5 for the input grid . We set the data-consistent acceptability threshold TA = 2 , which correspond to two standard deviations . The Hes1 and T-cell receptor models for the results were numerically integrated using the stiff ode solver ode15s with the default settings . To increase computational efficiency , the sparse grid code and the mathematical models were vectorized . The code was run on four 8-core Intel Xeon 3 . 4 GHz CPUs each with 16 GB of memory and running the Windows 2003 server platform with the MATLAB Parallel Computing Toolbox . Our input design MBDOE algorithm code is available upon request from the corresponding author . An upper bound on the number of model simulations used by our MBDOE algorithm can be approximated by: T e v a l = S G θ + N A ︸ ( a ) + m i n ( N u , S G u ) × N R A × N ( N + 1 ) 2 ︸ ( b ) + N R A ︸ ( c ) ( 21 ) where SGθ and SGu are the number of model simulations ( nodes ) used to create sparse grid interpolants over the uncertain parameter and input space , respectively . An upper bound estimate of these values can be calculated using a Sparse Grid toolbox function , spdim ( maxDepth , Dim ) assuming the maximum possible depth and the dimension of the uncertain parameter or input space . The number of simulations required to build an accurate interpolant may be quite a bit lower than this approximation when the model is smooth over the uncertain parameter and/or input space . The terms of this upper bound approximation are mapped to the steps of Fig 1 . The ( a ) term of Eq ( 21 ) is the total number of model simulations used to identify the representative parameters . The second term , ( b ) , is the number of simulations that determine the optimal input vector . For this approximation , the sparse interpolant on the input space is only created if the estimated maximum number of nodes , SGu required is less than Nu . Furthermore , we assume a worst case scenario where the input magnitude changes at all N admissible times resulting in the fraction , N ( N + 1 ) 2 . The third term , ( c ) , is the number of model simulations used create the scenario tree to determine the measurement pairs .
To illustrate the effectiveness of our MBDOE strategy to resolve dynamical uncertainty of a target system , we use a simple Hes1 oscillator toy model [41] . This example demonstrates how an optimal perturbation enhances the ability of the MBDOE to reduce the dynamical uncertainty . The Hes1 model was previously used to demonstrate a Bayesian design of experiments strategy [16] . This model describes the changes in the level of the Hes1 transcription factor that is important in somitogenesis . It is modeled with an ODE system that describes the changes of the levels of the Hes1 mRNA , m , the cytosolic protein , P1 , and the nuclear protein , P2 , as shown: d m d t = - k m + 1 1 + ( P 2 P 0 ) h , d P 1 d t = - k P 1 + ν m - k 1 P 1 , d P 2 d t = - k P 2 + k 1 P 1 . ( 22 ) A description of the parameters of the model and their nominal values are listed in Table 2 . The degradation rate , k , and protein transport rate , k1 , are assumed to be known and their values are set to the nominal value while the rest of the parameters are considered unknown . The uncertain parameter space , Ω , is initially set to have bounds of 0 . 1 and 10 times previously reported nominal parameter values . The experimental design space is 𝔻 , partially defined by an input α ∈ 𝕌 that modifies the transport rate , k1 , as α × k1 where 𝕌 = [0 . 01 , 2] with a δu = 0 . 05 . The input magnitude can be changed at admissible times , 𝓣 = {0 , 2 , 5 , 8 , 10 , 50 , 100 , 150 , 200} min . In this example , ub , is set to α = 1 ∀τ ∈ 𝓣 . The allowable measurement state space includes the Hes1 mRNA , m , and the total protein concentration , P1+P2 . The allowable sampling time space 𝕋 = [0 , 300] min was defined to specify measurements with a δt = 10 min . We want the algorithm to specify a minimum of 8 distinct measurements . The experiment is designed to decrease the dynamical uncertainty on three target states , m , P1 , and P2 under a target input α = 1 applied for the simulated time period . To evaluate the MBDOE algorithm in silico , we simulate a plant model for which we want to reduce the dynamical uncertainty . The plant model is initially simulated with unknown model parameters set at the nominal values shown in Table 2 . We generate 6 initial measurements of both mRNA , m , and total protein concentration , P1+P2 , at times t = 10 , 20 , 60 min with 10% additive Gaussian noise . Initial data identify the acceptable parameter space , ΩA , that is consistent with the data as defined by Eq ( 7 ) . The resulting optimal experiment design is in Table 3 . The optimal input sequence is specified by α values shown in Fig 2a and associated predicted measurements are shown on Fig 2b , superimposed on the representative dynamics for the measurable species to illustrate their connection with uncertainty . The greedy algorithm for the input selection is shown in Table 4 showing each iteration of input selection with the progressive predicted reduction in TDU values . The initial uncertainty and final uncertainty in the dynamics of the target states are superimpossed in Fig 2c to demonstrate the reduction of the region of uncertainty of the dynamics . The dynamical uncertainty regions associated with the mRNA , P1 , P2 target states are reduced by 84% , 81% and 86% , respectively . In all cases , this reduced uncertainty region enclose the true dynamics of the system . The measurement pairs are shown in the scenario tree in Fig 3a . the nodes of the tree specify the measurements that maximize uncertainty in the measurable species , ξ , while minimizing the TDU , γ , under the optimal input sequence , u* . In general , the values of ξ and γ decrease as you move down the tree and the predicted dynamics are constrained . To investigate how the measurement pairs contribute to the reduction of uncertainty in the dynamics of the target system , the total , γ , and individual variances are estimated assuming each predicted measurement has been taken ( moving down and from left to right along the tree , neglecting repeated measurements ) . These results are shown in Fig 3b and 3c . These figures show that the first five unique measurements from the tree are necessary to resolve the target system dynamics . To further explore the optimality of our MBDOE algorithm , we compare our results achieved with u* and M* to those of measurement only MBDOE . For this comparison , the measurement only MBDOE determined the optimal measurements , MT , assuming the applied input was the target input , uT . These results are summarized in Table 5 by the percentage reduction in the uncertain dynamical region for each target species and TDU . Our MBDOE algorithm outperforms all other experiment design combinations based on the TDU reduction . Although our MBDOE design is not the best in reducing the uncertain dynamical region for all target states , the results are comparable . The supplemental material contains the figures showing the uncertain dynamical regions for all measurement and input combinations in the Table . The supplemental material also contains figures that indicate the ability of our MBDOE algorithm to reduce the uncertain dynamical regions for different parameterizations of the Hes1 plant model . The computational efficiency of this MBDOE method partially arises from the use of an interpolation as a surrogate of the model to reduce numerical integration of ODEs . The sparse grid terminated with a relative accuracy of 0 . 7% with an absolute tolerance of 0 . 9 . The algorithm required only 137 model evaluations to build an interpolant which was sampled 10 , 000 times to identify 3852 acceptable parameters where NA = 2000 . The algorithm selected 48 representative parameters , NRA = 48 , to span the distinguishable dynamics generated by the acceptable parameters . For the input design , separate 1-D sparse grids are constructed for each admissible time of input change with 9 nodes . The total number model evaluation used for the complete experiment design is 18 , 716 where 2137 were for finding the representative parameters , 16 , 531 were used for the input selection , and 48 were used for the measurement selection . Clearly the majority of the model simulations were used to determine the optimal input signal . This is exceptionally large since we allowed the input to change at 9 time points and had fine input resolution with 40 different values . For comparison purposes , the Hes1 model experiment design in [16] required more than 30 , 000 model simulations to determine which species should be measured to provide the most information .
In this work , we have proposed an MBDOE algorithm that extends previous work to include the design of an optimal input together with optimal measurements to reduce dynamical uncertainty in biological systems . The global nature of the algorithm overcomes the challenges posed by traditional MBDOE designs that rely on the local FIM design . Furthermore , most MBDOE techniques that consider input design only compare a predefined set of input values and/or measurements . Our computationally efficient approach enables us to avoid this limitation and search over the input space for the optimal input values at pre-specified time points . Our strategy achieves a global design by implementing computationally efficient strategies using sparse grid interpolation , probability-weighted scenario trees , and dynamical representative parameters . An interpolant of the ODE system is created by strategically sampling the uncertain parameter space and is used to evaluate additional points by LHS without simulating the ODEs . Representative parameters are used to span the dynamics of the acceptable parameter space . Together , the representative parameters and the sparse grid interpolation are used to efficiently screen the dynamical variance on the input space and facilitate the probability-weighted scenario tree to identify associated optimal measurements that minimize the uncertainty in the target system dynamics . We have confirmed that our optimal input MBDOE algorithm will specify experiments that are more informative in their ability to reduce dynamical uncertainty over MBDOE techniques that only specify optimal measurements using 3D and 19D models . The optimization of the input signal is most important when a limited number of measurements are taken . The computational expense of the MBDOE method is highly dependent on the dimension of the unknown parameter space and the feasible input space . For high dimension models , a large number of model simulations may be required to explore the large dimension uncertain parameter space to identify the acceptable and representative parameters . This computational burden can be reduced by the use of the sparse grid interpolation tool if the model output is smooth over the uncertain parameter space . For large input spaces , the majority of the model simulations in the MBDOE algorithm may be used to determine the optimal input signal . The number of model simulations increases with more flexibility in the input feasible space in terms of: ( 1 ) number of inputs that could be applied , ( 2 ) number of allowable changes in the inputs magnitudes ( input resolutions ) , and ( 3 ) the number of admissible input times . In our examples , we specified a high degree of flexibility in the feasible input space for the lower dimension Hes1 model than the high dimension TCR model . As a result , the TCR model used 12 , 063 model simulations for the optimal input vector design while the Hes1 system required 16 , 531 model simulations . Historically , MBDOE methods have designed experiments sequentially , whereby information gained from a previous experiment is used immediately to inform the next experiment design [32 , 43 , 44] . This approach is prevalent with the local MBDOE strategies since it improves the accuracy of the parameter estimates that support the design of the next experiment . The sequential design becomes problematic when the number of uncertain parameters is large because a single experiment does not estimate all the parameters accurately [45] . A parallel design methodology improves the estimates of the parameters and reduces experimental costs by specifying multiple measurements . The parallel design in [15] uses scenario trees to specify all optimal measurements necessary to resolve the experimentally distinguishable dynamics . A hybrid design combines the advantage of the sequential design with the cost efficiency of the parallel design by specifying multiple experiments to perform and iterating with the results informing the next round of MBDOE . Our MBDOE design uniquely supports this hybrid design strategy . It uses a measurement scenario tree to determine a user-specified number of measurements as opposed to the previous scenario tree approach that indicated all measurements required to fully resolve the dynamics . Herein , the number of desired measurements controls the depth of the constructed scenario tree . ( If a tree is made too deep , we believe that many of the specified measurements would be sub-optimal since the actual dynamics only lie within a fraction of the predicted tree . ) Thus , if the desired dynamical resolution is not achieved by one application of our MBDOE algorithm , the design can be repeated while informed with the new additional experimental data . New experimental data restricts the acceptable parameter space further to generate a new more relevant optimal experimental design . Although our MBDOE algorithm is global , we cannot claim it is globally optimal . The algorithm makes optimal decisions within constraints . To make the problem tractable , we split the optimization problem into two sequential steps while the first specifies the optimal inputs and the second finds the associated measurement pairs . It is possible , that a better solution does exist and our algorithm misses it . Another source that may contribute to a sub-optimal result would be in the accuracy of the sparse grids . If the interpolants are not sufficiently accurate surrogates for the model responses , the results may be sub-optimal . In addition , we do not presume to have a good estimate of the number of acceptable parameter values needed to support the MBDOE since it is a function of the the biological system , the experimental setup , measurement noise , the mathematical model and its uncertainty . Hence , there is no exact formula for calculating the number of acceptable parameter vectors needed to support our experiment design algorithm . If the value is too small , the algorithm output would be sub-optimal and likely not repeatable . However , if repeated runs of our MBDOE algorithm produce similar results for increasing numbers of NA than it is likely to be sufficiently large . Thus , without putting constraints on the biological system model , NA must be sufficiently large to cover all distinguishable dynamics so that repeated application of our MBDOE algorithm derives similar optimal experiment designs . Overall , the proposed MBDOE strategy successfully extends previous MBDOE capabilities to design an optimal input with associated measurements that minimizes the uncertainty in the target system dynamics . The interpolation grid provides a computational efficient way to search for both parameter fits and optimal input over large uncertain spaces . The use of representative parameters , selected to span the dynamical space of the biological model , provides a means to sample the dynamical space uniformly enhancing the computational tractability of this approach . Furthermore , the probability weighted scenario tree designs modified from [15] supports input and multiple optimal measurement pair selection with fewer sampled parameters . Although the enhancement of the discrimination ability of the experiment is somewhat model dependent , for the examples we have presented herein , we have found that the ability to specify an optimal input in addition to optimal measurements has enhanced the ability to bound the expected target system dynamics . | Many mathematical models that have been developed for biological systems are limited because the complex systems are not well understood , the parameters are not known , and available data is limited and noisy . On the other hand , experiments to support model development are limited in terms of costs and time , feasible inputs and feasible measurements . MBDOE combines the mathematical models with experiment design to strategically design optimal experiments to obtain data that will contribute to the understanding of the systems . Our approach extends current capabilities of existing MBDOE techniques to make them more useful for scientists to resolve the trajectories of the system under study . It identifies the optimal conditions for stimuli and measurements that yield the most information about the system given the practical limitations . Exploration of the input space is not a trivial extension to MBDOE methods used for determining optimal measurements due to the nonlinear nature of many biological system models . The exploration of the system dynamics elicited by different inputs requires a computationally efficient and tractable approach . Our approach plans optimal experiments to reduce dynamical uncertainty in the output of selected target states of the biological system . | [
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| 2015 | Efficient Optimization of Stimuli for Model-Based Design of Experiments to Resolve Dynamical Uncertainty |
Nematode-trapping fungi are “carnivorous” and attack their hosts using specialized trapping devices . The morphological development of these traps is the key indicator of their switch from saprophytic to predacious lifestyles . Here , the genome of the nematode-trapping fungus Arthrobotrys oligospora Fres . ( ATCC24927 ) was reported . The genome contains 40 . 07 Mb assembled sequence with 11 , 479 predicted genes . Comparative analysis showed that A . oligospora shared many more genes with pathogenic fungi than with non-pathogenic fungi . Specifically , compared to several sequenced ascomycete fungi , the A . oligospora genome has a larger number of pathogenicity-related genes in the subtilisin , cellulase , cellobiohydrolase , and pectinesterase gene families . Searching against the pathogen-host interaction gene database identified 398 homologous genes involved in pathogenicity in other fungi . The analysis of repetitive sequences provided evidence for repeat-induced point mutations in A . oligospora . Proteomic and quantitative PCR ( qPCR ) analyses revealed that 90 genes were significantly up-regulated at the early stage of trap-formation by nematode extracts and most of these genes were involved in translation , amino acid metabolism , carbohydrate metabolism , cell wall and membrane biogenesis . Based on the combined genomic , proteomic and qPCR data , a model for the formation of nematode trapping device in this fungus was proposed . In this model , multiple fungal signal transduction pathways are activated by its nematode prey to further regulate downstream genes associated with diverse cellular processes such as energy metabolism , biosynthesis of the cell wall and adhesive proteins , cell division , glycerol accumulation and peroxisome biogenesis . This study will facilitate the identification of pathogenicity-related genes and provide a broad foundation for understanding the molecular and evolutionary mechanisms underlying fungi-nematodes interactions .
Nematode-trapping fungi are a heterogeneous group of organisms broadly distributed in terrestrial and aquatic ecosystems [1] , [2] . These fungi are capable of developing specific trapping devices such as adhesive networks , adhesive knobs , and constricting rings to capture nematodes and then extract nutrients from their nematode prey [2] , [3] , [4] . Most nematode-trapping fungi can live as both saprophytes and parasites [1] , [2] . They play important roles in maintaining nematode population density in diverse natural environments . Their broad adaptability and flexible lifestyles also make them ideal agents for controlling parasitic nematodes of plants and animals [2] , [4] . Arthrobotrys oligospora ( teleomorph Orbilia auricolor ) is one of the best-studied nematode-trapping fungi [2] , [5] . Strains of A . oligospora have been found in diverse soil environments including heavy metal-polluted soils and decaying wood [6] , [7] where they live mainly as saprophytes . In the presence of nematodes , A . oligospora enters the parasitic stage by forming complex three-dimensional networks to trap nematodes ( Figure 1 ) . The trapping initiates a series of processes including adhesion , penetration , and immobilization of nematodes [2] , [5] . The ability to trap nematodes makes it an attractive candidate agent for controlling parasitic nematodes of plants and animals . Indeed , two commercial biological nematicides , Royal 300 [8] and Royal 350 [9] , have been developed based on two species closely related to A . oligospora: A . robusta and A . irregularis . The formation of trapping devices by nematode-trapping fungi is an important indicator of their switch from the saprophytic to the predacious lifestyles [2] , [4] . Previous studies have identified the morphological characteristics of major trapping devices , including their evolution and phylogenetic distribution [4] , [10] , [11] , [12] . Recently , the gene expression profiles in trap cells and vegetative hyphae of the nematode-trapping fungus Monacrosporium haptotylum were analyzed using the microarray technology [13] , including examining the transcriptional response of M . haptotylum to the model nematode Caenorhabditis elegans at different infection stages [14] . However , at present , no genome sequence information is available in any nematode-trapping fungi and relatively little is known about the molecular mechanism underlying trap formation or nematode-fungi interaction . Such genome sequence information could help reveal the genetic features that allow these fungi to form nematode traps and provide important information to study the molecular mechanism of infection and their lifestyle transitions . Here the first genome sequence of a nematode-trapping fungus , A . oligospora Fres . ( ATCC24927 ) was reported . In addition , using the proteomics approach and quantitative PCR ( qPCR ) , proteins differentially expressed in response to nematode extract ( NE ) were identified and further investigated . The combined genomic , proteomic , and qPCR data led us to formulate the putative genetic and metabolic pathways involved in trap formation in A . oligospora .
The genome of A . oligospora strain ATCC24927 was sequenced to 36 . 6-fold coverage through a Sanger/pyrosequencing hybrid shotgun approach from multiple clone types ( Table S1 ) . The 40 . 07 Mb genome is similar in size to that of the model ascomycete fungus Neurospora crassa [15] . The A . oligospora genome was assembled into 215 scaffolds , containing a long-range continuity as reflected by N50 scaffold size of 2037 Kb and N50 contig size of 575 . 8 Kb ( Table S2 ) . The assembly represents 99% of the coding regions of the genome , as assessed by mapping 50 , 121 assembled transcript sequences ( 8 . 9 Mb ) to the genome assembly . Almost all of the transcripts ( 99 . 6% ) were mapped onto the genome . A total of 11 , 479 protein-coding genes were predicted: 23 . 6% belonged to multi-gene families and 44 . 6% were mapped in the KOG/COG database ( Figure 2A ) . The average gene density was one gene per 3 . 50 kb , with an average gene length of 1 . 69 kb , similar to that of N . crassa ( 1 . 67 kb ) [15] . Compared to N . crassa , genes in A . oligospora have more introns ( 2 . 8 VS . 1 . 7 ) but with a shorter average intron length ( 90 bp VS . 134 bp ) [15] . As is typical of ascomycete fungal genomes , approximately one-third ( 34 . 6% ) of the predicted A . oligospora genes lacked significant homologies to known proteins from public databases ( Table S2 ) . The orthologous genes between A . oligospora and other 10 fungal genomes were identified based on bidirectional best hits ( BBHs ) using BLAST [16] ( Table S3 ) . The other 10 genomes used for comparison were divided into non-pathogen ( Aspergillus nidulans , N . crassa and Saccharomyces cerevisiae ) and pathogen ( Fusarium graminearum , Magnaporthe oryzae , Verticillium dahliae , A . fumigatus , Coccidioides immitis , Histoplasma capsulatum and Chaetomium globosum ) groups . Based on orthology analysis , the genes in A . oligospora can be classified into four categories: ao ( only found in A . oligospora , 6157 genes ) , ao/pathogen ( found in A . oligospora and pathogen genomes , 961 genes ) , ao/pathogen/non-pathogen ( found in all genomes , 4249 genes ) , and ao/non-pathogen ( found in A . oligospora and non-pathogen genomes , 112 genes ) ( Figure 3A ) . The results showed that A . oligospora shared many more genes with pathogenic fungi than with non-pathogenic fungi . The genes shared between A . oligospora and other pathogenic fungi may be functionally related to pathogenicity in these fungi . A total of 529 orthologous proteins were found in all the 11 analyzed fungal genomes . These orthologous sequences were concatenated to infer phylogenomic relationships among these fungi using Neighbor-joining , Maximum parsimony and Maximum likelihood methods . The same tree topology was found by all the three phylogenetic methods ( Figure 4 ) . From the phylogenomic trees , Eurotiomycete fungi ( A . nidulans , A . fumigatus , C . immitis and H . capsulatum ) were clustered into one clade , Sordariomycete fungi ( F . graminearum , V . dahliae , C . globosum , N . crassa and M . grisea ) were clustered into a different clade , while A . oligospora formed a separate branch . Our result was consistent with results from previous phylogenomic analyses of the Ascomycota [17] , [18] . Our analysis revealed that 0 . 47% of the assembly consisted of repetitive sequences ( Table S4 ) , higher than that in the ascomycete wheat pathogen F . graminearum ( 0 . 10% ) [19] , but lower than that in N . crassa ( 3 . 15% ) [15] . The relatively low percentage of repetitive sequences in N . crassa was attributed to a genome-wide defense system known as repeat-induced point mutation ( RIP ) , a phenomenon hypothesized to be widespread among filamentous ascomycetes [15] , [19] . To determine whether RIP contributed to the low percentage of repetitive sequences in A . oligospora , the RIP indices for the whole genome as well as separately for the coding regions , non-coding regions , exons , introns , multigene families , and repetitive sequences were calculated . Using the default setting ( TpA/ApT≥0 . 89 , ( CpT+ApT ) / ( ApC+GpT ) ≤1 . 03 ) [20] , a positive RIP response was detected in the repetitive sequences with both RIP indices ( and very high AT content ) ( Table S5 ) , likely contributing to the lack of the repetitive sequences in A . oligospora . RIP could have similarly contributed to the relatively low proportion of genes in multigene families ( 25 . 11% ) in A . oligospora . For protein coding genes , although no positive RIP responses were detected using average RIP indices , a large number of regions contained signatures of RIP ( Table S6 ) . Specifically , using the 200-bp windows with 100-bp shifts , about one third of the genome sequences had RIP-positive sequences . The whole genome blast analysis against the pathogen-host interaction ( PHI ) gene database [21] identified 398 putative PHI genes in A . oligospora , 294 of which belong to 86 multigene families . The gene number expanded to 706 if single linkage transitive closure was applied . The distribution analysis of the othologous sequences showed that the putative PHI genes included 80 ao-specific genes and 73 genes only found in pathogen genomes , more than those in the ao/non-pathogen category ( Figure 3B ) . It should be noted that many putative PHI genes were not included in the pathogenicity-related gene families , suggesting that there are potentially more pathogenicity-related genes that remain to be experimentally confirmed . The number of genes in several gene families related to fungal pathogenicity was found expanded in the A . oligospora genome ( Figure 5 ) . For example , subtilisins , a group of proteases essential for infection [22] , [23] , [24] , [25] , were found in a greater number in A . oligospora ( 24 ) than in several model ascomycetes such as the animal pathogens A . fumigatus ( 4 ) , C . immitis ( 15 ) and H . capsulatum ( 3 ) , plant pathogens F . graminearum ( 15 ) , V . dahliae ( 16 ) and M . grisea ( 22 ) , and non-pathogens A . nidulans ( 3 ) , N . crassa ( 5 ) . Besides subtilisin genes , the A . oligospora genome also contains a larger number of genes within enzyme families of cellulases ( 58 ) , pectinesterases ( 18 ) and cellobiohydrolases ( 17 ) than other sequenced model fungi ( Figure 5 ) . As a comparison , the M . grisea genome contains the highest number of genes within the cytochrome P450 family ( P450; 112 ) ; the A . fumigatus genome contains the highest numbers of genes within enzyme families of non-ribosomal peptide synthases ( NRPS; 21 ) , chitinases ( 18 ) , and polygalacturonases ( 16 ) ; the V . dahliae genome contains the highest numbers of genes within enzyme families of polygalacturonases ( 16 ) , pectinesterases ( 29 ) , and pectate lyases ( 29 ) ; and the C . globosum genome contains the highest numbers of genes within enzyme families of cellulases ( 66 ) and xylanases ( 27 ) . Because of their ability to disrupt the physical and physiological integrity of the cuticles of nematode hosts during penetration and colonization , subtilisin-like serine proteases have been identified as important virulence factors in nematode-trapping fungi [23] , [26] , [27] . For example , previous studies demonstrated that a proteinase K-like serine protease PII in A . oligospora could efficiently degrade nematode cuticle [22] , [26] , [28] . The A . oligospora genome contains 24 genes encoding putative subtilases , which can be categorized into four subtilisin families ( Figure S1 ) . Among them , 20 belong to the proteinase K-like family , which can be further classified into five subfamilies ( SF1-SF5 , Figure S1 ) . The following four genes are clustered into one of the five sub-families , SF4: PII ( AOL_s00076g4 ) , P186 ( AOL_s00215g702 ) , P233 ( AOL_s00075g8 ) , and P12 ( AOL_s00170g103 ) . P12 shares a high nucleotide sequence identity ( 77 . 9% ) with the cuticle-degrading protease encoding gene spr1 of another nematode-trapping fungus Monacrosporium megalosporum [29] . When A . oligospora was exposed to nematode extracts for 10 h , the transcription levels of P12 and P186 increased by 5 . 9- and 23 . 4-folds ( Table S7 ) respectively , whereas those of PII and P233 did not change significantly . In order to identify the role of P186 in infection against nematodes , the gene P186 was disrupted by homologous recombination according to the method described by Colot et al . [30] . The disruption removed 107 bp from the open reading frame of P186 and was confirmed by PCR using genomic DNA as templates with primers ( P186-5F and P186-3R ) . The phenotypic properties and nematocidal activities of the mutants were compared with the wild strains . No obvious differences in phenotypic properties , such as growth rates , spores number , traps number and morphology , were found between the mutants and the wild strain . However , disruption of P186 greatly attenuated the pathogenicity of A . oligospora . The fatality rate of nematodes infected by P186-deletion mutants ( △P186 ) decreased by 24–32% ( Figure 6 ) at 24 h after infection . Our results suggested that P186 likely play major roles during nematode infection by A . oligospora . In contrast , the lack of a significant change in PII expression level upon NE induction is consistent with an earlier finding that PII gene disruption had only a limited effect on the pathogenicity of A . oligospora [22] . Some fungi can produce secondary metabolites , including small molecular toxins that kill host cells before infection , generate a necrotrophic stage during infection , or disable host cellular functions after infection [31] . So far , 179 nematicidal compounds belonging to diverse chemical groups have been identified from nematophagous fungi , three ( oligosporon , 4′ , 5′-dihydro-oligosporon and linoleic acid ) of which were from A . oligospora [32] . Several enzyme families are commonly involved in the synthesis of these secondary metabolites in fungi , such as PKSs , NRPSs , and P450s . Genomic analysis identified five putative PKS genes and seven NRPS genes in the A . oligospora genome ( Figure 5 ) . Among the PKS genes , AOL_s00215g283 is likely involved in the production of 6-methyl salicylic acid ( Figure S2 ) . However , the functions of the seven NRPS genes could not be predicted based on the phylogenetic analysis ( Figure S3 ) . Of the P450 genes , our search against the fungal cytochrome P450 database ( FCPD ) [33] found that the 36 P450 ( Table S8 ) genes in the A . oligospora genome could be classified into four classes ( Group I P450 , Group IV P450 , pisatin demethylase-like P450 , and CYP52 P450 ) . P450 genes are known to be associated with the biosynthesis of diverse classes of secondary metabolites in other fungi [34] . Although the above-mentioned genes may play important roles in pathogenicity , the formation of trap likely involves other genes and is a prerequisite for infection , and serving as the indicator for lifestyle switch from saprophytic to predacious stages in nematode-trapping fungi [1] , [2] , [3] , [4] . At present , little is known about the molecular mechanism of trap formation . To identify the proteins involved in trap formation , a proteomic study was performed and the profiles of intracellular proteins from A . oligospora cells at two developmental stages representing the early nematode extract ( NE ) induction stage ( 10 h after treatment with NE ) and the late stage of trap formation ( 48 h after treatment with NE ) ( Figures S4 and 2B ) were compared . As a negative control , mycelia incubated on medium without NE but with the solvent of NE ( sterile deionized water ) were analyzed . The expressions of 90 and 25 proteins were found up-regulated ( P<0 . 05 ) , while 16 and 94 were down-regulated ( P<0 . 05 ) at 10 h and 48 h , respectively . Most of the proteins up-regulated at 10 h were involved in translation , posttranslational modification , amino acid metabolism , carbohydrate metabolism , energy conversion , cell wall and membrane biogenesis ( Table S7 ) . The results suggest very active growth and metabolism during the transition from vegetative hyphae to trap cells . In contrast , compared to those at the saprophytic stage , the expressions of most proteins up-regulated at 10 h were found either down-regulated or unchanged at 48 h when the traps were already formed , consistent with the hypothesis that the proteins up-regulated at 10 h were likely related to trap formation in A . oligospora . Similar to other organisms , A . oligospora uses signaling cascades to alter its gene expression patterns in response to environmental changes . Genes encoding the components of common fungal signal transduction pathways are all found in the A . oligospora genome . Specifically , glycosylphosphatidylinositol-specific phospholipase C ( AOL_s00109g54 ) , mitogen-activated protein kinase ( MAPK , AOL_s00173g235 ) , serine/threonine protein phosphatase 2A ( regulatory subunit , AOL_s00007g146 ) , calcyclin binding protein ( AOL_s00054g214 ) , and Ca2+/calmodulin-dependent protein kinase ( AOL_s00078g95 ) were all up-regulated ( Table S7 ) during the formation of traps . This result is consistent with the importance of signal sensing and transduction in the shift from saprophytic to carnivorous lifestyles in A . oligospora . Both hyphal growth and trap formation require energy . In A . oligospora , the tricarboxylic acid ( TCA ) cycle was up-regulated in response to NE , as indicated by the enhanced expression of genes in the TCA cycle , such as citrate synthase ( AOL_s00079g361 ) , aconitase ( AOL_s00110g24 ) , isocitrate dehydrogenase ( AOL_s00075g141 ) , succinyl-CoA synthetase ( AOL_s00043g45 ) , and malate dehydrogenase ( AOL_s00210g140 and AOL_s00215g565 ) ( Table S7 ) . In addition , malate synthase ( AOL_s00112g112 ) and isocitrate lyase ( AOL_s00075g130 ) , two key enzymes in the glyoxylate cycle , were up-regulated at 10 h ( Table S7 ) , indicating that this pathway is also important for trap formation . It has been reported that the glyoxylate cycle is associated with fungal virulence [35] . Taken together , our results suggest that the TCA cycle and the glyoxylate cycle are actively involved in A . oligospora trap formation , possibly by providing energy and substrates for macromolecule biosynthesis . Cell division and cell cycle controls also play important roles during A . oligospora trap formation . In this study , the expression levels of Cdc37 ( a molecular chaperone , AOL_s00043g594 ) and Mih1 ( a phosphatases , AOL_s00176g31 ) ( Table S7 ) were up-regulated during the formation of traps ( 10 h ) . In S . cerevisiae and Schizosaccharomyces pombe , Cdc37 is required for maintaining the protein level of a cyclin-dependent kinase Cdc28 , a key enzyme for regulating the G1-S and the G2-M phase transitions [36] . Similarly , Mih1 promotes cell entry into mitosis by removing the inhibitory phosphorylation placed on Cdk1 by Wee1 in S . cerevisiae [37] . In addition , several cytoskeleton proteins such as two actin-binding proteins ( AOL_s00097g552 and AOL_s00079g186 ) , an actin-related protein ( AOL_s00007g186 ) , and a microtubule-binding protein ( AOL_s00097g636 ) ( Table S7 ) were significantly up-regulated at 10 h after exposure to NE . In summary , these proteins facilitate trap formation by increasing mitosis and cell proliferation . The fungal cell wall is a complex structure composed of chitin , glucan and other polymers [38] . Traps are derived from specialized vegetative hyphae that have thicker and more robust cell walls than typical vegetative hyphae [39] . A large number of genes related to cell wall biosynthesis were identified in the A . oligospora genome . Proteomics analysis revealed that the expression levels of several proteins involved in cell wall synthesis , e . g . glycosyltransferase ( AOL_s00097g268 ) , glycosidase ( AOL_s00083g375 ) , phosphoglucomutase ( AOL_s00054g87 ) ( Table S7 ) , were significantly up-regulated during trap formation ( 10 h ) . In addition , increased expressions at the mRNA level for 1 , 3-beta-glucan synthase ( AOL_s00054g491 ) , chitin synthases ( AOL_s00210g37 , AOL_s00075g119 and AOL_s00078g76 ) and glucosamine 6-phosphate synthetase ( AOL_s00076g99 ) were also detected using qPCR ( Table S7 ) . Increased expression of those enzymes required for the biosynthesis of glucan , chitin and glycan undoubtedly facilitate new cell wall formation during trap formation . Trap cells in nematode-trapping fungi contain numerous dense bodies that are related to peroxisomes [2] , [5] , [13] . A number of genes encoding peroxisomal biogenesis factors and related proteins were found ( Table S9 ) in the A . oligospora genome . While no significant change in protein level was found in cells at 10 h after exposure to NE , the qPCR analyses of 10 selected peroxisomal genes revealed that four peroxisomal genes were significantly up-regulated during trap formation ( Table S7 ) . A previous study demonstrated that in M . haptotylum , the transcription level of a peroxisomal membrane protein ( Peroxin-11 ) was up-regulated by 72% in nematode trapping knobs when compared to saprotrophic mycelia [13] . These data suggest that up-regulation of peroxisomal proteins is likely involved in the formation of dense bodies in trap cells in A . oligospora . Glycogen is present in most fungi as a carbon storage molecule . The expression levels of glycogen phosphorylase ( AOL_s00109g17 ) and hexokinase ( AOL_s00112g89 ) were significantly up-regulated during A . oligospora trap formation ( 10 h ) ( Table S7 ) , indicating accelerated glycogen degradation and glycolysis . In M . haptotylum , the glycogen phosphorylase ( gph1 ) gene was also reported to be up-regulated during knob formation [13] . Moreover , the expression level of glycerol 3-phosphate dehydrogenase ( AOL_s00054g748 ) , a key enzyme in the synthesis of glycerol from dihydroxyacetone phosphate , increased by 5 . 7-fold during the formation of traps ( Table S7 ) . Accumulation of glycerol in response to NE was also confirmed experimentally ( Figure S5 ) . In contrast , degradation of fatty acids that generate other substrates for glycerol synthesis was down-regulated as indicated by the decreased expression of several beta-oxidation enzymes such as thiolase ( AOL_s00210g122 ) and 3-hydroxyacyl-CoA dehydrogenase ( AOL_s00110g113 ) ( Table S7 ) . Based on the proteomic and qPCR analyses , glycerol production during trap formation in A . oligospora likely occurred via the glycogen degradation and glycolysis pathways . In appressorium-forming fungi , such as M . grisea , glycerol plays a key role in generating hydrostatic turgor pressure to breach the host cuticles by a mechanical force [40] . The results here are consistent with the hypothesis that glycerol accumulation in A . oligospora likely function similarly to that of M . grisea during the penetration of nematode cuticle . A previous study proposed that the capture of nematodes by A . oligospora was mediated by lectins located on the fungal cell surface [41] . An A . oligospora lectin ( AOL ) was subsequently identified [42] . However , AOL-deletion mutants showed similar saprophytic growth and nematode pathogenicity as their wild-type progenitor strain [42] . Our study revealed that seven genes encoding lectins with different sugar specificities were present in the A . oligospora genome ( Table S10 ) . Interestingly , none of the seven lectin genes , including AOL ( AOL_s00080g288 ) , showed any significant change in expression at either the mRNA or the protein level in response to NE . The adhesive proteins comprising extracellular fibrils on trap cell surface can aid in nematode capturing [2] , [5] . A total of 17 adhesion-associated protein-encoding genes ( Table S10 ) were found in the A . oligospora genome . Our transcriptional analyses via qPCR revealed that the expressions of five adhesin encoding genes were up-regulated at 10 h ( Table S7 ) . One up-regulated adhesion gene , AOL_s00076g567 , is a homolog of MAD1 , an adhesin in the entomopathogenic fungus Metarhizium anisopliae that mediates its attachment to insects [43] . The other four adhesion proteins ( AOL_s00043g50 , AOL_s00007g5 , AOL_s00210g231 and AOL_s00076g207 ) were up-regulated by 5 . 4- , 4 . 2- , 21 . 7- and 1 . 7-fold , respectively , during the formation of traps ( 10 h ) ( Table S7 ) . These data support previous findings that adhesions on trap surfaces are important for capturing nematodes . The combined genomic , proteomic , and qPCR data suggest a model of nematode trap formation in A . oligospora ( Figure 7 ) . In this model , A . oligospora recognizes nematode signals and activates a diversity of downstream cellular processes . These processes include ( i ) increased cell proliferation and septum formation with enhanced cell wall biosynthesis and cytoskeleton assembling; ( ii ) enhanced glycerol synthesis and accumulation leading to increased intracellular turgor pressure in preparation for host colonization and penetration within the nascent trapping cells; ( iii ) formation of dense bodies possibly involving the peroxisome biogenesis; and ( iv ) synthesis of adhesive proteins on the surface of the trapping cells to enhance nematode capture . These complex and coordinated processes are fueled by energy and building blocks supplied from the glyoxylate and TCA cycles .
Nematode trapping device is not only an important indicator of the switch from the saprophytic to the predacious lifestyle , but also a pivotal tool for capturing nematodes for nematode-trapping fungi [2] , [4] . However , due to the lack of genome data of this group of fungi , there is currently limited information about the molecular mechanism of trap formation . Previous studies were mainly focused on the morphology and morphogenesis of traps . In this study , the major biological pathways , and the proteins and genes likely involved in trap formation in A . oligospora were first identified by proteomic and qPCR analyses . And a model on the formation of nematode trapping device in A . oligospora was proposed based on the combined genomic , proteomic , and qPCR data . However , the detailed spatial and temporal dynamics among the cellular processes , and their interactions during trap formation , remain to be elucidated . A . oligospora is the first Orbiliomycete fungus to have its whole genome sequenced . Compared with other pathogenic and non-pathogenic fungi , A . oligospora contained abundant orphan genes not found in other sequenced fungi ( 53 . 64% ) ( Figure 3A ) , a result consistent with the phylogenomic analysis that A . oligospora is phylogenetically very distant from other sequenced Ascomycota ( Figure 4 ) . The A . oligospora specific genes may be related to its complex life-styles . Specifically , A . oligospora is not only a saprophyte , but also a nematode pathogen , a pathogen of other fungi , and a colonizer of plant roots [44] . The genes shared between A . oligospora and 10 other pathogenic and non-pathogenic ascomycete fungi ( 37 . 02% , Figure 3A ) likely represent housekeeping genes for this group of organisms . Interestingly , A . oligospora share many more genes with pathogenic fungi than with non-pathogenic fungi . Those shared between A . oligospora and pathogens likely contribute to fungal pathogencity in general . The distribution patterns of the pathogenicity-related gene families and putative PHI genes in different genome categories suggested that the ao-specific genes and the genes in the ao/pathogen category were more abundant than those in the ao/non-pathogen category . In fact , some pathogenicity-related gene families were completely absent in the ao/non-pathogen category . The genes specific to the ao/non-pathogen category are likely not involved in pathogenicity while the ao-specific genes may be very important for the pathogenicity of A . oligospora . Further analysis of PHI genes showed that carbohydrate-degrading enzymes ( for example , polygalacturonase , xylanase and pectate lyase ) represent a big proportion of ao-specific genes . Similarly , MFS transporter ( 11% ) , pectate lyase ( 6 . 8% ) and short-chain dehydrogenase ( 6 . 8% ) were the common genes specific to the ao/pathogens category . Subtilisins and other pathogenicity-related genes play very important roles during infections [23] , [34] , [45] . As is typical for pathogens and saprobes of animals , the A . oligospora contains abundant pathogenicity genes encoding subtilisins and chitinases . Increasing evidences showed that increasing the copy number of virulence genes can improve the pathogenicity of pathogenic fungi . This strategy has been successfully used in A . oligospora [22] and several fungi used for biocontrol , such as M . anisopliae [46] , Beauveria bassiana [47] and Trichoderma harzianum [48] . The qPCR analysis suggests that the proteases P12 and P186 likely play more important roles than PII during the infection process , and disruption of the gene P186 further confirmed that P186 is an important pathogenicity factor in A . oligospora . Therefore , increasing the copy numbers of P12 and P186 through genetic engineering may effectively improve the pathogencity of A . oligospora and other fungi . Moreover , no obvious phenotypic differences were found between the mutants and the wild strains , suggesting that the P186 may specifically work in pathogenicity without interfering with other metabolic and reproductive processes . The nematode-trapping fungi can kill nematodes by producing secondary metabolites [32] . Several classes of secondary metabolite producing genes were found in the A . oligospora genome , suggesting that the potential repertoire of compounds is quite diverse as predicted . Some of these genes were not found in other closely related species ( Figures S3 and S4 ) and could potentially contribute to producing novel metabolites . Further work may identify new nematicides from these secondary metabolite-producing genes in A . oligospora . Attraction and recognition are the early steps during A . oligospora – nematodes interaction [49] . Nematodes are likely attracted by compounds released from the mycelia of nematode-trapping fungi [50] . However , the chemical attractant ( s ) involved in nematode-fungal recognition remain largely unknown . Recently , it was found that the nematoxic bacterium Pseudomonas aeruginosa could produce acylated homoserine lactones ( AHL ) , the signal molecules in quorum sensing , as attractants for nematodes [51] . Interestingly , a homologous gene involved in biosynthesis of AHL was found in A . oligospora genome . Moreover , in our recent study , a “Trojan horse” mechanism of bacterial pathogenesis against nematodes was reported [52] , in which the bacterium Bacillus nematocida B16 lures nematodes by emitting potent volatile organic compounds ( VOCs ) , and seven VOCs are confirmed to lure nematodes , while the related genes had not been identified . These studies should help us identify the potential compounds involved in attraction and uncover the mechanism of recognition between the nematode-trapping fungi and nematodes . Previous studies showed that lipid droplets accumulate in the trophic hyphae of A . oligospora at later stages of infection , and new vegetative mycelium developed from the trap that had originally captured the nematode when the lipid droplets disappeared [12] . Our results showed that several peroxisomal proteins as well as the key enzymes of the glyoxylate cycle were up-regulated during trap formation , which suggested that part of the nutrients released from the nematodes might first be converted to lipids by the fungus . The lipids were then degraded via the beta-oxidation pathway located on the peroxisome and further metabolized through the glyoxylate cycle to support growth of new vegetative hyphae . However , some of the enzymes involved in the beta-oxidation pathway were down-regulated during the early stage of trap formation . Further studies focusing on individual gene knockouts will be necessary to determine the roles of these enzymes in lipid metabolism during trap formation . Fungi that are pathogenic to invertebrates , especially those targeting nematodes and insects , are of great importance for maintaining ecological balance in natural environments and for improving agricultural production [2] , [53] . Here the first genome of this group of fungi was reported and the key features related to their pathogenicity were described . The information presented here on a nematophagous fungus should facilitate our understanding of these fascinating carnivorous fungi , and provide a basis to analyze the similarities and differences between nematophagous and other pathogenic fungi . These data also have important ramification for the practical development of improved biological control agents . Our proposed model for trap formation based on the comprehensive genomic , qPCR and proteomics analysis could serve as a roadmap for further investigating the molecule mechanism underlying the transition between saprophytic and predatory lifestyles in fungi .
A . oligospora Fres . ( ATCC24927 ) was purchased from the American Type Culture Collection ( ATCC ) and maintained on cornmeal agar ( CMA ) . This fungus was originally isolated from soil in Sweden and provided to ATCC by Nordbring-Hertz B . The A . oligospora was sequenced by the ABI 3730 Sanger sequencing platform to a depth of 2 × . An additional 34 . 6 × sequencing data coverage was provided by the Roche 454 Genome Sequencer Titanium/FLX platforms . GS De Novo Assembler developed by Roche was used to assemble the sequencing data . Repetitive sequences in the genome assembly were identified by searching the Repbase database [54] using RepeatMasker and by de novo repetitive sequence search using RepeatModeler ( http://www . repeatmasker . org/RepeatModeler . html ) . Ab initio gene prediction was performed on the genome assembly by Augustus , GlimmerHMM , and SNAP trained with transcript sequences of A . oligospora from this study , and by GeneMark-ES formulated for fungal genomes . A final set of gene models was selected by EvidenceModeler [55] , combining ab initio gene predictions with supports by transcript alignments from A . oligospora and other fungal species . Predicted genes were annotated by BLAST searches against protein databases , and by InterProScan searches against protein domain databases . tRNAs were predicted by tRNAscan-SE [56] . Ribosomal RNAs were identified by a BLAST search with known rRNA modules of other fungal genomes . Other non-coding RNAs , including snRNAs and miRNAs , were predicted by searching the Rfam database [57] using Infernal ( http://infernal . janelia . org ) . Predicted proteins in A . oligospora were compared with the predicted proteins of 10 sequenced fungal genomes . All proteins were searched against all other proteins in these genomes using BLASTP . The matches with E≤1e−5 and at least 30% sequence identity [58] over 60% of both protein lengths [15] were taken as homologous sequences . Bidirectional best hits ( BBHs ) [16] from the homologous sequences were taken as orthologous sequences . A total of 529 orthologous proteins were obtained and concatenated to infer the phylogenomic relationships among these taxa with PHYLIP [59] using different methods , including Neighbor-joining ( NJ ) , Maximum parsimony ( MP ) and Maximum likelihood ( ML ) . Multigene families were constructed from the homologous sequences based on single linkage transitive closure [15] . A total of 2882 of the 11479 genes were clustering into 789 multigene families . Several gene families related to fungal pathogenicity were manually selected based on previous knowledge and gene annotations . The members of the pathogenicity-related gene families were expanded via homology search followed by clustering based on single linkage transitive closure . In addition , pathogenicity-related genes were also predicted by a whole genome blast analysis against the PHI gene database [21] . The pathogenicity-related gene families of subtilisin , NRPS and PKS were selected to perform phylogenetic analysis . NJ tree was obtained using MEGA 4 . 1 [60] using bootstrap analysis with 1 , 000 replicates . MP tree was constructed using PAUP*4 . 0b8 [61] with a heuristic search ( initial trees were obtained by 100 replicates with random addition and branch swapping with the TBR algorithm ) . Non-parameter bootstrap ( 1 , 000 replicates ) was performed to assess the support level for each node on the MP trees . ML tree was obtained using PHYML version 3 . 0 [62] . Bootstrap analysis with 100 replicates was applied . The RIP indices , TpA/ApT and ( CpA+TpG ) / ( ApC+GpT ) , were determined to detect RIP relics [20] , [63] , [64] . The AT content and RIP indices in all sequences were calculated . Windows of 500-bp and 200-bp with 100-bp shifts were performed separately , for the whole genome as well as individually for the coding regions , non-coding regions , exons , introns , multigene families , and repetitive sequences . RIP regions were detected in the 200-bp windows with 100-bp shifts with TpA/ApT ≥0 . 89 and ( CpA+TpG ) / ( ApC+GpT ) ≤1 . 03 . Quantitative PCR was conducted with 2 µl reverse transcribed product in a 7300 Real-Time PCR system ( Applied Biosystems , California , USA ) using Power SYBR Green PCR Master Mix ( Applied Biosystems ) . 18S rDNA gene was used as the internal control . Fold changes were calculated using the formula 2− ( ΔΔCt ) , where ΔΔCt is ΔCt ( treatment ) -ΔCt ( control ) , ΔCt is Ct ( target gene ) - Ct ( 18S ) , and Ct is the threshold cycle ( User's Manual for ABI 7300 Real-Time PCR System ) . Total proteins were extracted by following the method by Fernández-Acero et al . [65] . Approximately 1 mg of protein samples was brought to a final volume of 170 µl for 2-D analysis . Protein spots were manually excised from Coomassie Brilliant Blue G-250 ( BBI ) stained gels . Proteins were digested by trypsin for 16–20 h at 37°C . Peptides were analyzed by MALDI-TOF using a 4700 series Proteomics Analyzer ( Applied Biosystems ) . Proteins were identified by searching PMF generated against a local database using Mascot algorithm of the GPS software . The Whole Genome Shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession ADOT00000000 . Other genes , accession nos . PII , X94121/AOL_s00076g4; spr1 , AB120125; Cdc37 , Z47813/AOL_s00043g594; Mih1 , JO4846/AOL_s00176g31; Cdc28/Cdk1 , Z36029; Wee1 , X73966; Peroxin-11 ( PEX11 ) , EF419889; gph1 , AY635194; AOL , X97093; Mad1 , DQ438337/AOL_s00076g567 . | The fungus Arthrobotrys oligospora has multiple lifestyles . It's not only a nematode pathogen , but also a saprophyte , a pathogen of other fungi , and a colonizer of plant roots . As a nematode pathogen , A . oligospora forms adhesive networks to capture nematodes and is a model organism for understanding the interaction between these fungi and their host nematodes . In this study , the whole genome sequence of A . oligospora was reported . Our analyses of the proteome profiles of intracellular proteins from cells treated with nematode extracts for 10 h and 48 h revealed a key set of genes involved in trap formation . The changes in protein levels for some trap formation related genes were further confirmed by qPCR . The combined genome and proteome analysis identified the major genetic and metabolic pathways involved in trap formation in A . oligospora . Our results provide the first glimpse into the genome and proteome of this fascinating group of carnivorous fungi . The data should serve as a roadmap for further investigations into the interaction between nematode-trapping fungi and their host nematodes , providing broad foundations for research on the biocontrol of pathogenic nematodes . | [
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| 2011 | Genomic and Proteomic Analyses of the Fungus Arthrobotrys oligospora Provide Insights into Nematode-Trap Formation |
Chikungunya virus ( CHIKV ) is an emerging arbovirus , belonging to the Togaviridae family , Alphavirus genus , transmitted by Aedes spp . mosquitoes . Since 2007 , two different CHIKV strains ( E1-226A and E1-226V ) have been responsible for outbreaks in European countries , including Italy , sustained by Ae . albopictus mosquitoes . In this study , we assessed the susceptibility to the CHIKV E1-226V , strain responsible for the Italian 2007 outbreak , of eight Ae . albopictus populations collected in Northern , Central , Southern , and Island Italy , by experimental infections . Vector competence was evaluated by estimating infection , dissemination , and transmission rates ( IR , DR , TR ) , through detection of the virus in the bodies , legs plus wings , and saliva , respectively . Additionally , vertical transmission was evaluated by the detection of the virus in the offspring . The results of our study demonstrated that the Italian populations of Ae . albopictus tested were susceptible to CHIKV infection , and can disseminate the virus outside the midgut barrier with high values of IR and DR . Viral infectious RNA was detected in the saliva of three populations from Central , Southern , and Island Italy , also tested for TR and population transmission rate ( PTR ) values . No progeny of the first and second gonotrophic cycle were positive for CHIKV . This study strongly confirms the role of Ae . albopictus as a potential CHIKV vector in Italy . This may represent a threat , especially considering both the high density of this species , which is widespread throughout the country , and the increasing number of cases of imported arboviruses .
Chikungunya virus ( CHIKV ) is a zoonotic arthropod-borne virus ( Togaviridae family , Alphavirus genus ) , historically endemic in Africa . Since its first isolation during an outbreak in Tanzania in 1952 , several epidemics in African and Asian continents were reported . The re-emergence of CHIKV was unpredictable , with intervals from 7 to 20 years between consecutive epidemics [1–5] . In 2004 , CHIKV re-emerged , with an explosive onset in Kenya , spreading to the Comoros and the La Réunion islands and , in early 2005 , to other islands in the South-West Indian Ocean . These events were followed by an epidemic in the Indian subcontinent in 2005/2006 . In only 10 years , CHIKV spread across all five continents , from the Indian Ocean region to Asia , to Mediterranean Europe and Central America causing a series of large outbreaks [6–9] . In 2007 , for the first time , CHIKV reached the temperate climate countries of Europe , causing more than 200 autochthonous cases in North-Eastern Italy [10] . In 2010 and 2014 local transmission events were reported also in the South-East of France , with two and twelve cases respectively [11 , 12] , and one autochthonous case was reported in 2015 in the South-East of Spain [13] . More recently in August 2017 , other locally-acquired CHIKV infections , were reported in South-East France ( Le Cannet-des-Maures , Var Department ) , and in three municipalities in Central and South Italy ( Anzio and Rome , on Tyrrhenian coast , Latium region , and Guardavalle on the Ionic coast of Calabria region ) [14–17] . These autochthonous CHIK outbreaks clearly point out the high vulnerability of Europe to the transmission of tropical arboviruses . In urbanized areas , CHIKV transmission is sustained by the anthropophilic Aedes species , such as Ae . aegypti and Ae . albopictus ( known as the tiger mosquito ) able to cause large urban epidemics . In this context , humans act as amplifier hosts capable of developing high viremia ( e . g . 108−9 RNA copies/mL ) [18] , thus infecting other mosquitoes and contributing to the spread of the virus . To date , three virulent CHIKV genotypes have been identified: West African , Asian , and Eastern-Central-South African ( ECSA ) [19] . During the Réunion islands outbreak , the emergence of an Ae . albopictus-adaptive mutation ( E1-226V ) in the Indian Ocean Lineage-IOL strains ( ECSA genotype ) , provided a fitness increase of CHIKV with a shorter extrinsic incubation period ( EIP ) in the Ae . albopictus vector , which was widespread on the islands [18 , 20–24] . This adapted viral genomic variant was involved in the outbreaks occurring in North-Eastern Italy in 2007 and in the South-Eastern France in 2014 and 2017 where Ae . albopictus is widespread [10 , 16 , 25] . Thus , although Ae . aegypti was widely recognized as the main urban vector of CHIKV in tropical areas , Ae . albopictus is considered able to transmit CHIKV in temperate climate areas too . The presence of field-collected mosquitoes positive to the RNA virus highlighted the role of Ae . albopictus as CHIKV vector during the European outbreaks [26 , 27] . Moreover , experimental infection confirmed a high susceptibility of local European Ae . albopictus populations to the mutated ECSA CHIKV strain ( E1-226V ) [28–32] . Although the virus recently detected from the French index case is carrying the E1-226V mutation [16] , the strain responsible for the ongoing outbreaks in Central and Southern Italy , as well as the viral strain detected in France in 2010 , are not carrying this mutation [12 , 15 , 33] . This repeated circulation of both CHIKV strains in Europe has emphasized the importance of evaluating the vector competence of Ae . albopictus from different areas in order to assess the real risk of CHIKV epidemics in temperate zones and to support efficient surveillance and control strategies . This study aims to experimentally evaluate the vector competence of Ae . albopictus and to assess potential susceptibility for CHIKV ( E1-226V ) among mosquito populations , in particular of Central , Southern , and Island Italy . In addition , the mosquito progeny from both the first ( FGC ) and the second ( SGC ) gonotrophic cycle were analyzed in order to assess the possible virus overwintering .
This study was carried out in accordance with the recommendations of the Animal Experimentation protocol ( Decree no . 116/92 , European Directive 86/609/EEC ) . In accordance with this legislation the presence and approval of an Ethic Committee is not required; however , at the Istituto Superiore di Sanità ( Rome , Italy ) , the veterinarians of the Service for Biotechnology and Animal Welfare , performed the functions of local IACUCs . Blood was collected from the ear vein of the rabbit according to the European legislation for the care and the use of laboratory animals . Pig intestine epithelium , used for the membrane feeding system , is a commercially available product [34] . For the experimental infections , eight Italian Ae . albopictus populations , were used: one from Northern Italy: Legnaro ( Padua province , Veneto region ) ; three from Central Italy: Rome ( Latium region ) ; Borgo Vodice ( Latina province , Latium region ) ; Termoli ( Campobasso province , Molise region ) ; four from Southern Italy: Rende ( Cosenza province , Calabria region ) ; Marina di Zambrone ( Vibo Valentia province , Calabria region ) ; Cagliari ( Sardinia region ) and Sant’Antioco ( Carbonia-Iglesias province , Sardinia region ) . Collection sites of tested mosquito populations are reported in Fig 1 . For each population , about 800–1000 adult mosquitoes , originated from field-collections of eggs and larvae in the 2015–2016 summer season , were used to establish the laboratory colonies and were reared for several filial generations ( as shown in Fig 1 ) in the Insectarium of the Istituto Superiore di Sanità before experimental infection . Larvae and adults were reared and maintained following a standardized procedure [35] , mosquitoes were held in a climatic chamber maintained at 27±1°C , 70% relative humidity , and a 14h:10h light-dark cycle . Larvae were reared in a 0 . 3% sodium chloride solution and fed with dry cat food ( Royal Canin srl , 20151 Milan , Italy ) ; emerged adults were maintained in cages and supplied with a 10% sucrose solution . To ensure egg laying , mosquito females were provided a rabbit blood meal by membrane feeding apparatus , consisting of a pig intestine membrane covering the base of a glass feeder ( Vetro Scientifica srl , 00185 Rome , Italy ) containing the blood . For experimental infection 7-day-old mosquito females of the eight Italian Ae . albopictus populations were used . All populations were tested to exclude the presence of CHIKV and dengue virus . About 100 mosquitoes of each population were pooled ( 20 individuals ) , according to the geographic origin and sex , and analyzed by using quantitative Real Time PCR ( qRT-PCR ) . For the experimental infections , CHIKV strain CHIKV/ISS-2007/patient G . P . /M2V2 , isolated on VERO cells from the serum of a patient from Emilia Romagna outbreak in 2007 [10] , was used . The CHIKV stocks were obtained by propagation on VERO cells and then stored at -80°C in aliquots until processed . The viral titer used for experimental infection of CHIKV frozen stock was 6 . 8 log10 Plaque Forming Units/mL ( PFU/mL ) obtained by plaque assay on VERO cells . Experimental infections of mosquito populations were performed in BSL-3 cabinet using an infectious blood meal , composed of 2/3 rabbit blood with EDTA ( Ethylenediaminetetraacetic acid , Sigma-Aldrich Corp , Rockville , MD , USA ) and 1/3 viral seed , with a final concentration of 6 . 3 log10 PFU/mL . Female mosquitoes were allowed to feed for 60 min through a pig intestine membrane covering the base of a glass feeder containing the blood-virus mixture maintained at 37°C by a warm water circulation system . After the infectious blood meal , fully engorged females were transferred into new cages and maintained in a climatic chamber for 12 days , at the same insectarium conditions as described earlier . To determine if virus was present in the body ( head , thorax , and abdomen ) or legs plus wings of the tested mosquitoes , from three to nine specimens of each population were dissected at days 0 , 2 , 3 , 7 , and 12 post exposure ( d . p . e . ) . The length of viral EIP , and the trend of viral particles in the saliva samples of potentially infected females , were evaluated by collecting specimens at all d . p . e . , as reported above , from three Ae . albopictus populations from Central , Southern , and Island Italy ( Borgo Vodice , Rende , and Sant’Antioco , respectively ) . Briefly , after dissection of the legs and wings from the body , mosquitoes were forced to salivate and the proboscis was inserted into a quartz capillary filled with 3 μL of fetal bovine serum ( FBS , Sigma-Aldrich , St . Louis ) . One microliter of 1% pilocarpine ( Sigma-Aldrich , St Louis , MO ) [30] was applied on the thorax . After 30 min , the medium containing the saliva was expelled into a 1 . 5-mL tube containing 500 μL of Mosquito Diluent ( MD ) buffer ( Phosphate Buffer Saline , 20% heat-inactivated FBS , 1% penicillin/streptomycin/amphotericin B mix; Invitrogen , GIBCO ) . Bodies , legs plus wings , and saliva specimens were stored at -80°C until processed [35–37] . To detect a potential vertical transmission of the CHIKV , a sample of potentially infected females was allowed to lay eggs . Larvae from the FGC were reared up to adulthood in the climatic chamber . Samples of adults ( grouped by geographic origin and sex ) were obtained from the early ( 4 d . p . e . ) and late ( 7 d . p . e . ) ovipositions . At 12 d . p . e . an uninfected second blood meal was provided for the remaining females of the Borgo Vodice population and offspring from the SGC ( ovipositions of the 4 and 7 days after the uninfected meal ) were also reared and the adults collected . All samples from FGC and SGC were stored at -80°C and processed as pools of 5–30 specimens . For each mosquito , body and legs plus wings were cold homogenized separately , suspended in 1 mL and 0 . 8 mL of MD buffer , respectively , and centrifuged at 3000 x g for 30 min at 4°C . The supernatants were aliquoted and , together with mosquito’s saliva samples , were used for RNA extraction by using the QIAamp viral RNA kit in accordance with the manufacturer’s protocol ( Qiagen Inc . , Valencia , CA , USA ) . CHIKV titer of infected mosquitoes was evaluated by qRT-PCR , performed by using CHIKV TaqMan primers and probe [10] . Quantification of CHIKV in RNA samples was obtained comparing the crossing points of the values of the standard curve obtained from 10-fold serial dilutions of CHIKV stocks with estimated concentration by titration on VERO cells [35–37] . These values were expressed as plaque forming unit equivalents ( PFUeq ) . Viral isolation was carried out as described by Verani et al . [38] . Briefly , 100 μL of the supernatant fluid of saliva were seeded on a confluent VERO cells monolayer . After 1 hour of incubation at 37°C , 2 mL of medium , consisting of Dulbecco’s MEM , 2% FBS , 1% antibiotic-antimycotic mix ( Invitrogen , Gibco ) , was added . VERO cell cultures were examined daily for 14 days for cytopathic effect ( CPE ) . Vector susceptibility was evaluated by analyzing the following indexes: i ) the infection rate ( IR ) calculated as the number of CHIKV positive bodies with respect to the total number of fed females; ii ) the dissemination rate ( DR ) calculated as the number of specimens with CHIKV-positive legs plus wings among the number of specimens with CHIKV-positive bodies; iii ) the transmission rate ( TR ) defined as the number of mosquitoes with CHIKV-positive saliva among the number of specimens with CHIKV-positive bodies . The potential vector competence was expressed as population transmission rate ( PTR ) , calculated as the number of specimens with CHIKV-positive saliva with respect to the total number of fed mosquitoes [35–37 , 39 , 40] . The non-parametric Kruskal-Wallis test was used to compare the mean titer values in body and legs plus wings among all the mosquito populations tested . To evaluate trends in viral replication in bodies , legs plus wings , and saliva over time ( expressed in d . p . e . ) , nptrend ( nonparametric test ) , developed by Cuzick [41] , was used . The values of TR and PTR among Borgo Vodice , Rende , and Sant’Antioco were compared using Chi-squared test ( or Fisher-Yates test ) . Significant difference was established when p-values were lower than 0 . 05 . Data analyses were carried out with Stata 13 software ( StartCorp LP , Texas , USA ) .
Initially the study was performed to assess the susceptibility to infection and dissemination of CHIKV in eight Italian Ae . albopictus populations representative of the whole country . As shown in Table 1 all Ae . albopictus bodies analyzed at 0 d . p . e . showed qRT-PCR positive results with mean viral titers around 3–4 log10 PFUeq/mL . The analysis of the mosquito bodies exhibited an increase of mean viral titer from 0 to 7 d . p . e . showing that mosquitoes tested were infected and able to permit CHIKV replication in their body . Legnaro , Rome , Termoli , Rende , Marina di Zambrone , and Sant’Antioco reached the peak at 2 d . p . e . with values of 5 . 1±0 . 6 , 5 . 5±0 . 5 , 5 . 1±0 . 6 , 5 . 5±0 . 3 , 5 . 1±nd , and 4 . 8±0 . 3 log10 PFUeq/mL , respectively . Viral RNAs recovered from Borgo Vodice and Cagliari after CHIKV infection showed mean values approximately constant from 2 to 7 d . p . e . even if an increase of the mean titer was found at 7 d . p . e . ( 4 . 9±1 . 0 and 5 . 4±0 . 7 log10 PFUeq/mL respectively ) . Moreover , in all mosquitoes processed on the 12 d . p . e . lower RNA titers were detected if compared with 2 d . p . e . , suggesting a decreasing of the viral replication . The trend of the mean viral titers for all eight populations were comparable and no statistically significant differences were observed ( Kruskal Wallis test p = 0 . 825; nptrend values ranging from 0 . 259 to 0 . 791 ) . Regarding the IR , very high values in all dissected mosquitoes were observed at each collection time with the highest number of infected females starting from 3 d . p . e . ( values ranging from 67% to 100% ) . In particular , cumulative IR percentages , calculated as the total number of mosquitoes infected from 3 to 12 d . p . e . , were very high in all eight tested populations with values ranging from 79–100% ( Table 1 ) . After the 2 d . p . e . , disseminated infection was observed for Borgo Vodice , Termoli , Rende , Marina di Zambrone , Cagliari , and Sant’Antioco while , after 3 d . p . e . it was found in Legnaro and Rome with mean viral titers ranging from 3–4 log10 PFUeq/mL ( Table 1 ) . For all these populations the trend of the mean viral titers were comparable without any statistically significant differences ( Kruskal Wallis test p = 0 . 609; nptrend values ranging from 0 . 319 to 0 . 947 ) . A value of DR higher than 60% was observed in all specimens collected at 7 d . p . e . , with a proportion of the number of mosquitoes CHIKV positive in legs plus wings very high also at 12 d . p . e . ( range 67%-100% ) . Two out of 8 populations ( Marina di Zambrone and Cagliari ) showed a cumulative DR value of 100% , and high cumulative DR percentages were also obtained from the other populations ( range 73–91% ) showing a high susceptibility of all populations tested to CHIKV ( Table 1 ) In order to determine the length of viral EIP , known to be short in Ae . albopictus infected with CHIKV E1-226V variant , three populations representative of Central , Southern , and Island Italy , Borgo Vodice , Rende , and Sant’Antioco respectively , were monitored until 12 d . p . e . and saliva was analyzed . Even if the number of positive saliva was low and some of them were at the limit of detection , a viral trend similar to that obtained in bodies and legs plus wings ( Table 1 ) was observed over time . In addition , the viral titers trend in the saliva showed no significant differences among the three populations analyzed ( nptrend values ranging from 0 . 49 to 0 . 82 ) . As expected the EIP was very short and viral presence was detected at 3 d . p . e . with mean viral titer of 0 . 9 log10 PFUeq/mL for Borgo Vodice , 2 . 3 log10 PFUeq/mL for Rende and 1 . 0 log10 PFUeq/mL for Sant’Antioco ( Fig 2 ) . The maximum value was reached between 3 and 7 d . p . e . in Rende population showing the higher viral titer ( 2 . 3 log10 PFUeq/mL ) at day 3 . In accordance with the viral trend observed in bodies and legs plus wings ( Table 1 ) , the viral presence decreased to 0 . 3 log10 PFUeq/mL and 1 . 6 log10 PFUeq/mL for Borgo Vodice and Rende , respectively and it was undetectable for Sant’Antioco at 12 d . p . e . The highest PTR value was recorded at 3 d . p . e . in Sant’Antioco population ( 50% ) , while at 7 d . p . e Borgo Vodice ( 38% ) and Rende ( 40% ) were found to have the higher number of mosquitoes with positive saliva of those tested ( Fig 2 ) . Moreover , all saliva samples of the three populations tested induced CPE when seeded on VERO cells , confirming the presence of viable CHIKV in these samples . In Fig 2 , cumulative TR and PTR were also showed . Out of the total of infected mosquitoes , 23% of Borgo Vodice , 40% of Rende , and 31% of Sant’Antioco were able to secrete CHIKV by the saliva . Even if the highest cumulative TR and PTR values were recorded in Rende and Sant’Antioco populations , no statistically significant differences in percentages were observed among the tested populations ( p = 0 . 51 and p = 0 . 52 , respectively ) . Despite the high variability of CHIKV viral titers in the saliva and the low number of mosquitoes with positive saliva , these findings clearly demonstrate that in all three Ae . albopictus populations tested , CHIKV is able not only to infect and disseminate very efficiently , but also to reach the salivary glands . It must be taken into account that 2 out of 3 of the mosquito populations ( Rende and Sant’Antioco ) showed CHIKV positivity in their saliva at 12 d . p . e . with detectable value of 1 . 6 log10 PFUeq/mL in Rende ( Fig 2 ) . In order to detect a possible vertical transmission of CHIKV , adult specimens from the FGC ( 182 females and 204 males ) of the mosquitoes of the eight Ae . albopictus populations , exposed to the infected blood meal , were analyzed in pools . Two pools ( 8 females and 37 males ) of the Borgo Vodice population were also processed for the SGC . No evidence of vertical transmission was detected in both FGC and SGC progeny .
Understanding virus-vector interactions remains essential for risk assessment , and additional studies to evaluate differences in vector competence of Ae . albopictus to different CHIKV strains are needed for epidemic preparedness . Moreover , in absence of vaccine and/or specific treatment active surveillance has to be considered the most important approach to control CHIKV outbreaks for providing early warning and for applying appropriate vector control strategies . | Aedes albopictus is a proven vector of more than 20 different arboviruses and , as demonstrated by experimental infections , is an efficient vector of chikungunya virus ( CHIKV ) in several countries . In Italy this widespread species caused the first outbreak of CHIKV in Europe in 2007 ( Emilia-Romagna region ) and recently , after 10 years , two different outbreaks in the Central and Southern part of the country ( Latium and Calabria regions ) were reported . Symptoms of CHIKV are arthralgia and joint pain , skin rash , primarily of the trunk and limbs , but also commonly fever and myalgia . Complications of this disease are rare , but long-term sequelae often occur in a considerable number of patients in particular , in children , the elderly , and patients with chronic diseases . The recent circulation of CHIKV in Italy has highlighted the importance of investigating vector competence of Italian populations of Ae . albopictus from different areas . In this study , we experimentally infected several mosquito populations in order to assess the real risk of CHIKV epidemics in temperate zones and support efficient surveillance and control strategies . | [
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| 2018 | Vector competence of Italian Aedes albopictus populations for the chikungunya virus (E1-226V) |
Interleukin-1 ( IL-1 ) is a large cytokine family closely related to innate immunity and inflammation . IL-1 proteins are key players in signaling pathways such as apoptosis , TLR , MAPK , NLR and NF-κB . The IL-1 pathway is also associated with cancer , and chronic inflammation increases the risk of tumor development via oncogenic mutations . Here we illustrate that the structures of interfaces between proteins in this pathway bearing the mutations may reveal how . Proteins are frequently regulated via their interactions , which can turn them ON or OFF . We show that oncogenic mutations are significantly at or adjoining interface regions , and can abolish ( or enhance ) the protein-protein interaction , making the protein constitutively active ( or inactive , if it is a repressor ) . We combine known structures of protein-protein complexes and those that we have predicted for the IL-1 pathway , and integrate them with literature information . In the reconstructed pathway there are 104 interactions between proteins whose three dimensional structures are experimentally identified; only 15 have experimentally-determined structures of the interacting complexes . By predicting the protein-protein complexes throughout the pathway via the PRISM algorithm , the structural coverage increases from 15% to 71% . In silico mutagenesis and comparison of the predicted binding energies reveal the mechanisms of how oncogenic and single nucleotide polymorphism ( SNP ) mutations can abrogate the interactions or increase the binding affinity of the mutant to the native partner . Computational mapping of mutations on the interface of the predicted complexes may constitute a powerful strategy to explain the mechanisms of activation/inhibition . It can also help explain how an oncogenic mutation or SNP works .
Interleukin-1 ( IL-1 ) is a large family of cytokines ( small cell signaling proteins ) that mediate innate immune responses to defend the host against pathogens . The IL-1 family has 11 member proteins ( IL-1F1 to IL-1F11 ) and they are encoded by 11 distinct human and mouse genes [1]–[3] . The first discovered family members , IL-1α ( newly named IL-1F1 ) and IL-1β ( IL-1F2 ) , are secreted by macrophages and epithelial cells in response to pathogens and have strong proinflammatory properties leading to fever ( affecting hypothalamus ) and activation of T cells and macrophages . IL-1 family members have been intensely studied ( especially IL-1α and IL-1β ) unraveling their roles in a number of autoinflammatory diseases [4]–[6] . Signaling initiated by the IL-1 cytokines increases the expression of adhesion factors on endothelial cells resulting in immune cells ( such as phagocytes and lymphocytes ) migration to the site of infection . The autoinflammatory disease is a class of chronic inflammation with increased secretion of active IL-1β , thus blocking IL-1β is therapeutically beneficial [7] . IL-1α and IL-1β can induce mRNA expression of hundreds of genes , including themselves ( a positive-feedback loop ) , and their gene regulatory actions are conducted via a conserved signaling pathway [8] . Signal propagation mainly depends on mitogen-activated protein kinases ( MAPKs ) , MAPK kinases ( MKK/MAP2Ks ) , MKK kinases ( MKKK/MAP3K/MEKKs ) and the downstream proteins of MAPKs , finally leading to activation of transcription factors that regulate the expression of host defense proteins ( Figure 1 ) . The signal initiates by binding of IL-1α or IL-1β ligands to type I receptor ( IL-1R1 ) and propagates with the help of the co-receptor IL-1 receptor accessory protein ( IL-1RAP ) , forming a trimeric complex [9] . In this trimeric complex , the Toll- and IL-1R–like ( TIR ) domains on the cytoplasmic regions of IL-1R1 and IL-1RAP receptors get close to each other resulting in the recruitment of myeloid differentiation primary response gene 88 ( MYD88 ) , Toll-interacting protein ( TOLLIP ) [7] and IL-1 receptor-associated kinase 4 ( IRAK4 ) [10] , [11] . A stable complex is formed between IL-1 , IL-1R1 , IL-1RAP , MYD88 and IRAK4 [10] . MYD88 binding triggers phosphorylation of IL-1 receptor-associated kinases IRAK4 , IRAK2 and IRAK1 , leading to the recruitment and oligomerization of tumor necrosis factor-associated factor 6 ( TRAF6 ) [12]–[14] . TRAF6 and phosphorylated IRAK1 and IRAK2 dissociate and migrate to the membrane to associate with TGF-β-activated kinase 1 ( TAK1 ) and TAK1-binding proteins TAB1 and TAB2 [7] . The TAK1-TAB1-TAB2-TRAF6 complex migrates back to the cytosol , where TRAF6 is ubiquitinated and TAK1 is phosphorylated [7] . From this point , the signal can propagate via two main paths: IKK – IκB – NF-κB and/or MKK – MAPK/JNK/ERK ( Figure 2 ) . In the first path , phosphorylated TAK1 activates the inhibitor of nuclear factor kappa-B kinase subunit beta ( IKKβ ) and activated IKKβ phosphorylates the nuclear factor kappa-B inhibitor ( IκB ) which gets degraded so that nuclear factor kappa-B kinase ( NF-κB ) is released and migrates to the nucleus [7] . TAK1 can also activate mitogen-activated kinases ( MAPK ) p38 , c-Jun N-terminal kinases ( JNK ) and extracellular signal-regulated kinases ( ERK ) by interacting with MAP kinase kinase ( MKK ) proteins . Downstream in this path , are transcription factors such as c-Jun , c-Fos , c-Myc and ATF2 . MAP kinase signaling pathways , which are conserved among eukaryotes , mediate cellular events triggered by extracellular signals such as cytokine binding [15] and they are essential for IL-1 signaling ( Figures 1 and 2 ) . This pathway builds upon a triple kinase cascade consisting of a MAP kinase kinase kinase ( MKKK/MEKK ) , a MAP kinase kinase ( MKK/MEK ) and a MAP kinase ( MAPK ) and these kinases sequentially phosphorylate and activate each other [15] . The JNK and p38 MAP kinases , called stress activated MAP kinases , have roles in tumor suppression and can be both directly phosphorylated and activated by MKK4 , which is also a tumor suppressor [16]–[18] . The successive activation mechanism takes place as follows: MEKK interacts with inactive MKK and phosphorylates it; the complex dissociates , releasing the free and active MKK , which is ready to interact with inactive JNK to activate it [15] . Activation of JNK leads to disruption of the MKK-JNK interaction , freeing the active JNK to phosphorylate its downstream targets . There are several mechanisms through which stress activated MAP kinases regulate tumor suppression , including promoting apoptosis ( p53 , Bax , Bim/Bmf ) , inhibiting proteins that inhibit apoptosis ( Bcl2 , Bcl-XL , 14-3-3 , Mcl-1 ) , inhibiting tumor development ( TGF-β1 ) and tumor growth ( CDC25 , CyclinD1/CDK4 ) [16] . Somatic mutations were identified in the JNK pathway via large-scale sequencing analyses of human tumor cells [19] , [20] , and JNK3 encoding gene ( MAPK10 ) has been speculated to be a putative tumor suppressor gene as almost half of the brain tumors that were examined included mutations [21] . ERK1 and ERK2 , the other members of MAPK family , are also upregulated in tumors [22] . Recently , inflammation has been related to cancer [23]–[25] . Cancers are mostly due to somatic mutations and environmental factors , and chronic inflammation is implicated by most of these risk factors [26] . Chronic inflammation , due to autoimmune diseases or infections , causes tumor development via several mechanisms , including oncogenic mutation induction [26] . Oncogenic mutations and single nucleotide polymorphisms ( SNPs ) are key players in inflammation-related cancers and it is crucial to map the mutations/SNPs on the corresponding 3D structures of the proteins to gain insight into how they affect protein function [27] , [28] . SNPs that cause diseases , if not in the core of the protein , are frequently located in protein-protein interface regions rather than elsewhere on the surface [26] , [28] . Structural knowledge can clarify the conformational and functional impact of the mutation/SNP on the protein [27]–[29] . The effect of a functional mutation can be expressed by a change in the specificity of the interactions between a mutated protein and its partners [30] . Quantitatively , a mutation changes the binding free energies of the mutant's interactions with its partners with respect to the free energies of its interactions in the native form [30] . The functional impact of the mutation on the specificity differs . The mutation can destabilize the protein and/or its interaction , leading to ‘loss-of-function’; or can lead to a change in the specificity of protein-partner interactions , resulting in a ‘gain-of-function’ , or can gain new binding partners and hence a new biological function , i . e . result in a ‘switch-of-function’ [30] . Two recent studies used structural pathways for mapping mutations on protein-protein interfaces , one on smaller scale pathways [27] and the other on large scale [28] . Mosca et al . [27] mapped mutations onto proteins as an application of their useful computational modeling technique with the data limited to the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) complement cascade pathway and the interactions of complement component 3 ( C3 ) and complement factor H ( CFH ) which it includes . In a pioneering work , Wang et al . [28] explored genotype-phenotype relationships on a large scale . They systematically examined thousands of mutations and mapped them onto interaction interfaces and experimentally validating their predictions for the MLH1-PMS2 , WASP-CDC42 and TP63–TP73 interactions . These and other studies emphasized the need for computational methods for large-scale interactome studies [31] , [32] . We propose a method similar to ones used in the works of Mosca et al . [27] and Wang et al . [28] , while introducing the advantage of in silico mutagenesis to observe the effects of mutations on protein-protein interactions on a large scale . Here , we construct the IL-1 signaling pathway by combining the related pathways and information from the literature [8] , [33]–[35] ( Figure 2 ) . We observe that there are approximately 100 interactions between proteins that have experimentally identified 3D structures . However , only 15 of the interactions have structures of protein-protein complexes ( Figures 3A and S1 ) in the Protein Data Bank ( PDB , http://www . rcsb . org/pdb/ ) [36] . The structural coverage of the pathway is under 20z% . Our major aim is to expand the structural apoptosis pathway [37] , and map oncogenic mutations and SNPs to reveal their mechanism .
We are able to increase the structural coverage from 15% up to 71% by predicting the protein-protein complexes throughout the pathway using the PRISM algorithm [38]–[40] , such that the number of predicted structures of protein-protein complexes that have binding energies lower than −10 energy units is 74 out of 104 interactions ( Figure 3 ) . The predicted structures of complexes are also compared with the 15 available PDB structures of the complexes in the pathway . Except for three where the proteins interact with peptides , all PDB structures could be successfully reproduced via the predictions ( Table S1 ) . A large scale analysis of the distribution of oncogenic mutations and SNPs in the predicted and experimental structures of protein-protein complexes in the IL-1 pathway indicated that both oncogenic mutations and SNPs correspond significantly to interface regions and their ‘nearby’ residues ( Tables 1–4 and Table S2 ) with p-values 0 . 00013 and 0 . 0009 , respectively . Nearby residues are defined as the residues that are at most 6 Å away from interface residues in three dimensional space . Along similar lines , David et al . showed that SNPs , if not in the protein core , are more likely to occur at the protein-protein interfaces rather than the remaining surface [41] . Three oncogenic mutations and a SNP are observed either directly on the predicted interfaces of MKK4 with JNK2 and JNK3 ( the SNP is a computational hot spot residue in the MKK4-JNK3 interaction ) or as nearby residues ( Figure 4 ) . In Figure 4A , the predicted MKK4-JNK2 interaction has a binding energy of −12 . 29 energy units . Analysis of interface residues of the predicted complex reveals that MKK4 Ser251 and JNK2 Gly268 correspond to the interface region . These residues are important because the p . Ser251Asn mutation is involved in metastatic melanoma and p . Gly268Ala is a SNP . Figure 4B shows the predicted complex of MKK4-JNK3 . On MKK4 , Arg154 is mapped to the interface as a computational hot spot residue and its mutation to Tryptophan ( p . Arg154Trp ) is involved in colorectal adenocarcinoma . Moreover , Gln142 is a nearby residue of the interface and the MKK4 p . Gln142Leu mutation is involved in lung squamous cell carcinoma . Although it is known that MKK4 interacts with MAPKs ( JNK1/2/3 and p38α/β ) through its MAPK-docking site ( D-site ) [42] , here we identify a complementary binding interface between MKK4 and JNK2/3 . The D-site is located at the N-termini of MKK4 ( residues 37–52 ) , but the N-termini is missing in all of the 3D structures ( PDB codes: 3aln , 3alo and 3vut; including residues between 80 and 399 ) and hence could not be included in our predictions . However , we modeled a complementary interface , which is not at the D-site but in the kinase catalytic domain . This model can be explained by the finding that although the D-site facilitates the activation of MAPKs through MKK-MAPK signaling , the specificity is also affected by allosteric cooperativity among other binding motifs in the kinase catalytic domains [43] . The mutations involve important residues in the critical domains of MKK4 such as Ser251 in the activation loop and Gln142 in the αC-helix . Gln142 was also proposed by another study [30] to be in the interface of the MKK4-human kinase STK4 interaction and the p . Gln142Leu mutation is speculated to switch-off native partners , gathering non-native interaction partners instead . Also , the MKK4 protein has a large interface for the dimer formed in the experimentally identified crystal structure ( PDB code: 3alnAB ) which includes Gln142 . Similar to the dimer in the PDB , in our predictions both MKK4 and JNK3 interact with each other through their kinase domains , forming a large interface area including Gln142 as a nearby residue . Similarly , a putative interface of MKK-MAPK interaction was found to overlap with the ERK2 dimerization interface [44] . Wilsbacher et al . also listed the important regions on the MAPK for MKK-MAPK binding; one of them is the tip of the C helix [44] , which is in accordance with our model as Gln142 in the αC-helix of MKK4 is one of the mutants and found to be important in MKK4-JNK3 interaction . In short , the predicted interaction does not show a simple recognition ( D-site ) binding but rather involves a specificity binding model . In order to better understand the mechanism through which the mutations may relate to cancer , these residues are mutated computationally and the resulting predicted binding energies of the complexes are compared ( Table 5 ) . After individual energy minimization of the target structures and re-running PRISM on the energetically minimized wild type and mutant targets , we observe models of protein-protein complexes with slightly different interface residues and conformations for the minimum energy solutions . The comparisons are based on these reference interactions . For the MKK4-JNK3 interaction , results are also obtained using a different template ( 1p4oAB ) than the original ( 2ab0AB ) . None of the complexes predicted using the original template is favorable due to the predicted positive binding energy values after the minimization and we are unable to assess mutations based on unfavorable reference interaction . However , predictions of the MKK4-JNK3 interaction based on the 1p4oAB template clearly show the effect of p . Gln142Leu and p . Arg154Trp mutations as the wild type complex is still favorable after the minimization ( −12 . 66 energy units , Table 5 and Figure 5A ) . The mutation of Arg154 to Tryptophan abolishes the interaction as the predicted binding energy becomes positive ( 12 . 84 energy units ) , implying that the interaction is not favorable anymore ( Figure 5B ) . We can explain this change in terms of the specificity as an effect of a loss-of-function type mutation , as MKK4 cannot phosphorylate and activate JNK3 and its tumor-suppression function is lost . On the other hand , Gln142 is a nearby residue and its mutation to Leucine causes a dramatic decrease in the predicted binding energy ( −41 . 14 energy units ) with respect to the newly predicted reference MKK4-JNK3 interaction ( −12 . 66 energy units ) ( Table 5 and Figure 5C ) . We observed that the interaction not only still takes place after the mutation occurs but also it is predicted to have a tighter binding . The contribution of this change to cancer development can be interpreted in two ways: a stronger complex between MKK4 and JNK3 takes place and the interaction , previously transient , gets so strong that it becomes obligate and cannot dissociate so that the activated JNK3 cannot activate its downstream targets , resulting in tumor in predisposed persons; or the essentially constitutively active MKK4 phosphorylates the JNK3 targets and the elevated , unregulated JNK activation leads to tumors as shown previously [45] . A similar case was also previously shown for EGFR , the activation loop of which could not switch to the inactive conformation due to a mutation . Hashimoto et al . showed that single cancer mutations in kinase domains destabilized inactive states [46] . Moreover , our structural analysis explains the experimental data in the study of Ahn et al . [18] , who performed site-directed mutagenesis using human MAP2K4 ( MKK4 ) cDNA in order to create somatic mutants which are subjected to a mutant JNK1 as a substrate and concluded that p . Arg154Trp is one of the loss-of-function mutations whereas p . Gln142Leu is a gain-of-function mutation which results in a highly active MKK4 similar to a synthetic constitutively active mutant . In the second case study , the analysis of the predicted MKK7-JNK3 complex reveals the effect of the p . Arg178Cys and p . Arg178His mutations in MKK7 . Similarly , the individual targets are energetically minimized , in silico mutations are performed , and PRISM is re-run to obtain new values of predicted binding energies ( Table 5 ) . When the values are compared to that of the new reference MKK7-JNK3 interaction ( −11 . 66 energy units ) , a jump in both mutant cases ( Table 5 ) and a significant change in the conformations are observed , making the energies positive so that these interactions cannot take place anymore . Thus , the mutation of Arg178 to Cys and His are both inferred as loss-of-function mutations abrogating the interaction with the JNK3 partner . Since MKK7 is a tumor suppressor , the inhibition mechanism of the MKK7-JNK3 interaction could presumably lead to tumor progression due to the loss-of-function of MKK7 . Finally , we concentrate on two IL-1 pathway specific interactions , namely the interactions of IL-1 with receptors IL1R1 and IL1RAP . After mapping the oncogenic mutations and SNPs onto the interfaces of these receptors , we observe that the predicted structure for IL-1α-IL1R1 interaction is important due to containing 11 SNPs and 5 mutations on its interface and nearby residues ( see the last row of Table 2 ) . Comparison of the predicted binding interface to experimentally reported interactions reveals common residues , confirming the predicted complex structure of IL-1α-IL1R1 . In the work of Labriolatompkins et al . [47] , using oligonucleotide-directed mutagenesis , they determined seven important residues on the binding site of human IL-1α for IL1R1 binding . When we mapped these residues on the PDB structures and compared them to the interface of the predicted wild type IL-1α-IL1R1 interaction ( with −71 . 92 units of predicted binding energy ) , we observed that 4/7 residues ( Arg16 , Ile18 , Ile68 , Trp113 ) are in common and the remaining three residues ( Asp64 , Asp65 , Lys100 ) are nearby residues of the interface . After confirming that the predicted structure is in accordance with related data in the literature , the binding energies of the mutants are compared to the energetically minimized reference interaction ( −19 . 5 energy units ) . Being a ligand-receptor interaction , this complex has a large interface containing 75 residues in addition to 104 nearby residues . Intuitively , we expect to observe insignificant effects of SNPs or single point mutations on the interaction . The results are in parallel with our expectation and there are no significant changes in the predicted binding energies for the interactions with mutant proteins . However , we notice an exception for the p . Ile68Asn SNP on IL-1α ligand ( Table 5 ) . This SNP is observed to be very important for binding as it blocks the interaction of the mutant ligand not only with the wild type receptor but also with most of the mutant receptors ( Table 5 ) . This observation may be supported by the information that Ile68 is both a computational hot spot residue and experimentally determined as critical in binding [47] . Thus , importantly , this SNP may affect the innate immune system and inflammatory response as this interaction is critical for the initiation of IL-1 signaling , with the only alternative pathway being the IL1B ( IL-1β ) -IL1R1 interaction . However , interestingly this is not always the case . A ligand bearing the p . Ile68Asn substitution ( a computational hot spot ) appears to interact with a p . Ile276Thr mutant receptor ( a nearby residue of the interface ) with the energy of −40 . 64 , suggesting that this mutation on the receptor is in fact useful for canceling out the effect of the SNP on the ligand ( Figure 6 ) . Although these two residues are not in contact in three dimensional space ( with a distance of 16 . 1 Å between them ) , the change in the binding energy is significant implying that the difference stems from slight conformational changes . This observation reveals that compensatory changes do not necessarily involve residues in contact . The p . Ile276Thr mutation on IL1R1 is important: according to the COSMIC database [48] , [49] it is related to endometrioid carcinoma and IL1R1 expression was found to be increased in active endometriotic lesions [50] . This fact supports our finding that IL-1α-IL1R1 interaction is still favorable between this mutant receptor and IL-1α with the p . Ile68Asn SNP , and emphasizes the usefulness of our strategy . To conclude , in the absence of experimental data identifying the location of the mutations and SNPs on the protein structures , we are able to computationally clarify the mechanism of inhibition/activation by mapping these mutations on the interface of the predicted MKK4-JNK3 , MKK7-JNK3 and IL-1α-IL1R1 complexes; and our account is corroborated by available experimental data . Here , we reconstructed the IL-1 signaling pathway by combining related pathways and information in the literature , expanding the previously constructed structural apoptosis pathway . By predicting protein-protein complexes throughout the pathway using the PRISM algorithm , the structural coverage of the pathway increased up to 71% . The distributions of oncogenic mutations and SNPs in the predicted structures of protein-protein complexes indicated that they significantly correspond to interface and adjoining residues , and more importantly , in some cases to computational hot spot residues . While oncogenic mutation and SNP data are reported for single proteins , by mapping them onto interfaces we are able to determine the critical binding partners , interactions with whom are affected by the mutations . Additionally , in silico mutagenesis of the corresponding residues and comparison of the change in the binding energies between the wild type and mutant shed light on the mechanism of cancer development , inflammation and other potential diseases . The IL-1α-IL1R1 interaction bearing the p . Ile68Asn and p . Ile276Thr mutations provides one remarkable example in inflammatory response and cancer .
The 3D structures of protein-protein complexes in the reconstructed IL-1 signaling pathway are predicted by using the PRISM ( PRotein Interactions by Structural Matching ) [38]–[40] tool ( Figure 7 ) . PRISM is a large-scale protein-protein interaction prediction , modeling and structure assembly tool [40] that previously was successfully applied to several pathways [37] , [52]–[54] . Two input sets , the template and the target sets , are required by the PRISM algorithm to obtain protein-protein interaction predictions ( Figure 7 ) . The structurally nonredundant ( nonhomologous ) unique interface dataset described by Tuncbag et al . [55] is used as the template set in this work . The dataset was generated by hierarchical clustering of 49512 two-chain interfaces ( extracted from all of the available PDB complexes in the version of February 2006 ) into 8205 clusters . In this work , one representative interface for each of the clusters is combined into the template set . However , the number decreases from 8205 to 7922 due to the update in the PDB ( since 2006 ) and replacement of previous structures with the better ones under different PDB codes . PRISM is a prediction algorithm based on three-dimensional protein structures and therefore it can only be applied to target proteins with known ( experimental or high quality modeled ) structures . The target set may contain a minimum of two proteins and the number of proteins in the set can increase up to any desired number , that is , all the proteins in a given pathway . Here , the target set is composed of IL-1 related PDB chains , the interactions and complex structures of which we want to predict . We focused on every single one of these and applied the prediction algorithm for each of the binary interactions ( 104 edges ) between the targets that have 3D structures ( 50 nodes ) ( Table S3 ) . However , for one interaction there might be more than two structures in the target set as each of the proteins may have more than one experimentally identified structure . We included all available PDB codes for the target proteins in each run to have a complete picture of possible protein-protein complexes . The prediction algorithm has four main steps: target surface extraction , structural alignment of the template interface and the targets , transformation of the targets onto the template and eliminating collisions , and flexible refinement of the resulting complexes ( Figure 7 ) . Firstly , the surface residues of target chains are extracted via the Naccess program [56] which calculates the accessible surface area of residues . The criterion for a residue to be accepted as a surface residue is that its relative surface accessibility should be greater than 5% . In the second step , the structural comparison of the template interface chains with the target chain surfaces are carried out using the MultiProt structural alignment tool [57] based on some filters . For example , if the template chain has less than or equal to 50 residues , then 50% of the template residues should match the target surface residues; if larger than 50 , a 30% match of template to target residues is required . In addition , at least one ‘hot spot’ residue on the template interface should match one of the hot spots on the target surface . In the third step , the resulting set of target surfaces from the previous set are transformed onto the corresponding template interfaces to form a complex so that these two targets are potential partners interacting with each other through an interface similar to the template interface architecture . Following transformation , PRISM eliminates the residues of the target chains that collide with each other . Finally , the FiberDock algorithm [58] , [59] is used to refine the interactions allowing some flexibility , to resolve steric clashes of side chains , to compute the global energy of the complex and to rank the solutions based on the calculated binding energies . The calculated binding score is correlated with the experimental binding free energy and taken as the approximation of the binding free energy function [58] , [60] . We accept a threshold of −10 energy units for a favorable interaction [54] . The detection of computational hot spot residues that are used in the analyses is done by using HotRegion Server [61] . Once all possible structures of protein-protein complexes in the IL-1 signaling pathway are predicted , the analysis of the interfaces in terms of SNP and mutation distribution is done . In addition to mapping the available SNPs and mutations onto predicted complexes and checking whether they correspond to the interface , in silico mutagenesis is also performed to observe the change in the predicted binding energy of the wild type and mutant complexes . The list of SNPs and mutations related to the target proteins is obtained from LS-SNP/PDB which is a web tool for genome-wide annotations of human non-synonymous SNPs mapped to Protein Data Bank structures [62] and COSMIC ( Catalogue of Somatic Mutations in Cancer ) database [48] , [49] , respectively . Based on the list , 394 SNPs and 535 oncogenic mutations are mapped onto the target PDB structures of the proteins in IL-1 pathway and these numbers decrease to 371 and 302 , respectively when only the SNPs and mutations on the PDB surfaces are considered ( Table 1 ) . Then , these SNPs and mutations are mapped onto the interface residues and their nearby residues of both predicted and experimental interactions in the IL-1 pathway ( Table 2 ) . The statistical significance of these mappings are calculated by applying t-test to the following two sets: the ratio of mutations and SNPs mapped on interface and nearby residues to total interface size including nearby residues versus the ratio of mutations and SNPs on a noninterface surface region to total noninterface surface region ( Tables 3 and 4 ) . These ratios are calculated for each of the 77 interactions listed in Table 2 . Note that the surface residues of target chains are extracted via Naccess program [56] as described in the previous subsection . To understand the effect of oncogenic mutations and SNPs , those predicted to be at the interface ( or nearby residues ) are mutated in case studies using the FoldX plugin [63] for the YASARA molecular viewer [64] . There are two steps for computationally mutating the residues: energy minimization and residue change . The “RepairPDB” module of YASARA is used for energy minimization both before and after the residue change and the “BuildModel” module is used for mutation . For setting the FoldX options , default values are used except temperature , which is taken the same as the experimental temperature of the PDB structure . After obtaining the mutant structures , an energy minimization process is performed for the second time and the resulting structures are saved as PDB files and used as new targets for re-running the interaction prediction algorithm PRISM . The predicted binding energy values of the interactions are then compared to the reference values ( Table 5 ) . | Structural pathways are important because they provide insight into signaling mechanisms; help understand the mechanism of disease-related mutations; and help in drug discovery . While extremely useful , common pathway diagrams lacking structural data are unable to provide mechanistic insight to explain oncogenic mutations or SNPs . Here we focus on the construction of the IL-1 structural pathway and map oncogenic mutations and SNPs to complexes in this pathway . Our results indicate that computational modeling of protein-protein interactions on a large scale can provide accurate , structural atom-level detail of signaling pathways in the human cell and help delineate the mechanism through which a mutation leads to disease . We show that the mutations either thwart the interactions , activating the proteins even in their absence or stabilize them , leading to the same uncontrolled outcome . Computational mapping of mutations on the interface of the predicted complexes may constitute an effective strategy to explain the mechanisms of mutations- constitutive activation or deactivation . | [
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| 2014 | The Structural Pathway of Interleukin 1 (IL-1) Initiated Signaling Reveals Mechanisms of Oncogenic Mutations and SNPs in Inflammation and Cancer |
The majority of the proteins encoded in the genomes of eukaryotes contain more than one domain . Reasons for high prevalence of multi-domain proteins in various organisms have been attributed to higher stability and functional and folding advantages over single-domain proteins . Despite these advantages , many proteins are composed of only one domain while their homologous domains are part of multi-domain proteins . In the study presented here , differences in the properties of protein domains in single-domain and multi-domain systems and their influence on functions are discussed . We studied 20 pairs of identical protein domains , which were crystallized in two forms ( a ) tethered to other proteins domains and ( b ) tethered to fewer protein domains than ( a ) or not tethered to any protein domain . Results suggest that tethering of domains in multi-domain proteins influences the structural , dynamic and energetic properties of the constituent protein domains . 50% of the protein domain pairs show significant structural deviations while 90% of the protein domain pairs show differences in dynamics and 12% of the residues show differences in the energetics . To gain further insights on the influence of tethering on the function of the domains , 4 pairs of homologous protein domains , where one of them is a full-length single-domain protein and the other protein domain is a part of a multi-domain protein , were studied . Analyses showed that identical and structurally equivalent functional residues show differential dynamics in homologous protein domains; though comparable dynamics between in-silico generated chimera protein and multi-domain proteins were observed . From these observations , the differences observed in the functions of homologous proteins could be attributed to the presence of tethered domain . Overall , we conclude that tethered domains in multi-domain proteins not only provide stability or folding advantages but also influence pathways resulting in differences in function or regulatory properties .
A large proportion of proteins , coded in the genomes of diverse organisms , is constituted of more than one domain [1 , 2] . Multi-domain proteins have evolved from single-domain proteins through many duplication and adaptive events [3] . Duplication and shuffling of domains have led to the emergence of various unique and novel functions using an existing repertoire of domains [3–5] . Presence of multiple domains in proteins has been reported to confer structural stability [6] and folding and functional advantages [7] . Proteins can be decomposed into domains based on various criteria namely sequence , structure , function , evolution and mobility [8 , 9] . At the sequence level , domains are defined on the basis of conservation of residues over significant length; structural domains are defined on the basis of globularity and compactness; functional domains are modules in proteins which can function independently of other modules in the protein; evolutionary domains are protein modules propagating through evolution by recombination , transposition , shuffling etc . and protein modules with high correlated mobility are identified as domains according to the mobility definition [8] . It is important to note that a given protein may have different but equally valid domain annotations depending upon the basis of domain annotation [9] . Often domains in multi-domain proteins interact with one another . The role of domain-domain interfaces has been implicated in long-range allostery regulation [10–12] , the emergence of a new function [13] , the regulated mobility of the proteins [14] etc . In comparison to protein-protein interfaces , geometrical and chemical properties of domain-domain interfaces have been observed to be intermediate to interfaces in permanent and transient protein-protein complexes [15] . Domain interface size and linker length have been observed to influence the folding and stability of domains in multi-domain proteins [16] . The physiochemical nature of the domain-domain interface [15] , the associated energetic of domain-domain interface [6] and its influence on folding in multi-domain proteins [16 , 17] is well described . A recent review covers extensively the effect of domain tethering on the thermodynamics of the protein and its influence on the protein stability and folding [18] . But how protein domains behave in multi-domain proteins in comparison to single-domain proteins , has largely been unexplored and unaddressed , except some studies on the influence of tethering on the folding pathway [16 , 17 , 19 , 20] . In the current study , we have explored how protein domains behave in multi-domain systems in comparison to single-domain systems . For this , identical protein domains crystallized in two forms ( a ) tethered to other protein domains and ( b ) tethered to fewer protein domains than ( a ) or not tethered to any protein domain were studied . For example , full-length rat DNA polymerase β consists of three domains ( DNA polymerase β N-terminal; DNA polymerase β and DNA polymerase β catalytic ) . Crystal structures are available for full-length protein ( PDB id: 1BPD ) and the two C-terminal domains ( PDB id: 1RPL ) ( Fig 1 ) . For the study , we have compared the properties of the second and third domains in the two crystal forms . This comparison allowed us to study the influence of the first domain on the second as well as the third domains . Further comparative dynamics analyses of homologous protein domains were carried out to understand the functional relevance of tethering of domains . Analyses reveal an intricate coupling between the domains in multi-domain systems leading to alteration in dynamics in 18 protein pairs . Structural and energetics differences were observed in half the numbers of cases studied . Differential dynamics were observed for identical and structurally equivalent functional residues of the homologous protein domain pairs . Our observations strongly suggest that tethering of domains in multi-domain proteins changes the properties of constituent domains , thus regulating the function of the entire protein .
Differences in the conformation of domains were observed in comparative structural analyses of identical protein domain pairs crystallized in two forms ( a ) tethered to other protein domains ( henceforth referred as MD ) and ( b ) tethered to fewer protein domains than ( a ) or not tethered to any protein domain ( henceforth referred as ID ) . Distributions of RMSD and GDT values for the 20 protein domain pairs are shown in Fig 2A . To delineate the differences arising due to differences in crystal packing , RMSD and GDT distributions of the protein domain pairs were compared with the control dataset 1 . The control dataset 1 consists of pairs of identical monomeric proteins . Distributions of structural deviation of the protein domain pairs and control dataset 1 were observed to be significantly different ( two-sample KS test , p-value: 1 . 26e-06 ( RMSD ) , p-value: 8 . 14e-06 ( 100-GDT ) , Fig 2B and 2C ) . This suggests that structural deviations observed in the protein domain pairs are likely to be due to tethering of domains and not due to crystallization artefact . The upper quartile limits of RMSD ( RMSD > 1Å ) and GDT ( 100-GDT > 5 ) distributions of the control dataset 1 were taken as a cut-off to identify the protein domain pairs with significantly different conformations . RMSD and GDT distributions of the protein domain pairs suggest subtle changes in global conformation of the common protein domains for 10 cases ( 100-GDT ≤ 5 ) while 10 cases show substantial changes in the conformation ( 100-GDT > 5 ) ( Fig 2A ) . Since GDT and RMSD give an estimation of structural deviation over the entire length of a protein domain , significant structural deviations at local short stretches can be missed out . All the protein domain pairs were analyzed to identify stretches of residues showing significant structural deviation ( refer structural analysis section in materials and methods ) . Four categories of pairs were observed: ( i ) only domain-domain interface showed significant structural deviation , ( ii ) regions other than the domain-domain interface showed structural deviation but no structural deviations were observed at the domain-domain interface , ( iii ) structural deviations were observed both at domain-domain interface and regions other than the domain-domain interface and , ( iv ) no significant structural deviation was observed between the protein domain pairs . Representative examples of the 4 case types are shown in Fig 2E . 9 out of the 20 protein domain pairs showed structural changes at regions other the domain-domain interface ( S1A Fig ) . Further analysis of the regions with significant structural deviation shows ~14% of such regions harbors functional residues while ~24% harbors domain-domain interface residues . Functional significance of ~62% of the residues cannot be commented upon ( Fig 2D ) . It has to be noted that structural deviations were observed independently of the number of domain-domain interface residues . For example , despite no interaction between the domains in fibronectin , structural deviations are observed ( S1B Fig ) . The observations suggest that tethering of domains can alter the conformation of the constituent domains , with many residues apart from domain-domain interface residues showing significant structural deviation . Previous analyses by del Sol et al . have shown that network property , namely residue centrality of hemoglobin and NtrC differ between the inactive and the active state of the proteins [21] . Residue centrality measures the importance of the residue in maintaining the residue-residue communication network within the protein structure . Domains in conjunction with other domains can be treated as one of the states of the protein domain and the domains in the absence of tethered domains can be treated as another state of the protein domains . Hence , a network approach was undertaken to understand the differences in residue-residue contacts , if any , for the 20 protein domain pairs . To represent residue-residue communication numerically , a network parameter namely communicability centrality ( henceforth referred as coc ) is used . High communicability centrality measure of a residue implies its importance in residue-residue communication in the structure . The distribution of the coc score of ID is observed to be significantly different from MD ( two-sample KS-test , p-value < 2 . 2e-16 ) ( Fig 3A ) . Interface residues also show differences in the coc score between MD and ID ( two-sample KS-test , p-value: 1 . 05e-08 ) ( Fig 3B ) . Since interface residues form intensive contacts at the domain-domain interface in MD , we expected the coc scores to be lower for interface residues in ID than MD , but ~ 30% of the residues show higher coc in ID than MD ( Fig 3B ) . This observation suggests that rewiring of intra-domain residue-residue contacts of interface residues results on tethering of domains . Distribution of coc scores of non-interface residues is also observed to be different between MD and ID ( two-sample KS-test , p-value < 2 . 2e-16 ) ( Fig 3C ) , implying that on tethering of domains in a multi-domain system , many residues which are not part of interface region also undergo changes in the residue-residue contacts . The functional residues did not show a significant difference in the coc distribution ( S2A Fig , two-sample KS-test , p-value: 0 . 04 ) . It has to noted that ~7% ( 291 residues out of 4284 residues ) of residues show significant differences in centrality score ( |centrality score ( MD ) –centrality score ( ID ) | > 1 . 5 ) ( Fig 3A ) . These 291 residues belong to 12 domain pairs in the dataset . Only ~3% of these 291 residues form a part of domain-domain interface regions . Many residues showing a significant difference in coc score ( |centrality score ( MD ) –centrality score ( ID ) | > 1 . 5 ) showed low structural deviation ( S2B Fig ) implying that rewiring of the residue-residue contact can happen without any significant structural deviation . An example of the coc distribution of a domain pair ( fibronectin ) is shown in Fig 3D . Fibronectin domain shows differences in centrality score both at the domain-domain interface residues ( boxed as black in Fig 3D ) as well as residues other than domain-domain interface residues ( boxed as red in Fig 3D ) . Normal mode analysis was used to study the extent of influence of tethering on the dynamics of the constituent domains . Normal modes , accounting for 80% variance of the protein motion , were calculated for each MD and ID of the 20 protein domain pairs . To compare the flexibility of MD and ID normalized summed square fluctuation values were compared . The flexibility profiles were observed to be statistically different for all the domain pairs , except two ( Fig 4A and 4B , two-sample KS test , p-value < 2 . 2e-16 ) . To ensure that the differences are not an artefact of crystal packing , flexibility profiles of ID and MD were compared with two control datasets namely control dataset 2 and control dataset 3 respectively . The control dataset 2 was generated by in silico removal of the tethered domains from MD . The domains in the control dataset 2 ( referred to as AD ) are essentially identical to ID in sequence as well as length . The flexibility profiles of ID and AD were observed to be similar ( S3A Fig ) . The control dataset 3 was generated by in silico ligation of the ID with the tethered domain of MD . This was achieved by superimposing the ID onto MD , followed by in silico removal of the common domain from MD and then ligation of the remaining domains of MD with ID . The multi-domains in the control dataset 3 ( referred to as swapped domain ) are essentially identical to MD in sequence and length . The flexibility profiles of MD and swapped domains were also observed to be similar ( S3B Fig ) . The similarity of the flexibility profiles of the protein domain pairs and the control datasets ensured that the differences observed in the flexibility profiles of MD and ID are a consequence of the tethering of the domains in multi-domain systems than a crystallization artefact . The flexibility of the residues was observed to be different in MD and ID ( Fig 4A ) . ~32% of the residues show higher flexibility in ID than in MD , while ~22% of the residues have higher flexibility in MD than in ID . The rest of the residues have comparable flexibilities . Higher variance in the distribution of flexibility of residues is observed for MD than ID ( Fig 4B ) . The higher variance of the flexibility of residues in MD implies that many residues in MD show higher/lower flexibility than the mean flexibilty . To ascertain further , how the flexibility profiles of interface residues and functional residues differ in MD and ID , the flexibility distribution of the interface residues and functional residues were compared . The interface residues generally show higher flexibility in ID than MD ( Fig 4C ) . A majority of interface residues ( ~70% ) have higher flexibility in ID than in MD . But interestingly , ~30% of interface residues have comparable flexibility in MD and ID . Thus , some of the interface residues retain their rigidity in the isolated state as well . ~36% of the functional residues have higher flexibility in ID than MD while ~18% have higher flexibility in MD than ID ( Fig 4D ) . Hence many functional residues are rigid in MD than ID . Many residues which are neither part of interface nor functional residues show differences in the flexibility profile ( Fig 4D and S3C Fig ) . To ascertain whether the residues showing differences in fluctuation in MD and ID show structural deviation as well , we calculated the correlation between the two . A poor correlation ( Spearman correlation coefficient: 0 . 25 , S3D Fig ) was observed between the differences in fluctuation and structural deviation , suggesting tethering of domains can alter the dynamic properties of protein domain without significant structural conformation change . Residue-residue communication in protein domains is important for the function and structural integrity of proteins . Residues can relay information to other residues either by forming contacts or through synchronization of dynamics . To understand the influence of tethered domain on the synchronization of dynamics of residues in protein domain , the extent of correlation of fluctuation among residues ( henceforth referred as cross-correlation ) was studied . Higher number of residues with high cross-correlation value ( |cross-correlation| ≥ 0 . 7 ) was observed for MD ( ~22% ) as compared to ID ( ~10% ) ( Fig 5A ) . This observation implies that residues show tight coupling ( |cross-correlation| ≥ 0 . 7 ) in the case of MD but no or weak coupling in the ID ( |cross-correlation| < 0 . 7 ) . Moreover , clusters of high correlation were observed in the case of MD; which often corresponded to sub-domains or domains or super-secondary structures in the spatial coordinate . The matrices of MD and ID were observed to have a low similarity ( low Rv coefficient ) for all the domain pairs except two ( Fig 5B ) . A representative example ( fibronectin ) is shown in Fig 5C and cross-correlation matrices for 20 protein domain pairs are shown in S4 Fig . To ensure that differences are not observed due to crystal packing or other artefact , Rv coefficient between cross-correlation of ID and control dataset 2 and cross-correlation between MD and control dataset 2 were calculated ( S5 Fig ) . The comparison ruled out any other factor apart from tethering for the behavior observed . An important point to note here is that this characteristic has been observed irrespective of the number of interactions between the domains . For example , the domains in rat DNA polymerase β do not interact with each other but still , low Rv coefficient is observed ( 1BPD in Fig 5B ) . Molecular dynamic studies were carried out for 3 domain pairs from the dataset to study the synchronization of motions in the domain at all-atom level . These 3 pairs of domains were selected based on the number of interfacial residues between the domains . Tight coupling of motions was observed not only between the C-alpha of residues but also between the side-chains of residues in MD ( S6 Fig ) . While weak or no coupling was observed for side-chains of residues in ID . Thus , molecular dynamics analysis for 3 pairs showed that higher cross-correlation between residues in MD is manifested not only at the backbone level , as observed also from NMA , but also at the side-chain level . All the observations imply that tethering of domains in multi-domain proteins alters the flexibility as well as the synchronization of the fluctuations of residues of the constituent domains . From the network analysis of the structure of the 20 protein domain pairs , it was observed that certain residues show significant differences in the communicability centrality score . We further wanted to study whether this rearrangement in the intra-domain residue-residue contacts , as represented by communicability centrality score , changes the energetic stability of the residues and residue-residue contacts . Frustratometer algorithm [22] was used to study the effect of tethering on energetics distribution of residues . The algorithm calculates a parameter , single residue level frustration ( SRLF ) , for each residue in the structure . Two parameters , configurational frustration index and mutational frustration index , are calculated for all the contact pairs in the structure . SRLF measures the energetic stability of the residue with respect to every other amino acid at that position . Configurational frustration index measures the stability of the contact pair with respect to every other configuration the contact pair can take during the folding process . Mutational frustration index measures the stability of the contact pair with respect to every other amino acid combination at that position . Mathematically , frustration index is the Z-score of the energy of the native with respect to the decoys . A residue or a contact is considered as minimally frustrated if the frustration index is greater than 0 . 78 , highly frustrated if the frustration index is less than -1 and neutrally frustrated if frustration index is in between -1 and 0 . 78 [22] . The frustration indices were calculated for the 20 protein domain pairs . Though the distribution of SRLF of MD and ID were observed to be largely comparable ( two-sample KS test , p-value: 0 . 98 ) ( Fig 6A ) but ~12% ( region II , III , IV , VI , VII and VIII of Fig 6A ) of the residues showed differences in the single residue level frustration ( SRLF ) with 5 residues ( region III and region VII of Fig 6A ) showing drastic substitution from high frustration to minimal frustration and vice-versa . These 12% residues are distributed over the entire domain dataset i . e . each domain pair have at least one residue showing different frustration indices . Residues apart from domain-domain interface residues and functional residues were also observed to differ in the frustration index ( S7A Fig ) . Moreover , differences in the frustration index of MD and ID were observed to be independent of the structural deviation observed . Equivalent numbers of substitutions were observed at structural deviation greater than 1Å and lower than or equal to 1Å ( Fig 6D ) . Similar trends as that of SRLF were observed for configurational frustration and mutational frustration ( Fig 6B , 6C , 6E and 6F ) but a higher number of contacts showed differences in configurational frustration type as compared to mutational frustration type . Many residues which are neither domain-domain interface residues nor associated with function showed differences in the frustration type of contact ( S7B and S7C Fig ) . The differences suggest that when a protein domain tethers to another domain not only the stability of entire domain [6] or the folding rates differ as reported earlier [16] but the stability of the residues as well contact pairs changes for few cases . Since a larger number of contacts were observed to be configurationally frustrated ( higher the configurational frustration index; more stable the conformation during the folding process ) in comparison to mutationally frustrated , it hints that the domains may sample different conformations during the folding process in MD and ID , as have been reported earlier in literature for some multi-domain proteins [16–20] . To understand further the influence of tethering of domains on the function of proteins , a comparative analysis was performed for homologous domain pairs , where one member is a single-domain protein while the other member is a part of a multi-domain protein . Both the members are full-length gene products . Four pairs of proteins namely ( a ) phosphoribosylanthranilate isomerase from E . coli ( PDB id: 1PII ) and Jonesia denitrificans ( PDB id: 4WUI ) , ( b ) cyclophilin from Bos taurus ( PDB id: 1IHG ) and Homo sapiens ( PDB id: 3ICH ) , ( c ) sialidase from Micromonospora viridifaciens ( PDB id: 1EUT ) and Homo sapiens ( PDB id: 1SO7 ) and , ( d ) hexokinase-1 from Homo sapiens ( PDB id: 1HKC ) and Saccharomyces cerevisiae ( PDB id: 3B8A ) were studied . The four domain pairs have sequence identity in the range of 27–56% with RMSDs in the range of 1 . 3–2 . 2Å ( Fig 7 ) . Since the homologous proteins differ in their amino acid sequences , only the dynamic properties of the protein were compared . The dynamics of the proteins were studied using normal mode analysis . For the comparative analysis , in-silico multi-domain chimeras of the single-domain proteins were generated . This was achieved by superposing the single-domain protein on the multi-domain protein , followed by in-silico removal of the homologous domain from the multi-domain protein and ligation of the domains . This in-silico protein will henceforth be referred as a chimera . For the hexokinase-1 protein , since the two-functional domains show gene duplication , the chimera was generated by superposing the single-domain on both the domains of multi-domain . Thus the two halves of the chimera of hexokinase-1 are identical . The flexibility and the cross-correlation coefficient of the functional residues were compared between single-domain proteins , multi-domain proteins and the chimeras for understanding the influence of tethering of domains on the function of proteins . Only topologically equivalent and identical functional residues of the homologous domain pairs were compared to minimize the influence of nature of residues . Normalized square fluctuations of functional residues were compared between the single-domain and multi-domain proteins . The functional residues have lower flexibility ( normalized square fluctuation < 0 ) in both single-domain and multi-domain proteins ( Fig 8 ) . Residues important for function or structural integrity are known to show lower flexibility [23] . Nonetheless , the flexibility of functional residues is lower in the multi-domain proteins as compared to the single-domain proteins ( Fig 8 ) . The flexibility of functional residues in the multi-domain protein and the chimera is observed to be similar ( S8 Fig ) except in the case of sialidase . This observation implies that increase in the rigidity of functional residues is a consequence of tethering of domains in multi-domain proteins . The differences in the flexibility of the functional residues can contribute towards differences reported in the functions of homologous protein domains , which are discussed later . To further understand the alteration in the dynamic properties of the domain , cross-correlation of the functional residues were studied . High correlation of motions was observed among functional residues for multi-domain protein in comparison to single-domain proteins ( Fig 9 , upper row ) . The single-domain proteins showed weaker cross-correlation among residues for all the cases ( Fig 9 , middle row ) . The cross-correlation between functional residues was comparable between the multi-domain and chimera for all the cases , except hexokinase-1 ( Fig 9 , lower row ) . The observations suggest that alteration in the synchronization of motion is a consequence of tethering . For cyclophilin , the multi-domain protein is known to be less sensitive to cyclosporin as compared to single-domain cyclophilin [24] . Detailed analysis of cyclophilin single-domain protein showed the cyclosporin binding pocket shows low cross-correlation because of the closing movement of the pocket; but the multi-domain cyclophilin is superseded by domain-domain motion , where the functional residues move in the same direction resulting in high cross-correlation values ( S9 Fig ) . This differential dynamics can provide a rationale for the lower sensitivity towards cyclosporin of the multi-domain protein in comparison to single-domain protein . The closing movement of the functional residues in the single-domain protein can hold the ligand better than the observed motion of the residues in the multi-domain protein . The single-domain hexokinase-1 protein has higher Km ( 300 μM ) [25] as compared to multi-domain protein ( 32 μM ) [26] . The glucose-binding pocket is at the interface of sub-domains for both multi-domain and single-domain protein . The sub-domain movement in single-domain protein is superseded by the domain movement in multi-domain protein . The low-frequency global motion in multi-domain protein allows better-synchronized motion of the binding pocket as compared to single-domain protein ( Fig 9D ) . Weaker correlation between residues in single-domain hexokinase-1 as compared to multi-domain hexokinase-1 can explain the different Km , despite identical binding protein . From these analyses , we argue that tethering of domains influences the function of the constituent domains . Chimera hexokinase-1 also exhibited an interesting feature . Though the structure and sequence of the two protein domains in the chimeric hexokinase-1 is identical , the domains exhibited different flexibility profile ( S10A Fig ) . It has to be noted that while constructing the chimera of the yeast hexokinase-1 , a stretch of 9 amino acids from the C-terminal of the first domain and a stretch of 9 amino acids from the N-terminal of the second domain were removed to relieve short contacts at the domain-domain interface region and linker region . To ensure that the differences are not observed due to this specific amino-acids deletions , the flexibility profile of the natural single-domain yeast hexokinase-1 ( 3b8a in S10B Fig ) was compared with the in-silico generated model of the yeast single-domain hexokinase-1 with 9 amino acids deleted from the N-terminal ( 3b8a_N in S10B Fig ) and the in-silico generated model of the yeast single-domain hexokinase-1 with 9 amino acids deleted from the C-terminal ( 3b8a_C in S10B Fig ) . The flexibility profiles were observed to be identical ( S10B Fig ) , implying that the differences in the flexibility profile are only due to the tethering of domains and not due to deletion of the amino-acids . The observations suggest that the differences observed in the constituent domains of multi-domain protein depend on the order of the domain in the multi-domain proteins . The cross-correlation between the functional residues in the N-terminal and C-terminal domain also differs ( S10C Fig ) . A number of positively correlated motions were observed in the C-terminal domain than in N-terminal domain . 6 pairs of functional residues viz . 173–210 , 173–211 , 174–210 , 173–211 , 176–210 and 176–211 exhibit anti-correlation motion in the N-terminal domain while the same residue pairs exhibit positively correlated motion in the C-terminal domain . We hypothesize that the differences in the nature of correlation of the fluctuation of the functional residues in the N-terminal and C-terminal domain may have given rise to the differential functional activity of the two domains in human hexokinase-1 at the first duplication event during evolution . The C-terminal of the human hexokinase-1 is catalytically active while N-terminal is catalytically inactive .
Conformational and structural alterations have been observed in proteins as they bind to other proteins [27 , 28] . This line of thinking is extended in the current work to understand the structural , dynamic and energetic effects of tethering of protein domains in multi-domain proteins on the constituent domains . The extent of similarity between the physical and geometrical properties of protein-protein interaction and domain-domain interaction in multi-domain proteins [15] motivated us for the study . A dataset of 20 protein domain pairs of known 3-D structure has been used in the analysis . Each pair comprises of an entry with one or more domains of a multi-domain protein and the other entry has at least one additional domain tethered . Fifty percent of the protein domain pairs show differences in the global conformation on tethering . Rewiring of some intra-domain residue-residue contacts was observed in 12 protein domain pairs . Normal mode and molecular dynamics analyses of the domain pairs suggested that the flexibility of residues differs between domain in isolation and domain in multi-domain protein . Tight coupling of fluctuation was observed between residues in multi-domain proteins as compared to domain in isolation for all the domain pairs except one . These differences in the fluctuation and coupling of fluctuation are observed due to the shift from low-frequency local motion in isolated domain to low-frequency global motion in multi-domain systems . The stability of ~12% of residues and residue-residue contacts changed on tethering in all the domain pairs . Many of the differences in the intra-residue contacts , dynamics and energetics of the residues were observed without any significant structural deviation . These results strongly suggest that tethering of domains in multi-domain proteins influences the conformation , intra-domain residue-residue contact map , dynamics and the stability of residues and residue-residue contact of domains . Structural , dynamic and energetic differences were observed for many residues apart from domain-domain interacting residues in many domain pairs . These differences at regions spatially away from domain-domain interface could have allosteric origin; where the domain-domain interface region is the orthosteric site , the regions showing alteration are the allosteric site and the perturbation being tethering of domains . Allosteric alteration of proteins by altering the flexibility or correlated motion of the side-chains has been reported for some proteins [12 , 29–32] . For example , the isolated WW domain and PPIase domain of human Pin1 protein has been shown to retain substrate binding and isomerase activity in vitro; but genetic studies showed that the WW domain is essential for in vivo Pin1 activation [12 , 29 , 30] . The WW domain regulates the activity of the PPIase domain by altering the flexibility and the extent of correlation of motion of side-chain of the three catalytic loops without much conformational changes [12 , 29 , 30] . To gain further insights on how tethering of domains influences the function of proteins , comparative dynamics analyses were carried out for 4 pairs of homologous domains , where a member in a pair is a multi-domain protein and the other member is a single-domain protein which is a homologue of one of the domains in the other protein in the pair . In each pair , only identical and structurally equivalent functional residues were analyzed . Functional residues were observed to be more rigid in all the multi-domain proteins than the single-domain proteins . This rigidity of functional residues is observed due to superseding of the low-frequency local motion of the single-domain protein by the low frequency global domain-domain motion in the multi-domain proteins . The low-frequency global domain motion alters the synchronization of residue-residue motion of functional residues in multi-domain proteins as compared to single-domain homologues . Differences in the catalytic activity reported for these homologous domain pairs can be a manifestation of these alteration in fluctuations . Combined with our observations on the identical domain pairs , it can be concluded that tethered domains in multi-domain proteins influence the function of domains by affecting the dynamics of the domains . Identical functional residues were observed to have different dynamics depending on the domain order , as exemplified by the chimera hexokinase-1 in our study . The N-terminal and the C-terminal domains of the chimera hexokinase are identical in sequence and conformation , but the flexibility and the synchronization between functional residues differ between the two domains . Similar observation was made by Kirubarkaran et . al . Artificial two-domain proteins were generated by fusing the natural protein domains PDZ3 and SH3 with five artificial domains . Observed differences in the fluctuation of the residues in PDZ3/SH3 domains were found to be dependent on the order of the domain construct for many cases [33] . These observations suggest that domains are not tethered during evolution at random but as a design to modulate the function of the constituent domains . Since dynamic alterations are observed in all the domain pairs; irrespective of the number of interface residues , size of the constituent domains , directionality of domain order or the fold ( as defined in SCOP ) of the domains ( Table A and B in S1 Text ) , it can be concluded that dynamic allosteric regulation of domains is an intrinsic property of multi-domain proteins . This observation reinforces reports by others in literature that allostery is an intrinsic property of globular protein and allosteric regulation is prevalent in many multi-domain proteins [11 , 34–36] . Alteration in the dynamics of the domain without any significant conformational difference by the tethered domain can be a great tool by evolution to modulate the function of same domain in different multi-domain proteins without altering the fold or structure of the domain , which otherwise can be an expensive process . Alterations in the covalent structure of proteins such as post-translational modifications are known for causing changes in the conformation and/or nature of dynamics at the site of modification and around [37–40] . For example , phosphorylation of the activation loop of kinases such as cAMP-dependent kinase and CDK is well known to alter the conformation of the kinase extensively , enabling transition between inactive and active forms [41–43] . In our work , we considered pairs of identical domains , one in isolation and the other tethered to another domain . This pair can be viewed as though the domain in isolation is “modified” covalently in the other structure in the pair i . e . a domain and a domain linker region is covalently attached at one of N or C-terminus of the domain of interest . Clearly , this “covalent modification” in the terminus will have an influence on the structure/dynamics of the domain in the neighborhood of covalent attachment or possibly , even at a distant site . Interactions between the domain-domain linker and the flanking domains are common for all the examples studied in this work . Indeed , such interactions are present even in the examples where the direct domain-domain interactions are not present as the two domains are spatially well separated , for example cyclophilin and hexokinase-1 . We believe that interactions between domain-domain linker and the domain of interest play a significant role in conferring alterations in structure , dynamics and correlated motions we observe in comparison with isolated domains . Since alterations in dynamics were observed independent of the number of amino acids in the linker ( Table A and B in S1 Text ) , we believe that the effects depend on the presence of linker than the length of the linker . Role of linker residues in the allosteric communication between domains has been suggested by others as well in the literature [11 , 33 , 44–47] . All these observations suggest that the tethered domain and linker region can act as a scaffold for allosteric modulation of domains . The study presented here can be further exploited in designing new domain combination with desired activity .
Proteins in the datasets were structurally aligned using TM-align [51] . For the same-domain dataset , the structural variations were studied at the global and local level . Global Root Mean Square Deviation ( RMSD ) and Global Distance Test–Total Score ( GDT-TS ) [52] score were used to define global deviations . GDT-TS , henceforth mentioned as GDT , is used to define structural similarity between domains of identical sequences . Unlike RMSD it is largely insensitive to outliers arising especially due to differences in loop conformations . It is defined as the number of alpha carbons falling within a distance cut-off from the corresponding Cα of the other structure . MAXCLUSTER , an improved version of the maxsub algorithm [53] , with a cut-off of 4Å was used for calculation of GDT score . High GDT scores are indicative of a low structural deviation between the proteins . For studying structural variation at the local level , regions of residues that show significant structural deviation as compared to other regions of the structure were compared . For this , the distance between corresponding Cα atoms of the protein pairs after superimposing the structure onto each other was calculated . All the residues , whose distance between corresponding Cα ( s ) is more than twice the standard deviation from the mean of the distance distribution of all the residues , were identified as region showing significant structural deviation . For homologous protein domain pair , only RMSD has been calculated to quantify the structural differences . To capture differences , if any , in residue-residue communication within proteins; undirected and unweighted networks of protein structures were constructed . The network was constructed for repaired structures ( refer following section on dynamics ) . Each node in the network represents Cα and each edge represents the interaction between the nodes provided the distance between Cα atoms is less than or equal to 5Å . Network property namely communicability centrality was calculated using NetworkX [54] module of python . Communicability centrality quantifies the extent to which a node communicates with its neighbour . High communicability centrality measure of a residue implies its’ importance in inter-residue communication in protein structure . Numerically , it is the summation of all the closed walks of all lengths starting and ending at a node . To study dynamics of domains , we have used two approaches namely normal mode analysis ( NMA ) and molecular dynamics . Crystal structures were energy minimized using GROMACS package [55] with conjugate gradient as the energy minimization method . Prior to energy minimization , the structures were repaired for missing residues and missing atoms . The missing residues were modelled using Rosetta 3 . 4 [56] and missing atoms were built using WHAT IF 10 . 1a algorithm [57] . Normal modes were calculated by generating coarse-grained anisotropic network model ( ANM ) for proteins , with 15 Å as the cut-off for connecting the nodes . Distance-dependent spring constants ( the closer the nodes , stiffer is the edge ) were used for the edges . Calculation of normal modes as well as the associated calculations and analyses were done using the ProDy package [58] . For the analyses , only the normal modes contributing to 80% variations were studied and fluctuation values contributed by first five N-terminal residues and last five C-terminal residues were removed . Furthermore , correlation of fluctuation between each residue pairs , termed as cross-correlation , was compared . The similarity between cross-correlation matrices has been measured using distance independent measure called Rv coefficient [59] . Rv coefficient measures the closeness of a set of points represented as a matrix . It is a multivariate generalization of Pearson correlation coefficient . Molecular dynamics was performed to study the correlation of fluctuation at the all-atom level for 3 pairs . Molecular dynamics was performed using GROMACS package . The proteins were simulated using Charmm 27 force field [60] and SPC water model [61] in a dodecahedron box . The system was energy minimized using steep descent after addition of appropriate counter ions to balance the charges . The system was appropriately equilibrated for 100 ps using V-rescale and 100 ps using Parrinello-Rahman . The final production run was performed once for 400ns . Energetics calculation was performed only for a dataset of identical protein domains . As the homologous protein domains differ in sequence identity , it is futile to compare their energetics . Frustratometer algorithm [22] was used to perform the energetics calculation . The algorithm systematically perturbs each residue and contact to generate the decoys and compute energy according to Associative Memory Hamiltonian with Water-mediated interaction energy function ( AMW ) [62] . Then the energy of the native protein is compared with the energy distribution of the decoys to calculate the frustration index , which is the Z-score of the energy of the native with respect to the decoys . A residue or a contact is considered as minimally frustrated if the frustration index is greater than 0 . 78 , highly frustrated if the frustration index is less than -1 and neutrally frustrated if frustration index is in between -1 and 0 . 78 as defined in [22] . AMW is a coarse-grained energy function where the backbone is represented as Cα , O and the side chain is reduced to Cβ , the position of N and C is generated considering the ideal geometry of the peptide bond . AMW energy function consists of five non-local energy terms namely Lennard-Jones 6–12 potential , H-bond potential , compactness potential , burial potential and water-mediated interaction potential . A pair of amino acids is considered to form a contact if the inter Cα distance is less than or equal to 5Å . Each contact is perturbed either by mutating each interacting residue pair to every other amino acid pair but keeping all other interaction parameters same as the native structure . Then the effective energy of the native contact is compared with the decoys to access the energetic stability of the contact to mutation . So , it provides a qualitative measure of the energetic feasibility of mutation of such contacts . The frustration index calculated by this method is termed as mutational frustration index . Another way of perturbing the contacts is by displacing the location of each contact thus sampling the possible configurations which can be taken by the contacts during folding . The frustration index calculated in such a way is termed as configurational frustration index . Similar to contacts , each residue is perturbed to every other amino acid and other configurations to evaluate the stability of residue in the native structure to all these perturbation . The frustration index calculated by this method is termed as Single Residue Level Frustration ( SRLF ) . All the statistical analyses were performed using R package . | High prevalence of multi-domain proteins in proteomes has been attributed to higher stability and functional and folding advantages of the multi-domain proteins . Influence of tethering of domains on the overall properties of proteins has been well studied but its influence on the properties of the constituent domains is largely unaddressed . Here , we investigate the influence of tethering of domains in multi-domain proteins on the structural , dynamics and energetics properties of the constituent domains and its implications on the functions of proteins . To this end , comparative analyses were carried out for identical protein domains crystallized in tethered and untethered forms . Also , comparative analyses of single-domain proteins and their homologous multi-domain proteins were performed . The analyses suggest that tethering influences the structural , dynamic and energetic properties of constituent protein domains . Our observations hint at regulation of protein domains by tethered domains in multi-domain systems , which may manifest at the differential function observed between single-domain and homologous multi-domain proteins . | [
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| 2018 | Same but not alike: Structure, flexibility and energetics of domains in multi-domain proteins are influenced by the presence of other domains |
Protein aggregation into amyloid fibrils and protofibrillar aggregates is associated with a number of the most common neurodegenerative diseases . We have established , using a computational approach , that knowledge of the primary sequences of proteins is sufficient to predict their in vitro aggregation propensities . Here we demonstrate , using rational mutagenesis of the Aβ42 peptide based on such computational predictions of aggregation propensity , the existence of a strong correlation between the propensity of Aβ42 to form protofibrils and its effect on neuronal dysfunction and degeneration in a Drosophila model of Alzheimer disease . Our findings provide a quantitative description of the molecular basis for the pathogenicity of Aβ and link directly and systematically the intrinsic properties of biomolecules , predicted in silico and confirmed in vitro , to pathogenic events taking place in a living organism .
A wide range of proteins has been found to convert into extracellular amyloid fibrils , or amyloid-like intracellular inclusions , under physiological conditions [1 , 2] . Such proteins have largely been identified through their association with disease , although a number have been found to have beneficial physiological functions in organisms including , amongst others , bacteria [3] , yeast [4] , and humans [5] . Indeed , the ability to aggregate and assemble into amyloid-like fibrils has emerged as a common , and perhaps fundamental , property of polypeptide chains [1 , 6 , 7] . This discovery has stimulated extensive biophysical and mutational analysis of the underlying molecular determinants of amyloid fibril formation . These studies have resulted in the derivation of general models , based on physicochemical parameters , that both rationalise and predict the propensity of proteins to convert from their soluble forms into intractable amyloid aggregates in vitro [8–10] . The misfolding and aggregation of proteins in vivo , however , differ from similar processes taking place under in vitro experimental conditions , in that they occur in complex cellular environments containing a host of factors that are known to modulate protein aggregation and protect against any subsequent toxicity [11] . This difference between in vitro and in vivo experimental conditions represents a significant barrier to the development of a molecular understanding of protein aggregation in living systems and its consequences for disease . In this paper we describe the results of an approach designed to bridge this divide by expressing a range of mutational variants of Aβ42 in a Drosophila model of Alzheimer disease [12] and correlating their influence on the longevity and behaviour of the flies with their underlying physicochemical characteristics .
The expression of the Aβ42 peptide ( coupled to a secretion signal peptide ) in the central nervous system of Drosophila melanogaster results in both intracellular and extracellular deposition of Aβ42 , along with neuronal dysfunction , revealed by abnormal locomotor behaviour and reduced longevity [12–14] . Learning and memory deficits are also observed in flies expressing Aβ42 and to a lesser extent in those expressing Aβ40 . Importantly , the severity of the cognitive deficits is closely correlated with the magnitude of the locomotor and longevity phenotypes [14] . Our system , as with other recently developed invertebrate models of neurodegenerative disease , therefore produces clear , quantitative phenotypes that allow rapid and statistically robust assessments of the effects of mutations [15 , 16] . Using an algorithm described previously [8 , 10] we computed the intrinsic aggregation propensities ( Zagg ) of all 798 possible single point mutations of the Aβ42 peptide and also of the more toxic E22G Aβ42 peptide . A total of 17 mutational variants , with a wide range of aggregation propensities ( Table 1 ) , were then expressed throughout the central nervous system of Drosophila melanogaster , and their effects were compared to those of wild-type ( WT ) and E22G Aβ42 expression . The longevity of multiple lines of flies ( n = 4–6 independent lines ) for each variant was compared to that of flies expressing the WT or E22G Aβ42 peptide . This pooling of data from multiple independent lines for each Aβ42 mutant studied serves as a control for the potential variation in expression levels between transgene insertion sites . In addition , the locomotor ability of a representative selection of the Aβ42-variant-expressing flies was assessed to provide a measure of the early effects of the peptides on neuronal dysfunction . Examples of the results of this analysis are shown for four of the variants studied ( Figure 1 ) . Flies expressing the WT Aβ42 peptide have a median survival of 24 ± 1 d; flies expressing the E22G Aβ42 peptide associated with familial Alzheimer disease have a median survival of only 8 ± 1 d . In contrast , some of the peptide variants are less harmful . For example , flies expressing F20E Aβ42 have a median survival of 29 ± 1 d ( Figure 1A ) , and flies expressing I31E/E22G Aβ42 peptide have a median survival of 27 ± 1 d ( Figure 1B ) , representing substantial increases in longevity compared to WT and E22G Aβ42 flies . Furthermore , the longevity of these variants is comparable to that of flies expressing the Aβ40 peptide ( median survival = 30 ± 1 d; Figure S1A and S1B ) , which has been previously shown to be non-toxic when expressed both in transgenic flies [12 , 13] and in transgenic mice [17] . F20E Aβ42 and I31E/E22G Aβ42 flies also have very significantly improved locomotor ability compared to WT and E22G Aβ42 flies ( Figure 1C and 1D; Videos S1 and S2 ) and are comparable in locomotor performance to flies expressing the Aβ40 peptide ( Figure S1C and S1D ) . We also analysed a range of Aβ42 variants that were more harmful than the WT peptide; for example , flies expressing the E11G or M35F variants of the Aβ42 peptide have significantly shorter lifespans than WT Aβ42 flies ( median survival = 21 ± 1 and 15 ± 1 d , respectively; Figure 1E and 1F ) . Quantitative analysis of all 17 Aβ variants studied reveals a highly statistically significant correlation between the propensity of a variant to aggregate ( Zagg ) and its effect on the survival of the flies ( Stox ) ( Figure 2A; r = 0 . 75 , p = 0 . 001 ) . A significant correlation is also observed when we analyse the relationship between the predicted aggregation propensity ( Zagg ) of a representative selection of Aβ variants and their effects on mobility or locomotor performance ( Mtox ) ( Figure 2B; r = 0 . 65 , p = 0 . 009 ) . We have also verified that correlations exist between the measured aggregation rates ( Kagg ) and both Stox and Mtox for a representative selection of the Aβ42 variants , as we would expect from our predictions ( Figure 3 ) . Whilst our analysis reveals a significant relationship between the aggregation propensity of Aβ42 and its effects on neuronal integrity in vivo , it has also uncovered a small number of variants that do not conform to this trend , most notably the I31E/E22G Aβ42 peptide . In order to determine the significance of such divergent behaviour for the origins of Aβ42 pathogenicity , we selected one peptide whose effects on the longevity and mobility of the flies is well predicted by its Zagg ( F20E ) and one whose effects did not correlate with its Zagg ( I31E/E22G ) and performed further analysis of their aggregation in vitro and in vivo . The F20E mutation is predicted to reduce significantly the propensity of the Aβ42 peptide to aggregate ( Table 1 ) . Indeed , when we measure the rate of aggregation using thioflavin T fluorescence we find that F20E Aβ42 does aggregate significantly more slowly in vitro than the WT Aβ42 peptide ( t1/2 = 44 and 11 min , respectively; Figure 4A ) , in good accord with our predictions . The in vivo aggregation of the F20E Aβ42 peptide is also significantly reduced compared to that of the WT Aβ42 peptide . Anti-Aβ42 immunohistochemistry using a C-terminal-specific antibody that binds an epitope ( Aβ residues 35–42 ) [18] that does not include the residues being studied here , reveals progressive accumulation of Aβ42 in the brains of WT-Aβ42-expressing flies from 10 d of age , with extensive deposition being evident by day 20 ( Figure 4B ) . In contrast to this behaviour , flies expressing F20E Aβ42 show no signs of Aβ42 deposition at day 20 ( Figure 4C ) . Quantitative reverse transcription polymerase chain reaction ( qRT-PCR ) analysis of Aβ42 transcription levels was also carried out on WT Aβ42 and two independent lines of F20E Aβ42 fly brains to ensure that the reduced deposition and toxicity of the F20E Aβ42 peptide was not due to coincidentally lower transcription levels . In fact , the F20E Aβ42 transgene was transcribed at slightly higher levels than WT Aβ42 ( Figure S2 ) in both lines tested . That the F20E Aβ42 peptide does not form in vivo deposits , despite being able to form amyloid fibrils in vitro ( albeit significantly more slowly than WT Aβ42 ) suggests that the F20E mutation reduces the aggregation propensity of Aβ42 sufficiently to allow cellular clearance mechanisms such as proteases ( e . g . , neprilysin ) [13] to prevent its accumulation in vivo . We conclude , therefore , that the increased longevity and locomotor performance of F20E Aβ42 flies are indeed attributable to a measurable reduction in the aggregation propensity of this peptide in vivo , as predicted by our analysis . In the case of the I31E/E22G Aβ42 variant there appears to be no correlation between its predicted aggregation propensity ( which is very similar to that of the highly pathogenic E22G Aβ42 peptide; Table 1 ) and its effects on longevity and locomotor behaviour in the fly ( Figure 1B and 1D ) . However , studies of the I31E/E22G and E22G Aβ42 peptides in vitro show that , as predicted by our algorithm , they aggregate at very similar rates ( t1/2 = 7 and 4 min , respectively; Figure 4D ) . Furthermore , anti-Aβ42 immunohistochemistry reveals similar levels of deposition in the brains of both E22G- and I31E/E22G-Aβ42-expressing flies at 8 d of age ( Figure 4E and 4F ) that cannot be accounted for by variations in transcription level as measured by qRT-PCR ( Figure S2 ) . Together these observations confirm that our predictions of aggregation propensity are accurate for these peptides in vivo as well as in vitro . To determine the consequences of peptide deposition on the integrity of the brain , we looked for the presence of vacuoles , which are a well-documented sign of neurodegeneration [19] . Despite comparable levels of deposition , the vacuoles seen in the brains of E22G-Aβ42-expressing flies are entirely absent from the brains of I31E/E22G-Aβ42-expressing flies . In this case , therefore , the relationship between the presence of Aβ42 deposits and the functional and anatomical integrity of the brain does not appear to hold . This observation is reminiscent of the finding that there are cases in which the presence of Aβ plaques in the brains of elderly humans , and indeed in transgenic mouse models of Alzheimer disease , does not correlate with cognitive ability [20 , 21] . It has been proposed , in explanation of this finding , that the neuronal dysfunction and degeneration historically attributed to the presence of Aβ amyloid fibrils in the brains of patients with Alzheimer disease may in fact be caused by the concomitant presence of prefibrillar aggregates [22–24] . With this in mind , the unexpected in vivo effects of variants such as the I31E/E22G Aβ42 peptide prompted us to develop a second algorithm ( see Materials and Methods ) by analysing a set of data for which the rates of formation of protofibrils containing β-sheet structure have been reported [8] . This algorithm is able to predict the propensity of other polypeptides to form protofibrils . Whilst there are a few Aβ42 variants ( including I31E/E22G Aβ42 ) whose global aggregation propensities ( Zagg ) do not correlate well with their in vivo effects on neuronal dysfunction ( Figure 2 ) , we find that the predicted propensities of these variants to form protofibrillar aggregates ( Ztox ) correlate very strongly with their in vivo effects ( Stox , r = 0 . 83 , p < 0 . 00001; Mtox , r = 0 . 75 , p = 0 . 001; Figure 5 ) . We propose , therefore , that the effects of all Aβ42 variants in the flies can be directly attributed to their effects on the intrinsic propensities to form deleterious protofibrillar aggregates . It is extremely interesting in this regard that a comparison , using electron microscopy , of the morphology of E22G and I31E/E22G Aβ42 aggregates formed under identical conditions reveals the presence of a significant quantity of protofibrils in the former and only well-defined fibrils in the latter ( Figure S4 ) . Furthermore , we propose that it is possible to predict accurately in silico the in vivo effects of the Aβ42 peptide from a knowledge only of the intrinsic physicochemical properties of its constituent amino acids . We believe that this approach to understanding the determinants of protein misfolding in vivo will be applicable to many other diseases as we have demonstrated previously that the physicochemical parameters that determine the aggregation propensity of Aβ also determine the aggregation behaviour of a wide range of both disease- and non-disease-related proteins [10 , 25] . It is also remarkable that , despite the fact that the intrinsic aggregation propensities of typical protein sequences vary by at least five orders of magnitude [25] , we have been able to achieve profound alterations in the pathogenic effects of Aβ42 by increasing or decreasing its propensity to aggregate by less than 15% . This result suggests that proteins implicated in misfolding diseases are likely to be extremely close to the limit of their solubility under normal physiological conditions [26] , and consequently the small alterations in their concentration , environment , or sequence , such as occur with genetic mutations [27] or with increasing age [23] , are likely to be the fundamental origin of these highly debilitating and increasingly common conditions [28] . In conclusion , we have presented accurate , quantitative measurements of the relationships between the manifestations of neuronal dysfunction in a complex organism , such as locomotor deficits and reduced lifespan , and the fundamental physicochemical factors that determine the propensity of the Aβ42 peptide to aggregate into protofibrils . These results provide compelling evidence that , despite the presence within the cell of multiple regulatory mechanisms such as molecular chaperones and degradation systems [29] , it is the intrinsic , sequence-dependent propensity of the Aβ42 peptide to aggregate to form protofibrillar aggregates that is the primary determinant of its pathological behaviour in living systems .
Mutant Aβ42 expression constructs were produced by site-directed mutagenesis of the WT Aβ42 sequence in the pMT vector ( Invitrogen ) and were subcloned into the pUAST vector . Transgenic Drosophila expressing the desired Aβ42 variants were generated according to the procedures described by Crowther et al . [12] . All survival assays were carried out as described previously [12] . Survival curves were calculated using Kaplan–Meier statistics , and differences between them analysed using the log rank method . All survival times in the text are given as median ± standard error of the median . For previously characterised control lines expressing either WT or E22G Aβ42 , the survival of one representative line was measured . For each novel mutational variant of Aβ42 , between four and six independent lines were analysed ( n = 100 for each line ) in order to control for variability in expression levels between individual lines due to the varying location of transgene insertion . The effect of a mutational variant on survival ( Stox ) was calculated by comparing the survival time of the flies in which it was expressed ( Smut ) to the survival of Aβ40-expressing flies ( Smax ) used as a negative control in the same experiment: Stox = ( Smax − Smut ) /Smax . The locomotor ability of the flies was assessed in a 45-s negative geotaxis assay . Flies were placed in a plastic 25-ml pipette and knocked to the bottom of the pipette . The number reaching the top of the pipette ( above the 25-ml line ) and the number remaining at the bottom ( below the 2-ml line ) after 45 s was measured . The mobility index was calculated as ( ntop− nbottom + ntotal ) /2ntotal . Two representative lines were tested for each novel mutant Aβ42 and one line for each previously characterised control ( WT Aβ42 and E22G Aβ42 ) . Three independent groups of 15 flies each were tested three times at each time point for each line . Differences between genotypes were analysed by ANOVA . The effect of each mutational variant on locomotor performance ( Mtox ) was calculated by fitting the decline in mobility index over time to a straight line and then estimating the time at which each mutant line of flies had declined to a mobility index of 0 . 5 . Immunohistochemistry analysis was performed as described previously [12] on single representative lines for each genotype using the G2–11 anti-Aβ42 antibody ( The Genetics Company ) . Representative lines of F20E- and I31E/E22G-Aβ42-expressing flies were chosen to have median survivals within 1 d of the combined median survival determined for each genotype . The propensity to form amyloid aggregates ( Zagg ) was calculated using an approach described previously [10] . Briefly , for a given protein , Zagg is obtained by averaging the propensities that are above zero in the aggregation profile . All the propensities are normalised into a variable that has an average of zero and a standard deviation that equals one ( the normalisation is made using the propensities of a set of random sequences ) . In a profile there can be residues with a propensity larger than one , but these peaks are usually sparse and their contribution is diluted upon averaging . Consequently , sequences with an overall Zagg score larger than one are very rare . In order to calculate the propensity for forming protofibrillar aggregates ( Ztox ) , we developed a method based on an equation containing the same physicochemical contributions used to calculate the propensity for fibrillar aggregation , but with specific weights determined using a set of experimentally determined protofibrillar aggregation rates for the protein acylphosphatase [30] . A Web server for calculating Zagg and Ztox is available at http://rd . plos . org/10 . 1371_journal . pbio . 0050290_01 . All peptides were dissolved in trifluoroacetic acid and sonicated for 30 s on ice . The trifluoroacetic acid was removed by lyophilization and the peptides were then dissolved in 1 , 1 , 1 , 3 , 3 , 3-hexafluoro-2-propanol and divided into aliquots that were dried by rotary evaporation at room temperature . The amount of peptide in the aliquots was determined by quantitative amino acid analysis . The peptides were dissolved at a concentration of 30 μM in 50 mM NaH2PO4 ( pH 7 . 4 ) and incubated at 29 °C with continuous agitation . At regular time intervals , 5 μl of the peptide solution was removed and added to 100 μl of 20 μM thioflavin T in 50 mM Gly-NaOH ( pH 8 . 5 ) . Fluorescence intensity was measured at 440 nm excitation and 480 nm emission using BMG FLUOstar OPTIMA . The rate of aggregation ( k ) was determined by fitting the plot of fluorescence intensity versus time to a single exponential function y = q + Ae ( −kt ) [30] , and t1/2 was calculated using t1/2 = ln2/k . Twenty flies expressing each variant of Aβ42 were collected at day 0 ( i . e . , on the day of eclosion ) for each transgenic line to be analysed . The flies were then anaesthetised and decapitated , and the heads were collected and snap frozen in liquid N2 . Total RNA was extracted from the fly heads using the Qiagen RNeasy mini kit with on-column genomic DNA digestion using DNAse 1 . The concentration of total RNA purified for each line was measured using a NanoDrop spectrophotometer . One microgram of RNA was then subjected to reverse transcription using the Promega Reverse Transcription System with oligo dT primers . qRT-PCR was performed using a BioRad iCycler and Absolute QPCR SYBR Green Fluorescein Mix ( ABgene ) . Each sample was analysed in triplicate and with both target gene ( Aβ42 ) and control gene ( RP49 ) primers in parallel . The primers for the Aβ42 PCR were directed to the 5′ end of the signal secretion peptide sequence and the 3′ end of the Aβ coding sequence: forward , GCATTCGTGAATTCATGGCGAGCAAAGT; reverse , TACTTCTAGATCCTCGAGTTACGCAATCAC . The RP49 primers were designed across an intron to avoid amplifying any residual genomic DNA contamination: forward , ATGACCATCCGCCCAGCATCAGG; reverse , ATCTCGCCGCAGTAAACG . Relative expression levels were calculated using the Livak method . | A wide range of diseases , including diabetes and common brain diseases of old age , are characterised by the deposition of protein in the affected tissues . Alzheimer disease , the most common neurodegenerative disorder , is caused by the aggregation and deposition of a peptide called Aβ in the brain . We have previously developed a computational procedure that predicts a particular peptide or protein's speed of aggregation in the test tube . Our goal was to test whether the speed of aggregate formation that we observe in the test tube is directly linked to the brain toxicity that we see in our fruit fly model of Alzheimer disease . We made flies that produce each of 17 variant forms of Aβ and show that the toxicity of each variant is closely linked to the tendency of each variant to form small soluble aggregates . Our computational procedure has previously been shown to be applicable to a wide range of different proteins and diseases , and so this demonstration that it can predict toxicity in an animal model system has implications for many areas of disease-related research . | [
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| 2007 | Systematic In Vivo Analysis of the Intrinsic Determinants of Amyloid β Pathogenicity |
Aging and longevity are considered to be highly complex genetic traits . In order to gain insight into aging as a polygenic trait , we employed an outbred Saccharomyces cerevisiae model , generated by crossing a vineyard strain RM11 and a laboratory strain S288c , to identify quantitative trait loci that control chronological lifespan . Among the major loci that regulate chronological lifespan in this cross , one genetic linkage was found to be congruent with a previously mapped locus that controls telomere length variation . We found that a single nucleotide polymorphism in BUL2 , encoding a component of an ubiquitin ligase complex involved in trafficking of amino acid permeases , controls chronological lifespan and telomere length as well as amino acid uptake . Cellular amino acid availability changes conferred by the BUL2 polymorphism alter telomere length by modulating activity of a transcription factor Gln3 . Among the GLN3 transcriptional targets relevant to this phenotype , we identified Wtm1 , whose upregulation promotes nuclear retention of ribonucleotide reductase ( RNR ) components and inhibits the assembly of the RNR enzyme complex during S-phase . Inhibition of RNR is one of the mechanisms by which Gln3 modulates telomere length . Identification of a polymorphism in BUL2 in this outbred yeast population revealed a link among cellular amino acid availability , chronological lifespan , and telomere length control .
The observation that dietary restriction promotes longevity in organisms ranging from yeast to primates raises the expectation that molecular mechanisms mediating this lifespan extension may also be shared among species . In support of the idea that related genetic circuitry controls aging in different species are the findings that genetic or pharmacological modulations of the conserved nutrient responsive pathways , such as target of rapamycin ( TOR ) [1] or insulin-like-growth factor ( IGF-1 ) [2] , increase lifespan in a wide range of species including mammals . The budding yeast Saccharomyces cerevisiae has become a popular model for studying the genetic and molecular basis for variation in lifespan . Two different forms of aging have been studied in yeast . Replicative lifespan ( RLS ) is defined by the number of daughter cells that are generated by a budding mother cell whereas chronological lifespan ( CLS ) is defined as the ability of yeast cells to survive in stationary phase as judged by the their capability to reenter the cell cycle after nutrients are reintroduced [3] , [4] . The two types of aging in yeast are thought to have their counterparts in mammals as the aging of dividing stem cells or the aging of non-dividing cells such as neurons or muscle cells , respectively . In addition to replicative and chronological aging , mutant yeast cells dividing in the absence of telomerase components exhibit loss of viability [5] similarl to replicative senescence of human fibroblasts in culture [6] . Recent epidemiological studies of human populations demonstrated a correlation between reduced leukocyte telomere length and overall mortality [7] , suggesting a link between telomere maintenance and organismal aging . Furthermore , life stress has been shown to influence leukocyte telomere length [8] , establishing a role for environmental stress in telomere stability . Little is known about how these processes connect , though twin studies suggest that both telomere length regulation and longevity in humans have a strong genetic component [9] , [10] . Most of what we have learned about telomere maintenance mechanisms and the genetics of aging comes from model organisms where the effects of the single gene changes can be examined independently from other genetic alterations . However , because natural populations are genetically diverse , differences in aging and telomere maintenance are more likely to result from the integration of effects of polymorphisms at multiple loci . In order to gain insight into telomere maintenance in genetically diverse populations , we have previously employed an outbred yeast model consisting of 122 haploid progeny derived by a cross of vineyard RM11-1a ( RM ) and laboratory S288c yeast ( BY ) [11] . Parental strains differ at 0 . 5% of their nucleotides and the progeny have been genotyped at >3000 markers , allowing for quantitative trait locus ( QTL ) mapping . In a previous telomere length study , we identified several loci that control telomere variation in this cross [12] . In this study , we used the same outbred model to explore chronological aging as a complex trait . During the course of these studies , we found that that one of the loci that controls chronological lifespan is identical to a major locus found to control telomere length , suggesting a previously unrecognized link between the two yeast aging-related phenotypes . This was an intriguing finding because changes in telomere length are linked to DNA replication , while chronological aging occurs in non-dividing cells . Furthermore , the two phenotypes were regulated in opposite directions by this locus: strains that inherited the vineyard allele had shorter telomeres and longer lifespans . We found that a single amino acid substitution in Bul2 , a component of an ubiquitin ligase complex which polyubiquitylates amino acid permeases and regulates their presence at the cell membrane , controls cellular amino acid availability and is responsible for the variation in both telomere length and CLS . We also elucidated a pathway by which decreased cellular amino acid uptake conferred by the BUL2 polymorphism and the consequent inhibition of nutrient-responsive TOR1 signaling lead to reduced telomere length .
To determine chronological lifespan of the 122 haploid progeny ( segregants ) from the RM/BY cross , strains were grown in YPD medium in 96-well plates to stationary phase , where cells maintain metabolic activity but cease mitotic division . Chronological lifespan ( CLS ) studies are often done in synthetic media , where yeast lifespans can be analyzed in a few weeks [13] . Because of the observation that the use of synthetic medium in CLS studies exposes cells to lifespan-limiting acidification [14] , we decided to carry out segregant CLS analysis in YPD where acidification of the media during culture outgrowth is not a problem . After intervals of approximately 30 days , we harvested 1 µL of each stationary phase culture , spotted culture dilutions on YPD plates , and determined viability of cultures as the ratio of microcolonies after 24 hours of growth to the total cell number plated . We found excellent correlation ( R = 0 . 98 ) between the cell viability determined using our microcolony method and the viability measured using colony forming ability ( Figure S1 ) . The vast majority of cultures were found to be fully viable after the initial interval of 5 days in stationary phase ( Figure 1A ) . Further along in stationary phase , segregant culture viabilities decreased to an average of 70% after 31 days ( range 30–91% ) , 35% after 59 days ( range 11–62% ) and 20% after 100 days ( range 0 . 5–40% ) . The observed viability distributions of the chronologically aged segregant strains displayed several interesting features . First , the variation in viability between segregants was continuous , suggesting that multiple genetic loci control survival among the segregants . Second , we observed that the parental strains' phenotypes are in the middle of the range . Such transgressive segregation , in which the segregant progeny exhibit more extreme phenotypes than either parental strain , suggests the presence of compensatory genetic loci within both the RM and BY parental backgrounds . Finally , the rank of the segregant viabilities was not static , as illustrated by the changing order of the parental strains over time , which suggests that different genes are responsible for early and late viabilities . We used genome-wide linkage analysis to identify the loci ( QTL ) responsible for the variation in chronological lifespan . Each segregant strain has been characterized for BY or RM inheritance at 2 , 956 polymorphic markers across the genome [11] . Using genome-wide linkage analysis , phenotype distributions can be compared between segregants that inherit the BY or RM sequence at each locus . A significant difference between the two distributions establishes a linkage between the trait of interest and the genomic sequence near the tested polymorphic marker . We found that stationary phase survival is linked to several genetic loci , consistent with the observed continuous range in viability ( Figure 1B–1D , Table S2 ) . We also noticed that the strength of linkage of the mapped loci changes with time . The chromosome 13 linkage , for instance , has LOD scores >3 . 5 at 31 and 59 days , yet it has no role in controlling viability after 100 days in culture . On the other hand , the chromosome 14 linkage had the opposite temporal pattern: not significant at day 31 yet has LOD scores >3 . 5 at day 59 and 100 . The alteration of the relative importance of different loci at different time points suggests that cells depend on different cellular processes during early and late stages of chronological lifespan . Comparison between the genome scan for loci that control chronological lifespan and our previous analysis for loci that control telomere length ( Figure 1E ) revealed that the strongest linkage for chronological lifespan at day 31 ( chromosome 13 locus ) is congruent with a previously identified locus that controls telomere length [12] . The segregant strains which inherited the RM allele of chromosome 13 locus had longer CLS ( 65% versus 56% viability at 30 days ) and shorter telomeres ( 261 bp versus 286 bp ) compared to strains which inherited the BY allele of the locus . In order to determine whether other mutants with short or long telomeres exhibit either reciprocal effects or alterations in CLS in general , we examined a panel of deletion mutants known to have telomere length alterations and found no correlation between telomere length changes and CLS ( Figure S2 ) . Likewise , a more general comparison of CLS and telomere length , using data from the recent global CLS study [15] and our previous telomere length screen [12] , did not reveal any correlation between telomere length and CLS ( Figure S2 ) . While we found no general correlation between telomere length and CLS , the striking overlap of genetic linkage between telomere length and chronological aging in this cross led us to hypothesize that these two traits are both controlled by a common polymorphism and that identifying the responsible gene may reveal an unexpected link between telomere maintenance and chronological aging . Among the polymorphisms in the mapped region , we identified one in the coding region of BUL2 , a gene encoding a component of the Rsp5p E3-ubiquitin ligase complex involved in amino acid permease sorting . During growth in the presence of rich nitrogen sources , high affinity amino acid permeases , such as the general amino acid permease GAP1 and the proline transporter PUT4 , are polyubiquitylated by a complex consisting of Bul1 , Bul2 and Rps5 , which specifies vacuolar-targeting of permeases for degradation [16] , [17] . Cellular amino acid permease activity can be monitored using the toxic proline analogue ADCB , which is transported across the cell membrane via nitrogen-regulated PUT4 and GAP1 [18] . We found that the parental RM and BY strains exhibit a striking difference in ADCB sensitivity when grown with a rich nitrogen source ( Figure 2A ) . Consistent with higher permease activity and amino acid intake relative to the RM strain , the BY strain was not able to grow at concentrations of ADCB that were non-toxic to the RM strain . Genome-wide linkage analysis of ADCB sensitivity in the segregants demonstrates that the BUL2-containing locus underlies the parental differences in permease activity ( Figure 2B ) . The BY strain carries a single Leu883Phe substitution relative to the RM version of Bul2 , which is conserved among many fungal homologs ( Figure 2C ) and all but three of the sequenced S . cerevisiae strains ( F883L is present in S288c and the two baking isolates YS2 and YS9 ) [19] . Engineering the RM allele of BUL2 into the BY strain restored ADCB resistance , whereas substitution of the BY BUL2 allele into the RM strain resulted in ADCB sensitivity ( Figure 2D ) . These findings indicate that the BY BUL2 Phe883Leu polymorphism confers a loss of Bul2 function , similar to that of a bul2Δ mutant , and increases permease activity and amino acid uptake . We next evaluated whether the same BUL2 polymorphism that controls cellular permease activity also mediates chronological lifespan and telomere length variation . The replacement of BUL2 in the BY parental strain with the RM allele led to an increase in chronological lifespan ( from 55% to 65% viable cells at 30 days in YPD medium ) , which was similar in magnitude to the increase in chronological lifespan conferred by the RM BUL2 allele in the segregants ( Figure 3A , 3B ) . Conversely , the replacement of the RM BUL2 allele with the BY BUL2 allele in the RM parental strain decreased chronological lifespan ( 67% versus 62% ) after 30 days . We next examined the effect of BUL2 alleles on the time-dependant viability curves in both laboratory and vineyard background using the synthetic media that is commonly used for CLS studies . ( In order to minimize the viability reduction due to media acidification , we used buffered SC medium [14] ) . Consistent with previous reports , we observed that CLS is shortened in SC medium compared to YPD , however , restoration of BUL2 function using RM BUL2 allele in the laboratory strain extended chronological life span even more robustly than we have observed in YPD ( Figure 3C ) . BUL2 replacement in the vineyard strain with the hypomorphic BUL2BY allele shortened CLS and deletion of BUL2 led to further reduction in CLS ( Figure 3D ) , which parallels the effect of BUL2 allele replacement and BUL2 deletion on cellular permease activity in the vineyard strain , judged by increased ADCB sensitivity in the BUL2BY alleles and BUL2 deletion ( Figure 2D ) . The effects of BUL2 allele replacement on CLS results were also confirmed using standard colony formation metrics [13] . These findings demonstrate that the BUL2 polymorphism controls variation of chronological lifespan in the RM/BY cross . The average telomere length in the segregants that contain the BY allele of BUL2 was 286 bp , which is 25 bp longer than the telomere length average of segregants that contain the RM allele ( 261 bp ) ( Figure 4A ) . Therefore , if BUL2 is the responsible polymorphism for telomere length alteration , then the BUL2 allele replacement in the RM parental strain is expected to create a 25 bp increase in telomere length , while the allele replacement in the BY strain would have a modest telomere length reduction . We found that allele replacement of BUL2 in both parental strains led to alterations in telomere length as predicted by the segregant analysis: telomeres were found to be longer in the RM strains with BUL2 replaced by the BY allele and telomeres were shorter in the BY strains containing the RM BUL2 allele replacement ( Figure 4B ) . As expected from the segregant analysis , the effect of allele replacement was modest , but also consistent and reproducible , as shown by analysis of several independent strains . Deletion of BUL2 lengthened telomeres in the RM background , but had no effect in the BY background ( Figure 4C ) . These results demonstrate that the leucine residue substitution present in the BY parent creates loss of Bul2 function , leading to higher activity of amino acid permeases on cell membranes , reduced chronological lifespan , and increased telomere length . Reduced availability of cellular nitrogen and amino acids conferred by the restoration of Bul2 function is expected to reduce the activity of the nutrient sensitive TOR1 kinase . Since the region containing the BUL2 locus had been previously identified as a regulatory hotspot that controls abundance of many transcripts in this cross [20] , we evaluated whether these transcriptional alterations could be mediated by alterations in TOR1 activity . Consistent with this possibility , we found that the set of genes overexpressed in strains containing BUL2RM significantly overlaps with genes that were found to be overexpressed in response to amino acid deprivation ( p = 1 . 1×10−8 ) and rapamycin ( p = 1 . 2×10−3 ) ( Figure 5A , Table S3 ) [21] , known inhibitors of TOR1 activity [22] , [23] . Because reduction of TOR1 signaling has been shown to extend chronological lifespan [24] , [25] , the viability gain in chronological aging assays conferred by the restoration of Bul2 function can be explained by reduced activity of the nutrient responsive TOR pathway . Could the same gene network be mediating telomere length alterations conferred by BUL2 function ? To investigate this possibility , we re-examined data from our previous genome-wide telomere length screen [12] , focusing on deletion mutants of genes in the nitrogen signaling circuit . We reasoned that such mutants would likely affect telomere length through the same mechanism as BUL2 , thus we might gain insight into BUL2's mechanism of action on telomere length from known modes of action through these other nitrogen-signaling mutants . Among the mutants in genes involved in nitrogen signaling , we found that cells lacking TOR1 have modest reduction in telomere length and that cells lacking URE2 have strikingly short telomeres ( Figure 5B ) . In rich nitrogen environments , Ure2 binds to the transcriptional activator Gln3 and inactivates it through cytoplasmic sequestration [26] , [27] . Upon encountering nitrogen-limiting environments , Gln3 is released from its complex with Ure2 and translocates to the nucleus to upregulate nitrogen catabolite responses [28] . The short telomere phenotype in ure2Δ mutants is mediated by Gln3 , as we found that the deletion of GLN3 restored the short telomere lengths in ure2Δ cells back to wildtype lengths ( Figure 5C ) . We hypothesized that the reduced nitrogen availability occurring in cells with functional Bul2 ( i . e . the RM allele ) leads to increased Gln3 transcriptional activity and shorter telomeres . In order to evaluate whether transcriptional alterations previously mapped to the region containing the BUL2 locus [20] could be mediated by Gln3 , we compared the set of genes that are upregulated by the RM BUL2 allele with the genes that are upregulated in response to URE2 deletion . Of the 19 transcripts that are significantly upregulated in strains with the RM BUL2 allele , 10 transcripts were found to be overexpressed in our transcript array analysis of ure2Δ cells ( of which there were 208 transcripts ) , including known direct Gln3 targets such as BAT2 and DIP5 ( Figure 5A , Table S4 ) ( p = 8 . 5×10−11 ) [29] . These findings , along with previous reports which link loss of Bul2 to decreased Gln3 nuclear localization [30] , support a model in which restoration of Bul2 function leads to decreased cellular nitrogen availability , thereby promoting Gln3 transcriptional activity and reduction of telomere length . Could Bul2's effect on telomere length be mediated by Gln3 ? To address this question , we examined the effect of the BUL2 allele replacement in cells lacking GLN3 . We found that neither did the RM BUL2 allele in the BY gln3Δ strain shorten telomeres , nor did the BY allele replacement increase telomere length in the RM gln3Δ strain ( Figure 5D ) . The requirement of Gln3 for BUL2 allele-induced telomere alterations supports the idea that BUL2 telomere length changes are mediated by modulation of Gln3 transcriptional activity . These findings , along with previous reports which link loss of Bul2 to decreased Gln3 nuclear localization [30] , support a model in which restoration of Bul2 function leads to decreased cellular nitrogen availability , thereby promoting Gln3 transcriptional activity and reduction of telomere length . In order to determine the relationship of the telomere maintenance defect caused by the deletion of URE2 to other pathways that participate in telomere maintenance , we compared telomere lengths of ure2Δ single mutants and double mutants that were ure2Δ and deficient in either DNA damage signaling ( tel1Δ ) , telomerase ( tlc1Δ ) , or telomere-capping ( yku70Δ ) functions . The ure2Δ cells showed synthetic telomere length phenotypes with the yku70Δ , tel1Δ , and tlc1Δ mutants ( Figure S3 ) , suggesting that Ure2's effect on telomere maintenance acts independently from pathways involved in telomere extension , telomere-capping , and TEL1-mediated DNA damage signaling . Our previous study of telomere maintenance genes identified a significant subset of mutants involved in nucleotide biosynthesis as having altered telomere length [12] . For instance , loss of the ribonucleotide reductase large subunit RNR1 results in telomere shortening on par with loss of YKU70 or TEL1 . Since nitrogen availability dictates growth , we speculated that mimicry of nitrogen starvation created by increased nuclear Gln3 would induce cells to conserve nitrogen and restrict nucleotide synthesis , and this in turn would cause shortening of telomeres . We first examined transcript levels in ure2Δ cells , anticipating reductions in nucleotide biosynthesis gene expression , but found only modest decreases in RNR1 and other nucleotide genes unlikely to account for the magnitude of telomere shortening in ure2Δ mutants . However , among the upregulated genes in ure2Δ cells , we found a strong increase in expression of Wtm1 , an inhibitor of ribonucleotide reductase . Wtm1 protein levels were found to be almost 5-fold higher in ure2Δ cells compared to wildtype ( Figure 6A ) . In addition , allele replacement with BUL2RM in the BY background gave rise to a 50% increase in Wtm1 , while in the vineyards strain the replacement of BUL2 with the hypomorphic BUL2BY and BUL2 deletion decreased the Wtm1 protein level by 40% and 80% respectively ( Figure 6A ) . The ribonucleotide reductase complex assembles during S-phase and consists of large Rnr1 subunits and the two small subunits Rnr2 and Rnr4 . Unlike Rnr1 , which is always cytoplasmic , Rnr2 and Rnr4 are localized in the nucleus during G1 and translocate to the cytoplasm during S-phase [31] . This process is controlled by Dif1 , which promotes nuclear import , and Wtm1 , which anchors the small subunits Rnr2 and Rnr4 in the nucleus [32] , [33] . Based on our observation that Wtm1 expression increases in ure2Δ cells , we hypothesized that ure2Δ cells have increased nuclear retention of the small subunits Rnr2 and Rnr4 . As previously observed , we found that Rnr4-GFP is nuclear during G1 and cytoplasmic during S-phase in wildtype cells ( Figure 6B , 6C ) . While Rnr4-GFP is appropriately nuclear in ure2Δ cells during G1 , 56% of ure2Δ cells retain Rnr4-GFP in the nucleus during S-phase . We determined that this aberrant nuclear Rnr4 localization in ure2Δ is dependent on Wtm1 since ure2Δwtm1Δ double mutants have completely restored cytoplasmic localization of Rnr4-GFP . Rescue by WTM1 deletion is not merely due to loss of nuclear Rnr4 localization: more than 50% of wtm1Δ cells still maintain nuclear localization of Rnr4-GFP in G1 ( Figure 6C ) . Examination of strains with different BUL2 alleles revealed that alteration of Bul2 function has a small but reproducible effect on S-phase Rnr4-GFP localization ( Figure 6D ) . Both RM BUL2BY and RM bul2Δ strains had 2 . 6% of S-phase cells with nuclear Rnr4-GFP , which is a significant decrease from the 5 . 6% seen in the RM wildtype strain . The fraction of cells with nuclear Rnr4-GFP increases from 8 . 0% in BY wildtype to 10 . 3% in the BY strain with the RM allele of BUL2 and decreases to 5 . 6% of S-phase cells in the BY bul2Δ strain . These results suggest that cells with decreased TOR signaling , such as in ure2Δ mutants and cells with BUL2RM , form fewer ribonucleotide reductase complexes during S-phase due to increased Wtm1 expression . We then investigated whether deletion of WTM1 would rescue the ure2Δ telomere length shortening . Telomere length comparison of ure2Δ and ure2Δwtm1Δ mutants reveals that deletion of WTM1 partially rescues telomere shortening due to loss of URE2 ( Figure 7A ) . Along the same lines , we found that deletion of the Rnr1 inhibitor SML1 [34] also abrogates the ure2Δ short telomere length defect ( Figure 7B ) . These findings support our hypothesis that the shortened telomeres in ure2Δ cells are due , at least in part , to limitation of ribonucleotide reductase activity .
Examination of quantitative trait loci that regulate chronological aging and telomere length in the progeny from a cross between the laboratory strain S288c and a vineyard strain , RM11-1a , led to identification of a polymorphism in BUL2 which alters trafficking of amino acid permeases and cellular amino acid import . Loss of Bul2 function , conferred by the laboratory allele of the gene , initiates a cascade of events outlined in Figure 8 that centers on TOR , a nutrient-responsive protein kinase previously implicated in CLS control . Our study defines a novel downstream role for TOR signaling in the regulation DNA replication and telomere maintenance through Gln3-mediated assembly of ribonucleotide reductase during S-phase . Amino acids are powerful activators of TOR signaling not only in yeast , but also in multicellular eukaryotes . For Drosophila melanogaster larvae , amino acid deprivation inhibits TOR activity and leads to growth inhibition and reduced body size [35] . Similarly , Caenorhabditis elegans lacking the intestinal amino acid transporter pep-2 also have reduced body size [36] . Increasing evidence suggests that reduced intake of amino acids , which consequently reduces TOR activity , may be a key component of life-extending dietary interventions . Lifespan extension granted by dietary restriction in D . melanogaster was abolished by re-addition of amino acids [37] . Additionally , dietary reduction of a single essential amino acid , either tryptophan or methionine , was sufficient to confer lifespan extension in both mice and rats [38]–[40] . While dietary restriction studies in S . cerevisiae typically involve glucose restriction , our finding that restoration of Bul2 function and resulting reduction of cellular amino acid import extends CLS supports the idea that amino acid-mediated regulation of TOR signaling controls longevity . While several of the upstream molecular events that control TOR activity , such as growth factors and energy status , are understood in great detail [41] , we only have rudimentary knowledge of how cells sense amino acid sufficiency and transmit this signal to TOR . TOR forms two separate complexes: the rapamycin-sensitive TOR complex 1 ( TORC1 ) , which regulates growth , ribosome biogenesis , translation and lifespan , and the rapamycin-insensitive TOR complex 2 ( TORC2 ) involved in actin cytoskeleton organization and cell wall integrity [42] . Recent studies in mammalian cells have identified several components that are required for TOR activation by amino acids , including Rag GTPase heterodimers involved in the recruitment of TORC1 complex to the lysosomal membrane compartment [43] . In addition to their roles as activators of TOR , the S . cerevisiae Rag GTPase orthologs Gtr1 and Gtr2 [23] are also implicated in the retrieval of Gap1 and other high affinity amino acid permeases from the vacuolar trafficking pathway [44] , thus promoting their localization to the plasma membrane . Because the retrieval of Gap1 from the vacuolar targeting pathway is regulated by amino acid availability ( discussed below ) , these findings raise the possibility that the related amino acid-responsive pathway that controls TOR also controls recycling of high affinity transporters to the cell membrane . In contrast to the majority of the 23 amino acid permeases in yeast , which are constitutively expressed and import specific amino-acids with low affinity , high affinity permeases such as the general amino acid permease Gap1 and proline permease Put4 are highly expressed only during nitrogen limitation [16] , [45] , [46] . Gap1 and its related class of permeases have a high capacity for amino acid transport and are thought to scavenge amino acids for use as a source of nitrogen . Intracellular sorting is one of the mechanisms by which the quality of available nitrogen controls the presence of high affinity permeases at the cell membrane: during growth with a good nitrogen source such as ammonium , glutamate and glutamine , Gap1 is sorted to the vacuole for degradation [16] . When cellular nitrogen and amino acids levels are low , Gap1 is sorted to the plasma membrane . A complex consisting of Rps5 , Bul1 and Bul2 ubiquitylates Gap1 and specifies its sorting to the multivesicular endosome . From the endosome , Gap1 can be targeted either to the vacuole or trafficked to the plasma membrane depending of the amino acid availability [47] . The amino acid-regulated step in this process appears to be Gap1 retrieval from the endosome rather than Gap1 ubiquitylation . Nevertheless , ubiquitylation is a prerequisite for controlling Gap1 localization because in its absence , Gap1 never reaches the endosome and is constitutively targeted to the plasma membrane . Therefore , loss of Bul2 function , such as in cells with the BY allele of BUL2 , results in non-discriminatory import of amino acids and greater intracellular amino acid and nitrogen availability . Our finding that the common laboratory strain S288c carries a loss-of-function mutation in BUL2 , subsequently leading to indiscriminant amino acid uptake , is important for future studies that exploit yeast as a model for amino acid sufficiency and TOR signaling . Specifically , such studies should include strains with wild-type BUL2; for example , they could employ the allele substitution strains described here . The mutation in BUL2 adds to the list of genetic alterations in the standard laboratory strain that are not representative of other members of the species such as loss of function changes in AMN1 [48] and MKT1 [49] . Similar to the control of Gap1 , mammalian growth factor receptors are also regulated by ubiquitin-mediated trafficking . While yeast cells detect cellular resources directly through their import via permeases , multicellular organisms rely on growth factors such as IGF-1 , which also stimulates TOR activity through Akt-Tsc-Rheb signaling , to coordinate nutrient availability with growth [1] . Cell surface localization of the IGF-1 receptor ( IGF-1R ) has been shown to depend on ubiquitylation by Nedd4 , a homolog of the catalytic Rsp5 subunit of the Rsp5/Bul1/Bul2 ubiquitin ligase [50] . It is intriguing that Nedd4−/+ mice have reduced IGF-1 receptors on the cell surface and phenotypes consistent with reduced IGF-1 signaling , including decreased body size [51] , raising the possibility that they may share increased longevity with other IGF-1-related dwarf mice . Reduced amino acid import in cells with functional Bul2 inhibits TORC1 activity , consistent with our observation of increased activity of TOR-inhibited transcription factor GLN3 in cells containing the RM BUL2 allele compared with cells which have the BY allele of BUL2 . ( In favorable nitrogen conditions , high TORC1 activity sequesters Gln3 in the cytoplasm . ) Reduced TOR activity has been previously shown to extend both chronological and replicative lifespan in yeast [24] , [25] , [52] . Because reduced TOR activity extends lifespan also in higher eukaryotes [53]–[55] , there is great interest in understanding the downstream events that mediate this effect . Several mechanisms by which nutrients and TOR inhibition promotes CLS in yeast have been proposed , including reduced accumulation of acetate and/or acidification of culture media [14] , promotion of respiration and autophagy [56] , [57] , and increased activity of stationary phase and stress-responsive transcription factors [25] . CLS experiments are often carried out in synthetic media which is complicated by significant media acidification due to release of organic acids during fermentation ( the initial media pH of 4 . 2 decreases to <3 after cells reach stationary phase ) . A combination of acidic pH and high concentration of acetate in the media has been linked to reduction of cell viability [14] . Because our chronological aging assays are performed in rich media ( YPD ) , which has an initial pH of 6 . 0 that reduces only to 5 . 8 after cells reach stationary phase , or buffered synthetic media , acetate toxicity is an unlikely mechanism for CLS modulation in our study . A study by Bonawitz et al . linked reduction in TOR activity to increased cellular respiratory capacity [56] . While translation is generally inhibited by reduced TOR activity , Bonawitz et al . found that translation of mitochondrial proteins was enhanced and led to increased respiration during growth in glucose . Respiration becomes increasingly important for maintaining energy supplies and viability as cells transition from fermentative growth to stationary phase . The importance of respiration during the stationary phase transition is supported by the findings of two recent genome-wide studies that identified respiratory deficient mutants among those with the shortest CLS [15] , [57] . In the same studies , mutants defective in autophagy , another process stimulated by TOR inhibition , were also found to have short CLS . These observations suggest that autophagy and respiration constitute important mediators by which reduced TOR activity promotes CLS . The inhibition of TOR that occurs in cells during the post-diauxic shift and preparation for stationary phase also elicits specific transcriptional responses that are essential for maintaining viability during quiescence [25] . One target of TOR is the Rim15 protein kinase that translates nutrient limitation signals from TOR , as well as Ras/PKA and Sch9 , into upregulation of cellular responses necessary for survival in stationary phase [58] . Similarly to Gln3 , Rim15 is phosphorylated by the nutrient-sensing kinases and retained in the cytoplasm , but upon nutrient deprivation , dephosphorylated Rim15 translocates to the nucleus to activate transcription factors Gis1 and Msn2/4 , which upregulate genes necessary for post-diauxic shift [59] and stress response respectively [60] , [61] . Deletion of either RIM15 or its target transcription factors shortens CLS and abolishes benefits conferred by caloric restriction or mutations in Tor/Ras/Sch9 that mimic calorie restriction [25] . Since Rim15 and Gln3 are both directly regulated by TOR through cytoplasmic sequestration , we predicted that Gln3 , like Rim15 , would be essential for proper stationary phase transition and survival . In support of this idea , we have found that deletion of GLN3 in the vineyard strain dramatically shortens CLS ( Figure S4 ) and that alteration of Bul2 function did not affect CLS in gln3Δ mutants . However , consistent with previous reports [24] , [25] , we found that deletion of GLN3 in the laboratory strain increased CLS . The paradoxical increase in CLS in response to GLN3 deletion in the laboratory strain is in opposition to the CLS detriment conferred by the loss of function of other transcription factors such as Msn2/4 or Gis1 which are , similarly to Gln3 , upregulated during starvation . Furthermore , the opposing effect of GLN3 deletion in the laboratory and vineyard strains makes it difficult to determine the precise role of GLN3 as a mediator of CLS alterations in the cascade of events initiated by the BUL2 polymorphism . Serving as a central link between nutrient availability and growth , TORC1 regulates many cellular processes including ribosome biogenesis , protein translation , autophagy and respiration [1] . During the examination of how telomere maintenance is affected by amino acid import , we discovered that ribonucleotide reductase ( RNR ) complex assembly during S-phase is modulated by the TOR-responsive transcription factor Gln3 , defining a novel downstream role for TOR in DNA replication . We found that increased Gln3 activity , conferred by the deletion of URE2 , upregulates Wtm1 , which , in turn , promotes nuclear retention of the small RNR4 subunit in the nucleus . Deletion of WTM1 restores cytoplasmic localization of the small subunits and partially rescues the telomere length defect of ure2Δ cells . TORC1 inhibition by rapamycin was previously associated with genotoxic stress sensitivity and inability to maintain high Rnr1 and Rnr3 levels in response to DNA damage [62] . Using telomere length as a phenotype , we have uncovered a role of TORC1-responsive transcription factor GLN3 in modulation of RNR assembly during S-phase in response to cellular amino acid availability . TOR-mediated control of DNA replication adds further to TORC1's role in coordinating nutrient availability , growth and cell division . What is the relevance of our observation to mammalian and human aging ? Both dietary restriction and inhibition of TOR activity have been linked to lifespan extension in mice [40] , [55] . At the same time , epidemiological studies in humans have found an association between longevity and long telomeres [9] , [10] . Because our study demonstrates that dietary restriction and consequent reduction in TOR activity lead to reduction of telomere length , it will be important to determine whether reduced signaling in response to dietary restriction through this highly conserved nutrient and growth related pathway also reduces telomere length in mammals .
Experiments were carried out using standard YPD media ( 2% glucose , 1% yeast extract , 2% peptone ) unless otherwise noted ( ie . ADCB assays ) . The strains used in this study , listed in Table S1 , are from either the S288c ( BY ) or RM11-1A ( RM ) S . cerevisiae backgrounds . The segregant library has been previously described [11] , except that AMN1 has been deleted in each of the segregants to facilitate single cell viability analysis . ( The RM allele of AMN1 confers clumpiness , which precludes single cell analysis , whereas the S288c allele of AMN1 was previously shown to create a loss of AMN1 function [48] ) . Gene deletion mutants were either from yeast ORF deletion collection or were created using standard PCR transformation methods . For allele replacement , we PCR-cloned a fragment containing 1 kb of the 3′ end of BUL2 and 1 kb BUL2 downstream sequence from either the BY or RM strain using a 5′ primer with an XhoI site ( 5′- GGCTCGAGGATTGATGATACCGCCAGCCAATCACC ) and a 3′ primer with a HindIII site ( 3′- GGCCAAGCTTGCGGGAAAAAGGCCAAACTCTACG ) . These fragments were inserted between the XhoI and HindIII sites in pRS406 , a vector containing URA3 . We used site-directed mutagenesis ( QuikChange II kit , Stratagene ) to introduce the L883F polymorphism into the BY BUL2 vector . Allele replacement strains were generated using the “pop-in/pop-out” gene replacement method with the linearized BUL2 vector [63] . BUL2 allele replacement strains were first screened by sensitivity to ADCB and then PCR-sequenced to confirm the desired BUL2 polymorphisms . For each strain , 1 µL of saturated culture was inoculated into 150 µL of YPD ( 2% glucose ) or buffered synthetic complete media [14] in 96-well plates . Plates were then incubated for 2 days at 30°C , at which point they were foil-sealed to prevent evaporation and kept at 30°C for the remaining time . Strains were examined in triplicate . To assay viability , 1 µl of each resuspended culture was harvested , diluted in water , spotted onto solid YPD media , and incubated for 24 hours at 30°C . Microcolonies and cells that had not divided were counted using a microscope , with the total number of events ( n>200 for each culture ) used as the denominator to determine viability percentage . Additionally , colony formation unit ( CFU ) assays was used to determine viability in select RM and BY strains . Comparison between CFU and microcolony values obtained show that the two assays are highly correlative ( R = 0 . 98 ) ( Figure S1 ) . Genome-wide linkage analysis of segregant data was performed using the publicly available R/qtl software . Effects of RM/BY allele inheritance in the segregants were examined using R ( box plots ) and Excel ( student's t-test ) . Initial ADCB toxicity assays were carried out using 25 µg/mL ADCB ( L-Azetidine-2-Carboxylic Acid , Sigma-Aldrich ) dissolved in SD media ( 1 . 9 g YNB , 0 . 5% ( NH4 ) 2SO4 , 2% dextrose ) supplemented with leucine ( 80 µg/mL ) , lysine ( 60 µg/mL ) , and uracil ( 20 µg/mL ) to compensate for the auxotrophies present in the segregant library . Cells were inoculated into 150 µL media in 96-well plate and incubated at 30°C . Segregant growth in ADCB was quantified using absorbance at OD660 after 17 hours in 30°C . BUL2 allele replacement spot assays were carried out on solid SD media of the same composition with 25 µg/mL ADCB . Genomic DNA was harvested from saturated 3 mL cultures using a phenol∶chloroform DNA extraction . Telomere lengths were evaluated as described in Gatbonton et al . [12]: genomic DNA was digested overnight with XhoI , resolved by gel electrophoresis ( 0 . 5% TBE , 0 . 9% agarose gel , run for 360 V•hr ) and transferred to Hybond-N membrane . Terminal restriction fragments containing telomeres were visualized using 32P-labeled probes amplified from the Y′ subtelomeric sequence . Total RNA was harvested from 20 mL logarithmic phase cultures in biological triplicate using the hot phenol method previously described by Schmitt et al . [64] . Three competitive hybridizations for each experimental group ( ure2Δ versus wildtype ) were performed using three separate cultures , and the log2 of the expression ratio was calculated for every ORF . To assess the intrinsic variation of expression levels for different ORFs , wildtype versus pooled wildtype hybridizations were performed using three separate cultures . Arrays used were spotted oligo probe arrays generated by the Fred Hutchinson Cancer Research Center Genomics Resource . Probability of overlap with BUL2RM-upregulated transcripts was calculated using the binomial probability formula . Yeast whole cell extracts from 5 mL logarithmic phase cultures were harvested using the NaOH protein extraction method previously used by Thaminy et al . [65] and Kushnirov [66] . Proteins were resolved using SDS-PAGE ( 10% polyacrylamide gel , 120 V for 90 minutes ) and transferred to a nitrocellulose membrane . Proteins of interest were probed with antibodies against actin ( 1∶1000 dilution , Neomarkers ) or HA ( 1∶5000 dilution , Covance ) and visualized using HRP-conjugated IgG antibodies ( 1∶1000 , Vector Laboratories ) . Wtm1 blot intensity was quantified using ImageJ and normalized to actin intensity . The Rnr4-GFP strain was obtained from the commercially available Invitrogen/UCSF GFP-tagged collection and genes were deleted using standard PCR transformation protocols . Cells from logarithmic phase cultures were harvested and fixed using paraformaldehyde , as previously described by Biggins et al . [67] . To visualize nuclei , fixed cells were incubated with 1 µg/mL DAPI for 1 hour , washed once and resuspended in sorbitol . Cells were sonicated before visualization and scoring . At least 200 events for both S-phase and G1 cells were scored for wildtype , ure2Δ , wtm1Δ and ure2Δwtm1Δ strains . At least 500 S-phase cells were scored for RM and BY BUL2 allele strains . Images were captured using a Nikon E800 fluorescence microscope . | Dietary restriction promotes longevity in many species , ranging from yeast to primates , and delays aging-related pathologies including cancer in rodent models . There is considerable interest in understanding how nutrient limitation mediates these beneficial effects . Much of what we have learned about the genetics of aging comes from studying isogenic model organisms , where the effects of single gene changes can be examined independently of other genetic alterations . In order to explore a broader spectrum of genetic variation and to gain insight into aging-related phenotypes as polygenic traits , we analyzed the chronological lifespan of 122 S . cerevisiae strains derived from a cross between laboratory and vineyard yeast strains . The major genetic locus controlling chronological lifespan was found to be identical to a previously mapped locus that controls telomere length . Identification of the responsible polymorphism in BUL2 , a gene involved in controlling amino acid permeases , allowed us to establish a previously unrecognized link among cellular amino acid intake , chronological aging , and telomere maintenance . While human epidemiological studies have linked shortened telomeres with increased mortality , it is unclear how these processes are connected . Our results suggest that , in yeast , reduced amino acid uptake and consequent reduced nutrient signaling extend chronological lifespan but reduce telomere length . | [
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| 2011 | Natural Polymorphism in BUL2 Links Cellular Amino Acid Availability with Chronological Aging and Telomere Maintenance in Yeast |
Enterovirus A71 ( EV-A71 ) is an important emerging pathogen causing large epidemics of hand , foot and mouth disease ( HFMD ) in children . In Malaysia , since the first EV-A71 epidemic in 1997 , recurrent cyclical epidemics have occurred every 2–3 years for reasons that remain unclear . We hypothesize that this cyclical pattern is due to changes in population immunity in children ( measured as seroprevalence ) . Neutralizing antibody titers against EV-A71 were measured in 2 , 141 residual serum samples collected from children ≤12 years old between 1995 and 2012 to determine the seroprevalence of EV-A71 . Reported national HFMD incidence was highest in children <2 years , and decreased with age; in support of this , EV-A71 seroprevalence was significantly associated with age , indicating greater susceptibility in younger children . EV-A71 epidemics are also characterized by peaks of increased genetic diversity , often with genotype changes . Cross-sectional time series analysis was used to model the association between EV-A71 epidemic periods and EV-A71 seroprevalence adjusting for age and climatic variables ( temperature , rainfall , rain days and ultraviolet radiance ) . A 10% increase in absolute monthly EV-A71 seroprevalence was associated with a 45% higher odds of an epidemic ( adjusted odds ratio , aOR1 . 45; 95% CI 1 . 24–1 . 69; P<0 . 001 ) . Every 10% decrease in seroprevalence between preceding and current months was associated with a 16% higher odds of an epidemic ( aOR = 1 . 16; CI 1 . 01–1 . 34 P<0 . 034 ) . In summary , the 2–3 year cyclical pattern of EV-A71 epidemics in Malaysia is mainly due to the fall of population immunity accompanying the accumulation of susceptible children between epidemics . This study will impact the future planning , timing and target populations for vaccine programs .
Hand , foot and mouth disease ( HFMD ) is a common childhood disease , characterized by vesicles on the hands and feet , and ulcers in the mouth . Enterovirus A71 ( EV-A71 ) is one of the main causative agents of HFMD apart from coxsackieviruses ( CV ) A6 , A10 , and A16 [1–3] . EV-A71 , which belongs to the genus Enterovirus of the family Picornaviridae , is a small , non-enveloped , positive-stranded RNA virus . Substantial genetic diversity is observed in EV-A71 . EV-A71 can be divided into three major genotypes , A , B and C , based on a cut-off nucleotide divergence value of 17–22% [4] . Genotypes B and C can be further subdivided into subgenotypes B0-B5 and C1-C5 respectively based on cut-off nucleotide divergence of 10–14% [4 , 5] . In addition to self-limiting HFMD or herpangina , EV-A71 is also rarely associated with more severe neurological diseases such as encephalitis , meningitis , acute flaccid paralysis and neurogenic pulmonary edema [6 , 7] . Since its first isolation in California in 1969 , numerous epidemics of EV-A71 have been reported , mainly in Asia , including Singapore [8 , 9] , Malaysia [10 , 11] , Taiwan [12 , 13] and mainland China [14 , 15] . In Malaysia , HFMD epidemics due to EV-A71 were first documented in Sarawak , East Malaysia and Peninsular Malaysia in 1997 , with over 2600 children affected and 48 deaths [10 , 11 , 16] . EV-A71 epidemics with fatalities recurred in Peninsular Malaysia in late 2000 [10] and in late 2005 [11] , followed by an epidemic in Sarawak in early 2006 , with 6 deaths reported [10 , 11] . Further epidemics of EV-A71 occurred in 2008/09 and 2012 [17–19] . In Sarawak , a clear 3-year recurrent cyclical pattern has been shown , with EV-A71 epidemics occurring in 1997 , 2000 , 2003 and 2006 [1 , 20] . A cyclical pattern of EV-A71 epidemics occurring every 3–4 years has also been described in Japan [21 , 22] and Singapore [23] . Cyclical epidemics may be due to various factors , including changes in pathogen antigenicity , variations in host population immunity , and environmental drivers [24] . Shifts in genotype often accompany new epidemics [18 , 21] , but it is unclear whether these antigenic changes are the cause of recurrent epidemics . Most EV-A71 studies showed presence of cross-protective immunity against other genotypes following infection with a given genotype and high concordance in neutralization titers between the same genotypes [25–29] . Hence , changes in herd immunity are likely to be more important . As EV-A71 disease mainly affects young children below the age of 5 , cyclical patterns of epidemics could be due to the accumulation of a new generation of susceptible children every few years , enabling sustained transmission [29] . However , direct evidence for this is scanty , as most EV-A71 seroprevalence studies are point prevalence studies . In this study , we used 2 , 141 serum samples collected from children over an 18-year period encompassing 6 epidemics , and determined the association between seroprevalence rates and cyclical patterns of reported EV-A71 epidemics in Kuala Lumpur , Malaysia . We hypothesized that falls in EV-A71 seroprevalence rates were associated with new epidemics .
Overall national incidence rates of notified HFMD from 2006 to 2012 were available from the Ministry of Health , Malaysia . However , as the statutory notification of HFMD came into enforcement only in October 2006 , cases prior to this were underreported . The monthly numbers of HFMD cases for each of the 13 states and 2 federal territories were available only between 2008 and 2014 . The case definition for reporting HFMD is a child with mouth/tongue ulcers and/or maculopapular rash/ vesicles on the palms and soles , with or without a history of fever . This data is syndromic , without laboratory confirmation of the viral agent . As diagnostic virology facilities are not widely accessible , there is scanty data on causative viral agents . Consequently , EV-A71 epidemic years , with limited laboratory confirmation , were obtained from published reports [10 , 11 , 16 , 19] and defined as 1997 , 2000 , 2003 , 2006 , 2008/2009 , and 2012 . Population data for Kuala Lumpur and Malaysia was generated based on the 2010 Population and Housing Census performed by the Department of Statistics , Malaysia [30] . Monthly climatic data for Kuala Lumpur consisting of temperature ( °C ) , rainfall ( mm ) , number of rain days and ultraviolet radiance ( MJm2 ) were provided by the Malaysian Meteorological Department . Serum samples were randomly picked from archived residual sera collected for routine virology and bacteriology tests in the Diagnostic Virology Laboratory , University of Malaya Medical Center , in Kuala Lumpur , the capital of Malaysia . Samples from patients with suspected HFMD were excluded . A total of 1 , 769 sera from children aged between 1 to 12 years old , collected between 1995 and 2012 , were tested for EV-A71 neutralizing antibodies . Between 52 and 200 samples were collected for each year , except for 2009 , when only 30 samples from children were available . Samples were divided into 1–6 years ( pre-school ) and 7–12 years ( primary school ) age groups for most analyses . A further 372 serum samples from children <1 year were analyzed separately , as these may contain maternal antibodies . The study was approved by the hospital’s Medical Ethics Committee ( reference number 872 . 7 ) and the Medical Research and Ethics Committee of the Ministry of Health , Malaysia ( reference number NMRR-12-1038-13816 ) . Our institution does not require informed consent for retrospective studies of archived and anonymised samples . The selected serum samples were heat-inactivated at 56°C for 30 minutes . The neutralizing titer of each serum was determined by a microneutralization assay as described previously [8] , with modifications . Two-fold serial dilution of each serum sample was performed from 1:8 to 1:32 . An aliquot of 90 μl of each dilution was mixed with 90 μl of 1000 tissue culture infective dose ( TCID50 ) of EV-A71 strain UH1/PM/97 ( GenBank accession number AM396587 ) from subgenotype B4 . The serum-virus mixture was then incubated at 37°C for 2 hours in 5% CO2 . Each serum dilution was transferred into a 96-well plate in triplicate . A suspension of 100 μl containing 1 x 104 rhabdomyosarcoma ( RD , ATCC no . CRL-2061 ) cells was then added . Pooled positive sera of known titer were included in each assay as positive controls , using previously described criteria for reproducibility [8] . Wells containing diluted serum , virus alone , and uninfected RD cells were also included as controls . The plates were incubated at 37°C in 5% CO2 and examined for cytopathic effects ( CPE ) after 5 days . Neutralizing antibody titer was defined as the highest dilution that prevents the development of CPE in 50% of the inoculated cells . A sample was considered positive if the neutralizing titer was ≥1:8 [31 , 32] . Good cross-neutralization of serum against EV-A71 of different subgenotypes has been observed ( S1 Table ) . Nevertheless , sera from children ≤3 years collected in 2013 were used to verify the concordance of neutralization titers between the UH1 strain and a clinical virus isolate from subgenotype B5 ( GenBank accession number JN316092 ) isolated in 2006 ( 39 sera ) and a clinical isolate from subgenotype C1 ( GenBank accession number JN316071 ) cultured in 1997 ( 32 sera ) . These were the genotypes circulating in Malaysia between 1997–2012 [17] . High concordance in seropositive/seronegative status was obtained between UH1 and B5 virus ( 97% , 38/39 sera ) and UH1 and C1 virus ( 81% , 26/32 sera ) ; hence , this supports our use of the B4 virus alone for all the neutralization assays . We used phylogenetic analysis and selective pressure to investigate the role of genetic diversity in the cyclical patterns of EV-A71 epidemics . EV-A71 VP1 gene sequences of Malaysian isolates were retrieved from GenBank and aligned with Geneious R6 ( Biomatters Ltd , New Zealand ) . A total of 275 VP1 sequences reported from Malaysia between 1997 and 2012 were available for analysis ( S2 Table ) . The best substitution model was determined using jModelTest v0 . 1 . 1 [33] as the general time reversible model with rate variation among sites ( GTR+G ) . Phylogenetic trees were constructed using the Bayesian Markov Chain Monte Carlo method in BEAST 1 . 7 . 4 [34] , run for 30 million iterations with a 10% burn-in . All runs reached convergence with estimated sample sizes of >200 . The clock model was uncorrelated lognormal relaxed and the tree prior was coalescent GMRF Bayesian Skyride , allowing the generation of a plot of relative genetic diversity , which reflects the change in effective population size over time [35] . The maximum clade credibility tree was viewed using FigTree 1 . 4 [36] . Selective pressure analysis was performed using codon-based maximum likelihood methods implemented in the Datamonkey web server [37] . Amino acids were only selected when positively identified by the two different codon-based maximum likelihood methods , which were single likelihood ancestor counting and fixed effects likelihood . We used cross-sectional time series analysis to determine the associations between epidemic periods and changes in seroprevalence . EV-A71 epidemic periods were categorized based on reported epidemic years obtained from limited laboratory confirmation and published reports [10 , 11 , 16 , 19] . We categorized time of our study into six distinct clusters: 1995–1997 , 1998–2000 , 2001–2003 , 2004–2006 , 2007–2009 , and 2010–2012 . A cluster of time consists of months before the epidemic and during the epidemic year . Epidemic periods were defined as the years 1997 , 2000 , 2003 , 2006 , 2008 , 2009 , and 2012 . The seroprevalence rate was defined as the proportion of the samples tested which had a neutralization titer ≥1:8 . The association of seroprevalence and epidemic months were modeled using generalized estimated equations population average models adjusted for confounders which are biologically plausible or have been previously described; these factors were age ( 1–6 years and 7–12 years ) , and climatic variables ( monthly temperature , rainfall , rain days , and ultraviolet radiance ) . Interaction between seroprevalence , epidemic periods and age was also evaluated for possible heterogeneous effects or associations . Geometric mean titers ( GMT ) were calculated by log-transforming the positive neutralization titers , using a value of 64 for titers >1:32 . A two-sided type I error of 0 . 05 was used for statistical significance . Statistical analyses were performed using SPSS software version 22 ( IBM SPSS Software , USA ) and Stata version 12 ( Stata Corp , College Station , Texas , USA ) , and graphs were drawn using GraphPad Prism 5 ( GraphPad Software , USA ) .
Malaysia consists of Peninsular Malaysia , where most of the country's 16 states and federal territories are located , and East Malaysia , which consists of Sabah , Sarawak and Labuan . The monthly notified HFMD cases in each state and federal territory were available from 2008–2014 ( Fig 1 ) . The total annual HFMD cases in 2008–2014 were 15 , 564 , 17 , 154 , 13 , 394 , 7 , 002 , 34 , 519 , 23 , 331 and 31 , 322 respectively . In 5 of the 7 years , HFMD cases increased around March and peaked around May-June . Sarawak had the highest number of HFMD cases nationwide , while Selangor reported the highest number of cases among the states in Peninsular Malaysia . Only national overall HFMD incidence rates were available from 2006 . Details of causative viruses are generally not available , as most HFMD cases are clinically diagnosed and diagnostic virology facilities are not widely accessible . However , published reports of laboratory-confirmed EV-A71 epidemic years [2 , 3 , 11] were in accordance with cyclical reported HFMD activity from the available surveillance data , showing that EV-A71 epidemics occurred in Malaysia every 2–3 years , in 1997 , 2000 , 2003 , 2006 , 2008/9 , and 2012 . Age-specific incidence data was available only from 2011 to 2014 for the total HFMD cases nationwide and for the cases in Kuala Lumpur ( Fig 2 ) . The incidence rate of HFMD was the highest in those <2 years in both Kuala Lumpur ( 8 . 3 , 43 . 7 , 19 . 0 and 31 . 7 per 1000 population in 2011 , 2012 , 2013 and 2014 , respectively ) and Malaysia ( 4 . 8 , 22 . 9 , 17 . 9 and 20 . 6 per 1000 population in 2011 , 2012 , 2013 and 2014 , respectively ) . The rates decreased with increasing age , with the 7–12 years age group having the lowest incidence rates; in Kuala Lumpur , rates were 0 . 07 , 1 . 1 , 0 . 4 and 1 . 2 per 1000 population in 2011 , 2012 , 2013 and 2014 respectively , and overall in Malaysia , rates were 0 . 2 , 0 . 9 , 0 . 4 and 0 . 7 per 1000 population in 2011 , 2012 , 2013 and 2014 , respectively . EV-A71 seroprevalence was higher in the primary school 7–12 years age group ( 71 . 6% , 95% CI 68 . 2–74 . 7% ) compared to the preschool 1–6 years age group ( 52 . 8% , 95% CI 49 . 8–55 . 9%; P<0 . 001 ) overall , and in 16 out of the 18 years analyzed ( significantly different in 3 years; S3 Table ) . The overall seroprevalence and GMT were significantly higher in epidemic years ( seroprevalence 67 . 4% , 95% CI 63 . 8–70 . 9%; GMT 23 . 6 , 95% CI 21 . 8–25 . 5 ) compared to non-epidemic years ( seroprevalence 56 . 6% , 95% CI 53 . 6–59 . 5%; GMT 17 . 8 , 95% CI 16 . 7–19 . 0; P<0 . 001 ) ( Fig 3 ) . During epidemic years , the seroprevalence of children aged 1–2 years ( 52 . 5% , 95% CI 44 . 8–60 . 0% ) , 3–5 years ( 66 . 1% , 95% CI 58 . 7–72 . 8% ) , and 6–9 years ( 75 . 4% , 95% CI 69 . 1–80 . 8% ) were significantly higher compared to non-epidemic years ( 1–2 years old: 39 . 6% , 95% CI 33 . 9–45 . 5%; 3–5 years old: 51 . 6% , 95% CI 45 . 8–57 . 4%; and 6–9 years: 64 . 4% , 95% CI 59 . 1–69 ) ( Fig 3A ) . GMT also rose significantly during epidemic years ( 3–5 years old: 23 . 3 , 95% CI 19 . 8–27 . 3; 6–9 years: 26 . 7 , 95% CI 23 . 4–30 . 4; and 10–12 years old: 28 . 0 , 95% CI 23 . 5–33 . 4 ) compared to non-epidemic years ( 3–5 years old: 18 . 1 , 95% CI 15 . 7–20 . 9; 6–9 years: 18 . 1 , 95% CI 16 . 2–20 . 2; and 10–12 years old: 20 . 4 , 95% CI 18 . 0–23 . 1 ) ( Fig 3B ) . This is consistent with the observed general trend of EV-A71 seroprevalence spiking during reported EV-A71 epidemic years , and seroprevalence falling between epidemics ( Fig 4A ) . These results showed that younger children aged 1–6 years old had lower seroprevalence in non-epidemic years , indicating greater susceptibility , which may explain the higher HFMD incidence in this age group ( Fig 2 ) . The higher seropositive rates and GMT levels seen during epidemic years are likely to reflect recent infection . HFMD incidence in older children aged 7–12 years is considerably lower; thus , the higher GMT levels seen during epidemics are more likely to represent re-exposure to EV-A71 or milder infection resulting in under-reporting . Taken together , both the incidence rates and the seroprevalence data suggested that HFMD caused by EV-A71 affects susceptible children aged 1–12 years , and most frequently affects younger children aged 1–6 years . Many subgenotypes were co-circulating during EV-A71 epidemics . Subgenotypes B3 , B4 , C1 and C2 were present during the 1997 epidemic , but only subgenotypes B4 and C1 continued to circulate till 2001 and 2003 , respectively ( Fig 4C ) . After 2003 , subgenotype B5 became the sole genotype circulating in Malaysia . A Bayesian Skyride plot was used to estimate the evolutionary dynamics of EV-A71 in Malaysia over time ( Fig 4B ) . Sharp , transient rises of genetic diversity were observed in the reported epidemic years 1997 , 2000 , 2003 , 2006 , 2008/2009 , and 2012 . The decline in the effective population seen after the 1997 epidemic may coincide with purifying selection against subgenotypes B3 and C2 . The decline in the effective population observed after the 2000 and 2003 epidemics may indicate purifying selection against subgenotypes B4 and C1 , respectively . After 2006 , when only subgenotype B5 was circulating , interepidemic viral diversity showed overall decline punctuated by spikes during the epidemic years of 2008/2009 and 2012 . To further investigate the driving force of diversification in EV-A71 , selective pressure on the Malaysian EV-A71 VP1 was examined . The mean dN/dS ( ratio of nonsynonymous substitution rate to synonymous substitution rate ) was 0 . 058 , and evolution of EV-A71 was driven by strong purifying selection with over 62% of the analyzed codons under negative selection pressure . Two codons at positions 98 and 145 were under positive selective pressure , likely resulting in the emergence of new virus variants or lineage extinction . About 9 . 8% ( 27/275 ) of the sequences showed E98K , 3 . 6% ( 10/275 ) were E145Q , and 9 . 1% ( 25/275 ) were E145G and these could be observed in different genotypes across different EV-A71 epidemics ( S2 Table ) . These results showed that EV-A71 epidemics are characterized by peaks of increased genetic diversity , often with genotype changes . Evidence of strong negative selection and 2 codons with positive selection may explain the emergence of immune escape though the role in cyclical patterns of EV-A71 epidemics remains unclear . Interaction between seroprevalence , age and epidemic periods was first evaluated in the model for possible heterogeneous effects in different strata of age groups . The association between a 10% increase in monthly seroprevalence and odds of an epidemic was 1 . 41 ( 95% CI 1 . 16–1 . 71 ) in those aged 1–6 years and 1 . 23 ( 95% CI 0 . 94–1 . 61 ) in those aged 7–12 years . The association between a 10% decrease between the preceding and current months and odds of an epidemic was 1 . 13 ( 95% CI 0 . 95–1 . 35 ) in those aged 1–6 years and 1 . 05 ( 95% CI 0 . 82–1 . 33 ) in those aged 7–12 years . The model incorporating the interaction terms of monthly seroprevalence with age groups and changes between seroprevalence of preceding and current months with age groups was tested , but showed non-significant interactions ( P = 0 . 30 ) . This suggests that age did not modify the association between the two measures of seroprevalence and epidemic period . To further understand the relationship between recurrent EV-A71 epidemics and other factors such as seroprevalence , age and climatic variables , time series analysis was performed ( Table 1 ) . The monthly seroprevalence was positively associated with the odds of an epidemic period in the univariate analysis ( OR for every 10% increase in seroprevalence , 1 . 40; 95% CI 1 . 18–1 . 65; P<0 . 001 ) and multivariate analysis after adjusting for plausible confounding factors such as age , temperature , rainfall , rain days , and ultraviolet radiance ( adjusted OR for every 10% increase in seroprevalence , 1 . 45; 95% CI 1 . 24–1 . 69; P<0 . 001 ) . This means that every 10% increase in monthly seroprevalence is associated with 45% higher odds of an epidemic , which is consistent with the observation that seroprevalence rates are higher during epidemics . We then examined whether relative changes in seroprevalence over time were associated with epidemics . Every 10% decrease in EV-A71 seroprevalence between preceding and current months was not significantly associated with epidemics in univariate analysis; but there was a significant association in multivariate analysis ( aOR , 1 . 16; CI 1 . 01–1 . 35; P<0 . 034 ) . This shows that every 10% fall in monthly seroprevalence compared to the preceding month is associated with 16% higher odds of an epidemic .
In Asia , recurring epidemics of HFMD with associated severe neurological disease is a major public health concern . In Malaysia , HFMD became a statutorily notifiable disease only from October 2006 , although national surveillance data does not include the causative viral agents . A notable exception is Sarawak , the worst affected state in Malaysia , which established sentinel and laboratory-based surveillance of HFMD in 1998 , and clearly showed recurrent EV-A71 epidemics coinciding with large spikes in HFMD rates occurring at 2–3 year intervals [3 , 38] . We have found that national HFMD rates , which were not virus-specific , accorded with EV-A71 seroprevalence , spikes in genetic diversity of EV-A71 , and published reports of laboratory-confirmed epidemic years . Together , this showed that EV-A71 epidemics also occurred in similar 3 year cycles in Malaysia . We found clear support for our hypothesis , showing that statistically significant decreases in population seroprevalence ( as a proxy for immunity ) are temporally associated with subsequent epidemics , after adjustment for age , temperature , rainfall , rain days , and ultraviolet radiance . We identified seropositive children from as early as 1995 and 1996 , suggesting that EV-A71 was already circulating before the first documented epidemic in 1997 . The presence of seropositive young children in interepidemic years shows that ongoing transmission occurs between epidemics . This is supported by laboratory reports of EV-A71 isolated in low numbers during interepidemic years [3 , 12 , 17 , 39] . Based on the HFMD monthly distribution from 2008–2014 , a seasonal pattern was observed , with incidence peaking between May to June . In USA , HFMD epidemics occur during summer and autumn months [40] . Taiwan has also showed higher incidence in the summer months [41] and in Guangzhou , incidence peaked in April/May and September/October [42] . The location-specific factors leading to seasonal epidemics have not been clearly defined , but could include climatic factors , such as the association with relative humidity and mean temperature in Taiwan [43] , which may affect environmental survival of enteroviruses . In the present study , the overall likelihood of an epidemic was influenced by temperature and rain days , but not rainfall or ultraviolet radiance . The effects of these climatic factors on virus survival and spread will require further investigation . The relationships of HFMD with climatic variables remain to be explored in detail in Malaysia , particularly as individual states may have widely varying weather . The highest age-specific incidence of HFMD is seen in children <2 years old ( Fig 2 ) . This is consistent with the significant differences in age-specific EV-A71 seroprevalence seen between non-epidemic and epidemic years in those <2 years old , particularly in the <6 month ( from 47 . 7% to 64 . 0% , p = 0 . 016 ) and 6 months to 1 year age groups ( from 35 . 9% to 64 . 3% , p = 0 . 0016 ) . If an EV-A71 vaccine , such as the inactivated vaccine that has recently shown promise in phase 3 trials [44] , were introduced into routine immunization programs , children would have to be vaccinated at least by the age of 6 months , and possibly earlier [45 , 46] . As most children in Malaysia and other Asian countries [9 , 31] are seropositive by 5 years , an effective vaccine could prevent EV-A71 HFMD , as well as the severe associated neurological complications that mainly affect the very young [47] . The well-recognized cyclical pattern of EV-A71 epidemics seen in some countries has been attributed to the time taken for accumulation of enough susceptible children in the population . In Tokyo , the overall EV-A71 seroprevalence dropped to its lowest point in 6 years during the months just preceding an epidemic in 1973 , including an absence of antibodies in children <4 years old [48] . In Guangdong , China , seroprevalence gradually dropped from 2007 to 2009 , before a large epidemic in 2010 [49] . In Taiwan , there was evidence of fewer EV71 seroconversions in 1994–1997 , before the 1998 epidemic [47] . Our study is unusual as it charts seroprevalence over a long period of time , covering 18 years and 6 epidemics , and we showed that changes in population immunity in children appear to be the major driving force of the observed cyclical epidemics . Specifically , we demonstrated that falls in seroprevalence were clearly associated with higher odds of a subsequent epidemic . Seroprevalence in both 1–6 and 7–12 years age groups increased in epidemic years , suggesting that both groups are involved in disease burden and transmission . The higher HFMD rates seen in children aged 1–6 years is most likely due to their greater susceptibility ( as shown by their lower seroprevalence rates in non-epidemic years ) , but it may also be due to under-reporting in older children , who often have milder disease [38 , 50] . Estimation of the basic reproduction ratio ( R0 ) , or the number of secondary cases arising from an infectious case , has been widely used to study the dynamics of transmission of infectious diseases such as SARS and influenza [51] . The R0 of EV-A71 has been estimated as 5 . 48 , which is considered as moderately infectious [52] . The EV-A71 R0 was higher than the estimated CV-A16 R0 of 2 . 5 , suggesting that EV-A71 is more transmissible . For such a transmissible virus , the epidemic size is mainly dependent on the size of the susceptible population [53] . Following a viral epidemic , most of the population at risk would become immune . It may then take 2–3 years for the susceptible population to be replenished by newborns , and to be large enough for the R0 to increase to >1 , hence leading to a cyclical pattern of EV-A71 epidemics every 2–3 years . A similar study should be conducted to determine the R0 to further understand EV-A71 transmission dynamics in Malaysia . The present study also showed that Malaysian epidemics are characterized by peaks of increased genetic diversity , often with genotype changes . While the increased diversity may simply reflect a larger number of infections , we cannot exclude that new variants with antigenic changes may escape population immunity and contribute to cyclical epidemics . Although found in less than a quarter of Malaysian EV-A71 , the positive selection pressure sites found at positions 98 and 145 of the VP1 protein have been previously reported [5] . These mutations appeared at the terminal branches with changes from E98K , E145Q and E145G . Amino acid position 98 is part of the BC loop and position 155 is part of the DE loop , both of which are immunogenic loops of VP1 [17] . A recent study measured cross-reactive neutralizing antibody titers against viruses with mutations at residues 98 , 145 and 164 [54] . Up to 4-fold neutralization reduction was seen in sera from children , adults and rabbits tested against an EV-A71 VP1-98K/145Q/164E mutant , and all neutralization titers were ≥ 1:16 . However , viruses with all three mutations concomitantly have not yet been seen in nature . The significance of the antigenic variation will require more detailed longitudinal serological studies . If immune escape is not needed or plays only a minor role to produce the cyclical pattern of EV-A71 epidemics , a significant accumulation of susceptible children between epidemics will be enough to support large-scale transmission and another epidemic . Overall , in other published studies , EV71-infected children have detectable neutralizing antibody titers against all the EV71 genotypes [27] , and cross-protective immunity between genotypes is generally considered to be high [28 , 55] . Previous studies in humans , monkeys , rabbits and mice showed that neutralization antibody levels against different genotypes may vary , but overall human anti-serum generally does cross-neutralize strains of different genotypes ( S1 Table ) . Lower neutralization titers may not reflect antigenic shift sufficient to lead to immune escape . To date , no cases of recurrent EV-A71 infection have been reported , suggesting the presence of life-long protective immunity against EV-A71 . While enteroviruses clearly undergo antigenic evolution , complete immunological escape in EV-A71 seems to be rare , thus EV-A71 is generally considered to be a single serotype antigenically . Any possible clinical significance and contribution of reduced cross-protective immunity towards new epidemics will require further confirmation . Our study's findings may be a useful basis for future efforts to forecast EV-A71 HFMD epidemics in Malaysia . Occurrence of EV-A71 epidemics may be predicted by seroprevalence rates in children and influenced by temperature and number of rain days . The changing population immunity , the effects of climate variables on the survival and spread of EV-A71 in the environment , the change in virus genetic diversity , and changing probability of transmission of EV-A71 due to changes in host behavior under certain climatic conditions may explain the seasonal cyclical patterns . Time-series analysis of real-time , high-quality surveillance and seroprevalence data may provide efficient detection and effective forecasting of EV-A71 epidemics . Future research may also focus on the potential influence of other HFMD enteroviruses in the cyclical pattern of EV-A71 epidemics . The main limitation of this study is that we used a convenience sample of residual diagnostic sera from a single hospital . However , it would be difficult to otherwise obtain such an extensive collection of serum samples from healthy children over many years . When compared to a random cluster survey , convenience sampling has also been shown to give similar estimates of seroprevalence to 5 vaccine-preventable viral diseases [56] . The convenience sample used here is likely to be appropriate for this study . In conclusion , falls in seroprevalence in children aged 1–12 years old are the major driving force of the cyclical pattern of EV-A71 epidemics seen in Malaysia over 18 years . Nevertheless , possible interplay between seroprevalence with climatic variables and virus antigenic variations is evident and warrant future study . The highest age-specific incidence of disease , as shown by surveillance figures and seroprevalence rates , occurred in children <2 years . Together with the seasonal and cyclical patterns observed , this study has provided important data which will impact vaccine planning , timing and target populations for vaccine programs . | Enterovirus A71 ( EV-A71 ) is a major cause of hand , foot , and mouth disease ( HFMD ) in children . Since the first outbreak in Malaysia in 1997 , EV-A71 epidemics have occurred every 2–3 years , in 2000 , 2003 , 2006 , 2008/2009 , and 2012 . As the reasons for this cyclical pattern are not known , we hypothesize that it is due to changes in population immunity in children . In this study , we measured the EV-A71 neutralizing antibody prevalence in serum collected from children ≤12 years old between 1995 and 2012 , covering 18 years and 6 epidemics . HFMD incidence was highest in children <2 years , and seroprevalence increased with age , and was higher during epidemics compared to non-epidemic periods . Peaks in EV-A71 genetic diversity coincided with reported EV-A71 epidemics . Decreases in EV-A71 seroprevalence over time were significantly associated with subsequent epidemic periods . This suggests that epidemics lead to high levels of population seroprevalence; but during the 2–3 years between epidemics , the population of young children with no immunity is replenished and increases , making it more likely that a new epidemic will occur . This is the first study to show that the cyclical pattern of EV-A71 epidemics is associated with changes in EV-A71 seroprevalence . | [
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| 2016 | Cyclical Patterns of Hand, Foot and Mouth Disease Caused by Enterovirus A71 in Malaysia |
Members of the phylum Apicomplexa , which include the malaria parasite Plasmodium , share many features in their invasion mechanism in spite of their diverse host cell specificities and life cycle characteristics . The formation of a moving junction ( MJ ) between the membranes of the invading apicomplexan parasite and the host cell is common to these intracellular pathogens . The MJ contains two key parasite components: the surface protein Apical Membrane Antigen 1 ( AMA1 ) and its receptor , the Rhoptry Neck Protein ( RON ) complex , which is targeted to the host cell membrane during invasion . In particular , RON2 , a transmembrane component of the RON complex , interacts directly with AMA1 . Here , we report the crystal structure of AMA1 from Plasmodium falciparum in complex with a peptide derived from the extracellular region of PfRON2 , highlighting clear specificities of the P . falciparum RON2-AMA1 interaction . The receptor-binding site of PfAMA1 comprises the hydrophobic groove and a region that becomes exposed by displacement of the flexible Domain II loop . Mutations of key contact residues of PfRON2 and PfAMA1 abrogate binding between the recombinant proteins . Although PfRON2 contacts some polymorphic residues , binding studies with PfAMA1 from different strains show that these have little effect on affinity . Moreover , we demonstrate that the PfRON2 peptide inhibits erythrocyte invasion by P . falciparum merozoites and that this strong inhibitory potency is not affected by AMA1 polymorphisms . In parallel , we have determined the crystal structure of PfAMA1 in complex with the invasion-inhibitory peptide R1 derived by phage display , revealing an unexpected structural mimicry of the PfRON2 peptide . These results identify the key residues governing the interactions between AMA1 and RON2 in P . falciparum and suggest novel approaches to antimalarial therapeutics .
Plasmodium spp . , and P . falciparum in particular , are devastating global pathogens that place nearly half the human population at risk to malaria , leading to more than 250 million cases yearly and over one million deaths [1] . The success of the malaria parasite can be attributed to its intracellular lifestyle , invading host cells both in liver and blood stages . Invasion of red blood cells is an active process involving a moving junction ( MJ ) , which is formed by intimate contact between erythrocyte and parasite membranes and is thought to be coupled to the parasite's actin-myosin motor [2] , [3] . A number of merozoite antigens , either exposed on the surface or stored in secretory organelles , play a role in the invasion process [4] . One of these is Apical Membrane Antigen 1 ( AMA1 ) , a type-one transmembrane protein secreted from the micronemes to the merozoite surface and present at the MJ [5] , [6] . AMA1 is highly conserved in the Plasmodium genus [6] and , moreover , in the Apicomplexa phylum to which Plasmodium belongs [7] , [8] , suggesting a common functional role in diverse host cell invasion scenarios . In the apicomplexan organism Toxoplasma gondii , the receptor for AMA1 was shown to be Rhoptry Neck Protein 2 ( RON2 ) , a component of the parasite-derived RON protein complex that is secreted into the host cell during invasion and integrated into the host cell membrane [9] , [10] . This interaction was subsequently confirmed in P . falciparum as well [11] , [12] . Apicomplexans thus provide both receptor and ligand to drive active invasion . In many malaria-endemic regions , P . falciparum has become resistant to classic drugs , such as chloroquine , and is rapidly developing resistance to recently introduced drugs . Since both AMA1 and RON2 are specific to Apicomplexa and essential for invasion , interruption of the AMA1-RON2 interaction presents an ideal new target for the design and development of inhibitors . This is supported by the recent observation that the invasion-inhibitory peptide R1 [13] , [14] blocks interaction between AMA1 and the RON complex in P . falciparum [15] , but due to the polymorphism of AMA1 , the effectiveness of this peptide inhibitor is limited to a subset of parasite isolates . Interestingly , R1 does not prevent apical contact but no formation of a functional MJ ensues from this event [15] . Crystal structures of PfAMA1 in complex with invasion-inhibitory antibodies [16] , [17] have implicated a hydrophobic groove on Domain I ( DI ) of PfAMA1 as being critical for function . The topological nature of the PfAMA1 groove [18] is conserved in P . vivax AMA1 [19] and T . gondii AMA1 [20] , and contains a number of residues that are conserved or semi-conserved across Plasmodium species , as well as other members of Apicomplexa [21] , suggesting that it contributes to the receptor-binding site of AMA1 . This was recently confirmed by the crystal structure of TgAMA1 in complex with a synthetic peptide , TgRON2sp , which inserts in the groove of TgAMA1 [22] . Here , we report the crystal structure of the complex formed between PfAMA1 and peptide segments of PfRON2 , which , together with our previous structural results on the TgAMA1-TgRON2 co-structure [22] , highlights a conserved , crucial interaction in apicomplexan host cell invasion . Functional characterization of hot-spot residues driving AMA1-RON2 complex formation leads to a deeper understanding of key interactions occurring at the MJ of P . falciparum and reveals the molecular basis of cross-strain reactivity while preserving specificity for the species . We also describe the crystal structure of PfAMA1 in complex with the invasion-inhibitory peptide R1 [14] , and show that this peptide presents an intriguing structural mimicry of PfRON2 . Collectively , our results provide an important structural basis for designing cross-strain reactive molecules that inhibit invasion by P . falciparum .
From the 67-residue construct , PfRON2-5 , that we previously showed to have affinity for PfAMA1 [11] , and guided by the TgAMA1-TgRON2sp structure [22] , we synthesized two analogous PfRON2 peptides: PfRON2sp1 ( residues 2021–2059; numbering from the initiation methionine in PF14_0495 ) , and PfRON2sp2 ( residues 2027–2055 ) . Significantly , there is no polymorphism in this sequence among P . falciparum isolates . Both constructs incorporate a disulfide-bound β-hairpin loop proposed to be critical in complex formation [22] while PfRON2sp2 is truncated at both the N- and C-termini ( Fig . 1A ) . Since the extracellular region of PfRON2 is non-polymorphic , we determined the affinity of both peptides for PfAMA1 by Surface Plasmon Resonance ( SPR ) measurements using the 3D7 , CAMP , FVO and HB3 proteins to explore the possible effects of AMA1 polymorphisms . The affinity of PfAMA1 from 3D7 for PfRON2sp1 is 25-fold higher than for PfRON2sp2 ( Fig . 1B to E , Table 1 ) , highlighting a moderate , yet influential , role for the N- and C-terminal tails . Interestingly , KD values for the PfRON2sp peptides showed no significant variation in binding to PfAMA1 from the four strains . PfRON2sp1 and PfRON2sp2 were co-crystallized with the first two ectoplasmic domains ( DI , DII ) of recombinant PfAMA1 3D7 or CAMP strains , respectively . The co-structure of PfAMA1 3D7 PfRON2sp1 ( PDB entry code 3ZWZ ) was refined to 2 . 2 Å resolution , while PfAMA1 CAMP PfRON2sp2 ( PDB entry code 3SRI ) was refined to 1 . 6 Å resolution ( Tables 2 , 3 ) . The two co-structures overlay with a root mean square deviation ( rmsd ) of 0 . 81 Å in 304 Cα positions , and the two peptides alone overlay with a rmsd of 0 . 34 Å over the complete length of the modeled PfRON2sp2 ( 25 Cα ) ( Fig . 2A ) . These data confirm that the reduced affinity of PfRON2sp2 is due to the truncated N- and C-termini . Since PfRON2sp1 is more biologically relevant than its truncated counterpart , it is used for the following analyses unless otherwise noted . PfRON2sp1 , traced from Thr2023 to Leu2058 , includes a disulfide bridge between Cys2037 and Cys2049 and makes several direct contacts with PfAMA1 ( Fig . S1 ) , resulting in a total buried surface area of 3154 Å2 ( 1441 Å2 for PfAMA1 and 1713 Å2 for PfRON2sp1 ) . Overall , the binding paradigm established by TgAMA1-TgRON2sp [22] is maintained , with an N-terminal helix seated at one end of the AMA1 receptor-binding groove and extended through an ordered coil to a disulfide-closed β-hairpin loop , generating a U-shaped conformation ( Fig . 2A ) . Similarly , exposing a functional receptor-binding groove on AMA1 requires displacement of the extended non-polymorphic DII loop , which adopts a disordered state ( not modeled between Lys351 to Ala387 ) ; this region is stabilized by DI in apo PfAMA1 ( Fig . 2B ) . Intriguingly , the backbone of the N-terminal helix and additional coil of PfRON2sp1 ( 2024-QQAKDIGAG-2032 ) overlays remarkably well with a section of the apo PfAMA1 DII loop ( 360-YEKIKEGFK-368 ) ( rmsd<0 . 4 Å ) , which also includes a helical region ( Fig . 2B - box 1 ) . Three water molecules buried by the DII loop in the apo form are retained in the receptor-bound state and facilitate a network of hydrogen bonds that bridge PfAMA1 DI to either the DII loop or PfRON2sp in apo PfAMA1 or the receptor complex , respectively ( Fig . 2C ) . The majority of intermolecular contacts are formed by the segment Lys2027-Met2042 of PfRON2sp1 . An influential residue on PfRON2 appears to be Arg2041 , a residue specific to the P . falciparum species , located at the tip of the β-hairpin with its guanidyl group fitting snugly into a preformed pocket of PfAMA1 ( Fig . 2D ) . The invasion-inhibitory peptide R1 , comprising 20 residues ( VFAEFLPLFSKFGSRMHILK ) [14] , has been shown by nuclear magnetic resonance ( NMR ) to bind to the PfAMA1 hydrophobic groove , but this study gave little structural detail of the interaction [15] . We therefore crystallized PfAMA1 3D7 ( DI and II ) with R1 to compare with the PfRON2 complex . Surprisingly , two molecules of R1 are bound to PfAMA1 , which we denote respectively as the major peptide ( R1-major ) , lying deeply in the binding groove , and the minor peptide ( R1-minor ) , lying above R1-major and making fewer contacts with PfAMA1 ( Fig . 3 and Table S1 ) . Several solvent molecules bridge directly between PfAMA1 and R1-major . As in the PfAMA1-PfRON2sp complexes , the N-terminus of R1-major binds to a region of PfAMA1 that becomes exposed after displacement of the DII loop . R1-major makes several direct contacts with PfAMA1 ( 113 interatomic distances<3 . 8 Å ) , including 19 hydrogen bonds and a salt bridge between the amino group of Lys-P11 ( R1 peptide residues numbers are prefixed by P ) and the Asp227 carboxylate group of PfAMA1 ( Table S1A ) . Contacts made by R1-minor to PfAMA1 are fewer ( 26 contacts<3 . 8 Å ) and include only five hydrogen bonds ( Table S1B ) . Interactions between R1-major and R1-minor are maintained by a total of 24 interatomic contacts , including three hydrogen bonds ( Table S1C ) . In total , 3025 Å2 of molecular surface is buried between PfAMA1 and the two peptides , with R1-major contributing about 75% to this area . The buried surface between R1-major and R1-minor is 563 Å2 , reflecting the smaller number of close interatomic contacts between these two components . Since the structure of the PfAMA1 3D7-R1 complex revealed two bound peptide molecules , binding measurements of R1 to PfAMA1 3D7 were made by isothermal titration calorimetry ( ITC ) to examine the stoichiometry ( Fig . S2 ) . The measured KD of 145 nM is comparable with previous measurements by SPR [13] and the deduced stoichiometry was 1∶1 over the peptide concentrations used . This implies that the second binding site in the crystal structure ( R-minor ) has an affinity that could not be determined under the experimental conditions used for ITC but can be estimated to be at least 10-fold weaker than the major site . While R1-major follows the general contour of the receptor-binding groove , it does so in a linear rather than the U-shaped conformation adopted by PfRON2sp1 ( Fig . 4A ) . R1-minor occupies a similar region in space as the second strand of the PfRON2sp β-hairpin , contacting the same DI loop of PfAMA1 but running in the opposite direction to form a parallel two-stranded β-sheet with the major peptide ( Fig . 4A ) . Portions of R1-major exhibit structural similarity to PfRON2 , displaying a 1 . 2 Å rmsd in the twelve Cα positions ( PfRON2sp1 , Ala2031 to Met2042; R1-major , Phe-P5 to Met-P16 ) ( Fig . 4A ) . Moreover , sequence alignment based on the structural superposition reveals a remarkable similarity between the central regions of the two ligands; the segments Ala2031-Met2042 of PfRON2 and Phe-P5–Met-P16 of R1 have five identical amino acids and two conservative differences ( Fig . 4B ) . R1-major residue Arg-P15 contributes the most contacts to PfAMA1 and is positioned within the same pocket of PfAMA1 as PfRON2 Arg2041 ( Fig . 4A - box 3 ) where it maintains six of the seven hydrogen bonds observed for PfAMA1-PfRON2sp . Interestingly , while PfRON2 mimicry is observed in the cystine loop-binding region ( Phe2038/Phe-P12 to Arg2041/Arg-P15 ) , R1-major establishes clear anchor points in the hydrophobic groove different from PfRON2; Phe-P2 and Phe-P5 brace the peptide N-terminus in the region exposed by displacement of the DII loop , with Phe-P5 occupying the pocket left vacant by Phe367 of PfAMA1 ( Fig . 4A - box 1 ) . R1 is strain specific , binding to PfAMA1 from the 3D7 ( cognate antigen ) and D10 strains , but with much reduced affinity to the HB3 or W2mef proteins , as determined by ELISA [14] or SPR [13] measurements ( recapitulated in Table S2 ) . In contrast , PfRON2sp1 bound to all the PfAMA1 proteins tested ( Table 1 ) with a higher affinity than for R1 peptide . Consistent with these values , PfRON2sp1 displayed a higher capacity to inhibit red cell invasion by P . falciparum 3D7 than the R1 peptide ( Fig . 5 ) . Moreover , PfRON2sp1 shows cross-strain inhibition of invasion as expected from its biological function ( Table 1 ) , contrasting with the more restricted strain specificity of R1 ( Fig . 5 , Table S2 ) [14] . The PfAMA1 3D7-R1 crystal structure shows that three polymorphic residues ( 175 , 224 and 225 ) contact R1-major ( Table S2 ) . The 224 polymorphism , Met/Leu , is conservative and since contacts are formed by the main chain only , this should not affect R1 specificity . The 3D7 and D10 antigens both carry Tyr175 and Ile225; for the W2mef and HB3 antigens , residue 175 is Tyr and Asp , respectively , and residue 225 is Asn in both . Thus , polymorphisms at positions 225 and possibly 175 appear to be determinant for the 3D7 specificity of R1 at the major peptide-binding site ( Table S2A ) . R1-minor contacts polymorphic residue 230 , which is Lys in all strains studied ( Table S2B ) . As our data suggest a weak affinity for this binding site , however , it is unlikely that this polymorphism has a significant effect on the specificity for R1 . We examined these polymorphisms further using the mutant PfAMA1 Dico3 [23] , which differs only at residue 175 for the 3D7-contacting residues ( Table S2A ) , and a 3D7 mutant with the substitution Ile225Asp , which we call 3D7mut . The equilibrium KD , determined from the SPR steady-state responses to R1 binding , was 15 . 2±1 . 9 µM for 3D7mut and 22 . 3±3 . 3 µM for Dico3 , showing a reduction in affinity of over 200-fold with respect to the native 3D7 antigen ( Fig . 6 , Table S2C ) . This affinity is comparable to that observed for HB3 and W2mef [13] ( recapitulated in Table S2 ) , and confirms that both Tyr175 and Ile225 are important for the strain-specific recognition of R1 . Tyr175 , located at the tip of a flexible DI loop that is solvent-exposed in the apo antigen [18] , becomes buried by R1-major and forms a hydrogen bond to this ligand via the phenol group . Ile225 is also buried by R1-major , forming a pair of hydrogen bond via its main chain to the R1-major main chain . Guided by the similarities between the PfRON2sp and R1 co-structures , and the conservation of key contact residues ( Fig . 7A ) , we probed the functional importance of a subset of PfRON2 residues by testing the binding to BHK-21 cells expressing PfAMA1 of GST-PfRON2-5 fusion proteins carrying single alanine mutations at: Pro2033 ( aligns structurally with Pro7 of peptide R1 , which was shown to be critical for binding [24] ) , Phe2038 ( interacts with invariant residue Phe183 in the hydrophobic groove and aligns structurally with Phe12 of R1 ) , Arg2041 ( extensive contacts with PfAMA1 and structurally equivalent to Arg-P15 of R1 ) and Pro2044 ( the peptide bond Ser2043-Pro2044 is cis and is thus important for the β-hairpin conformation ) . Consistent with the structure , mutation of Arg2041 to Ala abrogated binding to PfAMA1 ( Fig . 7B ) . Similar effects were observed with Pro2044 , Phe2038 and Pro2033 mutations , the latter also shown to be a key residue in the TgAMA1-TgRON2 interaction [22] . Similarly , a subset of key PfAMA1 residues was also chosen for mutation: Phe183 ( an invariant residue that contributes to the hydrophobic groove and that interacts with Phe2038 of PfRON2 via aromatic interactions ) , Asn223 ( which makes important polar interactions with PfRON2 ) , residue 225 ( a polymorphic residue that contributes many contacts to PfRON2 in the structure both the CAMP ( Asn225 ) and 3D7 ( Ile225 ) complexes ) , Tyr234 ( which makes polar contacts to Arg2041 of PfRON2 ) and Tyr251 ( which has been suggested by previous studies to be important [12] , [25] ) . A clear role for Phe183 in the PfAMA1-PfRON2 complex formation was evident when expressed on the surface of BHK-21 cells and tested for their ability to bind GST-PfRON2-5 fusion protein ( Fig . 7C ) . A less pronounced role of Tyr234 was observed and none for the remaining residues , including Tyr251 . Although these conclusions differ from those of others [12] , [25] , these results are consistent with the limited contacts shown by this residue in the structures and with our earlier findings on the TgAMA1-TgRON2 interaction , where the equivalent TgAMA1 residue , Tyr230 , had a minimal effect on the binding .
The structure of PfAMA1 in complex with the extracellular region of its receptor PfRON2 and the accompanying functional analysis reveal atomic details of the interaction between two key partners at the MJ . The binding site on PfAMA1 includes the hydrophobic groove and a region that becomes exposed by displacement of the flexible DII loop from its apo conformation . Comparison of residues from both components at the PfAMA1-PfRON2 interface with those of other apicomplexan homologs underscores the separate co-evolution of the receptor-ligand pair in members of the phylum . The DII loop displays a strong propensity for mobility in P . falciparum [16] , [18] and P . vivax AMA1 structures [19] , particularly at its N- and C-terminal extremities ( weak or absent electron density ) ; the central region of the DII loop is more structured and stabilized by contacts with DI , and is better defined in some of these AMA1 structures . Here , we show that the DII loop is displaced by PfRON2sp , as well as by the R1 peptide . In T . gondii , the DII loop is 14 residues shorter than in the Plasmodium orthologs and appears less mobile [20] but nonetheless is readily displaced by TgRON2sp [22] . Flexibility may therefore have an important functional role: it protects a significant portion of the binding site in apo AMA1 against the host's immune response but can be readily displaced to extend the hydrophobic groove for effective binding to RON2 . The anti-PfAMA1 invasion-inhibitory monoclonal antibody 4G2 , which binds to the N- and C-termini of the DII loop [19] , probably prevents its displacement for effective binding to PfRON2 . The absence of polymorphisms in the DII loop in spite of immune targeting of this region underlines its important functional role [21] . We have previously demonstrated an evolutionary constraint on the AMA1–RON2 interaction within apicomplexan parasites [11] . Our functional analysis of the TgAMA1-TgRON2sp co-structure suggested that the cystine loop initially anchors the receptor to the hydrophobic groove , causing expulsion of the DII loop to promote interaction throughout the entire binding site [22] . Comparison of the TgAMA1-TgRON2sp and PfAMA1-PfRON2sp co-structures reveals that the cystine loop , while conserved across the two genera , is the most divergent region within the RON2 ( Fig . 8 ) . The separate co-evolution of the AMA1-RON2 pair in Apicomplexa is clearly illustrated by the difference between the cystine loop conformations of PfRON2sp and TgRON2sp . In particular , this allows Arg2041 to access the specific PfAMA1 pocket ( Fig . 8 ) , where it participates in an intricate network of polar interactions . From mutagenesis , we have demonstrated a crucial role of Arg2041 in complex formation ( Fig . 7B ) . Moreover , this region of the cystine loop also appears to play an influential role in species selectivity as superposition of PvAMA1 structure [19] onto PfAMA1-PfRON2sp shows that Arg2041 would be sterically hindered at the interface but Thr , the equivalent residue in PvRON2 from P . vivax , can be accommodated ( Fig . 9A ) . This accounts for our prior observation that the original 67-residue segment of PfRON2 does not bind to PvAMA1 [11] . An additional feature of the PfRON2sp cystine loop region is the presence of a cis peptide bond between Ser2043 and Pro2044; the Ser-Pro-Pro segment contributes negligible buried surface area but is important for maintaining the β-hairpin conformation for efficient complex formation . Sequence alignment reveals that the Pro duo ( Pro2044–Pro2045 ) is preserved in all analyzed Plasmodium species ( Fig . 8A ) and is thus likely important for specific recognition of AMA1 . We propose that it provides necessary internal structure at the tip of the cystine loop and places the disulfide bond in the proper orientation to brace the AMA1-RON2 interaction . The influential role of Pro2044 is confirmed by mutagenesis where substitution with Ala , which would disfavor the cis peptide bond , abrogates PfAMA1-PfRON2 binding ( Fig . 7B ) . While T . gondii does not share the conserved proline pair , its cystine loop is two residues shorter ( Fig . 8A ) , which mirrors the narrower groove of TgAMA1 . Altogether , the overall U-shape architecture of RON2 in complex with AMA1 appears to be remarkably well maintained within apicomplexan parasites but specific features are clearly visible in the cystine loop of PfRON2 and TgRON2 , highlighting how a receptor-ligand complex has evolved to maintain a common and crucial event in the biology of these parasites . Although the PfAMA1-PfRON2 interface is highly conserved , five polymorphic residues of PfAMA1 contact the non-polymorphic PfRON2sp [26] . Of these , however , only residue 225 ( Asn/Ile ) varies significantly . The remaining polymorphisms should not affect binding as they involve main chain contacts only ( residues 172 , 174 , 187 and 224 ) . Our study allows a detailed structural assessment of polymorphism at residue 225 since complexes with PfAMA1 from the 3D7 ( Ile225 ) and CAMP ( Asn225 ) strains were determined . The 3D7 and CAMP orthologs both maintain two hydrogen bonds between the main chain of residue 225 and PfRON2 Thr2039 . However , Ile225 presents a deep pocket to Arg2041 with apolar contacts formed between the aliphatic regions of these two side chains , while Asn225 presents a shallower pocket to Arg2041 with the Asn225 amide group stacking against the guanidyl group . Nonetheless , our binding studies by SPR show no significant difference in the affinity of these two PfAMA1 homologs for PfRON2sp2 . Sequence variations at PfRON2-interacting positions , 172 ( Glu/Gly ) , 187 ( Glu/Asn ) and 225 ( Ile/Asn ) are represented by the strains 3D7 , CAMP , FVO and HB3 that we have analyzed by SPR; the very similar KD constants , ranging from approximately 10 to 20 nM , confirm that these exert little effect in the strength of the interaction . Peptide R1 shows a more restricted specificity as it binds strongly to the cognate 3D7 and closely related D10 antigens but only weakly to orthologs that do not carry the same polymorphic amino acids at position 175 or 225 ( Table S2 ) . Tyr175 in PfAMA1 3D7 makes a hydrogen bond to the main chain of R1-major but , as this residue is located in a flexible loop with some freedom to adapt to the PfAMA1-R1 interface , it is unclear why the Asp175 polymorphism leads to reduced affinity . In the case of Ile225 of PfAMA1 3D7 , the main chain forms two hydrogen bonds to the main chain of R1-major but the preference of R1 for the Ile225 polymorphism remains unexplained as it contrasts with PfRON2sp where main chain hydrogen bonds are also formed by both Ile225 ( 3D7 ) and Asn225 ( CAMP ) to the main chain of PfRON2 . This emphasizes that specificity differences may present subtleties that are difficult to decipher . Here , the crystal structure of R1 in complex with the 3D7mut ( Ile225Asn ) and Dico3 ( Tyr175Asp ) mutants of PfAMA1 would provide invaluable insights into this question . Taken together , these results highlight that unlike the natural ligand PfRON2 , R1 , which was selected by phage display , is highly susceptible to polymorphisms . R1 exhibits a close structural similarity to PfRON2 , with the major/minor peptide pair displaying a similar boomerang form as PfRON2 , binding to the same region of PfAMA1 and following the same general contour of the binding-site groove . Our structural data show that binding of R1-minor is dependent upon prior binding of R1-major as it lies above the latter in the binding groove and makes fewer contacts to PfAMA1 . This , indeed , is consistent with the ITC measurements that show a stoichiometry of 1∶1 , indicating a weaker affinity for the minor peptide-binding site . R1-major is thus favored as the principle inhibitor of the interaction with PfRON2 , but this does not preclude a contribution by the minor peptide-binding site at high peptide concentrations . Therapeutic strategies aimed at inhibiting the interaction between PfAMA1 and PfRON2 should be very effective in treating malaria as they address a critical phase in the life cycle of the parasite and , importantly , should not be compromised by polymorphism since the PfAMA1-PfRON2 interface is highly conserved . Our results provide a structural basis for designing inhibitors against the most virulent malaria parasite . The PfRON2sp1 peptide used in this study has a very high affinity to PfAMA1 and is very efficient at inhibiting invasion . Moreover , in contrast to the less strongly binding peptide R1 , PfRON2sp1 is not strain specific . Structural details of the PfAMA1-PfRON2 interaction offer the possibility to design molecules with the desired specific inhibitory properties by in silico screening and structural validation . The binding of PfRON2 Arg2041 to a specific pocket on PfAMA1 could be a critical target region . Indeed , the important role played by Arg-P15 at the PfAMA1-R1 interface closely mirrors the equivalent interaction in the PfAMA1-PfRON2sp complexes and , interestingly , the same pocket is occupied by Arg and Lys in PfAMA1 complexes with the invasion inhibitory antibodies IgNAR [17] and 1F9 [16] , respectively ( Fig . 9B ) . Phe2038 ( corresponding to Phe-P12 in R1 ) is also a key residue , as its substitution by Ala affected binding . The importance of this sub-site is further highlighted by the concomitant loss in affinity when Phe183 ( with which it interacts ) was mutated in PfAMA1 . Collectively , these data provide a firm basis for designing molecules with optimal inhibitory properties to treat malarial infection .
( i ) Baculovirus insect cell expression: A synthetic codon-optimized gene encoding DI and DII of PfAMA1 3D7 [27] ( residues 104–438; numbering based on the initiation methionine , PF11_0344 ) ( GenScript ) was subcloned into a modified pAcGP67B vector ( Pharmingen ) for expression in insect cells using established protocols [20] . Final yield of recombinant protein was approximately 3 mg per L of culture . ( ii ) P . pastoris expression: Synthetic genes were optimized for PfAMA1 coding of residues 97–442 , from strains 3D7 ( Genbank accession number U33274 ) , CAMP ( accession number M34552 ) and HB3 ( accession number U33277 ) . Potential N-glycosylation sites were mutated and genes were cloned EcoRI-KpnI in the pPicZalpha A vector ( Invitrogen ) , resulting in an 11-residues sequence extension followed by myc-epitope and hexa-His tags at the C-terminus ) , expressed in P . pastoris , and purified as described [28] . Yield after purification was approximately 20 mg per L of culture . PfAMA1 FVO ( residues 25–545 , no tags , accession number AJ277646 ) was produced as described before [29] . The DiCo3 protein was modified compared to the published protein [23]; it includes the PfAMA1 FVO prodomain ( amino acids 25–96 ) and one additional mutation to minimize proteolytic cleavage Lys376–>Arg ( B . Faber , unpublished results ) . The PfAMA1 3D7mut ( Ile225–>Asn , residues 25–545 , no tags ) mutant was generated by site-directed mutagenesis ( Genscript ) and produced in P . pastoris in a similar fashion to the native protein [29] . A 39-residue peptide corresponding to residues 2021 to 2059 of PfRON2 ( PfRON2sp1 ) was synthesized by Kinexus ( Vancouver , Canada ) and disulfide cyclized . Lyophilized PfRON2sp1 was solubilized in 100% DMSO and subsequently diluted in HBS ( 20 mM HEPES pH 7 . 5 , 150 mM NaCl ) for use in co-crystallization and functional studies . Peptides PfRON2sp2 ( residues 2027 to 2054 ) and R1 were synthesized by PolyPeptide ( Strasbourg , France ) and solubilized in 3 . 5% DMSO for subsequent use . Crystals of PfAMA1 3D7 PfRON2sp1 were grown in 30% PEG400 , 100 mM Tris-HCl pH 8 . 5 , 200 mM tri-sodium citrate dihydrate and the protein ( 5 mg/mL final concentration ) incubated with PfRON2sp1 ( 1∶2 molar excess ) . A crystal in cryoprotectant buffer was flash cooled at 100 K and diffraction data were collected on beamline 9-2 at SSRL ( Stanford Synchrotron Radiation Laboratory , Stanford , US ) . Crystals of PfAMA1 CAMP PfRON2sp2 were obtained in 20% PEG 4000 , 0 . 1 M Tris/HCl pH 8 . 6 , 0 . 1 M sodium acetate and 20% isopropanol and the protein ( 6 . 4 mg/mL final concentration ) incubated with PfRON2sp2 ( 1∶5 molar excess ) . Diffraction data were collected from a crystal in cryoprotectant buffer at 100 K on beamline ID29 at European Synchrotron Radiation Facility ( Grenoble , France ) . Crystals of PfAMA1 3D7 R1 were obtained in 15% PEG 4000 , 0 . 1 M Tris/HCl pH 8 . 5 , 0 . 1 M sodium acetate and 10% isopropanol and the protein ( 5 . 4 mg/mL final concentration ) incubated with R1 ( 1∶6 molar excess ) . Diffraction data were collected at 100 K on beamline PROXIMA 1 at SOLEIL ( St . Aubin , France ) . Diffraction data were processed using Imosflm [30] or XDS [31] and Scala [32] in the CCP4 suite of programs [33] . Crystallographic parameters and data collection statistics are given in Table 2 . Initial phases were obtained by molecular replacement using PHASER [34] or AMoRe [35] with the unliganded PfAMA1 structure ( PDB 1Z40 ) . Tracing of the PfRON2 and R1 peptides , and addition of solvent molecules , was performed manually in COOT [36] and refinement was performed with Refmac5 [37] or autoBUSTER ( Global Phasing Ltd , Cambridge , UK ) . A summary of refinement statistics is given in Table 3 . All molecular representation figures were generated in the PyMOL Molecular Graphics System , version 1 . 2r3pre , Schrödinger , LLC . Coordinates and structure factors have been deposited in the Protein Data Bank with the following entry codes: PfAMA1-PfRON2sp1 , 3ZWZ; PfAMA1-PfRON2sp2 , 3SRI; PfAMA1-R1 , 3SRJ . SPR measurements were made with a Biacore 2000 instrument ( Biacore AB ) . AMA1 proteins diluted in 10 mM sodium acetate pH 4 . 5 for 3D7 , CAMP , HB3 and FVO strains , or pH 4 . 0 for 3D7mut and Dico3 , were covalently immobilized by an amine-coupling procedure on CM5 sensor chips ( GE Healthcare ) . The reference flow cell was prepared by the same procedure in absence of protein . Binding assays were performed at 25°C in PBS and 0 . 005% Tween 20 by injecting a series of peptide ( PfRON2sp1 and PfRON2sp2 on 3D7 , CAMP , HB3 and FVO , and R1 on 3D7mut and Dico3 ) concentrations at a constant flow rate of 5 µL/min . A heterologous peptide was used to verify the absence of non-specific binding . Peptide dissociation was realized by injecting the running buffer , and the surface was regenerated by injecting glycine/HCl pH 1 . 5 followed by SDS 0 . 05% . Control flow cell sensorgrams were subtracted from the ligand flow cell sensorgrams and averaged buffer injections were subtracted from analyte sensorgrams . For peptide R1 , steady-state signals ( Req ) were obtained directly from the plateau region of the sensorgrams , while for PfRON2sp peptides , estimated values of Req were obtained by extrapolation from the experimental curves since the association phase did not reach a final equilibrium state . All calculations were made using the BIAevaluation 4 . 2 software ( BIAcore AB ) . The saturation curves obtained by plotting Req versus the peptide concentration were fitted with a steady-state model to obtain the Rmax and the apparent equilibrium dissociation constants , KD . To normalize the response for the different ligands , these curves were reported as the percentage of bound sites ( ratio Req/Rmax ) versus the analyte concentration . . The P . falciparum cell cultures and the invasion assays were performed as described previously [11] . Briefly , highly synchronized P . falciparum 3D7 and HB3 schizonts ( 1 . 5% hematocrit , 1 . 5% parasitemia ) were incubated with R1 or PfRON2sp1 peptides . Blood smears were collected 16 hours post-invasion and used for ring-stage parasites counting . The results presented are representative of three independent experiments , each performed in triplicate . Cell binding assays using PfAMA1-expressing BHK-21 cells and recombinant GST-PfRON2-5 fusion proteins were performed as previously described [11] . Although not quantitative , this cell-binding assay truly reflects the interaction between AMA1 and RON2 as we carefully checked all the experimental steps as well as the image recording as described below . Transfections were carried out using Lipofectamine Reagent ( Invitrogen ) as instructed by the manufacturer with 3×105 BHK-21 cells grown on coverslips for 24 h in 6 well plates . Cells were grown for an additional 24 h post-transfection before subsequent analysis . Expression and correct folding of PfAMA1 ( and the mutants ) at the host cell surface was verified by IFA performed with or without permeabilisation , using antibodies either specific to the cytoplasmic tail ( anti-myc tag ) or specific to the extracellular ectodomain of PfAMA1 ( mouse mAb F8 . 12 . 19 [38] ) . For binding assays , coverslips from a same transfection experiment were washed in HBSS ( Invitrogen ) before addition of recombinant PfRON2-5 wild type or mutants diluted in HBSS at 10 , 1 or 0 . 1 µg/ml . Coverslips incubated with GST were systematically used as a control . After five washes in PBS to remove unbound protein , cells were fixed in 4% PAF and further processed for IFA as described above [11] . The binding characteristics of RON2 ( anti-GST labelling ) on the PfAMA1 mutant were only considered valid when its signal was identical to that of wild type PfAMA1 . All other micrographs were obtained with a Zeiss Axiophot microscope equipped for epifluorescence . Adobe photoshop ( Adobe Systems , Mountain View , CA ) was used for image processing . Matching pairs of images were recorded with the same exposure time and processed identically . The PfAMA1 and GST-PfRON2 mutated constructs were generated by site directed mutagenesis using Quickchange II XL protocol ( Stratagene ) . | Malaria arises from infection of erythrocytes by single-cell parasites belonging to the genus Plasmodium , the species P . falciparum causing the most severe forms of the disease . The formation of a moving junction ( MJ ) between the membranes of the parasite and its host cell is essential for invasion . Two important components of the MJ are Apical Membrane Antigen 1 ( AMA1 ) on the parasite surface and the Plasmodium rhoptry neck ( RON ) protein complex that is translocated to the erythrocyte membrane during invasion . The extra-cellular region of RON2 , a component of this complex , interacts with AMA1 , providing a bridge between the parasite and its host cell that is crucial for successful invasion . The parasite thus provides its own receptor for AMA1 and accordingly this critical interaction is not subject to evasive adaptations by the host . We present atomic details of the interaction of PfAMA1 with the carboxy-terminal region of RON2 and shed light on structural adaptations by each apicomplexan parasite to maintain an interaction so crucial for invasion . The structure of the RON2 ligand bound to AMA1 thus provides an ideal basis for drug design as such molecules may be refractory to the development of drug resistance in P . falciparum . | [
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| 2012 | Structural and Functional Insights into the Malaria Parasite Moving Junction Complex |
Among the natural compounds , terpenoids play an important role in the drug discovery process for tropical diseases . The aim of the present work was to isolate antiprotozoal compounds from Ambrosia elatior and A . scabra . The sesquiterpene lactone ( STL ) cumanin was isolated from A . elatior whereas two other STLs , psilostachyin and cordilin , and one sterol glycoside , daucosterol , were isolated from A . scabra . Cumanin and cordilin were active against Trypanosoma cruzi epimastigotes showing 50% inhibition concentrations ( IC50 ) values of 12 µM and 26 µM , respectively . Moreover , these compounds are active against bloodstrean trypomastigotes , regardless of the T . cruzi strain tested . Psilostachyin and cumanin were also active against amastigote forms with IC50 values of 21 µM and 8 µM , respectively . By contrast , daucosterol showed moderate activity on epimastigotes and trypomastigotes and was inactive against amastigote forms . We also found that cumanin and psilostachyin exhibited an additive effect in their trypanocidal activity when these two drugs were tested together . Cumanin has leishmanicidal activity with growth inhibition values greater than 80% at a concentration of 5 µg/ml ( 19 µM ) , against both L . braziliensis and L . amazonensis promastigotes . In an in vivo model of T . cruzi infection , cumanin was more active than benznidazole , producing an 8-fold reduction in parasitemia levels during the acute phase of the infection compared with the control group , and more importantly , a reduction in mortality with 66% of the animals surviving , in comparison with 100% mortality in the control group . Cumanin also showed nontoxic effects at the doses assayed in vivo , as determined using markers of hepatic damage .
Natural products have been a major source of drugs mainly for treating infectious diseases and cancer [1] . About 75% of anti-infective drugs approved from 1981 to 2002 are derived from natural sources . Many of them were isolated from plants and have shown antiparasitic activity . The first antimalarial drug , quinine , was isolated from Cinchona spp . and led to the development of other antimalarial drugs such as chloroquine , which is currently in use . More recently , the sesquiterpene lactone ( STL ) artemisinin has been isolated from the Chinese plant Artemisia annua , which has been used for over 2000 years to treat malaria . At present , this natural compound and its derivatives are used for treating chloroquine-resistant malaria . Both Chagas disease and leishmaniasis are protozoan diseases that cause significant morbidity and mortality in Latin America , whereas leishmaniasis also worldwide . According to the World Health Organization ( WHO ) , they are considered , among others , Neglected Tropical Diseases ( NTDs ) mainly affecting poor people in developing countries [2] . These parasitoses are often forgotten by governments and the pharmaceutical industry , due to economic reasons and a relatively limited market . Therefore , the development of new drugs for the treatment of these parasitic diseases remains a highly desirable goal . Chagas' disease or American trypanosomiasis is caused by the protozoan parasite Trypanosoma cruzi , which is transmitted by blood-sucking insects . This parasitic disease affects 8 million people , mostly in endemic areas of Latin America , but has now spread to other continents [3] . Up to 30% of patients develop heart failure and people usually die from sudden death caused by arrhythmias . The chronic phase of the disease can cause damage to the esophagus , colon or the autonomic nervous system in more than 10% of patients . It is estimated that Chagas' disease killed more than 10 , 000 people in 2008 [3] . In Argentina , 1 . 5 to 2 million people are affected and it is estimated that 15 people die of this parasitosis every week [4] . Leishmaniasis is caused by kinetoplastids from the genus Leishmania and is transmitted by sand flies . WHO estimates that almost 12 million people worldwide are infected with Leishmania spp . and 350 million are at risk of contracting this parasitic disease [5] . Cutaneous leishmaniasis is endemic in Northern Argentina covering an area of 500 , 000 km2 where it is common to find people coinfected with T . cruzi and Leishmania sp . [6] . Its incidence has increased during the last two decades mainly due to Leishmania braziliensis [7] . The clinical manifestations of these parasitoses depend on the Leishmania species involved , presenting three different clinical forms: cutaneous , mucocutaneous and visceral leishmaniasis . Most of the drugs currently in use for the treatment of American trypanosomiasis and leishmaniasis have severe drawbacks . The available treatments for Chagas disease are limited to the nitroaromatic compounds , benznidazole and nifurtimox , which were released in the 1970s . Even though these two drugs are active in the acute stage of infection , they are ineffective in the treatment of the chronic phase . They have toxic side effects and are not active against all T . cruzi strains [8] . The current antileishmanial therapy includes the use of pentavalent antimonials , amphotericin B , miltefosine or paromomycin , which have disadvantages in terms of the route of administration , parasite resistance , cost , teratogenic effects , length of treatment and toxicity [9] . Among the natural compounds , terpenoids display a wide range of biological activities such as anticancer and anti-inflammatory actions and are effective against infective agents such as viruses , bacteria and parasites [10] , [11] . Several terpenoids have been reported to have trypanocidal and leishmanicidal activities [12]–[15] . Recent research has shown the potential role of terpenoids as promising compounds against neglected protozoan diseases such as Chagas' disease and leishmaniasis [16] , [17] . An exhaustive and updated revision has been recently performed by Schmidt and coworkers [18] . These authors revised the antiprotozoal activities of the major biogenetic subclassses of terpenes focusing on STLs , diterpenes and triterpenes . Within the STLs , germacranolides , guaianolides , xanthanolides and pseudoguaianolides , have exhibited significant antiprotozoal activity , showing the potential of this class of compounds [18] . In the last decades , Asteraceae has been regarded as a promising family of plants because of the amount and variety of active compounds produced by the secondary metabolism . STLs and triterpenes isolated from this family have been reported as having trypanocidal and leishmanicidal activities [16]–[18] , [19] . In Argentina , the genus Ambrosia that belongs to this family is represented by three species: A . tenuifolia , A . scabra and A . elatior . Considering the limitations of current therapies for Chagas disease and leishmaniasis and our previous promising findings on the antiprotozoal activity of terpenoids from the genus Ambrosia [20]–[23] , the aim of the present work was to isolate further bioactive compounds from A . elatior and A . scabra .
The aerial parts of Ambrosia elatior L . ( BAF 707 ) and Ambrosia scabra Hook . & Arn . ( Asteraceae ) ( BAF 711 ) were collected in Buenos Aires Province , Argentina in May 2009 . The botanical identification was performed by Dr . Gustavo Giberti and a voucher specimen of each species was deposited at the Museo de Farmacobotánica , Facultad de Farmacia y Bioquímica , Universidad de Buenos Aires . Trypanosoma cruzi epimastigotes ( RA and K98 strains ) were grown in a biphasic medium . Cultures were routinely maintained by weekly passages at 28°C . T . cruzi bloodstream trypomastigotes from RA and K98 strains [24] were obtained from infected CF1 mice by cardiac puncture at the peak of parasitemia on day 15 postinfection . Trypomastigotes were routinely maintained by infecting 21-day-old CF1 mice . T . cruzi amastigotes were obtained from cultured cells infected with tripomastigotes . Leishmania braziliensis and Leishmania amazonensis promastigotes ( MHOM/BR/75/M2903 and MHOM/BR/75/M2269 strains , respectively ) were grown in liver infusion tryptose medium ( LIT ) . Cultures were routinely maintained by weekly passages at 26°C . Parasites were passaged 24 or 48 h previous to the experiments . Inbred male C3H/HeN mice were nursed at the Microbiology Department , Faculty of Medicine , University of Buenos Aires . All procedures requiring animals were performed in agreement with institutional guidelines and were approved by the Review Board of Ethics of IDEHU , CONICET , and conducted in accordance with the Guide for the Care and Use of Laboratory Animals of the National Research Council of Argentina [25] . Extraction of the aerial parts of A . elatior and A . scabra was done by maceration with dichloromethane∶methanol ( 1∶1 ) , as previously described [22] . The organic extract of A . elatior ( AE-OE ) was fractionated by column chromatography on Silica gel 60 with a gradient of hexane , ethyl acetate and methanol . Nine fractions ( F1AE–F9AE ) of 500 ml each were collected and taken to dryness . Each fraction was tested for trypanocidal activity on T . cruzi epimastigotes . Fractions F5AE , F6AE and F7AE were taken with ethyl acetate and afforded a crystalline compound named compound A , which was assayed for trypanocidal and leishmanicidal activities . Fractionation of organic extract of A . scabra has been previously described [23] . A pure compound , compound B , was obtained from fractions F5AS ( 70–74 ) by crystallization from ethyl acetate . Compound C was obtained as a white crystalline precipitate from fractions F5AS ( 75–77 ) , while fractions F5AS ( 125–135 ) afforded a white amorphous powder ( compound D ) . The structure elucidation of compounds A–D was performed by proton nuclear magnetic resonance ( 1H NMR ) and carbon NMR ( 13C NMR ) ( Inova NMR spectrometer; Varian , Palo Alto , CA ) 500 MHz in CDCl3 ( for compounds A , B and C ) and CDCl3:CD3OD ( 8∶2 ) ( for compound D ) , heteronuclear single quantum correlation ( HSQC ) ; heteronuclear multiple bond correlation ( HMBC ) ; correlated spectroscopy ( COSY ) ; electron impact-mass spectrometry ( EI-MS ) ( Agilent 5973 ) and infrared spectroscopy ( Bruker FT-IR IFS25 ) . Growth inhibition of T . cruzi epimastigotes and Leishmania spp . promastigotes was evaluated by a [3H] thymidine uptake assay as previously described [20] . Parasites were adjusted to a cell density of 1 . 5×106/ml and cultured in the presence of A . elatior organic extract , the fractions and purified compounds for 72 h at final concentrations ranging from 1 to 100 µg/ml . Benznidazole ( 5 to 20 µM; Roche ) and Amphotericin B ( 0 . 27–1 . 6 µM; ICN ) were used as positive controls ( data not shown ) . The percentage of inhibition was calculated as 100−{[ ( cpm of treated parasites ) / ( cpm of untreated parasites ) ]×100} . The trypanocidal effect of the pure compounds was also tested on bloodstream trypomastigotes of RA and K98 strains as previously described [20] . Briefly , mouse blood containing trypomastigotes was diluted in complete liver infusion tryptose medium to adjust the parasite concentration to 1 . 5×106/ml . Parasites were seeded ( 150 µl/well ) by duplicate into a 96-well microplate , and 2 µl of each compound/well at different concentrations or control drug ( benznidazole ) was added per well . Plates were incubated for 24 h and the remaining live parasites were counted on a hemocytometer . Results are expressed as [live parasites in wells after compound treatment/live parasites in untreated wells]×100 . To evaluate the effect of the compounds on intracellular forms of T . cruzi , 96-well plates were seeded with nonphagocytic Vero cells at 5×103 per well in 100 µL of culture medium and were incubated for 24 h at 37°C in a 5% CO2 atmosphere . Cells were washed and infected with transfected blood trypomastigotes expressing β-galactosidase [26] at a parasite/cell ratio of 10∶1 . After 24 h of coculture , plates were washed twice with PBS to remove unbound parasites and each pure compound was added at different concentrations in 150 µl of fresh complete RPMI medium without phenol red ( Gibco , Rockville , MD ) . Controls included infected untreated cells ( 100% infection control ) and uninfected cells ( 0% infection control ) . The assay was developed by the addition of chlorophenolred-β-D-galactopyranoside ( CPRG ) ( 100 µM ) and 1% Nonidet P40 , 5 days later . Plates were then incubated for 4–6 h at 37°C and the absorbance was measured at 595 nm in a microplate reader ( Bio-Rad Laboratories , Hercules , CA ) . Percentage inhibition was calculated as 100–{[ ( absorbance of treated infected cells ) / ( absorbance of untreated infected cells ) ×100} and the IC50value was estimated . Compounds A and B were combined with each other , as well as compound A and benznidazole , to evaluate a potential interaction among them . Trypanocidal activity was evaluated on RA epimastigotes by a [3H] thymidine uptake assay as previously described [20] . The fractional inhibitory concentrations ( FICs ) were calculated as the ratio of the IC50 of one compound in combination and the IC50 of the compound alone . The FIC index ( FICI ) for the two compounds was the FIC of compound A plus the FIC of compound B . The fractional inhibitory concentration index ( FICI ) was interpreted as follows: FICI≤0 . 5 synergy , FICI>4 . 0 antagonism , FICI = 0 . 5–4 addition [27] . Vero cells were assayed to determine viability by the MTT method [22] . Cells ( 5×105 ) were settled at a final volume of 150 µl in a flat-bottom 96-well microplate and were cultured at 37°C in a 5% CO2 atmosphere in the absence or presence of increasing concentrations of the pure compounds . After 24 h , 3- ( 4 , 5-dimethylthiazol-2yl ) -2 , 5-diphenyltetrazolium bromide ( MTT ) was added at a final concentration of 1 . 5 mg/ml . Plates were incubated for 2 h at 37°C . The purple formazan crystals were completely dissolved by adding 150 µl of ethanol and the absorbance was detected at 595 nm in a microplate reader . Results were calculated as the ratio between the optical density in the presence and absence of the compound multiplied by 100 . Groups of five C3H/HeN male mice ( 6 to 8 weeks old; 27 . 2±0 . 9 g ) were infected with 500 bloodstream T . cruzi trypomastigotes by the intraperitoneal route . Five days after infection , the presence of circulating parasites was confirmed by the microhematocrit method [23] . Mice were treated daily with compound A or benznidazole ( 1 mg/kg of body weight/day ) for five consecutive days ( days 5 to 10 postinfection ) by the intraperitoneal route . Drugs were resuspended in 0 . 1 M phosphate buffered saline ( PBS , pH 7 . 2 ) , and this vehicle was also employed as a negative control . Levels of parasitemia were monitored every 2 days in 5 µl of blood diluted 1∶5 in lysis buffer ( 0 . 75% NH4Cl , 0 . 2% Tris , pH 7 . 2 ) by counting parasites in a Neubauer chamber . The number of deaths was recorded daily . Groups of five C3H/HeN male uninfected mice were treated with PBS or compound A as described above , in order to evaluate the potential in vivo toxicity of the compound . On day 7 post treatment , blood samples were collected by cardiac puncture . Serum activities of alanine aminotransferase ( ALT ) , aspartate aminotransferase ( AST ) and lactate dehydrogenase ( LDH ) were determined as markers of hepatic damage . Assays were carried out by ultraviolet spectrophotometry following the specifications of the kit's manufacturer ( Wiener Lab , Buenos Aires , Argentina ) . The results are presented as mean±SEM . The level of statistical significance was determined by using one-way analysis of variance ( ANOVA ) , with GraphPad Prism 5 . 0 software ( GraphPad Software Inc . , San Diego , CA ) . Long rank test was used for survival curves . Comparisons were referred to the control group . P values of <0 . 05 were considered significant .
The dichloromethane∶methanol organic extract ( AE-OE ) from the aerial parts of Ambrosia elatior was evaluated in vitro against T . cruzi epimastigotes . This extract was active with a growth inhibition of 93 . 7±2 . 0% at a concentration of 100 µg/ml . The extract fractionation by chromatographic techniques using Silica gel 60 with a gradient of hexane , ethyl acetate and metanol yielded nine fractions ( F1AE to F9AE ) , which were assayed for their in vitro trypanocidal activity on epimastigotes at 100 , 10 and 1 µg/ml ( Table 1 ) . The results of this experiment showed that at a concentration of 10 µg/ml , fractions F5AE , F6AE and F7AE were the most active with percentages of growth inhibition of 94 . 7±0 . 5% , 75 . 3±4 . 8% and 76 . 8±1 . 4% , respectively ( Table 1 ) . A pure compound ( compound A ) was further isolated from fractions F5AE , F6AE and F7AE . It was identified by spectroscopic methods as the STL 1α , 3β , 4β , 5β , 8β , 10β-3 , 4-dihydroxy-11 ( 13 ) -pseudoguaien-12 , 8-olide or cumanin ( Figure 1 ) . An exhaustive search in the active fraction F5AS from A . scabra resulted in the isolation of three other compounds , B , C and D . The analyses of spectroscopic data allowed to identify compounds B and C as the STLs ( 2′R , 3aS , 6s , 8s , 8aR ) -octahydro-8-hydroxy-6 , 8-dimethyl-3-methylene-spiro[7H-cyclohepta[b]furan-7 , 2′ ( 5′H ) -furan]-2 , 5′ ( 3H ) -dione psilostachyin and its 5-epimer cordilin or epipsilostachyin , respectively . Compound D was identified as the sterol glycoside sitosterol-3-O-β-D-glucopyranoside or daucosterol ( Figure 1 ) . We analyzed the antiparasitic activity of the isolated compounds at different stages of Trypanosoma cruzi and Leishmania sp . The results of the trypanocidal activity of cumanin , cordilin and daucosterol on T . cruzi epimastigote forms are shown in Figure 2 . The STLs were active with 50% inhibition concentration ( IC50 ) values of 12 µM and 4 µM for cumanin , and 26 µM and 44 µM for cordilin against RA and K98 epimastigotes , respectively . Daucosterol exhibited lower activity with a growth inhibition of 40 . 7±2 . 8% at 100 µg/ml ( 174 µM ) . The combinatory effect of the two most active STLs , psilostachyin and cumanin , on epimastigotes of T . cruzi was analyzed by an isobologram . The fractional inhibitory concentration index ( FICI ) was 0 . 97±0 . 11 ( Mean±SD ) for the combination of these drugs , indicating there is neither antagonism nor synergism; however , an additive effect could be assessed in the trypanocidal activity between cumanin and psilostachyin ( Figure 3 ) . We neither found antagonism nor synergism interaction between cumanin and benznidazole , the current reference drug to treat Chagas disease ( FICI: 0 . 95±0 . 05 ) ( Mean±SD ) ( Figure 3 ) . The trypanocidal effect of the pure compounds was tested on bloodstream trypomastigotes obtained from infected mice . Parasites were seeded into a 96-well microplate and different concentrations of each compound were added to each well . After 24 h incubation , the remaining live parasites were counted on a hemocytometer . In Figure 4 it can be appreciated that the two STLs , cumanin and cordilin , showed activity against RA trypomastigotes with IC50 values of 180 µM and 90 µM , respectively . The sterol glycoside was active with an IC50 of 142 µM . In order to analyze the relevance of the compounds in different T . cruzi populations , K98 trypomastigotes were incubated in the presence of cumanin , cordilin and daucosterol , showing IC50 values of 170 µM , 83 µM and 184 µM , respectively ( Figure 4 ) . To analyze the effect of the compounds on the replicative forms , Vero cells were infected with transfected trypomastigotes expressing the β-galactosidase gene . After 24 h incubation , all the parasites outside the cells were removed by washing and different concentrations of the compounds were added to the wells . Five days later the cells were disrupted with detergent and β-galactosidase activity was determined with chlorophenol red-β-D-galactopyranoside , as a direct estimation of the number of parasites . Since the effect of psilostachyin was not previously reported , the four isolated compounds were included in the assay . Figure 5 shows that cumanin and psilostachyin were able to inhibit amastigote replication . Both STLs were active with approximately 95% inhibition at 25 µg/ml , and IC50s of 8 µM and 21 µM , respectively . Neither cordilin nor daucosterol were active against the intracellular forms of T . cruzi . After the observed effect of cumanin , psilostachyin , cordilin and daucosterol on T . cruzi , we analyzed the inhibitory activity of these compounds on Leishmania promastigotes . The IC50 values against L . amazonensis were 3 µM , 10 µM and 55 µM , for cumanin , psilostachyin and cordilin , respectively . We found that cumanin revealed leishmanicidal activity with growth inhibition values greater than 80% at a concentration of 5 µg/ml ( 19 µM ) , against both L . braziliensis and L . amazonensis ( Figure 6 ) . Psilostachyin and cordilin displayed high leishmanicidal activity against L . braziliensis ( 82 . 8±7 . 8 and 83 . 03±0 . 01% at a concentration of 1 µg/ml , respectively ) . These results showed that cumanin was active against the two species reported as the main cause of tegumentary leishmaniosis in Argentina: L . braziliensis and L . amazonensis [6] . To analyze the effect of the active compounds in vivo , a group of mice was inoculated with a deadly number of trypomastigotes of the RA strain and parasitemia was determined 5 days after to confirm the effectiveness of the infection . Mice were then treated with cumanin or benznidazole , the drug currently used to treat infected humans , for 5 consecutive days . Parasitemia and survival were periodically recorded . Figure 7A shows how cumanin was able to dramatically reduce the number of circulating parasites in the acute phase of T . cruzi infection compared with the PBS-treated control . When we analyzed the area under the parasitemia curve ( AUC ) we observed an important decrease in the number of circulating parasites in cumanin treated mice comparing with controls ( AUC: 382 and 800 , respectively ) . This protection was more important at the peak of parasitemia , on day 22 postinfection , when control mice showed 8 times more parasites than cumanin-treated ones ( 8 . 2 and 1 . 2×107 parasites/ml , respectively , p<0 . 01 ) . As shown in Figure 7A , parasitemia values were slightly higher than those obtained with benznidazole but no significant differences were found between the two compounds . The ability of cumanin to control infection was also reflected in the significant survival of the treated mice , respect to the control ( p = 0 . 0086 ) . Figure 7B shows the important reduction in mice mortality observed in cumanin-treated mice , where 100% of PBS-treated mice died between days 22 and 26 postinfection , while 66% of cumanin-treated mice survived the T . cruzi infection by the end of the experiment on day 100 ( not shown ) . Again , cumanin-treated mice did not show any significant differences with respect to those treated with the approved drug for this parasitosis . Hepatic toxicity of the STL was evaluated through the determination of a panel of hepatic-linked enzyme markers . Serum levels of AST , LDH and AST were measured at 7 days post treatment . Table 2 shows that cumanin treated mice exhibited similar levels of the analyzed enzymes to those of PBS-treated control mice , suggesting that cumanin is not hepatotoxic in vivo at the doses used .
In a previous investigation we have reported the isolation of STLs from Argentinean Ambrosia species , some of which have shown significant trypanocidal and leishmanicidal activities [20]–[22] . These promising results prompted us to continue with the search for other antiprotozoal compounds from Ambrosia scabra and its related species , A . elatior . The organic extract of A . elatior showed significant activity against T . cruzi , being able to inhibit 94% of epimastigote growth at a concentration of 100 µg/ml . When this extract was chromatographed on a Silica gel column , 9 final fractions ( F1AE–F9AE ) were obtained . Fractions F5AE , F6AE and F7AE displayed the highest trypanocidal activity against the non-infective form of T . cruzi and afforded compound A . The structural elucidation of this substance was based on the analysis of its spectral data and was identified as cumanin , which has been previously reported in Ambrosia artemisiifolia [28] and Ambrosia cumanensis [29] . We recently found that the F5AS fraction of the organic extract of A . scabra was very active against T . cruzi . From F5AS fraction we isolated the STL psilostachyin C , whose anti-epimastigote and anti-trypomastigote activity had been previously reported [22] . In this manuscript we report that further isolation steps from A . scabra fraction F5AS yielded other three compounds ( B , C , D ) . Compounds B and C were identified as the STLs , psilostachyin and cordilin , respectively , and compound D as daucosterol . The identification of all these compounds was performed by analysis of their spectral data . Psilostachyin had been previously isolated from A . tenuifolia by our group [20]; however , this is the first report of its presence in A . scabra , and the first time the isolation of cordilin and daucosterol from A . scabra has been reported . Psilostachyin and cordilin are diasteroisomers that only differ in the spatial configuration of the hydroxyl and methyl groups on C-5 ( Figure 1 ) . T . cruzi is genetically highly diverse [30] . While RA , belonging to TcII-DTUs , is a highly virulent pantropic/reticulotropic strain , K98 ( TcI ) is a low virulence myotropic strain [31] . In order to analyze the relevance of cumanin , cordilin and daucosterol in different T . cruzi populations , K98 and RA strains were evaluated for their trypanocidal activity . Even though the epimastigote stage of the parasite is non-infective , it is easy to cultivate and therefore , it is useful for a preliminary screening test [32] . Cumanin exerted significant in vitro trypanocidal activity against this parasite form , showing an IC50 value 12 µM and 4 µM for RA and K98 strains . Cordilin was less active with an IC50 26 µM and 44 µM , for the two strain analyzed . Daucosterol displayed even lower trypanocidal activity . However , in the search for new potential trypanocidal candidates , it is necessary to analyze the activity of the compounds on the infective forms of the parasite . Consequently , cumanin , cordilin and daucosterol were tested against trypomastigotes and all the isolated compounds were evaluated against intracellular amastigotes , including psilostachyin that had not been previously evaluated against this parasite form [20] . Cordilin and cumanin showed low activity on trypomastigotes ( IC50 = 89 µM and 180 µM , respectively . The two STLs psilostachyin and cumanin inhibited the growth of the intracellular forms of T . cruzi with IC50s values of 21 µM and 8 µM , respectively . Cordilin and daucosterol did not display any activity against amastigotes . The fact that cumanin is active against amastigotes is of particular interest , since the DNDi organization prioritizes the development of drugs that are useful during the indeterminate and chronic phases of the infection where parasites remain intracellular [33] . The ability of compounds to inhibit the intracellular growth of T . cruzi amastigotes is a more rigorous and relevant test of anti-T . cruzi activity , as it is applied to a stage which is the predominant and replicative form in mammals cells . The impairment of amastigotes replication upon cumanin treatment could lead to a reduction in tripomastigotes release from the cells and the subsequent low parasitemia observed in mice . Nowadays , the association of compounds could be an interesting strategy for the control of Chagas disease . Thus , psilostachyin and cumanin were tested together on epimastigotes to assess their possible interaction . An additive effect was observed between these two compounds . Considering the incidence of the Leishmania spp . in South America and the existence of patients co-infected with these parasites and T . cruzi [34]–[36] , the effect of the four compounds on L . braziliensis and L . amazonensis promastigotes was also evaluated . Cumanin , psilostachyin and cordilin revealed significant leishmanicidal activiy against both L . amazonenzis and L . brazilensis , while daucosterol leishmanicidal activity was lower . Trypanocidal activity of STL is highly influenced by stereochemical or structural differences [37] . Schmidt et al . [38] demonstrated that the STL helenalin was more active against T . cruzi and T . b . rhodesiense than its diasteroisomer mexicanin , which differs from the former only in the spatial orientation of an OH group . In the case of the two epimeric STLs isolated from A . scabra , cordilin and psilostachyin , the behavior of the two compounds against non-infective and infective forms of T . cruzi was very different . We have previously reported that psilostachyin is very active against epimastigotes and trypomastigotes showing IC50 values of 4 µM and 3 µM , respectively [20] . Those values compared with the results herein reported for cordilin with values of 26 µM ( epimastigotes ) and 89 µM ( trypomastigotes ) signal that psilostachyin is more active than cordilin . In the present study , we have also demonstrated that psilostachyin is more active than its 5-epimer on the intracellular form of the parasite . Thus , these results support the fact that stereochemistry plays an important role in the biological activity . The administration of cumanin to T . cruzi-infected mice ( 1 mg/kg/day ) produced a significant reduction in parasitemia levels , even lower than that produced by benznidazole , when compared with PBS-treated control mice . An 8-fold reduction in parasitemia levels was observed on day 22 postinfection compared with control . Moreover , cumanin was able to reduce the number of parasites during the whole course of the infection . More importantly , 66% of the mice survived the deadly challenge with trypomastigotes while only 20% of benznidazole-treated mice survived , compared with 100% mortality in the PBS-treated mice group . It should be noted that the dosis of 1 mg/kg/day used was irrespective of cumanin and benznidazole IC50 values . Interestingly , cumanin is more active on intracellular amastigotes than on free trypomastigotes , which is not the case for psylostachyin , since it is very active in both stages ( Figure 5 and reference [20] ) . This phenomenon could be attributed to the fact that cumanin differently affects both parasite stages somehow interacting in different metabolic pathways that deserve to be further investigated . An alternative hypothesis suggesting that cumanin affects both parasite stages similarly as well as the cells infected by amastigotes , consequently killing the cell host and parasites , cannot be sustained since cumanin showed to be nontoxic for non-infected cells at the concentration used ( Table 2 ) . The determination of in vitro and in vivo toxicity is a very important point in a drug discovery process [17] . Cumanin and cordilin displayed low cytotoxicity on Vero cell line at concentrations of up to 100 µg/ml ( data not shown ) . Moreover , cumanin-treated mice ( non-infected ) exhibited similar serum levels of hepatic-linked enzyme markers than those in the PBS-treated control mice ( Table 2 ) . Thus , by using in vitro and in vivo toxicity assays , we demonstrated the non-toxic effect of cumanin at the doses used . These results are in agreement with those reported by Lastra et al . [39] , who found that cumanin produces low cytotoxicity on peritoneal murine macrophages . As it was mentioned in the introduction section , different types of terpenoid compounds have shown to display antiprotozoal activity [18] , [40] , and among them , several STLs having an α , β-unsaturated-γ-lactone moiety have been reported to show trypanocidal and leishmanicidal activities [18] , [41] , [42] . Thus , the STLs psilostachyin and cumanin can be considered interesting lead molecules for the development of drugs for Chagas' disease . Variability in the outcome and morbidity of T . cruzi infection might be associated , at least in part , with the complex population structure of the parasite . Taking this into account , the fact that cumanin shows high activity at different stages and strains of T . cruzi highlights the importance of searching new drugs against Chagas disease . In addition , the leishmanicidal activity shown by these compounds in a preliminary assay could be an interesting fact to consider , since the endemic areas of Chagas disease and leishmaniasis usually overlap in Latin America and co-infected patients have been reported [34]–[36] . Despite the advances in the biology of protozoan parasites like T . cruzi and Leishmania sp . , the discovery and development of safer drugs to treat American trypanosomiasis and/or leishmaniasis still constitute a challenge . The results presented in this work demonstrate that terpenoids , particularly STLs , are an interesting group of natural compounds that could become good candidates for antiprotozoal chemotherapy . Further studies involving the evaluation of different targets will be useful to understand the mechanisms of action of the isolated STLs . | In addition to the primary metabolism necessary for life , plants have a secondary metabolism that generates compounds , which aid in their growth and development . A common role of secondary metabolites is defense mechanisms to fight off animals , pest and pathogens . Pharmacognosy takes advantage of the rich source of compounds produced by plants , selecting and processing natural products for medicinal use , resulting in a wide range of anticancer , anti-inflammatory and anti-infective drugs currently in use . Chagas disease and leishmaniasis are parasitic diseases caused by protozoa transmitted by sucking insects . According to the World Health Organization ( WHO ) they are considered as Neglected Tropical Diseases that are especially endemic in low-income populations in developing countries . The drugs currently in use were developed more than 50 years ago and have severe drawbacks . The authors found that chemical compounds called sesquiterpene lactones , obtained from plants of the Asteraceae family , have anti-protozoan activity against different parasite stages . The identified compounds were also able to control parasitic infection in mice without any toxic effects . Thus , these sesquiterpenes lactones could be interesting chemical compounds having pharmacological properties , whose chemical structure could be modified leading to potent and safer drugs to treat these parasitoses . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
]
| []
| 2013 | Natural Terpenoids from Ambrosia Species Are Active In Vitro and In Vivo against Human Pathogenic Trypanosomatids |
Barley ( Hordeum vulgare L . ) Mla alleles encode coiled-coil ( CC ) , nucleotide binding , leucine-rich repeat ( NB-LRR ) receptors that trigger isolate-specific immune responses against the powdery mildew fungus , Blumeria graminis f . sp . hordei ( Bgh ) . How Mla or NB-LRR genes in grass species are regulated at post-transcriptional level is not clear . The microRNA family , miR9863 , comprises four members that differentially regulate distinct Mla alleles in barley . We show that miR9863 members guide the cleavage of Mla1 transcripts in barley , and block or reduce the accumulation of MLA1 protein in the heterologous Nicotiana benthamiana expression system . Regulation specificity is determined by variation in a unique single-nucleotide-polymorphism ( SNP ) in mature miR9863 family members and two SNPs in the Mla miR9863-binding site that separates these alleles into three groups . Further , we demonstrate that 22-nt miR9863s trigger the biogenesis of 21-nt phased siRNAs ( phasiRNAs ) and together these sRNAs form a feed-forward regulation network for repressing the expression of group I Mla alleles . Overexpression of miR9863 members specifically attenuates MLA1 , but not MLA10-triggered disease resistance and cell-death signaling . We propose a key role of the miR9863 family in dampening immune response signaling triggered by a group of MLA immune receptors in barley .
Plants have evolved two major classes of immune receptors for pathogen recognition and defense activation [1] . Pattern recognition receptors ( PRRs ) detect conserved pathogen associated molecular patterns ( PAMP ) and trigger PAMP-triggered immunity ( PTI ) [2] , whilst intracellular NB-LRR ( NLR ) receptors recognize isolate-specific pathogen effectors and trigger effector-triggered immunity ( ETI ) , often accompanied by a hypersensitive reaction ( HR ) [3] , [4] . PTI and ETI are believed to share highly overlapping signaling networks for effective immune responses [5] . A plant genome may contain hundreds of genes encoding NLR receptors [6] . Improper activation of some NLR receptors is accompanied by autoimmunity that is harmful to plant growth [7] , [8] , thus , expression of NLR genes is likely under tight control at different levels . Plant small RNAs are noncoding RNAs that largely fall into two groups , microRNAs ( miRNAs ) and small interfering RNAs ( siRNAs ) . Plant small RNAs have important roles in plant growth and development , abiotic stress responses and antiviral defense responses [9]–[11] . Increasing evidence indicates that plant small RNAs also participate in PTI and ETI responses against bacterial and fungal pathogens [12] , [13] . For example , the miR393 and miR393* pair are involved in Arabidopsis PTI responses against Pseudomonas syringae pv . tomato ( Pst ) [14]–[16] and hvu-miR398 is regulated by the barley Mla NLR immune receptor , influencing ETI responses to the powdery mildew fungus [17] . Further , the natural antisense ( NAT ) RNA , nat-siRNAATGB2 , and a long siRNA , lsiRNA-1 , contribute to Arabidopsis RPS2-mediated ETI responses to Pst ( avrRpt2 ) [18] , [19] . Interestingly , recent examples indicate that plant small RNA pathways are required for defense responses against Verticllium dahliae [20] and are themselves targets of effectors secreted by oomycetes and fungi [21] , [22] . MicroRNAs are 21–24 nucleotides ( nt ) endogenous small RNAs that repress gene expression in plants and animals [23] . In plants , the majority of miRNAs originating from MIR genes are processed by the DICER-LIKE1 ( DCL1 ) enzyme from long primary miRNA transcripts that form imperfect stem-loop structures [9] , [10] , [24] . The mature miRNAs are loaded into an ARGONAUTE ( AGO ) protein to form functional RNA-induced silencing complex ( RISC ) for target gene silencing [25] . Plant miRNAs mediate target silencing through at least two modes of action , i . e . , target mRNA cleavage and translational repression [24] , [26] . Unlike animal miRNAs , plant miRNAs are highly complementary to their targets and earlier studies have shown that plant miRNAs guide target mRNA cleavage via AGO slicers [27]–[29] , thus guiding RNA cleavage was believed to be the major activity of plant miRNAs [9] . Increasing evidence has indicated that plant miRNAs also inhibit target mRNA translation [30]–[36] , and in some cases , plant miRNAs may regulate targets through both mRNA cleavage and translation inhibition [30] , [32] , [35] . Recent work has demonstrated that the ALTERED MERISTEM PROGRAM1 ( AMP1 ) -dependent activity of miR398 in repressing translation of the CSD2 gene on the ER [37] , further clarifying the role of plant miRNAs in inhibition of target gene translation . The role of plant miRNAs in modulating immunity has recently been shown in diverse plant species , particularly their involvement in post-transcriptional control of NLR receptors [38]–[41] . In Medicago truncatula , highly abundant miRNA families , e . g . , miR2109 , miR2118 and miR1507 , act as master regulators and target sites encoding highly conserved domains of a large group of NLR receptors [38] , and this regulatory circuit can be extended into non-legume plant species , e . g . , potato [38] . In N . benthamiana , two miRNAs , miR6019 and miR6020 , were shown to regulate the TIR-NB-LRR ( TNL ) -type N receptor gene and to attenuate N-mediated resistance against Tobacco mosaic virus [39] , while in other Solanaceae species the miR482/2118 superfamily and additional miRNA families target both the TNL- and CC-NB-LRR ( CNL ) -type receptor genes [39] , [40] . Moreover , ath-miR472 , a miR482-related Arabidopsis miRNA , was recently reported to act together with RDR6 to target CNL genes , including RPS5 and SUMM2 , and to modulate both PTI and ETI against bacterial pathogens through post-transcriptional regulation of a subset of CNL genes [41] . Plant NLR-targeting miRNAs are in general of 22-nt in length , and they are able to trigger the production of secondary siRNAs that form a feed-forward regulation loop to amplify the suppression effect on NLR genes [38]–[42] . These trans- or cis-acting small interfering RNAs are usually in phase with the 5′-end of the cleavage site of the target transcripts , thus called phased secondary siRNAs ( phasiRNAs ) [43] . While miRNAs of 22-nt rather than 21-nt are believed to be the preferential triggers of phasiRNAs [44]–[46] , the asymmetrical miRNA duplex structure is also important in triggering secondary siRNAs [47] . Despite the involvement of miRNAs and phasiRNAs in modulating plant immunity against pathogens in diverse plant species , the functional relevance of NLR-targeting miRNAs and the secondary siRNAs has not been well addressed in plant-fungal interactions , particularly for Poaceae species . In barley , Mildew resistance locus a ( Mla ) contains a large number of alleles encoding NLR immune receptors that have verified or anticipated race-specific disease resistance activity against the powdery mildew fungus , Blumeria graminis f . sp . hordei ( Bgh ) [48] . Recent findings indicate that upon pathogen recognition MLAs dynamically integrate PTI and ETI immune signaling pathways in both nuclear and cytoplasmic compartments to mediate defense responses [49] , [50] . Upon activation MLA associates in the nucleus with transcription factors ( TFs ) , i . e . , WRKY and MYB TFs , to initiate extensive transcriptional reprogramming that is critical for effective immune responses [51]–[55] , whereas MLA triggers cell-death signaling in the cytosol through as yet unknown components that are probably conserved in monocots and dicots [56]–[58] . Recent data show that Mla mediates control of barley miR398 that represses chloroplast HvSOD1 accumulation , thus de-repressing HR cell-death signaling in response to Bgh challenge [17] . Previous studies have also shown that the activity of some MLAs is tightly regulated at post-translational level by the co-chaperone Required for Mla12 resistance 1 ( RAR1 ) [59]–[61] . Nevertheless , whether and how Mla alleles are regulated at post-transcriptional level in barley remains unknown . Here , we show that members of the Triticeae-specific miR9863 family differentially regulate a subset of barley Mla alleles at the post-transcriptional level . Unique single-nucleotide-polymorphism ( SNP ) sites in mature miR9863s and the miR9863-binding site of Mla alleles determine regulation specificity . Furthermore , we demonstrate in barley and N . benthamiana that miR9863s trigger the biogenesis of phasiRNAs and together these small RNAs form a regulation network for controlling Mla expression . Overexpression of miR9863 members specifically attenuates MLA1 , but not MLA10-mediated disease resistance against Bgh in barley or induction of cell-death signaling in N . benthamiana , signifying the miR9863 family in fine-tuning immunity triggered by MLA receptors .
To investigate the role of miRNAs in barley ( Hordeum vulgare L . ) resistance to the powdery mildew pathogen , Bgh , we constructed small RNA ( sRNA ) libraries using samples derived from healthy and Bgh-infected leaves of the near-isogenic line P01 that harbors the Mla1 resistance allele [62] . Deep sequencing and data analyses of the sRNA libraries identified miRNAs that were either up- or down-regulated after Bgh infection . We searched in our sequence data for miRNAs potentially targeting Mla alleles and found that members of one miRNA family are complementary to the coding sequence for the region adjacent and with two-amino acids overlapping with the RNBS-D motif in Mla1 ( Fig . 1A and 1D ) . Amino acid sequence alignment indicated that this potential miRNA target site is highly conserved among Mla alleles , as well as some R genes from wheat and related species ( S1 Figure ) . The same miRNA family was also ascertained by an independent small RNA sequencing project [63] and described in previous small RNA deep sequencing studies in barley and bread wheat ( Triticum aestivum L . ) . This family was originally designated in wheat as miR2009 with four members , i . e . , miR2009a , miR2009b , miR2009c and miR2009d ( S1 Table ) [64]–[66] . However , the plant miR2009 was not formally included in miRBase20 . 0 , while a sea urchin miRNA was already registered as miR-2009 [67] . Thus , the name of the wheat miR2009 was most likely assigned arbitrarily and thus bears the same name as that of the sea urchin miRNA . Therefore , we have renamed the plant miRNA family with its own unique designator in miRBase20 . 0 , miR9863 . We set out to search for barley and wheat EST sequences containing miR9863 members , and to BLAST wheat and barley genome databases . We found that miR9863a could be aligned to the wheat EST , CK193889 , while miR9863c aligned to another wheat EST , DR736484 ( S2A Figure ) . Interestingly , miR9863b and miR9863d overlap by 17 nucleotides , and cluster with miR9863a on the same wheat EST ( i . e . , CK193889 ) with 244 nt in-between ( S2A Figure; S1 Table ) . In barley , miR9863b and miR9863d also align to one barley cDNA sequence , AK364228 , overlapping by 17 nt ( S2A Figure; S1 Table ) . Since miR9863b and miR9863d are two miRNAs that likely resulted from overlapping processing of the same locus , we designated these two miRNAs as miR9863b . 1 and miR9863b . 2 ( Fig . 1A; S1 Table ) [68] . The miRNA and miRNA* sequence and the flanking EST sequences together can form a typical hairpin structures , with the miRNA and miRNA* on the stem region ( Fig . 1B; S2A Figure ) . Thus , these analyses indicate that the miR9863 family is present in the Triticeae , and members of this family could be potential regulators of Mla alleles . Next we attempted to isolate the precursors of the miR9863 family from both barley and wheat . While we could readily amplify all three progenitors from wheat , i . e . , MIR9863a , MIR9863c and MIR9863b , we could only amplify two progenitors from barley , MIR9863a and MIR9863b , but not MIR9863c ( Fig . 1B ) . MIR9863c also was not present in an independent deep sequencing data set of barley cv . Morex , both from non-inoculated and Bgh inoculated leaves [63] . It is possible that miR9863c may be genotype specific in barley , or an intron-derived microRNA . Sequence analysis revealed that the MIR9863a progenitor from barley and wheat are highly conserved , and the same is true for MIR9863b ( S2A Figure ) . Since MIR9863a and MIR9863b are aligned in tandem on the same wheat EST ( CK193889 ) , this suggests the existence of a wheat polycistron of MIR9863a-MIR9863b , which may not be present in barley ( S2A Figure ) . To test whether these potential miRNA progenitors can generate miRNAs , we transiently expressed them in N . benthamiana by Agrobacterium-mediated infiltration and found the accumulation of 22-nt mature miRNAs in all cases ( S2B Figure ) . Further , we examined the expression of the miR9863 family in different plant species ( S3 Figure ) . Interestingly , we found that they are expressed in all tested barley and bread wheat cultivars , but do not accumulate in other tested monocots , e . g . , Brachypodium distachyon , rice ( Oryza sativa ) and maize ( Zea mays ) , nor in the dicots N . benthamiana and A . thaliana ( S3 Figure ) . Although searching of the monocot small RNA library using miR9863 sequences found some sorghum small RNAs matching miR9863a [63] , however , we could not further retrieve any flanking sequences from sorghum EST or genome database that can form a miRNA precursor structure [69] . Furthermore , searching of several sRNA libraries from Brachypodium distachyon [66] , [70] , [71] , which has been served as a model for the Triticeae tribe , did not find any matching sequences to the miR9863 sequences . We also screened NCBI genome and EST databases of rice and Arabidopsis using the mature miR9863 sequences ( allowing no more than 2 mismatches ) , but failed to identify a single match . Together , these suggest that the MIR9863 loci may be Triticeae-specific innovation . To examine the function of the miR9863 members in directing Mla1 transcript cleavage in barley , we conducted 5′-RACE using Bgh infected P01 ( harboring the Mla1 allele ) leaf samples . We could detect three major cleavage products from Mla1 transcripts after gel-electrophoresis ( Fig . 1C , marked by ‘a’ , ‘b’ , and ‘c’ ) . Sequencing of clones derived from band ‘a’ revealed two cleavage positions within the miR9863-binding site in Mla1 transcripts ( Fig . 1C and 1D ) . To test the specificity of miR9863a binding , we conducted site-directed mutagenesis by replacing the ‘T’ with an ‘A’ at the position next to the 1st cleavage site , and ‘CT’ with ‘GA’ across the 2nd cleavage site , resulting in Mla1-T1266A and Mla1-CT1270GA . This should result in a loop at the respective cleavage site in the miRNA/target duplex ( S4A Figure ) . We next determined the efficiency of miR9863a on wild-type ( WT ) Mla1 and the two Mla1 variants by transient expression in N . benthamiana . Upon co-expression of MIR9863a with WT Mla1 , MLA1 protein was decreased to a level that was barely detectable by Western blot analysis ( S4B Figure , lane 2 ) . By contrast , MLA1-CT1270GA or MLA1-T1266A protein abundance was not affected or only slightly reduced ( S4B Figure , lanes 3 and 4 , 5 and 6 , respectively ) , compared to the empty vector ( EV ) controls ( S4B Figure , lane 1 ) . These results suggest the strict pairing at the 10th to 11th nt and the 15th nt positions in the miR9863a-binding site of Mla1 is necessary for proper regulation of miR9863a . Sequencing of clones derived from band ‘b’ and ‘c’ revealed two additional cleavage sites downstream of the miR9863-binding position ( Fig . 1C and 1D ) . We speculate that these two cleavage sites were derived from in-phase degradation triggered by secondary siRNAs ( see below ) . In summary , the potentially Triticeae-specific miR9863 family has four members , and some members of this miRNA family direct the cleavage of Mla1 transcripts at the miRNA-binding site . Functional differentiation is usually observed among different miRNA members that belong to the same family [31] , [72] , [73] . To determine the specificity of individual miR9863 members for Mla1 , we co-expressed C-terminal HA-tagged MLA1 with progenitor MIR9863a , MIR9863b or MIR9863c , respectively , in N . benthamiana through Agrobacterium-mediated infiltration ( Fig . 1B; Fig . 2 ) . MLA1 accumulation was fully or significantly blocked by the expression of MIR9863a or MIR9863b but not by MIR9863c , compared to EV control ( Fig . 2A , 2B and 2C ) . Since MIR9863b should simultaneously generate miR9863b . 1 and miR9863b . 2 ( Fig . 1B ) , we could not differentiate the effect of these two miRNAs ( Fig . 2B ) . To address this , we utilized the Arabidopsis precursor Ath-MIR173 as a backbone and constructed artificial miRNA expression vectors , aMIR9863a , aMIR9863b . 1 and aMIR9863b . 2 ( S5 Figure ) [17] , [44] , [74] . In N . benthamiana these aMIRNA precursors could respectively generate miR9863a , miR9863b . 1 , or miR9863b . 2 to a comparable level ( Fig . 2D ) . Further co-expression of aMIR9863a with Mla1 fully repressed MLA1 accumulation in N . benthamiana ( Fig . 2E , lanes 1 and 2 ) . Importantly , aMIR9863b . 1 expression could largely block MLA1 accumulation whilst aMIR9863b . 2 expression has only a subtle effect , resulting in accumulation of MLA1 at ∼15% , or ∼60% of that in EV control , respectively ( Fig . 2E , lanes 3 and 4 , lanes 5 and 6 ) . These results demonstrate that Mla1 is differentially regulated by miR9863 members , with the highest efficiency specified by miR9863a and miR9863b . 1 , less so by miR9863b . 2 , and none by miR9863c . It is interesting to note that miR9863a and miR9863b . 2 differ markedly in their regulation of Mla1 , but have only a single nucleotide difference ( Fig . 1A; S6A Figure ) . To further characterize this observation , we introduced a ‘T’ to ‘C’ point mutation in the MIR9863a progenitor , resulting in MIR9863a-T9C that should produce a miR9863a variant sequentially same as miR9863b . 2 ( S6B Figure ) . Indeed , unlike MIR9863a that fully blocked MLA1 accumulation , MIR9863a-T9C co-expression with Mla1 led to high MLA1 level to ∼80% of that in EV control ( S6C Figure , panel a ) ; RNA gel-blotting detected equal expression level of the respective miRNAs ( S6C Figure , panel b ) . These data indicate that a single nucleotide difference between miR9863a and miR9863b . 2 can influence the regulation of Mla1 . Cloned Mla alleles encode highly sequence related CNL-subtype NLR receptors [48] , [59] , [75]–[78] . Sequence alignment using 29 Mla short sequences covering only the miR9863 target site shows that these Mla sequences are almost identical except for 1 or 2 adjacent nucleotides ( Fig . 3A ) . These two adjacent nucleotides were predicted to complement with the 2nd–3rd nt of miR9863a/b . 2 or the 7th–8th nt of miR9863b . 1 , respectively . Based on these SNP haplotypes , 29 Mla alleles were classified into three groups , group I , II and III ( Fig . 3A ) . To determine whether there are differences in miR9863 regulation on different Mla groups , we randomly selected from each group two Mla genes , i . e . , Mla28 and Mla32 from group I , Mla2 and Mla6 from group II , and Mla10 and Mla12 from group III , and each of them was co-expressed with precursor MIR9863a or MIR9863b in N . benthamiana , respectively ( Fig . 1B , Fig . 3B ) . Western analysis revealed that the accumulation of MLA28 and MLA32 ( group I ) was fully blocked by expression of MIR9863a and partially blocked by expression of MIR9863b ( Fig . 3B , left panels ) . By contrast , the accumulation of MLA2 and MLA6 ( group II , Fig . 3B middle panel ) , or MLA10 and MLA12 ( group III , Fig . 3B right panel ) were not affected by expression of MIR9863a or MIR9863b , compared to the EV control . Together , these data strongly suggest that only group I Mla members are regulated by miR9863a and miR9863b . 1/b . 2 in planta . To further verify the above finding that 1–2 SNPs in the Mla allele miR9863-binding site dictate miRNA specificity , we conducted targeted mutagenesis at these nt positions for each individual Mla from groups I , II , or III ( Fig . 4A ) . First , we replaced the ‘TC’ ( nt at 1278 and 1279 ) in Mla1 ( from group I ) with ‘GC’ or ‘GA’ to mimic the SNP haplotype of group II or III Mla , and reciprocally we replaced ‘GC’ in Mla2 and Mla6 , as well as ‘GA’ in Mla10 and Mla12 to ‘TC’ to mimic the SNP haplotype of group I Mla ( Fig . 4A ) . Upon co-expression of MIR9863a with WT Mla1 or two Mla1 variants ( Mla1-TC1278GC , Mla1-TC1278GA ) , we could not detect by Western analysis any WT MLA1 accumulation , but the levels of two MLA1 variants are comparable to that in the EV control ( Fig . 4B ) , suggesting loss of miR9863a effects on mutated Mla1 genes . In contrast , when MIR9863a was co-expressed with WT Mla2 and Mla6 , or the respective Mla variants ( Fig . 4C ) , as well as with WT Mla10 and Mla12 , or their variants ( Fig . 4D ) , we detected the expression of the WT MLAs similar to EV control , but no accumulation was detected for all mutant variants , compared to the EV control ( Fig . 4C and 4D ) . This indicates a gain of function for miR9863a for Mla variant genes that mimic the group I Mla SNP haplotype . We obtained similar gain of function results for MIR9863b ( S7 Figure ) . These results suggest that miR9863 targeting specificity on Mla alleles is largely determined by the two SNP compositions in the miR9863-binding site of each Mla . To further confirm miRNA-Mla specificity in barley , we conducted virus-induced gene silencing ( VIGS ) to knock-down expression of the miR9863 family in barley ( Fig . 5 ) . We first optimized a VIGS system by coupling a modified Barley stripe mosaic virus ( BSMV ) -induced gene silencing system with the expression of a short tandem target mimic ( STTM ) , i . e . , STTM-miR9863 , designed to bind all barley miR9863 members ( Fig . 5A ) [79] , [80] ( see Materials and Methods ) . BSMV viral particles harboring the STTM-miR9863 structure were obtained from N . benthamiana and used to infect the near-isogenic barley line P01 ( Mla1 ) or P03 ( Mla6 ) ( Fig . 5B , left and right half ) . As expected , we observed significantly reduced miR9863 accumulation in both P01 and P03 by BSMV: STTM-miR9863 , compared to the BSMV-STTM-EV control by RNA blot analysis , while the accumulation of the non-related miR156 was not affected in both lines ( Fig . 5B , panel a ) . Further qRT-PCR analyses validated the reduction of miR9863a/b . 2 or miR9863c/b . 1 in both lines infected with BSMV: STTM-miR9863 , compared to BSMV-STTM-EV ( Fig . 5B , panel b ) . Importantly , the reduced level of these miRNAs was inversely correlated with the increased Mla1 transcript level in P01 , in contrast to the unchanged Mla6 transcript level in P03 ( Fig . 5B , panel c ) . These data further verified that miR9863 family members specifically regulate group I Mla alleles . In summary , our results demonstrate that only a subset of Mla alleles are regulated by miR9863a and miR9863b . 1/b . 2 , and two SNPs in the miR9863-binding site of Mla dictate miR9863 specificity . Apart from many Bgh-responsive miRNA families in the sRNA libraries derived from infected barley leaf tissue ( above , and see Materials and Methods ) , we identified additional sRNAs that perfectly match the Mla1 sense and antisense strand and are aligned downstream of the miR9863 target site ( Fig . 6A , upper panel ) . These sRNAs are predominantly 21-nt in length ( Fig . 6B ) and biased for adenosine ( A , ∼35% ) and uridine ( U , ∼20% ) at the 5′-end position ( Fig . 6C ) . Of the two abundant sRNAs at the 5′-proximal , one starts at 126 bp downstream of the miR9863a cleavage site ( i . e . , 1397 bp in Mla1 ) , which corresponds to the 7th 21-nt register from the cleavage site; and the other starts at 504 bp downstream of the miR9863a cleavage site and corresponds to the 25th 21-nt register from the cleavage site ( Fig . 6A , lower panel ) . These sRNAs are likely phasiRNAs [43] derived from the Mla1 transcripts , thus , for convenience of further analysis these two 21-nt register siRNAs are designated as phasiRNAI and phasiRNAII , respectively . To understand whether the biogenesis of these phasiRNAs are directly linked to miRNA action on its target , we co-expressed MIR9863b or MIR9863a with Mla1 in the heterologous N . benthamiana and then quantified the expression level of phasiRNAI and phasiRNAII ( Fig . 6D , 6F and 6G ) . We found that phasiRNAI was significantly increased to ∼8 or 45 fold ( Fig . 6F , bars 3 and 5 ) , and phasiRNAII was increased to ∼2 to 2 . 5 fold ( Fig . 6G , bars 3 and 5 ) , compared to EV co-expressed with Mla1 ( Fig . 6F and 6G , bar 2 ) . These results indicate that miR9863b . 1/b . 2 and miR9863a trigger the biogenesis of phasiRNAs with Mla1 in N . benthamiana , and are consistent with our sRNA deep sequencing data obtained from barley . Previous studies have demonstrated that miRNAs of 22-nt , rather than 21-nt , trigger secondary siRNA [38] , [39] , [43]–[45] . To test whether this also applies to miR9863 for Mla-dependent secondary siRNA production , we engineered the precursor of MIR9863b or MIR9863a by adding a cytosine in the miR9863* sequence to remove the asymmetric structure , resulting in MIR9863b21 and MIR9863a21 ( Fig . 6D ) . This construct is predicted to produce 21-nt miRNAs that retain target-binding and cleavage activity [39] , [44] , [45] . Indeed , upon co-expression of each of these precursors with Mla1 in N . benthamiana , we detected by RNA gel blotting that MIR9863b21 and MIR9863a21 predominantly generated 21-nt miRNAs while the natural MIRNA precursors produced 22-nt miRNAs ( Fig . 6E , panel a ) . Significantly , compared to the natural MIRNA precursors , MIR9863b21 and MIR9863a21 less effectively repressed MLA1 accumulation ( Fig . 6E , panel b ) . Interestingly , we also observed marked reduction of secondary siRNAs , representing by phasiRNAI and phasiRNAII , in samples expressing mutated precursors ( Fig . 6F and 6G , bar 4 and 6 ) , compared to the WT precursors ( Fig . 6F and 6G , bar 3 and 5 ) . Together , these data suggest that the ineffective Mla1 regulation by the 21-nt-long miRNAs is partially due to the reduced level of secondary siRNAs . This was further supported by co-expressing MIR9863a or MIR9863a21 with a short Mla1 fragment encoding only the ARC domain fused with the mYFP-3HA tandem tag ( S8 Figure ) . In samples expressing miR9863a21 of 21-nt , we detected almost no phasiRNAs by RNA gel blotting , compared to the substantial phasiRNA accumulation in samples expressing 22-nt natural miR9863a ( S8B Figure , panel a ) . Similarly , the level of phasiRNAs was inversely correlated with the abundance of MLA1_ARC fusion protein ( S8B Figure , panel b ) . In summary , these results demonstrate that 22-nt natural miR9863s are critical for the biogenesis of phasiRNAs of 21-nt in barley and N . benthamiana , and that these secondary phasiRNAs are important for effective miR9863 regulation of Mla target genes . The sorting of miRNAs to different AGO proteins is complex and influenced by several factors [24] , [25] , for example , the 5′ nucleotide identity and the length of miRNAs , and miRNA duplex structure . Arabidopsis AGO1 has been shown to associate with miRNAs with 5′ U [81] and to trigger the production of secondary siRNA when binding 22-nt miRNAs [25] . AGO genes have not yet been formerly annotated in barley genome although some sequences share high similarity with AtAGO1 . To test whether AGO1 might be involved in miR9863-mediated regulation of Mla , we took advantage of the Tobacco rattle virus ( TRV ) mediated-VIGS to knock-down AGO1 in N . benthamiana using AGO4 as a control ( S9 Figure ) . Arabidopsis AGO4 was shown to associate primarily with 24-nt siRNAs [81] , [82] . As a technical control , the TRV-VIGS silencing of PHYTOENE DESATURASE ( NbPDS ) worked effectively , resulting in clear photobleaching phenotype on the upper systemic leaves ( S9C Figure ) . Both NbAGO1 and NbAGO4 possess two alleles each , i . e . , NbAGO1-1 and NbAGO1-2 , NbAGO4-1 and NbAGO4-2 , and these alleles share high sequence similarity [83] . Indeed , in our TRV-VIGS assays we observed co-suppression of both alleles of NbAGO1 or NbAGO4 when either one of the alleles was targeted for silencing ( S9B Figure , top and middle panel ) . Interestingly , upon co-expression of Mla1 with MIR9863b or MIR9863a , MLA1 accumulation was similarly suppressed in TRV: 00 treated plants or NbAGO4-silenced plants ( S9D Figure , 1st , 4th and 5th column ) . This is in contrast to NbAGO1-silenced plants , where MLA1 accumulation was unaffected , as compared to the TRV: 00 controls ( S9D Figure , 2nd and 3rd column ) ; the expression level of miR9863b or miR9863a was similar in all samples , however . Together , these data indicate that NbAGO1 rather than NbAGO4 is required for miR9863-mediated regulation of Mla1 in N . benthamiana . Our data so far have showed that miR9863-directed Mla transcript cleavage and degradation play a role in the regulation of Mla . To examine whether translational inhibition directed by miR9863 may contribute to MLA suppression , again we co-expressed MIR9863a with Mla1 in N . benthamiana and then quantified Mla1 transcripts in parallel with MLA1 protein levels ( Fig . 7 ) . We monitored the level of three Mla1 amplicons ( amplicon 1 , Mla1407–783; amplicon 2 , Mla11228–1582; amplicon 3 Mla12465–2621 ) , positioned respectively upstream , over and downstream of the miR9863a cleavage site to reflect the levels of transcription , cleavage and cleavage-triggered decay of Mla1 transcripts ( Fig . 7A ) [84] , at 16 , 24 , and 36 hours-post-Agro-infiltration ( hpai ) . The corresponding MLA1 protein level was determined in samples collected 2 hrs later for each time point , allowing sufficient time for translation ( Fig . 7B ) [84] . At 16 hpai , we detected a basal level of the three Mla1 amplicons , but no accumulation of MLA1 protein ( Fig . 7B , lanes 2 and 3 ) . Later , at 24 hpai , we detected increased accumulation of Mla1 amplicons , as well as the presence of MLA1 protein in samples co-expressing the EV control ( Fig . 7B , lane 4 ) . However , the levels of the three Mla1 amplicons in samples co-expressing MIR9863a were 49–67% of that in EV control and MLA1 protein remained undetectable in the same samples ( Fig . 7B , lane 5 ) . At 36 hpai , Mla1 amplicons and MLA1 protein further increased in the EV control ( Fig . 7B , lane 6 ) , but the three Mla1 amplicons in MIR9863a-expressing samples were only 29–39% of the EV control ( Fig . 7B , lane 7 vs . lane 6 ) . It should be noted that these Mla1 transcript levels in the MIR9863a-expressing samples at 36 hpai are higher than those in the EV controls at 24 hpai ( Fig . 7B , lane 7 vs . lane 4 ) , yet MLA1 protein accumulation was not observed at 36 hpai , even though MLA1 protein was detected in the EV control at 24 hpai ( Fig . 7B , lane 7 vs . lane 4 ) . These findings suggest that in N . benthamiana Mla1 transcript levels have been uncoupled from accumulation of MLA1 protein in samples that Mla1 was co-expressed with miR9863 , indicating apart from miR9863a-mediated transcript cleavage and decay , miR9863a-directed translational repression also plays a role in the regulation of Mla1 in this heterologous expression system . To investigate the role of miR9863 members in MLA-mediated disease resistance to Bgh , we used a well-established single-cell transient assay [77] , [85] . Plasmid constructs of miRNA precursor are co-delivered with a β-glucuronidase ( GUS ) reporter into barley epidermal cells by particle bombardment , and then the haustorial index ( HI% ) is scored to assess the frequency of fungal haustoria in transformed cells upon fungal inoculation ( Fig . 8A and 8B ) . First , we respectively delivered plasmids of EV , MIR9863a , MIR9863c , or MIR9863b into near-isogenic barley line P01 ( Mla1 ) and then inoculated conidiospores of Bgh K1 ( AVRa1 ) to activate MLA1-mediated immune responses ( Fig . 8A , left panel ) . We observed a low HI% of ∼3% in leaves receiving EV control , likely due to MLA1-triggered immunity against Bgh K1 ( Fig . 8A , bar 1 ) , whereas in leaves expressing MIR9863a or MIR9863b , we observed significantly increased HI% to ∼22% or 15% ( Fig . 8A , bars 2 and 4 ) , indicating MLA1-triggered immunity was compromised by the expression of miR9863a or miR9863b . 1/b . 2 . The HI% was similar in P01 leaves expressing MIR9863c to that in the EV control ( Fig . 8A , bar 3 ) , consistent with that miR9863c does not interact with the Mla1 allele ( Fig . 2C ) . Further , plasmids of MIR9863a , MIR9863c and MIR9863b were respectively delivered into leaves of near-isogenic barley line P09 ( Mla10 ) and inoculated with Bgh isolate A6 ( AVRa10 ) ( Fig . 8A , right panel ) , and we observed comparable HI% of ∼6% to 7% in leaves expressing EV or respective miR9863 precursors ( Fig . 8A , bars 5 to 8 ) , suggesting that expression of any miR9863 members had no effect on Mla10-mediated disease resistance to Bgh , consistent with that Mla members of group III are not miR9863 targets ( Fig . 3B ) . Taken together , overexpression of miR9863a and miR9863b . 1/b . 2 specifically attenuates MLA1 but not MLA10-mediated disease resistance against Bgh in barley . To exclude non-specific effects on barley defense responses due to miRNA overexpression , for example basal immunity , we delivered EV alone or together with MIR9863b into leaves of Golden Promise ( GP ) that is susceptible to both Bgh isolates K1 and A6 ( Fig . 8B ) . HI% scored on GP leaves expressing EV control , or co-expressing EV with MIR9863b , was ∼50% upon inoculation with Bgh K1 or A6 , suggesting that expression of miR9863b . 1/b . 2 does not interfere with basal defense to Bgh ( Fig . 8B , bars 1 and 2 , bars 5 and 6 ) . Furthermore , functional Mla1-mYFP or Mla10-mYFP fusion [52] was co-delivered into GP leaves with either EV or MIR9863b , respectively ( Fig . 8B , bars 3 and 4 , 7 and 8 ) . Mla1-mYFP co-expression with EV or MIR9863b resulted in contrasting HI% for Bgh K1 ( AVRa1 ) , ∼24% vs . ∼45% ( Fig . 8B , bars 3 and 4 ) , indicating again that MLA1-mediated responses against Bgh K1 was compromised by miR9863b . 1/b . 2 expression; in contrast , Mla10-mYFP co-expression with EV or MIR9863b resulted in similar HI% of ∼25% to Bgh A6 ( AVRa10 ) , thus retained MLA10-triggered disease resistance to Bgh A6 ( Fig . 8B , bars 7 and 8 ) , consistent with the finding that Mla10 is not a miR9863 target ( Fig . 3 , Fig . 4 ) . MLA triggered cell-death is conserved in dicot plants [56] , [57] , thus , we reasoned that miR9863 targeting MLA may also affect this MLA activity in dicots . To test this possibility , we co-expressed MIR9863a or MIR9863c respectively with Mla1 ( Fig . 8C ) , or with Mla10 ( Fig . 8D ) , in N . benthamiana . Mla1 co-expression with EV effectively triggered cell death detected by trypan blue staining at 36∼48 hpai , whereas this cell-death was completely abolished in Mla1 co-expression with MIR9863a ( Fig . 8C , left panel ) , but not with MIR9863c ( Fig . 8C , right panel ) . Alternatively , Mla10 co-expression with EV triggered strong cell-death that was unaffected by expressing either MIR9863a or MIR9863c ( Fig . 8D , left and right ) . These results indicate that miR9863a retains regulation specificity in suppressing MLA1 , but not MLA10 , to induce cell-death in dicot plants . To further understand the regulation of miR9863 on Mla target in the context of Bgh infections , we analyzed the expression of Mla1 , miR9863 members and two phasiRNAs during incompatible barley-Bgh interactions ( S10 Figure ) . A barley transgenic line expressing functional Mla1-HA fusion driven by Mla1 native promoter [61] was inoculated with Bgh K1 ( AVRa1 ) and gene expression was monitored up to 72 hrs post inoculation ( hpi ) . Mla1 transcripts were induced at 16 hpi by Bgh K1 and reached the highest level at 24 hpi ( S10A Figure ) . Similar results were previously reported for the expression of Mla6 and Mla13 during incompatible interactions [76] , [86] . During the same period of time from 16 to 24 hpi , we also detected by stem-loop qRT-PCR the increased expression of miR9863a/b . 2 and miR9863c/b . 1 , as well as phasiRNAI and phasiRNAII ( S10B and C Figure , Fig . 6A ) , noting that here we cannot distinguish miR9863a from miR9863b . 2 , or miR9863c from miR9863b . 1 , due to only one nt difference . Accumulation of Mla1 transcripts dropped from ∼4 . 0 fold at 24 hpi to ∼1 . 6 fold at 48 to 72 hpi ( S10A Figure ) , and inversely , the expression level of miR9863c/b . 1 , and the two phasiRNAs was increased and reached to the highest level at 48 hpi and sustained at high level up to 72 hpi ( S10 B and C Figure ) . Interestingly , miR9863a/b . 2 expression was induced earlier ( at 16 hpi ) than miR9863c/b . 1 ( at 24 hpi ) , and the former maintained at similar level while the latter continued to increase up to 72 hpi ( S10B Figure ) . These data confirm the induction of Mla gene expression in the early phase ( 16∼24 hpi ) during incompatible interactions [76] , [86] and suggest that , in the later phase of the interactions , the decreased Mla1 expression level is coupled to the induced or sustained expression of miR9863s and the phasiRNAs .
Although miRNAs regulating NLR-encoding genes have recently been demonstrated in a wide variety of dicot species and perennial woody plants [38]–[41] , [87] , convincing evidence for similar regulatory mechanisms have been scarce in well characterized monocot species , for example , rice and maize . Based on earlier work , it was postulated that NLR genes in Poaceae species were not regulated by endogenous miRNA and siRNA regulatory networks [43] . However , regulation of barley Mla alleles by the miR9863 family demonstrates that these mechanisms are present in Poaceae species . Moreover , previous small RNA sequencing studies have identified various members of the miR9863 family [63]–[66] , [88] , suggesting functional conservation in barley and wheat . Through data mining and mRNA expression analyses , we show that the miR9863 family appears to be absent in dicot species N . benthamiana and A . thaliana , as well as monocots rice ( O . sativa ) , maize ( Z . mays ) and B . distachyon , at least at the depth of coverage in current miRNA databases ( S2 and S3 Figures ) . Although some putative sorghum small RNAs were found sequentially matching miR9863a , it appears that the miR9863 family is mainly expressed in barley and wheat , suggesting that miR9863 family might be Triticeae-specific . Since functional Mla homologs and orthologs exist in wheat , it will be interesting to know whether miR9863 members regulate these genes or other NLR genes as well ( S1 Figure ) [89] , [90] . Unlike animal miRNAs , plant miRNAs largely depend on their high sequence complementarity to target mRNAs for post-transcriptional gene regulation [9] . Indeed , Arabidopsis and rice genome-wide assessments of sequence variation have revealed lower levels of nucleotide variation and divergence in miRNAs and target binding sites than in their flanking sequences [73] , [91] , leading to hypothesis that strong purifying selection plays an important role in the interaction between plant miRNAs and their target binding sites . Somewhat surprisingly , similar findings were also reported in humans [92] , [93] . Nevertheless , few SNP variations in miRNAs and their target binding sites have been identified in plants [73] , [91] , [94] , [95] , and some of them have been shown to contribute to phenotypic variations in Arabidopsis [95] . Our analyses of miR9863s and Mla target sequences have uncovered the role of miR9863 family SNPs in determining target specificity and potentially phenotypic variations ( Figs . 1 , 2 and 3; S6 Figure ) . The miR9863a and miR9863b . 2 differ by only one SNP at the 9th nt position ( Fig . 1A ) , however , they account for ∼60–80% difference in Mla1 target protein accumulation upon coexpression in N . benthamiana ( Fig . 2A , 2E; S6 Figure ) . Similarly , one SNP variation at 2nd nt position in miR9863c and miR9863b . 1 can fully dictate the regulation specificity on Mla1 ( Fig . 1A; Fig . 2B , 2C and 2E ) . In this context , it is worth noting that the NLR-targeting miR482/2118 superfamily contains at least 9 SNPs and 31 isoforms across diverse plant species [38] , [40] . Since many isoforms of the miR482/2118 family are species or genera specific , it is possible that the SNP variations within members of this superfamily have evolved to allow specific isoforms to regulate species-specific NLR genes and other targets as well [40] . A single or two adjacent SNPs in Mla alleles can dictate the regulation specificity of the miRNA members on Mla , thus , group I Mla but not group II or III Mla are targets of miR9863 members ( Figs . 3 and 4; S7 Figure ) . The above findings are supported not only by miRNA expression in the heterologous N . benthamiana system , but also by miRNA silencing in barley using BMSV-VIGS coupled with STTM technology ( Figs . 4 and 5 ) . Moreover , single cell transient expression assays in barley have demonstrated that overexpression of MIR9863a or MIR9863b specifically attenuated Mla1- but not Mla10-mediated disease resistance ( Fig . 8A ) ; similarly , miR9863a expression in N . benthamiana blocked Mla1- rather than Mla10-triggered cell death ( Fig . 8C and 8D ) . Therefore , miR9863 variants paired with their respective Mla alleles supports the notion that SNP variations in the miRNA and the target binding site are important in determining regulation specificity as well as in phenotypic variations in plants . We believe that this is particularly crucial for the control of the large NLR gene family , to allow for balancing the fitness cost and effectively coping with the fast evolving pathogen isolates . Although the Mla locus encodes highly related alleles apart from other dissimilar RGH families [48] , [77] , [96] , only a subset of these alleles are regulated by three out of four miR9863 family members presented here . We postulate that specificity between Mla and miR9863 is determined by SNP variation ( s ) in members among miR9863 family as well as in the binding site of target Mla alleles ( see above ) . Nonetheless , the evolutionary and functional relevance of Mla-miRNA regulation remains unclear . To shade light on this aspect , we utilized the cDNA sequences encoding full-length MLA , CC , NB-ARC or LRR domain to respectively construct a phylogeny tree of the 29 Mla alleles ( S11 Figure ) . When each individual Mla is mapped in accordance with subgroup identity based on SNPs in the target site , i . e . , group I , II and III ( see Fig . 3A ) , we observed more similar architecture for the phylogeny tree derived from full-length or NB-ARC cDNA sequence than to the others ( S11 Figure , A and C ) . Most interestingly , in the NB-ARC phylogeny tree , Mla alleles of group II and III comprise one more recent and distinct branch whereas group I Mla constitute a more complex pattern , indicating group II and III Mla are evolutionarily closer to each other than to group I Mla with regard to regulation by miR9863 . Indeed , we show that this control only applies to group I Mla alleles . This is reminiscent of the proposed parallel evolutionary pathways for the Mla family , i . e . , one branch contains Mla1 ( from group I ) and the other branch contains Mla6 and Mla13 ( from group II and III , respectively ) [59] . Thus , we speculate that the evolution of the Mla family might be influenced by the adaptation of miR9863 . In light of this , natural SNP variations might be fixed within the miR9863 binding site of group II and III Mla , which result in the deregulation of the miRNAs and subsequent formation of a Mla clade distinctive from group I Mla members . Additional evidence to support evolutionary pathways differentiating group I Mla alleles from group II and III comes from the differential Rar1-dependency of Mla alleles . RAR1 is believed to act as a cochaperone and assist NB-LRR proteins of low accumulation level to reach threshold steady-state level for effective immunity [60] , [61] , [97] , [98] . For example , MLA6 accumulates only one fourth the level of MLA1 , and thus depends on RAR1 to maintain a threshold level to mediate disease resistance [61] . Interestingly enough , while Mla1 from group I is Rar1-independent , Mla alleles from group II or III , such as Mla6 , Mla9 , Mla12 , Mla13 , Mla22 and Mla23 , excepting Mla7 , are all Rar1-dependent [59] , [99] ( Fig . 3; S10 Figure ) . One would hypothesize that , in barley and barley powdery mildew interactions , Mla-miR9863 adaptations are likely fixed for Mla alleles whose expression level and protein accumulation are high , for example those of group I; whereas for Mla natural variants , like members of group II and III that are unstable or with lower accumulation level , the miR9863 regulation might be unnecessary and thus released . Nevertheless , how the evolution of Mla branches is coupled with miR9863 regulation as well as the cochaperone RAR1 requires future investigation . In the present study , we leveraged the heterologous N . benthamiana expression system to investigate the regulation of barley miR9863 on its barley NLR targets . This system allows the testing of individual components in the absence of unknown confounding factors in the barley host . Indeed , despite the apparent absence of the miR9863 family in the dicotyledonous N . benthamiana and Arabidopsis ( S3 Figure , and discussion above ) , transient expression of barley and wheat miR9863 progenitors or artificial miRNA precursors in N . benthamiana can generate respective mature miRNAs of the correct size ( Fig . 2D; S2B Figure ) , suggesting that a conserved small RNA biogenesis machinery exists in both monocots and dicots . This is further corroborated by the observation of direct coexpression of 22-nt miR9863 members and Mla1 in N . benthamiana which enhanced the production of 21-nt phasiRNAs , and that many of these miR9863 members were identified in sRNA libraries derived from Mla1-containing barley isogenic line ( Fig . 6 ) . VIGS of NbAGO1 in N . benthamiana resulted in the loss of miR9863a or miR9863b control of Mla1 . This suggests that AGO1 may be functionally conserved in N . benthamiana and in barley ( S9 Figure ) . This is consistent with previous observation that plant AGO1 has the capacity to bind 22-nt miRNAs and trigger the production of secondary siRNAs [25] . It would be interesting in future experiments to test whether in barley 22-nt miR9863 members are sorted into AGO1-containing complex where they direct Mla transcript cleavage . Lastly , previous studies found that barley MLA1-triggered immunity against Bgh fungal isolate is fully retained in Arabidopsis and that MLA-triggered cell-death signaling is likely conserved in Arabidopsis and N . benthamiana [56]–[58] . Similarly , recent studies show that expression of some wheat Pm alleles , conferring resistance against B . graminis pathogens , also triggers cell-death responses in N . benthamiana [100] , [101] . Interestingly , another study also demonstrates that interfamily transfer of the dual TIR-type NLR genes , RPS4/RRS1 , from Arabidopsis into other Brassicaceae plants and Solanaceae species , confers broad-spectrum or isolate-specific disease resistance to fungal or bacterial pathogens [102] . These examples suggest that some plant NLR receptors may engage evolutionarily conserved downstream signaling components for triggering immune responses . Here , coexpression of miR9863a with Mla1 or Mla10 in N . benthamiana attenuates Mla1- but not Mla10-triggered cell-death responses ( Fig . 8 ) , together with above-mentioned retained miR9863 specificity on Mla alleles in N . benthamiana , might imply a conserved post-transcriptional regulatory machinery acting on heterologously expressed barley Mla NLR genes .
Barley ( Hordeum vulgare L . ) , bread wheat ( Triticum aestivum L . ) and Brachypodium distachyon plants were grown in a growth chamber under a 16 hrs/8 hrs , 20°C/18°C day/night cycle , respectively , with 70% relative humidity; Rice ( O . sativa ) was maintained in a growth chamber under a 16 hrs/8 hrs , 28°C/26°C day/night cycle , respectively , with 90% relative humidity; Maize ( Z . mays ) was grown in a chamber at 22°C under a 16 hrs/8 hrs day/night cycle under 70% relative humidity; N . benthamiana and A . thaliana were grown in greenhouse at 24±1°C with a 16 hr light period . Blumeria graminis f . sp . hordei ( Bgh ) isolates K1 ( AVRa1 , vira6 , vira10 , vira12 ) and A6 ( AVRa6 , AVRa10 , AVRa12 , vira1 ) were maintained on barley cultivar ‘I10’ ( containing Mla12 ) and ‘P01’ ( containing Mla1 ) , respectively , and kept at 70% relative humidity , and a 16 hrs/8 hrs , 20°C/18°C day/night cycle . Seven-day-old barley leaves of barley isogenic line P01 were inoculated with Bgh A6 and K1 for 22 hrs and total RNAs were isolated using TRIzol solution ( Invitrogen 15596-026 ) according to the manufacturer's instructions . Small RNAs of 18–30 nt were excised and isolated from 5 to 10 µg total RNAs electrophoresed on 15% polyacrylamide denaturing gel , and then were ligated with 5′ and 3′ adapters . The ligated small RNAs were used as templates for cDNA synthesis followed by PCR amplification . The obtained libraries were sequenced using the Solexa sequencing platform ( BGI , Beijing ) . The mature miRNAs sequences were used in a BLASTn search against the barley genome sequencing database ( http://webblast . ipk-gatersleben . de/barley/ ) and bread wheat expressed sequence tag ( EST ) database ( http://www . ncbi . nlm . nih . gov/ ) . The secondary structure of flanking sequence around perfectly matched site was predicted using the RNA-folding program Mfold [103] . Primers for cloning of MIR9863 precursors were designed according to the flanking sequences of the hairpin structures . Primers ( J19/J20 ) for hvu-MIR9863a cloning were designed according to the sequence of tae-MIR9863a . hvu-MIR9863b was PCR-amplified using barley ‘Morex’ genomic DNA as template ( primers , J15/J16 ) ; tae-MIR9863 cluster containing tae-MIR9863a and tae-MIR9863b was amplified using bread wheat ‘Chancellor’ genomic DNA as template and primer J05/J06; tae-MIR9863c was amplified using bread wheat ‘Chancellor’ genomic DNA as template and primer J07/J08 . All primer sequences are shown in supplemental S2 Table . RLM-RACE kit ( TaKaRa , Code D315 ) was used for 5′ RACE according to the manufacturer's instruction . Total RNAs were isolated from 7-day-old leaves of barley line P01 infected with Bgh K1 spores , and mRNAs were enriched from 100 µg of total RNAs using the PolyATtract mRNA isolation kit ( Promega , Z5210 ) . The RNA Oligo adaptor was ligated to mRNAs without calf intestinal phosphatase treatment . For the first round PCR , the 5′ RACE Outer Primer J01 together with Mla1 gene specific outer primer J03 were used . Nested PCR amplification was performed using the 5′ RACE Inner Primer J02 and Mla1 specific inner primer J04 . Plasmids constructed in this study are based on several expression vectors previously described and listed in supplemental S3 Table . CTAPi-GW-3HA was used as the transient expression vector for Mla genes in N . benthamiana , and derived plasmids were constructed using Gateway technology ( Invitrogen ) following the instructions of the manufacturer . For construction of miRNAs overexpression constructs used in N . benthamiana expression assays , indicated MIRNAs precursor sequences were PCR amplified , double digested by KpnI/HindIII , and ligated into 35S-pKANNIBAL vector . Precursor aMIR9863a , aMIR9863b . 1 and aMIR9863b . 2 were constructed by replacing the miR173/miR173* duplex in ath-MIR173 with amiR9863a/amiR9863a* , amiR9863b . 1/amiR9863b . 1* or amiR9863b . 2/amiR9863b . 2* ( S5 Figure ) using overlapping primers J35/J36/J37 , J31/J32/J33 and J36/J37/J38 , respectively . pTRV2-derivatives used in TRV-VIGS analysis were constructed based on pTRV2-LIC by ligation-independent cloning ( LIC ) as described previously [104] . J44/J45 , J46/J47 , J48/J49 and J50/J51 were used for amplifying anti-sense fragment from cDNA of N . benthamiana for silencing NbAGO1-1 , NbAGO1-2 , NbAGO4-1 and NbAGO4-2 , respectively . pTRV2-LIC vector was digested with PstI and ligated with PCR fragments . For BSMV mediated silencing of miR9863 members in barley , STTM-miR9863 and STTM-EV were first designed and amplified by using overlapping primer groups J52/J53/J54 and J55/J56 , according to Fig . 5A and Yan and associates [80] . Then PCR products were cloned into pCaBS-γbLIC vector through LIC strategy as described above . For single-cell transient overexpression of miR9863 members , the tae-MIR9863a , tae-MIR9863c or hvu-MIR9863b precursors with attB sites were cloned into pUbi-GATE vector by Gateway technology described above . Similarly , pUbi-GW-mYFP vector was used for expression of Mla1 or Mla10 cDNA . Point mutations for different Mla cDNAs or MIRNA precursors were introduced using one-step site-directed mutagenesis as described previously with minor modifications [105] . The entry vectors harboring wild-type Mla allele cDNA or 35S-pKANNIBAL vectors containing indicated wild-type MIRNA precursor sequences were used as templates for PCR reaction to introduce mutations . Total RNAs were extracted from plant materials using TRIzol solution , and treated with RNase-free DNase I ( TaKaRa ) . About 2 µg of total RNA and M-MLV Reverse Transcriptase ( Promega ) were further used for reverse transcription . For coding genes reverse transcription , first-strand cDNA was synthesized using Oligo ( dT ) 18 . For small RNA reverse transcription , specifically designed stem-loop reverse transcription primers were used , and followed the procedures described by Chen and colleagues [106] . Primer J71 , J72 , J75 and J76 were used for miR9863a/b . 2 , miR9863c/b . 1 , phasiRNAI and phasiRNAII , respectively; primer J81 was used for U6 ( U6 stands for U6 spliceosomal RNA ) , and obtained cDNA was diluted 10 times and used for further analysis . Real-time qPCR was performed using StepOne real-time system ( Applied Biosystems ) and GoTaq qPCR Master Mix ( Promega , A6001 ) ; for the determination of three Mla1 amplicons ( Fig . 7 ) , primer pairs J65/J66 , J67/J68 and J69/J70 were used; Primer J73 , J74 , J77 and J78 were respectively used with J79 to quantify the level of miR9863a/b . 2 , miR9863c/b . 1 , phasiRNAI and phasiRNAII; Primer pair J80/J81 and J82/J83 were used for the detection of U6 and Actin . Semi-quantitative RT-PCR was performed as described previously [56] . Primer pair J61/J62 , J63/J64 were designed to determine the silencing efficiency of NbAGO1 and NbAGO4 , respectively . RNA gel blot analysis was performed as described previously with minor modifications [107] . Total RNAs of 10 to 15 µg were separated on a 15% polyacrylamide denaturing gel by electrophoresis and cross-linking was performed as described previously [108] . The complementary sequences corresponding to miRNAs were used as probes after labeled with [γ-32P]ATP using T4 polynucleotide kinase ( New England Biolabs ) . Probe J84 , J85 , J86 and J88 were used to detect the mature miR9863a , miR9863b . 1 , miR9863b . 2 and miR156a signal , respectively; probe J87 was used for Mla1 derived phasiRNAs in S8 Figure according to Chen and colleagues [44] . Agrobacterium-mediated transient expression in N . benthamiana was performed as described previously [56] , [109] . For single expression , Agrobacterium suspensions expressing gene of interest were infiltrated into 4- to 5-week old N . benthamiana leaves . For co-expressions , Agrobacterium suspensions expressing MIRNA precursor were first infiltrated , and 24 hrs later , Agrobacterium suspensions expressing gene candidate was infiltrated into the same position . Samples were collected from the infiltrated sites at 36 hrs post infiltration . Total soluble proteins were extracted using 180 µl of 2× Laemmli buffer [110] from 60 mg leaf samples , and detected by immunoblotting as described previously [56] . Rat anti-HA antibody ( 1∶5000; Roche , 11867423001 ) and anti-rat IgG conjugated with horseradish peroxidase ( HRP ) ( 1∶10000; Sigma , A5795 ) were used for HA-tagged proteins detection . The levels of β-actin were determined using an anti-β-actin antibody ( 1∶1000; CWBIO , CW0264 ) coupled with anti-mouse IgG conjugated with HRP ( 1∶75000; Sigma , A9044 ) . Single-cell transient gene expression assay using biolistic delivery of plasmid DNA into barley epidermal cells was performed as previously described [77] . For MIRNAs overexpression , the β-glucuronidase ( GUS ) reporter gene was mixed with respective plasmids ( molar ratio 1∶1 ) before coating of gold particles . Barley leaf epidermal cells were transformed with the biolistic particle delivery system ( Bio-Rad , Model PDS-1000/He ) , and incubated 4 hrs before inoculation of Bgh spores . To identify transformed cells , bombarded leaves were fixed in solution at 48 hrs after Bgh infection and further stained for GUS activity . For MIRNAs and Mla co-expression , the GUS reporter and MIR9863b plasmids were co-coated with Mla1-mYFP or Mla10-mYFP plasmids ( molar ratio 1∶1∶1 ) . Trypan blue staining was described previously [56] . Briefly , N . benthamiana leaves were boiled for 5 min in staining solution , and were then de-stained in 2 . 5 g ml-1 chloral hydrate in distilled water for at least 3 days . TRV-based virus induced gene silencing assay was performed as described [56] , [111] . Briefly , pTRV1 and pTRV2-derived constructs ( pTRV2: EV , pTRV2: NbPDSas , pTRV2: NbAGO1-1as , pTRV2: NbAGO1-2as , pTRV2: NbAGO4-1as , pTRV2: NbAGO4-2as ) were transformed into A . tumefaciens strain GV3101 . Cell suspensions containing pTRV1 and pTRV2-derived constructs were collected and mixed at 1∶1 ratio and infiltrated into the third to fifth leaf of three-week-old N . benthamiana plants . Three weeks after infiltration , the upper newly expanded leaves were selected for further analysis . For BSMV-mediated STTM-VIGS assay of miR9863 members , constructs of pCaBS-α , pCaBS-β , and pCaBS-γbLIC derivatives ( pCaBS-γSTTM-EV and pCaBS-γSTTM-miR9863 ) were transformed into the A . tumefaciens strain EHA105 , respectively . The Agrobacterium suspensions of OD600 = 0 . 8 were mixed at 1∶1∶1 ratio and infiltrated in N . benthamiana leaves . The N . benthamiana sap was extracted from leaves with BSMV symptom at about 12 days post infiltration , and inoculated to the first two emerging leaves of barley leaves . About 15 days later , the newly grown upper barley leaves with virus symptom were collected for further analysis . Sequence data for genes used in this article can be found under GenBank accession numbers AY009939 ( HvuMla1 ) , GU245938 ( HvuMla2 ) , GU245939 ( HvuMla3 ) , AJ302292 ( HvuMla6 ) , AY266444 ( HvuMla7 ) , GU245940 ( HvuMla8 ) , GU245941 ( HvuMla9 ) , AY266445 ( HvuMla10 ) , AY196347 ( HvuMla12 ) , AF523683 ( HvuMla13-1 ) , GU245942 ( HvuMla16-1 ) , GU245943 ( HvuMla18-1 ) , GU245944 ( HvuMla18-2 ) , GU245945 ( HvuMla19-1 ) , GU245946 ( HvuMla22 ) , GU245947 ( HvuMla23 ) , GU245948 ( HvuMla25-1 ) , GU245949 ( HvuMla27-1 ) , GU245950 ( HvuMla27-2 ) , GU245951 ( HvuMla28 ) , GU245952 ( HvuMla30 ) , GU245953 ( HvuMla31-1 ) , GU245954 ( HvuMla32 ) , GU245955 ( HvuMla34 ) , GU245956 ( HvuMla35 ) , GU245957 ( HvuMla36-1 ) , GU245958 ( HvuMla37-1 ) , GU245959 ( HvuMla38-1 ) , GU245960 ( HvuMla39-1 ) , KJ619975 ( hvu-MIR9863a ) , AK364228 ( hvu-MIR9863b ) , CK193889 ( tae-MIR9863a , tae-MIR9863b , tae-MIR9863a/b cluster ) , DR736484 ( tae-MIR9863c ) , DQ321488 ( NbAGO1-1 ) , DQ321489 ( NbAGO1-2 ) , DQ321490 ( NbAGO4-1 ) , DQ321491 ( NbAGO4-2 ) , ACZ65507 ( HchMla1 ) , ADX06722 ( TmoMla1 ) , AGP75918 ( TmoSr35 ) , KF031291 ( AetSr33 ) , ACG63536 ( TduLr10 ) , AAG42168 ( TaeYr10 ) , AAY21626 ( TaePm3a ) , AAQ96158 ( TaePm3b ) , ABB78077 ( TaePm3c ) and AAY21627 ( TaePm3d ) . | Plants rely on cell-surface and intracellular immune receptors to sense pathogen invasion and to mediate defense responses . However , uncontrolled activation of immune responses is harmful to plant growth and development . Small RNAs have recently been shown to fine-tune the expression of intracellular immune receptors and contribute to the regulation of defense signaling in dicot plants , while similar processes have not been well documented in monocot grain crops , such as barley and wheat . Here , we show that , in barley , some members of the miR9863 family target a subset of Mla alleles that confer race-specific disease resistance to the powdery mildew fungus . These miRNAs act on Mla transcripts by cleavage and translational repression . Production of a type of trans-acting small RNAs , designated as phasiRNAs , enhances the effects of miRNA regulation on Mla targets . We propose that Mla-mediated immune signaling is fine-tuned by the miRNAs at later stage of MLA activation to avoid overloading of immune responses in barley cells . | [
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| 2014 | The miR9863 Family Regulates Distinct Mla Alleles in Barley to Attenuate NLR Receptor-Triggered Disease Resistance and Cell-Death Signaling |
Enhancing brown fat activity and promoting white fat browning are attractive therapeutic strategies for treating obesity and associated metabolic disorders . To provide a comprehensive picture of the gene regulatory network in these processes , we conducted a series of transcriptome studies by RNA sequencing ( RNA-seq ) and quantified the mRNA and long noncoding RNA ( lncRNA ) changes during white fat browning ( chronic cold exposure , beta-adrenergic agonist treatment , and intense exercise ) and brown fat activation or inactivation ( acute cold exposure or thermoneutrality , respectively ) . mRNA–lncRNA coexpression networks revealed dynamically regulated lncRNAs to be largely embedded in nutrient and energy metabolism pathways . We identified a brown adipose tissue–enriched lncRNA , lncBATE10 , that was governed by the cAMP-cAMP response element-binding protein ( Creb ) axis and required for a full brown fat differentiation and white fat browning program . Mechanistically , lncBATE10 can decoy Celf1 from Pgc1α , thereby protecting Pgc1α mRNA from repression by Celf1 . Together , these studies provide a comprehensive data framework to interrogate the transcriptomic changes accompanying energy homeostasis transition in adipose tissue .
Obesity has reached an epidemic proportion in both developed and developing nations , resulting in a steep rise in healthcare expenses and a growing population with associated comorbidities and chronic illnesses [1] . An attractive approach for obesity therapy is to augment the mass and activities of thermogenic brown adipose tissue ( BAT ) or promote white adipose tissue ( WAT ) to take on BAT-like features [2–8] . BAT , the classical thermogenic adipose tissue , is located in the interscapular region in mammals and is specialized to metabolize lipids for heat generation as a defense against cold temperatures . A second category of thermogenic adipocytes , referred to as beige adipocytes , are dispersed in WAT , especially in the subcutaneous depot . Beige adipocytes exhibit WAT phenotypes basally but can be induced to take on BAT features and exert BAT-like function , a process referred to as browning , by stimuli such as cold exposure , beta-adregenic agonist stimulation , or extensive physical exercise [2 , 5 , 6] . Recent studies from our group and others have revealed a new class of regulators known as long noncoding RNAs ( lncRNAs ) , which govern multiple aspects of adipocyte biology [9–14] . Through earlier global profiling studies , we identified a set of lncRNAs that were required for the differentiation of murine white adipocytes [15] . Xiao et al . , reported adipogenic differentiation-induced noncoding RNA ( ADINR ) as a necessary regulator for human adipocyte differentiation via remodeling Cebpα locus methylation [16] . Lin’s group identified Blnc1 , a BAT-enriched , Ebf-2–regulated lncRNA , which was important for the thermogenic differentiation of brown and beige adipocytes [17] . In another study , we integrated genome-wide surveys of transcription by RNA sequencing ( RNA-seq ) and chromatin state by chromatin immunoprecipitation sequencing ( ChIP-seq ) to depict the transcription landscape in mouse BAT and WAT [18] . We constructed a comprehensive catalog of lncRNAs in adipose and identified a set of brown adipose tissue–enriched lncRNAs ( lncBATEs ) . One of them , lnc-BATE1 , was subsequently demonstrated to be necessary for the proper development and maintenance of mature brown adipocytes [18] . Despite this recent progress , our understanding of the role of lncRNAs in the energy metabolism of adipose still remains at its infancy . Specifically , very few lncRNAs have been functionally characterized , cellular mechanisms utilized and influenced by lncRNAs remain poorly understood , and the dynamic regulation of lncRNA expression and function during adipose tissue’s adaptation to various environmental stimuli awaits further exploration . To address some of these gaps , we systemically generated parallel profiles of mRNA and lncRNA transcriptomes during WAT browning , BAT activation , and BAT inactivation . The transcriptome quantification revealed sets of stimulus-responsive lncRNAs , and the mRNA–lncRNA correlation analysis further illuminated profound associations between mRNAs and lncRNAs during the dynamic adipose remodeling , suggesting that lncRNAs’ engagement in adipose regulation may go well beyond our current understanding . More specifically , we identified the lncRNA lncBATE10 ( A530050N04Rik ) as a novel regulator that functions by decoying a repressor of Pgc1α gene expression , CUGBP Elav-Like Family Member 1 ( Celf1 ) . Together , our work establishes a framework to study the function of lncRNAs in energy homeostasis and dynamics in adipose .
We first set off to illustrate the changes in mRNA and lncRNA transcriptomes during WAT browning . Multiple stimuli can induce a BAT-like program expression in WAT , but the similarities and differences among them have not been systemically explored . We induced WAT browning with 3 widely used conditions , including chronic cold exposure ( 4°C for 7 days ) , β-adrenergic agonist treatment ( CL316243 , 1mg/kg , for 7 days ) , and a previously established swimming exercise protocol [19] ( Fig 1A ) . Animals under cold and exercise conditions showed significant reductions in body weight ( S1A Fig ) , and animals under all 3 conditions displayed increased food intake ( S1B Fig ) . Epididymal WAT ( eWAT ) mass decreased significantly in all conditions; inguinal WAT ( iWAT ) decreased in response to cold and exercise but not CL316243 treatment ( S1C Fig ) . Interestingly , BAT mass didn’t change significantly upon CL316243 and cold treatment , but was enlarged by almost 300% after extensive swimming training ( S1C Fig ) . The pathophysiological significance of the enlarged BAT responsive to exercises warrants further investigation but is beyond the scope of this study . Haemotoxylin and Eosin ( H&E ) staining for iWAT revealed a remarkable reduction of adipocyte size under all conditions ( S1D Fig ) . Cellular phenotypes in the cold- treated samples most resembled that of BAT , suggesting that it is a stronger stimulus for browning than the other 2 . To depict the global transcriptome change , we conducted RNA-seq and quantified the expression of mRNAs and lncRNA . For the lncRNA analysis , we employed the multiexonic lncRNA catalog ( approximately 1 , 500 lncRNAs ) established in our earlier work , which was stringently filtered by PhyloCSF score , CPC analysis , and ribosome release score [18] . Hierarchical clustering analysis of mRNA expression profiles demonstrated that samples within each condition tightly clustered with each other ( Fig 1B ) . Notably , the clustering of treatments based on lncRNA expression entirely mirrored the pattern observed for mRNAs ( Fig 1C ) . Thus , the lncRNA profile , similar to the more studied mRNA profile , appears to be a robust molecular signature of adipose tissue states and can be used to characterize adipose tissue responses to different environmental stimuli . We observed 1 , 062 , 837 , and 673 up-regulated and 1 , 201 , 681 , and 826 down-regulated mRNAs upon cold treatment , CL316243 treatment and exercise , respectively ( absolute fold-change ≥2-fold , false discovery rate [FDR] ≤ 0 . 05 ) , with 301 common up-regulated and 496 common down-regulated genes in all 3 conditions in iWAT ( Fig 1D and 1F ) . As expected , we detected a clear induction of Ucp1 , Elvol3 , Cidea , and many mitochondria-related genes ( S1E Fig ) . To identify biological processes that are enriched under the different treatments , we performed gene-set enrichment analysis ( GSEA ) by querying the Gene Ontology Biological Processes ( GOBPs ) as well as custom gene sets consisting of highly specific BAT- and WAT-expressed genes based on the expression across 29 different mouse tissues [18] . The top GOBPs were largely shared across conditions , although some condition-specific processes were also identified . The up-regulated pathways included the custom BAT-specific gene set , cellular respiration , and energy derivation by oxidation , indicating an acquirement of BAT-like phenotype , while the down-regulated pathways included the custom WAT-specific gene set and several immune-function–related GOBPs ( S1F Fig ) , indicative of a loss of WAT features and remodeling of the resident immune cells during browning , consistent with recent reports [20–22] . A parallel analysis of the lncRNAs showed 91 , 57 , and 49 were up-regulated and 98 , 101 , and 98 down-regulated lncRNAs in response to cold treatment , CL316243 treatment and exercise , respectively ( absolute fold-change ≥2-fold , FDR ≤ 0 . 05 ) , with 17 common up-regulated and 42 common down-regulated lncRNAs under all conditions ( Fig 1E and 1G ) . Based on the RNA-seq data , we selected15 up-regulated lncRNAs and 6 down-regulated lncRNAs during browning for real-time PCR validation; 14 out of 15 and 6 out of 6 lncRNAs could be successfully validated , respectively ( S1G–S1J Fig ) . In the absence of functional annotation for the majority of lncRNAs ( unlike mRNAs ) , we inferred likely function of the regulated lncRNAs based on their tissue-specific expression profiles across 29 mouse organs and cell types as described before [18] . The up- and down-regulated mRNAs as well as lncRNAs showed the strongest overlap with BAT-enriched and immune-cell–related gene sets , respectively ( hypergeometric P < 0 . 016 ) ( Fig 1H and 1I ) , which suggests that similar to mRNAs , the regulated lncRNAs may also function in BAT-related physiological processes and immune cell remodeling . To investigate the probable molecular basis for the dynamic regulation of lncRNAs , we examined the evidence for enrichment of Peroxisome proliferator-activated receptor gamma ( PparΥ ) and PR domain containing 16 ( Prdm16 ) binding sites within the promoter regions of regulated mRNAs and lncRNAs under the various browning conditions . To do this , global occupancy maps of PparΥ and Prdm16 in BAT and WAT were downloaded from publicly available ChIP-seq data [23 , 24] . In WAT , PparΥ targets 71% , 68% , and 63% of the promoter regions of the up-regulated lncRNAs responsive to 4°C cold exposure , CL316243 and exercise , respectively; in BAT , PparΥ binding sites were observed in 80% , 78% , and 74% of the promoters of induced lncRNAs , along with a significant overlap with Prdm16’s binding sites ( S2 Fig ) . Importantly , under each browning condition , the overlap between transcription factor ( TF ) s’ occupation sites and the up-regulated gene’s promoters was highly significant for both mRNA and lncRNA genes , with the strongest overlap observed for the 4°C treatment ( FDRs in the range of 5 . 3 X 10−3 to 2 . 2 X 10−16 for mRNAs and 2 . 6 X 10−1 to 1 . 32 X 10−4 for lncRNAs ) ( Fig 1J ) , suggesting that PparΥ and Prdm16 are likely to play a role in regulating mRNA and lncRNA expression . As an example , displayed in Fig 1K , lncBATE10 gene expression was dramatically induced during browning and found to contain multiple sites for PparΥ and Prdm16 co-occupation in its promoter and gene body . We next examined the differential transcriptomic changes during BAT activation by 6 hours cold exposure , or its inactivation by thermoneutrality ( 30°C for 7 days , referred to as whitening hereafter ) ( Fig 2A ) . Respectively , 682 and 232 mRNAs were up- and down- regulated during activation and whitening , with 143 genes overlapping ( hypergeometric P < 6 . 86E−193 ) ( Fig 2B ) ; conversely , 500 and 239 were down- and up-regulated during activation and inactivation with 58 genes overlapping ( P < 2 . 88E−56 ) ( Fig 2C ) . Thus , genes influenced by these 2 stimuli were regulated in opposite directions . For example , thermogenic markers such as Dio2 , Pgc1α , Ucp1 , and Elvol3 were induced by cold activation but suppressed during whitening ( S3A Fig ) . Pathway enrichment analysis by GSEA further indicated a gain of WAT-specific gene set expression and a loss of BAT-specific gene set expression during whitening ( S3B Fig ) . Interestingly , post-transcriptional regulatory functions such as RNA process and translation were strongly induced during BAT activation and repressed by BAT inactivation ( S3B Fig ) , suggesting that such regulation is likely an integral component of the regulatory network governing BAT function . Based on the transcriptomic changes characterized under 5 distinct conditions ( 3 conditions for browning and 2 conditions for activation and inactivation of BAT ) , we next sought to identify genes that were significantly regulated under multiple conditions . Genes that were up-regulated during browning and BAT activation but down-regulated during BAT whitening were of particular interest . In at least 4 out of the 5 conditions , 60 mRNAs were differentially regulated ( FDR ≤ 5% , ≥2-fold absolute change ) and included genes such as Ucp1 , Dio2 , Fabp3 , and other key genes in fatty acid metabolic process ( S3C–S3E Fig ) . Using the same filters , we identified 6 lncRNAs , including lncBATE10 , that were induced in at least 4 out of 5 conditions ( Fig 2D and 2E ) . BAT loses its BAT features during whitening while iWAT gains BAT features during browning . To determine if these phenotypic transitions were reflected in the transcriptomic signatures relevant to each condition , we performed principal components analysis ( PCA ) on mRNA expression signals from all 18 samples included in this study ( Fig 2F ) . Overall , a significant proportion of the treatment differences could be ascribed to gene expression with nearly 60% of the total variation captured in the first 2 principal components . Notably , the unstimulated iWAT and BAT ( plus cold-activated BAT ) samples occupied opposite ends of the PCA plot . The iWAT samples induced by exercise , CL316243 , and cold exposure gradually shifted towards BAT; conversely , the BAT subjected to whitening moved towards WAT . The cold-exposure treatment of iWAT created sufficient transcriptomic changes to shift the samples far enough to overlap with the whitened BAT samples , demonstrating that notwithstanding their distinct lineages of origin , the transcriptomes of iWAT and BAT can be coerced to reflect the transcription patterns of each other under conditions of browning and whitening . LncRNA PCA analysis revealed a picture remarkably consistent with the mRNA PCA studies ( Fig 2G ) . Samples were readily separated into different clusters according to treatment conditions; the movement towards merging between WAT after browning and BAT after whitening was also observed . Thus , the dynamic changes of both mRNA and lncRNA transcriptome appear to be tightly coordinated in these physiological processes , suggesting an intrinsic functional connection between mRNA and lncRNAs . To identify what these functional associations might be , we next explored the correlation patterns between mRNAs and lncRNAs . Although the function of lncRNAs cannot be reliably predicted by comparative genomics due to their poor conservation even between closely related species [25 , 26] , hypotheses on lncRNA function may be inferred from the mRNA–lncRNA correlations during dynamic physiological processes [27] . To predict lncRNAs’ function systematically , we selected lncRNAs and mRNAs that were regulated in at least 3 of the 5 examined conditions , resulting in a total of 819 mRNAs and 79 lncRNAs . We calculated the partial correlation between each lncRNA and mRNA across all 18 samples and used the partial correlation matrix to construct a mRNA–lncRNA network using GeneNet ( Fig 2H ) . We then interrogated the putative biological function of each lncRNA by testing the overrepresentation of biological processes among the mRNAs significantly coexpressed and correlated with the lncRNA expression . Clustering of the full gene coexpression network further revealed 3 major clusters ( Fig 2H ) . The major functional pathways represented by the genes in each cluster were determined by querying GOBP terms via the PANTHER classification system [28] . For each cluster , we grouped the genes into 4 broad functional categories , including metabolic process , immune system process , cellular process , and others . Focusing on the highly significant overrepresentations within each category , oxidative phosphorylation and respiratory electron transport were enriched in clusters 1 and 2 , whereas fatty acid beta oxidation was enriched only in cluster 2 . In contrast , cluster 3 genes were strongly overrepresented in processes related to immune function . Our earlier study had demonstrated that lncBATE1 was needed for a full BAT program expression in both brown and white adipocytes [18] . In our mRNA–lncRNA network , lncBATE1 was linked to Cebpβ , Dio2 , Ucp1 , Fabp3 , Elvol3 , and many other lipid metabolism genes , proving the effectiveness of our approach ( S4A Fig ) . Notably , lncBATE1 was also linked to another lncRNA , lncBATE10 , that was coexpressed with Ucp1 , Dio2 , Dgat1 , and other genes involved in lipid catabolism ( S4B Fig ) . This finding suggests a functional role of lncBATE10 also in BAT-related processes . LncBATE10 is about 1 . 7 kb in length , located in an intergenic region , and composed of 4 exons spanning a 10-kb genomic region in chromosome 18 ( S5A Fig ) . To precisely determine its 5′ and 3′ end , we performed 5′ and 3′ rapid amplification of cDNA ends ( RACE ) and found 1 major transcript . Its 5′ and 3′ ends are consistent with the annotation of a RIKEN cDNA clone ( A530050N04Rik ) ( S5B Fig ) . We performed real-time PCR to examine lncBATE10 distribution in cytosol and nucleus and found that it was distributed in both compartments ( S5C Fig ) . LncBATE10 is highly enriched in BAT in comparison with eWAT , iWAT , and other major tissues detected by Northern blot and real-time PCR ( Fig 3A and 3B ) . It is also highly abundant in BAT; according to fragments per kilobase of exon per million reads ( FPKM ) ( approximately 30 ) , it ranks in the top 3 most abundant lncRNA transcripts in our catalog ( S11 Data ) ; even compared with mRNAs , it still falls into the top 10% of most abundant transcripts ( S2 Data ) . Using diluted standard assay , we estimated that each brown adipocyte in BAT may contain approximately 230 lncBATE10 molecules ( S5D Fig ) . To test whether it is regulated during differentiation , we isolated primary brown and white preadipocytes , as previously described [18] , and examined the time-course of lncBATE10 expression during in vitro differentiation . LncBATE10 was up-regulated >100-fold during brown adipocyte differentiation and , consistent with the tissue-enrichment data , its expression was >10-fold higher in the cultured brown than white adipocytes at Day 4 and Day 6 ( Fig 3C ) . To determine whether lncBATE10 expression correlated with BAT activity , we activated BAT by exposing mice ( 8 weeks old , male ) to 4°C for 6 hours or inactivated BAT by hosting mice at thermoneutrality ( 30°C ) for 7 days , followed by real-time PCR analysis . Compared to controls , lncBATE10 was induced by approximately 7-fold ( Fig 3D ) and repressed by approximately 60% ( Fig 3E ) upon BAT activation and inactivation , respectively . Furthermore , we confirmed the induction of lncBATE10 during iWAT browning induced by cold exposure , swimming exercises , and CL316243 treatment ( Fig 3F ) . These results strongly suggest that lncBATE10 may have a functional role in BAT and iWAT browning . To determine the biological function of lncBATE10 , we infected primary brown preadipocytes with retroviral shRNAs targeting lncBATE10 and then induced cells to differentiate . We achieved more than 70% knockdown efficiency at day 5 of differentiation with 2 different shRNA constructs ( Fig 3H ) . LncBATE10 knockdown did not cause a detectable difference in cell morphology under microscope or lipid accumulation assessed by Oil-Red-O ( ORO ) staining ( Fig 3G ) . Real-time PCR analysis revealed only a mild reduction in pan-adipogenic markers including Cebpa , Pparϒ , and Fabp4 ( Fig 3I ) . However , depletion of lncBATE10 significantly impairs the expression of BAT-selective genes such as Ucp1 and Pgc1α at mRNA levels and protein levels ( Fig 3J and 3K ) . To determine the influence of loss-of-lncBATE10 at a genome-wide level , we performed RNA-Seq for the RNAs extracted from control and knockdown cells , followed by GSEA on pathways extracted from Reactome pathway database . The top down-regulated pathway was related to respiratory electron transport ( FDR < 0 . 001 ) , a hallmark of BAT function ( Figs 3L and 4G ) . Because BAT shares a common lineage origin with skeletal muscle and can be phenotypically converted to WAT under certain conditions , we further examined the gene expression of WAT and muscle markers and found little change upon lncBATE10 knockdown ( S5E and S5F Fig ) . To further assess the effect of lncBATE10 on the activation of brown adipocytes , we treated differentiated brown adipocytes ( Day 5 ) with 1 uM norepinephrine ( NE ) for 4 hours . As expected , NE treatment significantly stimulated the expression of thermogenic Ucp1 and Pgc1α ( S5G Fig ) , but their induction was blunted by lncBATE10 depletion ( Fig 3M ) . Therefore , lncBATE10 is indispensable for the full induction of a BAT-selective gene program . To test whether gain-of-lncBATE10 is sufficient to promote the BAT gene program , we cloned lncBATE10 into a retroviral vector , infected primary brown preadipocytes , and induced them to differentiate . We didn’t observe any significant change in lipid accumulation and cell morphology as well as BAT marker expression ( S5H and S5I Fig ) . Thus , lncBATE10 was required but not sufficient to promote BAT program , suggesting that the endogenous lncBATE10 abundance may be abundant enough to support normal differentiation , or lncBATE10 needs additional cofactors to achieve its functional influence . To determine the influence of lncBATE10 overexpression on global gene expression , we conducted RNA-seq , followed by GSEA analysis . Interestingly , most up-regulated pathways were related to nutrient metabolism , including amino acid degradation , fatty acid metabolism , tricarboxylic acid ( TCA ) cycle , glycerolipid metabolism , butanoate metabolism , and adipocytokine pathway , indicating a broad role lncBATE10 in regulating metabolism pathways ( S5J Fig ) . To further test whether lncBATE10 may act during late stage of brown adipogenesis instead of lineage determination , we overexpressed lncBATE10 in an immortalized brown preadipocyte line , but didn’t observe significant effects on BAT-selective marker expression ( S5K Fig ) . To determine the function of lncBATE10 in white adipocytes , we knocked down lncBATE10 in primary white adipocyte culture . We didn’t observe significant effects on cell morphology , lipid accumulation ( Fig 4A ) , or pan-adipogenic markers ( Fig 4B and 4C ) . However , we detected significant down-regulation in the expression of BAT-selective genes ( Fig 4D and 4E ) . We further examined the genome-wide effects of lncBATE10 knockdown by performing RNA-seq and GSEA , which revealed pathways related to respiratory electron transport as the most significantly down-regulated ones ( FDR < 0 . 001 ) ( Fig 4F and 4G ) . Interestingly , the influenced pathways in brown and white adipocytes largely overlapped , suggesting a similar role of lncBATE10 in both cell types ( Fig 4G ) . To determine the function of lncBATE10 in browning of white adipocytes , we knocked down lncBATE10 in white adipocyte culture and chronically treated cells with NE or a combination of NE and rosiglitazone . Drug treatment could markedly induce BAT-selective markers such as Cidea and Ucp1 , but these markers were blunted by knocking down of lncBATE10 ( Fig 4H and 4I , S6A–S6C Fig ) . To test whether lncBATE10 was sufficient to promote white adipocyte browning , we overexpressed lncBATE10 in iWAT adipocytes ( S6D–S6G Fig ) and 3T3-L1 cells ( S6H Fig ) , but didn’t observe any significant change in marker expression . Thus , lncBATE10 is necessary but not sufficient for BAT-selective program expression . To test the function of lncBATE10 in white fat browning in vivo , we generated adenoviral sh-lncBATE10 , and locally injected the control virus and sh-lncBATE10 virus into each side of iWAT . After 48 hours recovery from surgery , we induced browning by exposing animals to 4°C for 24 hours and then harvested tissue to examine BAT-selective markers . lncBATE10 was successfully knocked down by approximately 80% , which was accompanied by significant down-regulation of BAT-selective markers , including Ucp1 , Dio2 , Pgc1α , et al . but not the pan-adipogenic marker PparΥ ( Fig 4J ) . Western blot was further performed to confirm the reduction of Ucp1 and Pgc1α protein levels ( Fig 4K ) . Thus , lncBATE10 induction is necessary for WAT browning in vivo . Next , we investigated the regulatory mechanisms governing lncBATE10 expression . As shown in Fig 3D , lncBATE10 could be activated by acute cold exposure , which stimulates BAT primarily through beta-adrenergic receptor-cAMP pathway . To test this regulation more directly , we treated cultured brown adipocytes with NE and cAMP for 4 hours and observed a rapid induction of lncBATE10 in both conditions ( Fig 5A ) , demonstrating a regulatory role of cAMP pathway in lncBATE10 expression . cAMP is known to regulate downstream genes such as Ucp1 and Pgc1α by phosphorylating and activating a transcription factor , cAMP response element-binding protein ( Creb ) [29–31] , so we suspected that the transcription of lncBATE10 may be controlled by Creb . Sequence analysis by MatInspector [32] found a few putative Creb binding sites in the promoter region of lncBATE10 ( Fig 5B ) . To determine if 1 or more of these candidate sites were functional , we cloned a series of truncated promoters into a luciferase reporter and transfected these constructs into 293 cells to measure promoter activity in the presence and absence of Forskolin . Forskolin significantly increased the luciferase activity for the 2 . 6 kb promoter construct , indicating that this promoter harbors the regulatory element responsive to cAMP signaling ( Fig 5 ) . The promoter remained active until we truncated the Creb binding site immediately upstream of the transcription start site ( TSS ) , which fully abolished forskolin-induced promoter activity ( Fig 5C ) . Furthermore , we made 4 site-specific mutations at this binding site and found that these mutations were sufficient to abrogate promoter activity ( Fig 5D and 5E ) . We further performed ChIP-PCR to detect the binding between Creb and the identified binding site in brown adipocytes before and after Forskolin treatment , which revealed a significant increase of binding signaling upon Forskolin treatment ( Fig 5F ) . Together , these data demonstrate that lncBATE10 is regulated by the cAMP-Creb axis . To understand the mechanism of how lncBATE10 functions , an essential step is to determine its protein partners . We transcribed lncBATE10 in vitro , labeled the transcripts with biotin during transcription , incubated the labeled RNA with brown adipocyte lysates , and used streptavidin beads to pull down lncBATE10 with its associated proteins for mass spectrometry analysis ( S7A Fig ) . As expected , the identified proteins were highly enriched for RNA binding proteins ( RBPs ) and were closely associated with RNA processes ( S9 Data , S7B Fig ) . We identified 34 proteins with more than 10 unique peptides and identified 3 RBPs , HuR , Celf1 , and Celf2 , that were highly enriched in the lncBATE10 pulldown assay compared to the antisense control ( >10-fold enrichment ) ( S9 Data ) . We further performed Western blot to confirm the retrieval of these RBPs by lncBATE10 ( Fig 6A ) . Since the RNA-pulldown assay was performed in cell lysate but not a native cellular environment , the interactions between RBPs and lncBATE10 could have been an artifact of the assay conditions . To address this issue , we conducted RNA-immunoprecipitation assay , in which antibodies against HuR , Celf1 , or Celf2 were incubated with mouse brown adipocyte lysates to pull down the endogenous proteins , followed by real-time PCR analysis to examine the levels of lncBATE10 retrieved by each immunoprecipitation ( IP ) . The IP for Celf1 gave a very strong and consistent enrichment signal ( >20 fold ) ( Fig 6C ) , and therefore Celf1 was chosen for further studies . Celf1 is a well-studied RBP , and one of its known functions is to bind the 3′UTR of its target mRNAs to promote RNA degradation and repress translation [33–36] . We postulate that lncBATE10 may function as a sponge to trap Celf1 , which otherwise may repress some factors important for BAT differentiation and activation . We examined a set of mRNAs encoding factors important for BAT function , including Prdm16 , Pparα , Ebf2 , Pgc1α , Cebpβ , and Pparγ , and found that Pgc1α mRNA can be consistently retrieved by Celf1 IP ( Fig 6C ) , suggesting a model in which lncBATE10 protects Pgc1α mRNA by titrating away Celf1 . To test this model , we used retroviral shRNA to knock down lncBATE10 in brown adipocyte culture ( Fig 6B ) and then conducted RNA immunoprecipitation ( RIP ) for Celf1 . Despite the decreased Pgc1α mRNA upon lncBATE10 knockdown described above ( Fig 3J ) , the reduced lncBATE10 expression nonetheless resulted in an increased Pgc1α enrichment by Celf1 IP ( Fig 6C ) , providing a line of evidence supporting the competition model . To test whether lncBATE10 may affect Celf1’s expression directly , we performed Western blot to examine the expression of Celf1 in the lncBATE10 knockdown cells . We didn’t observe a significant difference ( S7C Fig ) , supporting that lncBATE10 functions through the completion model . To provide a more detailed molecular basis for the competition model , we examined the sequences of lncBATE10 and Pgc1α mRNA to look for putative Celf1 protein binding site ( CBS ) , which , according to previous studies , should be “UGU”-enriched regions [35 , 37–39] . Two such fragments ( approximately 60 nt ) were readily identified in lncBATE10 and the 3′UTR of Pgc1α mRNA ( Fig 6D ) . To test whether these regions can mediate Celf1 binding , we amplified and in vitro transcribed these 2 fragments for RNA-pulldown assay . Both fragments were sufficient to pull down Celf1 and thereby possess Celf1 binding sites ( Fig 6E ) . Identification of the precise Celf1 sites allows us to test whether the competition between lncBATE10 and Pgc1α mRNAs is mediated through these sites . We conducted RIP against Celf1 in the presence and absence of the Pgc1α RNA fragment containing Celf1 binding site ( Pgc1α-CBS ) ( S7D Fig ) . If this fragment is sufficient to compete with lncBATE10 for Celf1 , less lncBATE10 should be detected in the IP . As expected , our real-time PCR analysis shows that the presence of Pgc1α-CBS can cause >10-fold reduction of lncBATE10 enrichment in Celf1 IP ( Fig 6F ) , strongly arguing that the competition between lncBATE10 and Pgc1α is mediated through the identified binding site . To test whether the CBS can function in the context of Pgc1α 3’UTR , we repeated the RNA-pulldown and the competition assays using an approximately 1-kb 3′UTR fragment with or without the CBS , which led to similar results ( Fig 6G and 6H ) . Thus , the CBS is functional in Pgc1a’s 3′UTR context . Data presented above have demonstrated a competition between lncBATE10 and Pgc1α for Celf1 binding , but whether Celf1 can repress Pgc1α mRNA expression was not rigorously tested . We overexpressed Celf1 using a retroviral vector in primary brown preadipocytes followed by differentiation , and found Pgc1α to be significantly repressed at both mRNA and protein levels ( Fig 6G and 6H ) , supporting a repressive role of Celf1 on Pgc1α mRNA . We noticed that the adipogenesis per se was moderately inhibited upon Celf1 overexpression , so the reduced Pgc1α levels may involve indirect effects from adipogenesis . To preclude the indirect effect during adipogenesis , we differentiated primary brown preadipocytes for 5 days and then transfected a small interfering RNA ( siRNA ) targeting Celf1 into mature brown adipocytes . Both Pgc1α and lncBATE10 were increased ( S7E Fig ) , which further supports a repressive role of Celf1 on Pgc1α mRNA . However , further studies will be needed to dissect whether Celf1 exerts its effects on Pgc1α at RNA stability and/or translational efficiency . To test whether Celf1 represses Pgc1α mRNA through the identified binding sites , we cloned these fragments ( approximately 170 bp ) harboring Celf1 binding site from lncBATE10 and Pgc1α mRNA ( Fig 6K ) into a luciferase reporter and transfected these constructs into 293 cells to test their effects on luciferase activities . Sharing a 99% sequence similarity with the mouse gene , 293 cells express a human Celf1 . As expected , inclusion of Celf1 binding sites results in a dramatic decrease of luciferase activity ( Fig 6L ) , which can be significantly derepressed by knockdown of endogenous CELF1 ( Fig 6M and 6N ) . We further transfected these constructs into primary brown preadipocytes and observed similar results ( S7F–S7H Fig ) . Thus , the Celf1 binding sites identified in our studies appear to be sufficient for Celf1-mediated repression of Pgc1α mRNA activity .
Coincident with the onset of the obesity epidemic and the realization that increase of BAT mass and activity can improve metabolic health , there has been an upsurge of interest in understanding the detailed mechanism underlying brown fat development and WAT browning . Although it is well known that protein factors play regulatory roles in different aspects of adipocyte biology[40 , 41] , our understanding about noncoding genes in lineage-specific development of adipocyte is still at its infancy . Here , we present a comprehensive picture of the dynamic transcriptome changes during WAT browning induced by different stimuli and BAT whitening induced by thermoneutrality . Our data demonstrates that both mRNA and lncRNA transcriptomes of BAT and WAT could be coerced to reflect the cellular features of each other under appropriate conditions . We further built a regulatory network by integrating the regulated lncRNAs and mRNAs according to their coregulation , from which the function of each lncRNA could be inferred by analyzing its neighboring coexpressed mRNAs . Our work serves as a resource to study the dynamic regulation in adipose at various energetic states and provides a roadmap to investigate the function of lncRNA in adipose biology . Our study further identified lncBATE10 as a downstream effector of cAMP signaling pathway and to be necessary for the expression of a full BAT-selective gene program . Since cAMP pathway activates the expression of Pgc1α and lncBATE10 simultaneously , this may have a synergistic effect on the activity of Pgc1α . Mechanistically , we propose that lncBATE10 serves as a decoy to titrate away Celf1 , which otherwise targets and represses Pgc1α mRNA ( Fig 6M ) . This working model is reminiscent of a few other lncRNAs: pseudogenes of the tumor suppressor , Pten , have been proposed as decoys for miRNAs that repress Pten mRNA [42]; Gas5 has been shown to function as a decoy to deprive glucocorticoid receptor from DNA to prevent transcription of certain genes during starvation [43]; another recent lncRNA , P21-associated noncoding RNA DNA damage-activated ( PANDA ) , titrates transcriptional factor NF-YA away from target chromatin region and prevents p53-mediated apoptosis [44] . Thus , decoy could be a common mechanism used by many lncRNAs to modulate their targets in trans . Because both lncBATE10 and Pgc1α are highly enriched in BAT , an outstanding question is whether Celf1 may target other BAT-selective transcripts . Based on gene expression enrichment in BAT in comparison with WATs ( S12 Data ) , we identified a list of BAT-selective genes ( 269 genes ) . We scanned their 3′UTR regions with a 100-bp window and calculated the UGU nucleotide counts in each window . We ranked the 100-bp regions in 3’UTRs based on their UGU counts ( S12 Data ) . The CBS identified in the Pgc1a 3′UTR ranks in the very top group ( ≥20 UGU ) , indicating that it is a top target for Celf1 . Interestingly , a few more genes including Clic5 , Idh3a , Acot11 , Rnf152 , and Ppp1r3b and Ddhd2 also contain such a high-UGU region , suggesting that they may also be targets of Celf1 . Moreover , since lncBATE10 harbors a CBS , it is likely that Celf1 can regulate the stability of lncBATE10 and other lncRNAs with CBS . Further experimental evidence will be needed to confirm their interactions with Celf1 . We demonstrate that lncBATE10 can decoy Celf1 , which otherwise represses Pgc1α mRNA; however , it should be noted that this competition model doesn’t provide a complete picture of how lncBATE10 functions . Celf1 is likely to interact with multiple mRNA targets , so protection of Pgc1α mRNA by lncBATE10 is not a complete picture of the mechanism . Nonetheless , our work has depicted a dynamic picture of transcriptome during adipose tissue energy homeostasis changes and has identified lncBATE10 as a novel effector in the cAMP pathway that is necessary for the BAT-selective gene expression in brown and white adipocytes .
All mice were hosted at the animal vivarium at DUKE-NUS Medical School . All animal procedures were performed according to guidelines set forth by the Singapore SingHealth Research Facilities Institutional Animal Care and Use Committee and approved by the same committee under protocol IACUC 2016/SHS/1179 . Total RNAs from tissues and cultured cells were extracted using Trizol ( Life technology ) and purified using RNeasy kit ( Qiagen ) . RNA libraries were prepared using NEBNext Ultra RNA Library Prep Kit for Illumina and sequenced on the Illumina HiSeq2000 platform . Equal amounts of adipose RNA from each of the 4 different animals were pooled together as 1 sample for RNA-seq library preparation . RNA-seq data from this study have been deposited at the National Center for Biotechnology Information Gene Expression Omnibus ( accession number GSE86338 https://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? token=cnevggmadtafpen&acc=GSE86338 GSE79169 https://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? token=kjwnesigjzktpwj&acc=GSE79169 ) . C57BL6 mice were obtained from InVivos and subsequently bred in house . All mice were hosted at the animal vivarium at DUKE-NUS Medical School . For the browning experiment , 12-week-old male mice were housed individually in at 4°C for 7 days; for β-adrenergic agonist treatment , CL316243 ( Sigma ) was injected daily at 1 mg/kg for 7 days . Mouse swimming exercise was conducted according to a previously established program with a few modifications [19] . Briefly , the swimming exercise started at 8 weeks old . The protocol started at 10 minutes , 2 times daily . The exercise time increased 10 minutes each day until 90 minutes , 2 times per day was reached . After that , the protocol ended after another 2 weeks . The water was incubated at 30°C to keep mice from getting hypothermia during swimming . For acute cold challenge to activate brown fat , 8-week old mice were individually housed at 4°C for 6 hours . For thermoneutrality treatment , mice were housed at 30°C for 7 days . The generation , amplification , and purification of recombinant adenoviruses for expression of lncBATE10 or LacZ shRNA were conducted as described previously [48] with modifications . Sequences for lncBATE10 shRNAs are as follows: lncBATE10 shRNA: GCTTCTCCTGAACCAACAAGA , LacZ shRNA: CTACACAAATCAGCGATTT . Purified adenoviruses were titered with Adeno-X Rapid Titer Kit ( Clontech ) . Adenoviruses were injected at 100 ul per subcutaneous adipose depot ( 1010pfu/ml ) . Seven-week-old C57BL/6 male mice were anesthetized . Hairs located at the inguinal area were removed with a trimmer , the surgical wounds were disinfected with 70% ethanol , the underlying skin was opened , and the inguinal adipose tissue was exposed . Adenoviruses of lncBATE10 shRNA or shLacZ ( control ) were injected into the left and right inguinal adipose tissue , respectively . Animals recovered for 48 hours and then were housed in 4°C degree for 24 hours . Adipose tissues from inguinal depots were excised , and RNA was extracted for real-time PCR . Primary preadipocytes were cultured in 10% NBCS medium and induced to differentiate with regular differentiation cocktail as described before [49] . Briefly , interscapular BAT or iWAT from 6 to 8 approximately 4-week-old mice were pooled together , minced , and digested in 0 . 2% collagenase , which were subsequently filtered by 40 um cell strainer and centrifuged to collect stromal vascular fraction ( SVF ) cells at the bottom . SVF cells were cultured for downstream experiments . Every batch of SVF cells was considered as one biological replicate , and at least 3 biological replicates were performed . 3T3-L1 cells were maintained in DMEM containing 10% bovine calf serum and then differentiated according to the instruction from ATCC . Retroviruses were produced by the cotransfection of retroviral plasmids and packing plasmid pCL-Eco into 293T cells . Culture medium was changed to fresh medium at approximately 16 to 18 hours after transfection , and viruses were collected at 48 hours after transfection . Primary preadipocytes or 3T3-L1 preadipocyte at approximately 60% to 70% confluence were infected with fresh viruses , followed by standard differentiation procedure . All the plasmids used in this study were cloned using standard method . Full-length lncBATE10 expression plasmid was cloned into lncEXP retroviral expression vector [18] . ShRNA oligos were designed by using Invitrogen Block-iT RNAi Designer and cloned into pSUPER . retro . puro vector . LncBATE10 promoter fragments were amplified from mouse genome DNA and cloned into pGL3-Basic vector between KpnI and HindIII restriction sites . Creb site mutation introduced in lncBATE10 promoter was achieved by overlapping PCR using primers harboring mutated sequences . CBSs from lncBATE10 and Pgc1α 3′UTR were amplified from cDNA , respectively , and then cloned into Psicheck2 vector . Mouse Celf1 expression plasmid was constructed using a retroviral expression plasmid , pXZ201[49] . 5′ and 3′ RACE experiments were carried out as previously described . Each band visualized in agarose gel was recovered and cloned into pGEM-T easy vector . The transcription start and end sites of lncBAT-10 were determined by sequence alignment with mouse genome sequence . Cell staining by ORO , Hoechst , and Mitotracker was carried out as previously described [18 , 49] . Total RNA from tissues or cell samples was isolated as described . cDNA was made with random primers using M-MLV ( Promega ) . Sybr Green-based quantitative real-time PCR ( qPCR ) was performed using an Applied Biosystems 7900HT Fast Real-time PCR System . The mouse housekeeping gene RPL23 was examined in parallel as an internal control for data normalization . Primer sequences can be found in S10 Data . Protein samples resolved on a 4%–15% TGX gel ( Bio-Rad ) were transferred onto PVDF membrane using standard protocol . Membranes were blocked with 2% BSA , sequentially incubated with indicated primary antibody and horseradish peroxidase-conjugated secondary antibody . Specific bands were visualized and recorded with a ChemDoc MP Image System ( Bio-Rad ) . Primary antibodies against Ucp1 , Celf1 , and Celf2 were purchased from Abcam . Primary antibodies against Pparg , Pgc1α , HuR , and a-tubulin were obtained from Santa Cruz Biotechnology . Cytoplasmic and nuclear lysates and RIP were prepared as described before with minor modifications [18] . Cytoplasmic and nuclear lysates were prepared as described before with minor modifications [18] . Briefly , 4-day differentiated primary brown adipocytes ( 5 X 106 to 1 X 107 ) were washed with PBS , resuspended in 1-ml hypotonic buffer ( 10 mM Tris-HCl , pH 7 . 4 , 10 mM KCl , 2 mM MgCl2 , 1 mM DTT , 1 mM PMSF , 1X protease inhibitor , 0 . 4 U/ul RNase Inhibitor [Bioline] ) and chilled on ice for 15 minutes with gentle shaking every 5 minutes . Cells were transferred to a glass dounce homogenizer and disrupted with 20 strokes . Nuclei were pelleted by centrifugation at 3 , 300 X g for 10 minutets at 4°C , and cytosol fraction was transferred to a 1 . 5 ml eppendorf tube . Cytoplasmic lysates were further cleaned by supplementing KCl to a final concentration of 150 mM , followed by centrifugation at 20 , 000 rpm for 10 minutes at 4°C . Nuclei were resuspended in 1-ml nuclear isolation buffer ( 25 mM Tris-HCl , pH 7 . 4 , 150 mM KCl , 2 mM MgCl2 , 1 mM DTT , 0 . 5% IGEPAL , 1 mM PMSF , 1X protease inhibitor , 0 . 4 U/μl RNase inhibitor [Bioline] ) mechanically disrupted using a dounce homogenizer with 100 strokes and centrifuged at 20 , 000 X g for 10 minutes at 4°C . Cytoplasmic and nuclear lysates were mixed at a volume ratio of 1:1 and precleaned with ( 20 μl ) Dynabeads Protein G with continuous rotation at 4°C for 30 minutes . 1-ml precleaned cell lysates were incubated with 5 μg of indicated antibody at 4°C for 2 hours , and then further incubated with Dynabeads Protein G ( 40 μl ) at 4°C for 1 hour . RNA-protein complexes immunoprecipitated with protein G beads were washed 5 times with RIP buffer ( 25 mM Tris-HCl , pH 7 . 4 , 150 mM KCl , 2 mM MgCl2 , 0 . 05% IGEPAL ) . Ten percent of each sample was kept for western blot , and the rest was used for RNA extraction and subsequent qPCR examination . Cell lysates from primary brown adipocytes were prepared as described above . The RNA pulldown was performed as described in our earlier study [18 , 50] . In vitro–transcribed biotinylated lncBATE10 RNA or antisense RNA was denatured at 90°C for 2 minutes in RNA structure buffer ( 20 mM Tris-HCl , pH 7 . 4 , 0 . 2 M KCl , 20 mM MgCl2 , 2 mM DTT , 0 . 8 U/μl RNase inhibitor ) , then chilled on ice and supplemented with RNA structure buffer , followed by incubation for 1 hour at room temperature to allow for the proper refolding of RNA . Restructured RNAs were conjugated to 200 ul Dynabeads M-280 ( Life Technologies ) at room temperature for 1 hour . RNA-bound beads were then incubated with 1-ml cell lysates ( nuclear + Cytoplasmic fraction ) for 3 hours at 4°C . After washing 5 times with nuclear isolation buffer ( 25 mM Tris-HCl , pH 7 . 4 , 150 mM KCl , 2 mM MgCl2 , 1 mM DTT , 0 . 25% IGEPAL ) , RNA-Protein complexes were eluted from beads with 100 ul 2 mM biotin for a 3-hour rotation at room temperature and subject to mass spectrometry for unknown proteins or western blot for known proteins . The mass spectrometry was performed in LCMS-TripleTOF 5600 System in protein and proteomics center in NUS . Denatured RNA from mouse BAT , iWAT , and eWAT were resolved in 1% formaldehyde agarose gel and transferred to Hybond-N+ membrane ( GE healthcare ) . The membrane was UV-crosslinked , prehybridized in ULTRAhyb buffer ( Ambion ) at 68°C for 30 minutes , and hybridized with in vitro–transcribed biotin-labeled lncBATE-10 antisense RNA probe for 16 hours . After stringent washing , the hybridization signal was developed using BrightStar BioDetect Kit ( Ambion ) according to manufactures’ instructions and recorded by ChemiDoc Imaging System ( Bio-Rad ) . For the promoter assay , 293T cells cotransfected with lncBATE10 promoter reporters and pRL-CMV vector were treated with vehicle or forskolin for 2 hours before cell lysis . Reporter activities were measured using Dual-Luciferase Reporter Assay System ( Promega ) on a Tecan infinite M200 Microplate Reader . For the 3′UTR assay , 293T Cells transfected with shCELF1 Psicheck2 plasmid were split at a ratio of 1:5 after 48 hours of transfection , followed by transfection again 10 hours after cell attachment with reporters with or without CELF1-binding sequences . Mouse brown preadipocytes infected by viral shCELF1 were transfected with 1 μg reporter plasmid DNA 48 hours after infection and collected 24 hours after transfection for luciferase activity measurement . The statistical analyses for RNA-seq data are described in the Data Analysis Procedures section above . Student t test was used to compare 2 groups of samples; 1-way ANOVA was used to compare 3 or more groups of samples to correct multiple comparison . P value < 0 . 05 was considered as significant . | Fat accumulation is a major health problem in many countries , but unlike white fat—which stores calories—brown fat is packed with mitochondria to burn energy . Therefore , promoting “browning” of white fat and enhancing brown fat activity are seen as promising therapeutic strategies to fight obesity . Long noncoding RNAs ( lncRNAs ) , once largely believed to be functionally irrelevant , are receiving special attention because of the recent realization of their important role in many biological processes . Here , we performed a series of transcriptome analyses , including lncRNAs , during white fat browning and brown fat activation . Based on the mRNA–lncRNA coexpression network , we identified brown adipose tissue–enriched lncRNA 10 ( lncBATE10 ) as a new regulator in brown adipocyte differentiation . Loss of lncBATE10 impaired expression of a brown fat–selective program in brown adipocytes and during browning of white adipocytes . We further showed that lncBATE10 could facilitate browning of white fat and brown fat activation by promoting Pgc1a expression . Taken together , we depicted a comprehensive noncoding transcriptome network during white fat browning and brown fat activation and identified lncBATE10 as a novel regulator in these processes . | [
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| 2017 | Dynamic transcriptome changes during adipose tissue energy expenditure reveal critical roles for long noncoding RNA regulators |
West Nile virus is globally wide-spread and causes significant disease in humans and animals . The evolution of West Nile virus Kunjin subtype in Australia ( WNVKUN ) was investigated using archival samples collected over a period of 50 years . Based on the pattern of fixed amino acid substitutions and time-stamped molecular clock analyses , a single long-term lineage ( or topotype ) was inferred . This implies that a bottleneck exists such that regional strains eventually die out and are replaced with strains from a single source . This was consistent with current hypotheses regarding the distribution of WNVKUN , whereby the virus is enzootic in northern Australia and is disseminated to southern states by water-birds or mosquitoes after flooding associated with above average rainfall . In addition , two previous amino acid changes associated with pathogenicity , an N-Y-S glycosylation motif in the envelope protein and a phenylalanine at amino acid 653 in the RNA polymerase , were both detected in all isolates collected since the 1980s . Changes primarily occurred due to stochastic drift . One fixed substitution each in NS3 and NS5 , subtly changed the chemical environment of important functional groups , and may be involved in fine-tuning RNA synthesis . Understanding these evolutionary changes will help us to better understand events such as the emergence of the virulent strain in 2011 .
West Nile virus ( Family Flaviviridae; Genus Flavivirus ) is a mosquito-borne virus which can cause disease in humans , horses and birds . In humans , the disease can manifest as a fever with rash and , infrequently , neurological disease including meningitis , encephalitis and acute flaccid paralysis [1] . Introduction of West Nile virus into the Americas in 1999 resulted in tens of thousands of cases of human disease causing over 1 , 500 deaths , and has devastated bird populations [2] . The virus is now globally distributed , with the exception of Antarctica [3] . There are 7 lineages of which two comprise the majority of circulating strains [4] . In Australia , a strain of West Nile virus called Kunjin virus ( WNVKUN ) is endemic [5] . It has historically been considered more benign than strains circulating globally , but can cause rare cases of non-fatal encephalitis in humans [6] . WNVKUN has been included in Lineage 1b [3] , and a single Australian topotype was previously inferred on the basis of oligonucleotide fingerprinting [7] . In 2011 , the largest outbreak of equine encephalitis in over 30 years occurred in south-eastern Australia . Over 1 , 000 horses were infected , with a case fatality rate of 10–15% [8] . Surprisingly , only one case of human disease was reported , despite widespread virus activity [9] . The major aetiological agent of the outbreak , WNVNSW2011 , was subsequently shown to be neuroinvasive , but was less virulent than the highly pathogenic North American WNVNY99 strain in mouse models [10] . Hence , this unprecedented outbreak was an opportunity to explore the drivers causing an apparently benign strain to become virulent in horses . Sequencing revealed that WNVNSW2011 carried two known virulence markers that were not present in the prototype WNVKUN strain isolated in 1960 [10] . One was a glycosylation site at position 154 of the envelope . A second was a phenylalanine at amino acid 653 in the RNA polymerase protein encoded by the NS5 gene . Further sequencing revealed that all recent strains isolated , with the exception of the prototype strains , carry both these virulence markers [11] , and an additional 4 neuroinvasive strains that had been isolated in the previous two decades were identified . These genotypic changes may be fully or partially responsible for the observed increases in virulence . However , the drivers of the 2011 outbreak still remain unclear and are probably a result of the complex interplay between known , and possibly undetermined , viral genetic virulence determinants; increased fitness in the major WNVKUN vector Cx . annulirostris; or a convergence of high mosquito and waterbird populations associated with the widespread La Niña driven rain event [11 , 12] . Only the original 1960 prototype strains and a handful of coding-complete WNVKUN isolates from the 1980s until the present are currently available in GenBank . As most of the amino acid substitutions of the known determinants of increased neurovirulence of recent WNVKUN strains occurred at least before 1984 , there is inadequate sequencing of strains from the early 1960s until that time . In order to better understand the emergence of the neurovirulent strain responsible for the 2011 equine epidemic , and the evolution of the virus , additional sequence data is required . We accessed archival material collected over 5 decades and performed next generation sequencing to generate the data required to discriminate WNVKUN strains at a significantly higher resolution than previous analyses . Changes in the envelope protein were observed , consistent with immune pressure , and also in key replicative proteins which would subtly change the chemical environment of active sites . The analysis revealed a stable genome with a single lineage which was subjected to mostly purifying selection .
Archival material in the QIMR Berghofer Medical Research Institute collection was obtained over the period from 1960–2011 , and was isolated from mosquitoes collected from the Australian states of Queensland , New South Wales , and South Australia ( S1 Table ) . Isolates were stored frozen at -80°C until required . The isolates were passaged twice in C636 Aedes albopictus cells prior to sequencing . Tissue culture supernatants of cultured isolates were processed ( excluding the ultracentrifugation step ) , RNA was extracted , and nucleic acids pre-amplified using sequence-independent amplification ( SIA ) as described previously [13] . High throughput sequencing was performed on one of two platforms . Library construction and sequencing on the Ion Torrent Personal Genome Machine ( PGM™; Thermo Fisher Scientific ) was as described previously [13] . The SIA reaction products were subjected to tagmentation , indexing and library amplification following the manufacturer’s recommendations as described in the Nextera XT kit ( Illumina ) . Sequencing was performed on an Illumina HiSeq2500 , at the Australian Genome Research Facility . At least 18 million 125 bp paired-end reads ( i . e . 9 million pairs ) were obtained for each sample . A virus consensus sequence was obtained using Geneious R8 and MRM61C ( accession number D00246 ) as a reference sequence . Nucleotide sequences were aligned using the MAFFT plugin ( algorithm , FFT-NS-ix2; scoring matrix , 200 PAM/k = 2; gap open penalty , 1 . 53; offset value , 0 . 123 ) in the Geneious R8 package [14] . For comparison , an amino acid substitution was defined as a change that was different from at least two separate prior isolates . It was considered fixed when it occurred at least two subsequent times , at least once in a subsequent year and different sampling location . The codon adaptation index ( CAI ) and expected CAI ( eCAI ) were calculated as previously described using the server available at http://genomes . urv . es/CAIcal/ . For selection analysis , single likelihood ancestor counting ( SLAC ) and internal fixed effects likelihood ( IFEL ) analyses were performed as previously described with an optimized substitution model , using the server available at http://www . datamonkey . org/ . The data were checked first for evidence of recombination using RDP v4 . 56 [15] . Unless otherwise indicated , statistical tests used were those incorporated into the software . For phylogenetic analysis , the optimal substitution model was determined using jModelTest [16] for 88 different models . Using the optimized general time reversible model with invariant sites ( GTR+I ) , a phylogenetic tree was constructed using maximum likelihood in MEGA v7 . 0 . 14 [17] with 1 , 000 bootstrap replicates . The tree was rooted by setting WNVNY99 as the out-group . For molecular clock analysis with time-stamped data , the BEAST v1 . 8 . 1 package [18] was used with a log-normal relaxed clock . A chain length of 10 , 000 , 000 was run , sampling every 1 , 000 . The best supported tree was obtained using the utility TreeAnnotator and a burn-in of 1 , 000 . The optimal tree was graphically modified in TreeGraph2 [19] . 3D biomolecule graphics were done using the viewer in Geneious R8 . PDB files containing atomic resolution data ( core , 1SFK; envelope , 2I69; NS1 , 4OIE; helicase , 2QEQ; MTase , 2OY0; polymerase , 2HCN ) were obtained from the Protein Data Bank ( http://www . rcsb . org/pdb/home/home . do ) . WNVKUN data were used , and , where this was unavailable , WNVNY99 was used instead . Coloured projections of sequence conservation onto protein structure were conducted using the ConSurf server ( http://consurf . tau . ac . il/2016/ ) .
Data from 31 isolates were assembled and the consensus sequences aligned with sequence data from other strains available in GenBank . The alignment revealed a remarkable degree of conservation for an RNA virus over the 50 year collection period . Interestingly , the alignment revealed that the substitutions that were previously shown to be associated with virulence [11] had been present for many years . The phenylalanine at 653 of the RNA polymerase , which corresponds to the antagonist of type I interferon-mediated JAK-STAT pathway , was present in all strains with the exception of the MRM61 prototype and its derivatives . The N-Y-S glycosylation motif , present at amino acids 154–156 of the envelope protein , which first appeared in the 1960s , was partially fixed in the sampled population by the 1970s , and completely fixed in all isolates by the 1990s . Therefore , this change probably went to fixation sometime in the 1980s . This conclusion was supported by previous sequencing of the envelope protein of isolates from this decade ( J . H . Scherret , unpublished data ) . Hence , the two known changes from the 1960 prototype strain were mostly likely circulating for at least two decades prior to the 2011 equine outbreak . There were a large number of amino acid substitutions apparent in the alignment that occurred in single isolates , or a small number of isolates that were collected around the same time , but that failed to go to fixation in the population . These substitutions may be biologically significant for individual strains circulating in a region for a certain period of time . However , the substitutions that go to fixation are most relevant to the understanding of the long-term evolution of the virus . Hence , further analysis will focus on such substitutions . There were 35 sites where fixed amino acid substitutions occurred in the genome of WNVKUN over the course of 5 decades ( Fig 1A; S2 Table ) . Of note , is the fact that once fixed , these substitutions neither reverted nor were they replaced , and were then permanently maintained in the future virus population regardless of their origin in Australia . For example , the I49V substitution in NS5 , previously suggested as an evolutionary marker [11] , was identified as one such fixed substitution . This characteristic maintenance of fixed substitutions is indicative of a single long-term lineage of WNVKUN in Australia . The only exception to this observation occurred in two sites of the core protein . Firstly , a S11N substitution that occurred in the 1960s reverted back in the 2000s . Secondly , amino acid 114 of the core protein was substituted twice , once in each of the 1970s and 1990s . The fixed substitutions were distributed across the genome’s single large open reading frame ( ORF ) ( Fig 1A ) . There was a minimum of one and a maximum of 6 fixed substitutions per processed viral protein . Changes were not evenly distributed over time ( P < 0 . 05; χ2 test ) , with twice as many fixed substitutions occurring in the 1960s than the other decades . This indicated that the period was a hot-spot for non-synonymous nucleotide change . There was some indication of temporal clustering , with all 6 of 7 fixed substitutions in the surface proteins occurring during the 1960s and 70s , and the two fixed substitutions in NS2B occurring during the 2000s ( Fig 1B ) . The fixed amino acid substitution density was lowest for both NS3 and NS5 in comparison with all the other viral proteins ( Fig 1C ) . Both these proteins are critical for RNA synthesis . Therefore , this may indicate some constraint on the replicative machinery which limits the number of possible changes to the protein structure and function before viability is compromised . A root-to-tip divergence analysis was conducted to determine whether a molecular clock was applicable to the WNVKUN time-stamped sequence data . A strict linear correlation was found between the age of the isolates and the root-to-tip distance ( Pearson correlation coefficient r = 0 . 97 , P < 0 . 001; coefficient of correlation R2 = 0 . 93 ) indicating a molecular clock could be applied ( Fig 2 ) . A phylogenetic tree was constructed using BEAST ( Fig 2 ) with an optimized substitution model ( GTR+I ) . This tree morphology was very similar to a tree constructed using the maximum likelihood model with the same data that had not been time-stamped ( S1 Fig ) . Consistent with the conclusions reached from observing the pattern of fixed substitutions , a single lineage was apparent . Multiple clades were present when multiple isolates were collected at a single time point and location . For example , at both Mitchell River Mission in the 1960s and Charleville in the 1970s there were strains that could be grouped in multiple clades . However , after several years , these minor lineages died out to be replaced by the single main lineage . This result , in combination with the analysis of fixed substitutions , strongly suggested a single source or focus from which this lineage is continuously replenished . This may be either a physical and environmentally favourable site , permissive of virus replication; or alternatively , a climatic or seasonal restriction resulting in a bottleneck effect . Finally , from this analysis a rate of 6 . 924×10−4 nucleotide substitutions/site/year was calculated which was comparable to values calculated for West Nile virus [3] , and other flaviviruses such as dengue [20] , over longer genetic distances . Rates of synonymous ( dS ) to non-synonymous ( dN ) change were calculated across the WNVKUN genome to determine selection pressure . Firstly , the sequences were checked for evidence of recombination using RDP software . The isolate SA2011 was determined to be a possible recombinant ( P < 0 . 05 for 6 different models ) , and was therefore omitted to prevent a problem with the selection analysis . For the other included sequences , the SLAC method detected 9 codons undergoing significant ( P < 0 . 05 ) negative selection ( Fig 3A ) . An alternative method called IFEL , which detects selection along internal branches , detected 20 codons also undergoing negative selection ( Fig 3B ) . Only one codon ( codon 231 of envelope ) was determined to be under positive selection ( P < 0 . 05 ) . However , as this was an asparagine to serine substitution that did not go to fixation , its significance is unclear . Hence , with the exception of possibly one codon in the envelope protein , purifying selection is the major selection pressure being exerted on WNVKUN . To what extent adaptation to the host’s codon usage influences the life-cycle of WNVKUN is unknown . To explore this question , a measure of codon usage referred as the codon adaptation index ( CAI ) was used [21] . CAI was calculated for WNVKUN using mosquito ( Culex tritaeniorhynchus ) , human ( Homo sapiens ) , horse ( Equus caballus ) and bird ( Phalacrocorax carbo ) codon usage data sets ( Table 1 ) . The CAI values between the mosquito and all of the vertebrates ( human , horse and bird ) were significantly different ( P < 0 . 001 ) . Hence , there was a codon usage bias towards vertebrate hosts in comparison with the mosquito host . To establish whether the observed usage bias was due to codon preference or simply a function of nucleotide composition , an expected value of CAI ( eCAI ) was determined [22] . This value was calculated using the nucleotide and amino acid frequencies for 500 random sequences at a pre-determined probability ( P < 0 . 05 ) , and a ratio of CAI:eCAI greater than one indicated a preference for codon usage . All eCAI values were subjected to a Kolmogorov-Smirnov test and were found to follow a normal distribution . Mosquito CAI/eCAI was not significant ( 0 . 93 ) , indicating that usage bias was most likely due to nucleotide composition in that case . CAI:eCAI ratios were for the vertebrates were borderline for significance ( 0 . 99 ) and so it was difficult to be conclusive in that case , but may indicate a codon preference with vertebrates . This result compares with a recent study looking at codon preferences in 449 WNV strains with a clear codon preference towards human usage [23] . The inability to find a clear codon preference in our data may be due to sequence differences between global WNV strains , and WNVKUN strains circulating in Australia . To gain a better understanding of how fixed amino acid substitutions affected the function of the virus , these changes were related to known WNV protein structures at atomic resolution , where this information was available . For the envelope protein , there were 3 fixed substitutions that could be placed within its structure . The envelope protein is the main viral surface protein and contains the majority of the B cell epitopes of WNV [24] . A K310R substitution in envelope ( Fig 4 ) was within the lateral ridge of domain III ( DIII-lr ) which contains the major virus neutralization epitope [24 , 25] . Another two fixed substitutions that could be placed within the envelope structure ( K44R and F156S ) were both within domain I , proximal to the interface between domains I and III . The substitution at amino acid 156 is part of the glycosylation motif associated with increased virulence [10] . All of these fixed substitutions were surface accessible , consistent with changes due to selection pressure from humoural immune responses . There was a fourth L483F fixed substitution in the transmembrane domain . However , this is not shown on the figure as it was not included in the structure when this was originally determined . Finally , the positively-selected amino acid at 231 is also shown ( Fig 4 ) . It is a relatively conserved residue within the genus Flavivirus , suggesting it may have an important biological function . The non-structural proteins are responsible for replication of the virus . To investigate their evolution in WNVKUN , the positions of fixed amino acid substitutions were identified in the structures of viral proteins NS1 , NS3 , and NS5 ( S2 and S3 Figs; Figs 5 and 6 ) , as atomic scale information was also available for these proteins . NS3 contains a viral protease and a helicase which facilitates RNA synthesis ( Fig 5 ) . There were two substitutions that could be determined within the dimeric structure of NS3: one was K382R and the other was N465S . Amino acid 465 showed a relatively high degree of sequence conservation , suggesting an important functional role . It is immediately adjacent to domain VI of the helicase/NTPase [26] . The substitution was a change from an asparagine to a serine group . Whilst this was a conservative amino acid change , it may have resulted in a subtle shift in the environment surrounding the helicase catalytic groups . The substitution at amino acid 382 was at a variable site , is presumably due to stochastic drift and , therefore , of probably low biological significance . The NS5 protein is crucial for RNA synthesis , and has two important functional groups: a methyltransferase activity ( MTase ) responsible for transcript capping ( Fig 6 ) , and the RNA polymerase ( S2 Fig ) . A V183I fixed substitution , buried within the protein structure , was immediately adjacent to a residue in the methyltransferase catalytic tetrad ( K61-D146-K182-E216 ) [27] . The functional domain of which this residue forms a part is important , as K182 directly participates in deprotonation of the 2’-OH group in the ribose . A K182A mutant is attenuated in cell culture further demonstrating the domain’s importance [27] . This valine to isoleucine fixed substitution in WNVKUN at amino acid 183 was another example of a conservative amino acid change , resulting in a subtle change in the chemical environment of the catalytic site . In addition , there were two surface accessible substitutions: one I49V and the other R101K . Both of these were either variable or only slightly conserved , and so were most likely due to stochastic drift . The changes in NS1 and the polymerase domain of NS5 were not near important functional residues , so their structures are provided as supplemental data only .
The most remarkable finding of this work is the stability of the genome over a period greater than 50 years , consistent with other recent observations [28 , 29] . Over that period , the picture for West Nile virus in Australia is one of overwhelming purifying selection . There have been a maximum total of 6 fixed substitutions , and in most cases less , for individual viral proteins over that time . Of the changes that have occurred , most are in regions of variable sequence , implying stochastic drift; or where they are in possible functional domains , the changes are conservative in nature , resulting in minor changes at most . From these results , it was hard to deduce any change , or changes , that occurred which have led to enhanced virulence in some strains . Two substitutions were identified previously that were linked to potential enhanced virulence [10] . To better understand the emergence of virulent strains in this study , fixed substitutions were related to protein structure where structural information was available . There will be many other regions left unexplored by this analysis , and therefore , there is much potential to explore this issue further . However , this question is complicated by the possibility that virulence may be related to a single change , or a complex interaction involving changes at multiple sites , making it difficult to reach definitive conclusions . Our analysis indicates that both of the previously mentioned changes linked to virulence may have been present for over two decades . In particular , the phenylalanine at amino acid 653 of the RNA polymerase was present in all strains except the prototype and its derivatives . A second substitution resulted in the N-Y-S glycosylation motif , and a previous study indicated that most recent WNVKUN strains are indeed glycosylated [11] . As discussed above , this may indicate a trend to glycosylation at this site in recent decades . However , it has been suggested that the absence of the glycosylation site in the prototype and related strains are due to sequence changes occurring during growth in cell culture [9] . Alternatively then , the possibility that the lack of a glycosylation motif in the isolates prior to the 1970s may be a consequence of growth in cell culture cannot be eliminated , without directly sequencing the original material . Unfortunately , due to the length of time since the original collections , this material is no longer available . RNase-based oligonucleotide fingerprinting over 26 years ago indicated a single topotype for WNVKUN across Australia [7] . The high resolution genome sequencing in this study seems to concur with that early genome analysis . Both the nature of accumulation of fixed substitutions and time-stamped molecular clock analysis strongly suggests a single lineage , and seems to be independent of the site of virus collection . However , due to the sporadic and regional nature of collection , sampling occurred only at one site per year . In order to be conclusive about the observation of a single topotype , multiple samples will have to be obtained at relevant sites around Australia , preferably over multiple years . Complicating this picture , mosquito surveillance suggests circulation of strains different from the main lineage at a low frequency in enzootic regions [11] . Given the probability of a single topotype , it is interesting to speculate on the implications for the virus’s life-cycle and its dissemination . The maintenance of a single long-term lineage requires disseminated strains to eventually die out , to be replaced by another strain which is introduced from a single source . This could be either a location where virus replication occurs in the vertebrate host reservoir or mosquito vector , or alternatively , a local seasonal or climatic restriction which acts as a bottleneck on growth of the virus . In the longer term , these situations would prohibit the circulation of regional strains , and the seeding by a single source . This conclusion from the analysis is in agreement with current hypotheses regarding the epidemiology of WNVKUN . The virus is thought to be enzootic in a bird-mosquito transmission cycle in northern Australia , and is disseminated to the southern states after periods of above average rainfall [30] . Hence , the life-cycle presents opportunities for bottlenecks due to both location and climate , and for dissemination . There is one further implication of a single lineage for WNVKUN in Australia . The 2011 equine outbreak was associated with a more virulent strain of the virus [10] , which led to concerns that this strain may establish and also cause serious human disease in the future . If in the long-term WNVKUN strains die out and are re-seeded from a single source , then more virulent local strains may only circulate for several years before being replaced . WNVKUN is enzootic in Western Australia ( WA ) , and most active in the Pilbara region [31] . Isolates collected in WA are less virulent than those associated with the 2011 outbreak [11] . Hence , it is possible that the trend in southern states of Australia may be toward less virulence given sufficient time to replace regional strains . | West Nile virus is endemic in Australia , and is considered benign in relation to strains that circulate globally . In 2011 , a more pathogenic variant emerged which caused disease in horses . To understand the evolution of the virus , and as a background to the emergence of the pathogenic strain , we used high throughput sequencing combined with bioinformatics tools to obtain an overview of the evolution of the virus over 50 years . A single lineage regardless of the collection site was apparent . This was also supported by the pattern of changes in sequence between the isolates . The most significant finding was that the single lineage nature of the virus’s evolution infers that regional strains circulate for some years before becoming extinct . The regional strains must then be replaced by continual re-seeding , most likely by waterbirds that disseminate the virus across the continent after above average rainfall . There were changes in the nucleotide sequence that had become established at a population level . These were related to the structure of the viral proteins: in particular the envelope protein , the helicase ( NS3 ) and methyltransferase domain of NS5 . There were two changes in catalytic domains which may indicate some fine-tuning of replication . | [
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| 2016 | Archival Isolates Confirm a Single Topotype of West Nile Virus in Australia |
The World Bank Loan Project ( WBLP ) for controlling schistosomiasis in China was implemented during 1992–2001 . Its short-term impact has been assessed from non-spatial perspective , but its long-term impact remains unclear and a spatial evaluation has not previously been conducted . Here we compared the spatial distribution of schistosomiasis risk using national datasets in the lake and marshland regions from 1999–2001 and 2007–2008 to evaluate the long-term impact of WBLP strategy on China's schistosomiasis burden . A hierarchical Poisson regression model was developed in a Bayesian framework with spatially correlated and uncorrelated heterogeneities at the county-level , modeled using a conditional autoregressive prior structure and a spatially unstructured Gaussian distribution , respectively . There were two important findings from this study . The WBLP strategy was found to have a good short-term impact on schistosomiasis control , but its long-term impact was not ideal . It has successfully reduced the morbidity of schistosomiasis to a low level , but can not contribute further to China's schistosomiasis control because of the current low endemic level . A second finding is that the WBLP strategy could not effectively compress the spatial distribution of schistosomiasis risk . To achieve further reductions in schistosomiasis-affected areas , and for sustainable control , focusing on the intermediate host snail should become the next step to interrupt schistosomiasis transmission within the two most affected regions surrounding the Dongting and Poyang Lakes . Furthermore , in the lower reaches of the Yangtze River , the WBLP's morbidity control strategy may need to continue for some time until snails in the upriver provinces have been well controlled . It is difficult to further reduce morbidity due to schistosomiasis using a chemotherapy-based control strategy in the lake and marshland regions of China because of the current low endemic levels of infection . The future control strategy for schistosomiasis should instead focus on a snail-based integrated control strategy to maintain the program achievements and sustainably reduce the burden of schistosomiasis in China .
Schistosomiasis japonica , a disease caused by the trematode Schistosoma japonicum , has a documented history of more than 2100 years in China [1] . It severely impacts the health of residents within endemic areas , causing substantial morbidity such as wasting , weakness , ascites and growth retardation [1] , [2] , [3] . Recognizing the large public health and socio-economic impact of this disease , the government of China initiated a large-scale schistosomiasis control program in the mid-1950s and achievements have been monitored during nearly 60 years of continuous endeavor [2] , [4] , [5] . At present , the schistosomiasis endemic regions have been largely reduced and confined to seven provinces along the Yangtze River: five provinces of Hunan , Hubei , Anhui , Jiangxi and Jiangsu in the lake and marshland regions and two in the mountainous regions , Yunnan and Sichuan provinces [6] , [7] , [8] . Many projects or programs have contributed to this success . Among others , the World Bank Loan Project ( WBLP ) targeted at schistosomiasis control in China has played an important role during the period of 1992–2001 . Zhang&Wong [9] and Chen et al . [10] evaluated the impact of the WBLP strategy before and shortly after the end of the project . These authors concluded that the original objectives of the WBLP strategy– to control schistosomiasis morbidity – had been met , but that snail infested areas had increased to a certain degree and that snail infection fluctuated at low levels . After the termination of the WBLP strategy , there was a consistent gap between available funding and the financial resources required to maintain program achievements and to make further progress [10] . Many researchers have reported that schistosomiasis prevalence has rebounded in some regions , even where the criteria of transmission interruption or control had been previously met [11] , [12] . A national survey carried out in 2004 confirmed the re-emergence of schistosomiasis in China [13] . This led us to question the long-term impact of the WBLP strategy and investigate a more sustainable strategy for schistosomiasis control in China [2] , [14] . There are two potential limitations regarding the previous assessments of the success of the WBLP strategy . One is that previous studies only evaluated the short-term impact of the WBLP strategy [9] , [10] . The WBLP's control strategy focused on the large-scale use of chemotherapy to control morbidity in humans and livestock , an approach which has been frequently questioned regarding its sustainability . For example , the compliance rate of chemotherapy can decrease to a great extent because of fatigue with repeated treatments [6] , [15] . The long-term impact of the WBLP strategy could be different from the short-term situation . The second potential limitation is that earlier assessments were only based on a non-spatial perspective . That is , they used the magnitude of the absolute number of cases to evaluate the overall control effect , but neglected to consider the spatial aspects which can provide some new and even different results . It is well known that a certain number of cases in a region could be due to two completely different scenarios , a clustered risk profile with cases concentrated in only a few areas and a random risk profile with cases occurring nearly randomly throughout the region . Different risk patterns require a distinct control strategy and decision-making process . So evaluating the long-term impact of the WBLP strategy from a spatial perspective would be valuable for future planning of schistosomiasis control . In this study we used the national datasets from the five provinces in the lake and marshland regions from two periods , 1999–2001 and 2007–2008 , with the aim of assessing the long-term impact of the WBLP strategy on schistsomiasis . We compared the changes of the spatial risk distribution of schistosomiasis between the two study periods and contrasted the compositions of the two random effects of spatially correlated and spatially independent heterogeneities .
Our study was carried out in the lake and marshland regions of schistosomiasis in the middle and lower reaches of the Yangtze River , which included the five provinces of Hunan , Hubei , Anhui , Jiangxi and Jiangsu . According to the latest report ( 2009 ) on the schistosomiasis situation , it was estimated that 98 . 7% of the snail-infested areas and 97 . 8% of the S . japonicum infected people in China were concentrated in these five provinces [16] . These provinces included 261 schistosomiasis endemic counties . Of these , 115 reached the criterion of transmission interruption , 57 achieved transmission control and 89 had ongoing transmission [16] , [17] . It is obvious that the lake and marshland regions should be the focus for China's schistosomiasis control program . The WBLP project started in 1992 . It was completed at the end of 1998 in Anhui , Jiangxi , and Jiangsu provinces and continued until the end of 2001 in Hubei and Hunan . The three provinces that completed the project in 1998 continued to carry out schistosomiasis control activities using their own funds and according to the operational plan set out by the WBLP [10] . Thus , these provinces underwent similar stages of the schistosomiasis control strategy . The county-level prevalence data on S . japonicum infection in the lake and marshland regions were obtained from the national annual report on schistosomiasis . These data were first collected through village-based field surveys using a two-pronged diagnostic approach ( screening by a serological test on all residents of 5 to 65 years old and then confirmation by a parasitological test ) , then reported to the towns and finally summed at the county level , with only the county-level totalized databases made available to us . This system of recording and annual reporting had been in place since 1999 [18] . The diagnostic criteria and diagnostic approaches for schistosomiasis cases were as per the national guidelines [19] . Prior to and including during 2004 , these data were maintained and managed by Fudan University ( formerly Shanghai Medical University ) . Beginning 2005 , this task was taken over by the National Institute of Parasitic Diseases in Shanghai ( Formerly the Institute of Parasitic Diseases ) , Chinese Center for Disease Control and Prevention . The national databases for the lake and marshland regions during the two periods of 1999–2001 and 2007–2008 were obtained from the corresponding institutes , respectively . The total number of schistosomiasis cases and population at risk in each county were used to estimate the prevalence of schistosomiasis and to analyze the dynamics of spatial risk distribution and spatial heterogeneities of schistosomiasis-related risk between the two study periods . County-based digitized polygon maps in the lake and marshland regions were obtained for the five study provinces [5] , [6] . Digitized maps of the Yangtze River and Dongting and Poyang Lakes were also obtained . Attribute data on schistosomiasis were linked to the county maps to establish the spatial database and facilitate the visualization of the results . During the 10-year study period , the administrative boundaries of counties changed slightly . To simplify the analysis and for comparability of the results , the administrative divisions in 2008 were used as the standard and data from the other study years were modified accordingly . Two types of topological manipulations were involved , the merging and splitting of polygons , which have only ignorable impacts on this study because the county-level data is much too unspecific to really capture the dynamics of schistosomiasis . The former operation combined two or more polygons into a single , new polygon; and the number of schistosomiasis cases and the population at-risk were then summed to produce the new polygon's disease data . The latter operation divided one polygon into two or more new polygons , in which the number of schistosomiasis cases in each new county was estimated using the proportion of the population of the original county that was present in each of the new counties . All data manipulation was undertaken within ArcGIS9 . 2 software ( Environmental Systems Research Institute , Inc . , Redlands , CA , USA ) . The analysis consisted of four procedures . Firstly , crude prevalence of schistosomiasis was calculated and summarized for those counties with reported cases using conventional descriptive statistics ( e . g . , median and quartiles ) . Secondly , the counties in the lake and marshland regions were classified into five classes according to the dynamics of their prevalence status: 1 . ) non-endemic counties where no cases were reported during the two study periods; 2 . ) unchanged endemic counties where schistosomiasis cases were continuously reported across years; 3 . ) disappeared endemic counties where cases were continuously reported in 1999–2001 , but no cases were reported in 2007–2008; 4 . ) newly appeared endemic counties where cases were continuously reported in 2007–2008 , but no cases were reported in 1999–2001; and 5 . ) fluctuating endemic counties where cases were reported in one or two years of 1999–2001 and one year of 2007–2008 . Maps of these categories of endemicity were created using ArcGIS9 . 2 software . Thirdly , a Bayesian random-effect model-which was first introduced by Clayton and Kaldor in 1987 [20] and developed further by Besag et al . in 1991 [21]- was built to analyze the spatial distribution of schistosomiasis for different time points . In this model for estimating relative risk ( RR ) , area-specific random effects are decomposed into two latent components: one component represents the effects of schistosomiasis-related risk factors that vary in a structured manner in space ( referred to as correlated heterogeneity , CH ) and another component indicates the effects from schistosomiasis-related risk factors that vary in an unstructured way among areas ( referred to as uncorrelated heterogeneity , UH ) . The model is formulated as [22] , ( 1 ) ( 2 ) where is the number of reported schistosomiasis cases in county i; is the expected number of schistosomiasis cases in county i; is the expected or predicted RR in county i; is the overall level of risk assuming the effects of UH and CH are zero; is the correlated heterogeneity ( CH ) which was modeled using the conditional autoregressive ( CAR ) structure and is the uncorrelated heterogeneity ( UH ) that was modeled using a Gaussian distribution , for which the following formulas are used , respectively: ( 3 ) ( 4 ) where is the weight of the neighbor j for area i , if i , j are neighbors , otherwise ; and are precisions and their inverses are the variance of and , respectively . Bayesian methods were used to fit the spatial model , implemented using WinBUGS1 . 4 . 1 software ( Imperial College and MRC , London , UK ) . Parameters and control the variability of CH and UH effects , for which prior distributions were specified using the same vague gamma prior distributions: Gamma ( 0 . 5 , 0 . 0005 ) . For the baseline risk , a vague normal prior distribution: N ( 0 , 0 . 0001 ) was used . Model fitting was carried out using two separate chains starting from different initial values , and 30 , 000 iterations were run: the first 10 , 000 samples were discarded as burn-in and the remaining 20 , 000 iterations from each chain were used for parameter estimation . Convergence was checked by visual examination of the time series plots of samples from each chain and by computing the Gelman and Rubin diagnostic statistic . Finally , the estimated parameters of the model ( overall risk , the variation of the UH and CH components ) were summarized in a table . The predicted RR , the posterior probability of RR>1 , the ratio of UH to CH and the model residuals were exported and linked with the digitized polygon maps using ArcGIS9 . 2 software to display their spatial distributions .
The number of counties reporting schistosomiasis cases and the overall crude prevalence increased slightly from 1999 to 2001 during the later period of the WBLP strategy and then decreased during 2007–2008 , but the prevalence was still higher than that in 1999 . The overall variation ( 95% CI ) in the prevalence showed a tendency of continuous decline except for a slight rebound in 2001 ( Table 1 ) . Figure 1 shows the distribution of the unchanged , disappeared , new appeared and fluctuating endemic counties . The number ( proportion ) of endemic counties for each type were 110 ( 64 . 71% ) , 25 ( 14 . 71% ) , 4 ( 2 . 35% ) , and 31 ( 18 . 24% ) , respectively . The four newly appeared endemic counties in 2007–2008 were all located in Hubei province . Different types of endemic counties were intermingled along the Yangtze River and the Poyang and Dongting Lakes . Exceptions are one fluctuating and one disappeared endemic county , which were isolated and located in Jiangxi province . Besides , the fluctuating and disappearing counties are mainly on the geographical margins of the endemic areas . From Table 2 , we see that the overall risk of schistosomiasis in the counties tended to decrease gradually and that the risk in 2008 was reduced to less than half of the risk in 1999 . The changes in variation of the CH and UH components were different . The former fluctuated across years and reached a peak in 2008 whereas the latter first decreased from 1999 to 2001 , then increased again from 2007 and finally rebounded to a level that was similar to 1999 . Except for the year of 1999 , the variation in the CH component was greater than the variation in the UH component . The magnitude of the relative risk generally decreased during the study period and the counties with posterior expected RR>1 were mainly located in the areas surrounding Poyang and Dongting Lakes . In the southern part of Yangtze River , the areas with RR>1 were relatively stable in space; while in the northern part of Yangtze River , the spatial distribution of predicted risk counties spread northwards around the Dongting Lake , but was compressed in areas near Poyang Lake ( Figure 2 ) . Figure 3 shows that the counties with the highest probability of greater than average risk ( i . e . , RR>1 ) were mostly confined to Poyang and Dongting Lakes and their neighboring counties . For the counties where the posterior probability was high in the lower reaches of the Yangtze River in 1999–2001 , the posterior probability was reduced in 2007–2008 . In the endemic regions around the Poyang and Dongting Lakes , schistosomiasis risk was dominated by the UH effects of schistosomiasis-related risk factors in 1999 , but the CH effects were dominant in the other years . In contrast , in the lower reaches of Yangtze River the primary component of the heterogeneity effects for schistosomiasis risk was relatively unstable ( Figure 4 ) . The model residuals ranged between −1 . 40 and 1 . 65 . No obvious outliers were identified , so the distribution of the residuals after adjusting for the effects of the UH and CH components were not displayed here ( data are available upon request ) .
This study presents an application of Bayesian methods to evaluate the long-term impact of the chemotherapy-based WBLP strategy on the spatial distribution of schistosomiasis japonicum . It is widely reported that the transmission dynamics of schistosomiasis are closely related to socio-economic , climatic , demographic , biological and environmental factors [23] , [24] , [25] . When studying the epidemiology of schistosomiasis and evaluating the impact of control strategies , it is impossible to consider all the potential risk factors related with interested diseases because either the information is unavailable or disease mechanisms are unclear . Hence , previous reports have only included the most important factors or those of specific interest [4] , [5] , [26] , [27] , [28] , [29] , [30] , [31] , which will contribute to bias in effect estimates because of unadjusted effects from risk factors that have been ignored . From the spatial perspective , all the potential risk factors related with studied diseases can be divided into two latent components of random effects , spatially correlated heterogeneity ( CH ) and uncorrelated heterogeneity ( UH ) . This idea has been mathematically demonstrated within the well known Besag , York and Mollié model [21] , which permits us not only to analyze and predict the disease risk more accurately , but also to study and compare the dynamics of the two latent components . For schistosomiasis , CH could encompass the combined effects of all the unmeasured environmental factors ( e . g . , normalized difference vegetation index ( NDVI ) , land surface temperature ( LST ) , rainfall ) , socio-economic factors ( e . g . , income ) , and the biological factors of the intermediate host Oncomelania hupensis ( e . g . , density of snails ) , which are affected by the other two factors . Among others , the snails are the focus for schistosomiasis control , so the transmission control strategies are closely related with CH such as mollusciciding , environmental modification and liming the banks along canals and drainage ditches . Unfortunately , this was not the focus for WBLP's schistosomiasis control strategy . By contrast , UH could encompass spatially unstructured risk factors , which might include demographic factors ( e . g . , age , gender , education level , and occupation ) , human behavioral factors ( e . g . , frequency of contact with infected water , personal protections ) and livestock-related factors . Here , the reservoir hosts ( human and cattle ) are the intervention targets and the morbidity control strategy with chemotherapy-based population treatment emphasizes the observed UH . Hence , the dynamic changes in CH and UH can help signify the control effect of schistosomiasis to a certain degree and direct future control emphasis . From our study , we see that the prevalence of schistosomiasis has been greatly reduced and maintained at a low level . The prevalence during 2007–2008 was reduced further , but still in the same order of magnitude ( 10−3 ) . This suggests that the chemotherapy-based WBLP strategy has had little effect under the low endemic levels of schistosomiasis in China and some new control strategies are needed [32] , [33] . Also , we found that the spatial distribution of schistosomiasis risk during 2007–2008 was only slightly reduced and <10% endemic counties in 1999–2001 were declared to be free of cases in 2007–2008 . The most severely affected areas were located along the Yangtze River , in the areas of the great lakes ( Dongting and Poyang Lakes ) , and their surroundings [1] , [34] , [35] , where the predicted RR>1 and its posterior probability were high . The spatial distribution of current schistosomiasis risk seemed to be stable and was not obviously compressed in space . This was confirmed by the intersecting distributions of fluctuating , newly appeared , disappeared and unchanged endemic counties . Combined with the above results , we may conclude that continuing a chemotherapy-based control strategy following the decade-long WBLP is likely to contribute little to further schistosomiais control under the current situation of low morbidity levels . We also conclude that it is difficult to further restrict the spatial distribution of schistosomiasis endemic regions using current control methods . The rebounding of prevalence during 1999–2001 and the increasing RR in some counties during 2007–2008 , which were partly due to reduced compliance with drug use and the persistence of extensive snail habitats , suggests that the impact of the WBLP strategy was inconsistent . This has been frequently highlighted by many researchers who suggest that snail control should be given more attention for sustainable control of schistosomiasis [2] , [5] , [6] , [14] , [36] . The variation in UH was decreased from 1999 to 2001 and then increased again from 2007 to 2008 , reflecting a good short-term control effect of chemotherapy-based WBLP strategy , but the long-term effect may not be optimal . The variation in CH showed a tendency of increasing and in 2008 it was over 3 times as that of 1999 . This may suggest the risk from the intermediate host snails increased continuously , possibly because snail control was not emphasized within the WBLP strategy . For the predicted high risk regions around the Dongting and Poyang Lakes , the ratios of UH to CH were over 1 only occurred in 1999 , suggesting that CH has become the main component of current schistosomiasis risk in those regions where transmission control strategy focusing on snail control should be implemented . While for the lower reaches of Yangtze River , it is more complicated for the changes of UH to CH . The possible reason is the control effect was not only affected by itself , but also was influenced by the schistosomiasis epidemics in the upriver provinces where the cercaria could be brought to the downriver provinces following the water flows . So morbidity control strategy in the lower reaches of Yangtze River should be maintained until the snails in the upriver provinces have been well controlled . Besides , the frequent flooding of the Yangtze River , water resource development projects ( e . g . , Three Gorges Dam Project and South-to-North water transfer project ) [37] , [38] , climate change/global warming , anti-flood policies ( returning reclaimed land to lake , leveling dykes between main levees and building new towns for resettlement ) , mobility of populations , the frequent trade of livestock and increased tourism and travel to endemic regions are all important drivers for the fluctuation , ( re ) -emergence and spread of schistosomiasis and contribute to the continuing challenge of schistosomiasis control , especially sustainable control . Experiences in China and Japan indicate that controlling the intermediate host snail should result in a more sustainable impact compared to other control approaches [17] , [39] . The primary CH component of current schistosomiasis risk identified in this study also implies that a future control strategy should shift to transmission control strategy with snail control as an emphasis , which would have a sustainable impact on controlling schistosomiasis and be helpful for facilitating the ultimate elimination of schistosomiasis in China . There are two potential shortcomings in our study that warrant discussion . One limitation is that the quality of reported schistosomiasis data from different regions may be inconsistent . Case data were collected through a bottom-up disease reporting system aimed at monitoring the national disease status , so the data reliability was largely based on the quality of data reported by local institutions . Another limitation is that the diagnostic approach for schistosomiasis is not 100% sensitive and specific . The parasitological test ( e . g . , Kato-Katz technique ) has a low sensitivity , while serological tests ( e . g . , indirect hemagglutination assay , IHA ) have low specificity [40] . According to expert opinion , the estimated sensitivity and specificity of the Kato-Katz technique are about 20–70% and 95–100% , respectively and for the IHA technique about 90–95% and 85–90% , respectively [41] . Therefore , some correction methods for the prevalence estimates are needed for a precise analysis . Wang et al . ( 2008 ) reported a simple Bayesian approach to correct the reported prevalence of schistosomiasis by considering the uncertainties of the diagnostic approaches [42] , but they assumed that the diagnostic methods had the same uncertainties for all levels of endemicity . In fact this is not the case and it could introduce other biases to the results . The best possible solution may be to develop a correction method weighted according to prevalence , where the determination of appropriate weights is the most important issue . This is an area of future work . In conclusion , we conducted a comparative study of the spatial epidemiology of schistosomiasis using two datasets from 1999–2001 and 2007–2008 in the lake and marshland regions to evaluate the long-term impact of WBLP strategy on China's schistosomiasis status . The WBLP strategy appears to have had a good short-term impact on schistosomiasis control , but its long-term effect has not been ideal . It has successfully reduced the morbidity of schistosomiasis to a low level , but can not contribute further to current schistosomiasis control considering the low endemic level in China . The WBLP strategy has also failed to reduce the geographical range of affected counties . To achieve this and for sustainable control , transmission control strategies focusing on snails should become the next priority in the two most seriously affected regions surrounding the Dongting and Poyang Lakes . Whereas in the lower reaches of the Yangtze River , WBLP's morbidity control strategy should be continued for some time until the snails in the upriver provinces are well controlled . | Schistosomiasis japonica is an important disease in China with a documented history of more than 2 , 100 years . The World Bank Loan Project ( WBLP ) implemented during 1992–2001 contributed greatly to China's schistosomiasis control . This study shows that the long-term impact of WBLP strategy on schistosomiasis control was not ideal . It can only maintain the morbidity of schistosomiasis at a low level , but can not reduce it further . Also , the WBLP strategy could not effectively compress the spatial distribution of schistosomiasis risk . To achieve further reductions in schistosomiasis-affected areas , and for sustainable control , focusing on controlling the intermediate host snail in the lake and marshland regions was suggested to be the next step to interrupt schistosomiasis transmission within the two most affected regions surrounding the Dongting and Poyang Lakes . While in the lower reaches of the Yangtze River , the WBLP's morbidity control strategy may need to continue for some time until snails in the upriver provinces have been well controlled . | [
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| 2012 | Long-Term Impact of the World Bank Loan Project for Schistosomiasis Control: A Comparison of the Spatial Distribution of Schistosomiasis Risk in China |
Staphylococcal enterotoxins ( SEs ) produced by Staphylococcus aureus are known as causative agents of emetic food poisoning . We previously demonstrated that SEA binds with submucosal mast cells and evokes mast cell degranulation in a small emetic house musk shrew model . Notably , primates have been recognized as the standard model for emetic assays and analysis of SE emetic activity . However , the mechanism involved in SEA-induced vomiting in primates has not yet been elucidated . In the present study , we established common marmosets as an emetic animal model . Common marmosets were administered classical SEs , including SEA , SEB and SEC , and exhibited multiple vomiting responses . However , a non-emetic staphylococcal superantigen , toxic shock syndrome toxin-1 , did not induce emesis in these monkeys . These results indicated that the common marmoset is a useful animal model for assessing the emesis-inducing activity of SEs . Furthermore , histological analysis uncovered that SEA bound with submucosal mast cells and induced mast cell degranulation . Additionally , ex vivo and in vivo pharmacological results showed that SEA-induced histamine release plays a critical role in the vomiting response in common marmosets . The present results suggested that 5-hydroxytryptamine also plays an important role in the transmission of emetic stimulation on the afferent vagus nerve or central nervous system . We conclude that SEA induces histamine release from submucosal mast cells in the gastrointestinal tract and that histamine contributes to the SEA-induced vomiting reflex via the serotonergic nerve and/or other vagus nerve .
Staphylococcal enterotoxins ( SEs ) produced by Staphylococcus aureus ( S . aureus ) have emetic activity and are causative agents of bacterial food poisoning . The primary symptoms of staphylococcal food poisoning include nausea , abdominal cramping and vomiting , which develop up to 1–6 h after ingestion of the causative foods contaminated with S . aureus [1] . In 1930 , Dack et al . showed that staphylococcal food poisoning is not due to S . aureus cells , but caused by intoxication with SEs in the contaminated foods [2] . These toxins are also superantigens , which have the ability to activate a large amount of T cells [3] . These emetic and superantigenic activities make SEs a public health concern . Five major serological types of SEs ( SEA to SEE ) , so-called “classical SEs” , have been characterized [3] . Notably , new types of SEs and SE-like toxins ( SEG to SElV , SElX and SElY ) have also been reported [3–10] . Although the mechanism of superantigenic activity and the amino acid residues in the active site of SEs have been clarified , the exact molecular and cellular mechanisms of their emetic activity still remain unclear . We have previously elucidated the mechanism of SEA-induced emesis using a small emetic animal model , house musk shrew ( Suncus murinus ) model . This study has revealed shown that SEA binds with submucosal mast cells and evokes mast cell degranulation [11] . We also have demonstrated that 5-hydroxytryptamine ( 5-HT ) is a key molecule in SEA-induced emesis in house musk shrews [12] . Notably , the primates have been recognized as the standard model for detecting the emetic activity of SEs [13 , 14] . Therefore , it is necessary to conduct experiments in a primate model . However , the high cost and limited availability of primates have led to a reduction in the investigation of SE-induced emesis using this model . The common marmoset is a New World monkey that is small in size ( average height of approximately 20 cm ) . This monkey can give birth twice a year . Hence , the handling and breeding of this animal are easier than those of other primates . Furthermore , its complete genome sequence and the transgenic marmosets have been reported [15] . This information makes this animal attractive for investigation of SE-induced emesis . In the present study , we established a new emetic animal model using the common marmoset and analyzed the emetic activity of SEA . In addition , we used histological and pharmacological techniques for common marmosets and their gastrointestinal ( GI ) tracts to clarify the mechanism of emesis induced by SEs .
In order to investigate the suitability of common marmosets for the emetic assay , monkeys were administered representative SEs SEA , SEB , SEC and SEI and the emetic response was observed for 5 h ( Table 1 ) . Five out of 8 common marmosets received 50-μg/kg of SEA and all 11 monkeys that had ingested 250-μg/kg of SEA exhibited vomiting responses from 45 to 202 min after administration . The vomiting responses occurred 4 to 9 times after 50 μg/kg of SEA treatment and 2 to 28 times after 250 μg/kg of SEA treatment . The emesis-inducing activity of SEB was comparable to that of SEA . Furthermore , ingestion of SEC induced emesis in 5 of 8 common marmosets . SEI and a new staphylococcal enterotoxin-like toxin named SEIY [10] exhibited an emetic response in common marmosets , although the frequency of the emetic response was lower than that of other SEs . By contrast , toxic shock syndrome toxin-1 ( TSST-1 ) , which is a non-emetic staphylococcal superantigen , showed no emetic response . The results indicated that common marmosets respond to the emetic activity of SEs . SEs are also superantigens and have the ability to activate a large amount of T cells [3] . To clarify whether common marmosets respond to the superantigenic activity of SEs , the proliferation of common marmoset PBMCs stimulated with SEs , including SEA , SEB , SEC , SEI , SElY and TSST-1 , was measured . The representative results from three experiments are shown in Fig 1 . Over 1 pg/ml of SEB and SEI was able to induce PBMC proliferation . However , the minimum concentration of SEA and SEC required to induce PBMC proliferation was 100 pg/ml . To achieve substantial lymphocyte proliferation , these SEs required concentrations that were two orders of magnitude higher compared with SEB and SEI . The mitogenic activity of SElY and TSST-1 was low in comparison with that of other SEs . In the cases of SElY and TSST-1 , the minimum concentration required to induce PBMC proliferation was 10 and 100 ng/ml , respectively . These results suggested that PBMCs of common marmosets responded to the superantigenic activity of SEs . We further explored the target cells of SEA in the GI tract of common marmosets . The GI tract was collected and each segment , including the stomach , duodenum , jejunum , ileum , cecum and colon , was incubated with 1 μg/ml of SEA for 60 min at room temperature . SEA-treated tissues were then processed for SEA immunostaining using anti-SEA polyclonal antibody . SEA-immunopositive cells were present in the submucosa of the jejunum and ileum ( Fig 2 ) . Only a few SEA-immunopositive cells were detected in stomach and colon . No SEA signal was detected in the mucosa or submucosa of the duodenum and cecum . Spindle-shaped morphology of SEA-positive cells was indicated in the submucosa of the jejunum and ileum . Notably , binding of SEA to enterochromaffin cells was not observed in the luminal epithelium of the GI tract . To confirm anti-SEA antibody signal specificity , SEA-treated GI tract sections were stained with non-immunized rabbit IgG primary antibody . Our previous study , which used house musk shrews , demonstrated that the target of SEA is submucosal mast cells [11] . In the present study , SEA-binding cells in the GI tract of common marmosets morphologically resembled connective tissue-type mast cells ( Fig 2 ) . Therefore , we investigated whether SEA binds with mast cells in the submucosal jejunum and ileum of common marmosets . Sections of GI tract were incubated with 1 μg/ml of SEA for 60 min at room temperature . The sections of SEA-treated jejunum and ileum were processed for double- immunofluorescence staining using anti-SEA antibody and antibody against FcεRIα , an IgE receptor that is known as a mast cell marker . As expected , SEA and FcεRIα signals were co-localized in the submucosal sections of the ileum ( Fig 3 ) . A similar result was observed in the jejunum sections ( Fig 3 ) , suggesting that SEA binds with mast cells . To further confirm whether the detected cells were mast cells , the ileal sections were stained with anti-tryptase monoclonal antibody ( mAb ) , which recognizes tryptase on the mast cells . Double staining with antibodies against SEA and tryptase revealed that almost all of the SEA-binding cells were tryptase-immunopositive ( Fig 4A ) . Additionally , SEA-immunopositive cells in the stomach and colon were also identified as mast cells ( Fig 3 ) . These results indicated that the target cells of SEA in the GI tract of common marmosets are most likely submucosal mast cells . In addition , SEA-treated ileal sections were stained with anti-5-HT mAb and anti-histamine mAb . As shown in Fig 4B and 4C , submucosal mast cells exhibited positive histamine signals , but not 5-HT . Intact and degranulated mast cells are distinguishable by their metachromatic staining , particularly with toluidine blue [16 , 17] . Thus , we further investigated whether the interaction between SEA and mast cells could induce degranulation by using toluidine blue staining . Specimens of the intestinal tract were removed from common marmosets at 2 h after injection with 500 μg of SEA in the intestinal loop . The results of toluidine blue staining showed that metachromatic mast cells , which are virtually identical to mast cells , were present in both the mucosa and submucosa in the GI tract . To estimate the extent of degranulation in the submucosal mast cells quantitatively , we compared the number of metachromatic-staining cells in toluidine blue-stained jejunum sections between SEA and phosphate-buffered saline ( PBS ) injection ( Fig 5A ) . The results revealed that the number of metachromatic cells in the SEA-injected loop significantly was decreased in the jejunum submucosa compared with PBS-injected specimens ( Fig 5A and 5B ) . These results suggested that mast cell degranulation occurs after SEA administration . We investigated whether chemical mediators , such as histamine , are released from SEA-induced mast cells . The sections of common marmoset jejunum were incubated with 0 , 4 , 20 or 100 μg/ml of SEA for 2 h at 37°C in a CO2 incubator . Following this , histamine and 5-HT release in the culture supernatant fluid was measured . Notably , SEA induced the release of histamine in the jejunal sections incubated with 20 and 100 μg/ml of SEA ( Fig 6A ) . In order to confirm whether the observed SEA-induced histamine release caused by mast cell degranulation , the jejunum sections were incubated with mast cell stabilizer disodium cromoglycate ( DSCG ) during SEA-stimulation . DSCG suppressed SEA-induced histamine release in a dose-dependent manner ( Fig 6B ) . However , 5-HT release from the jejunum was not affected by SEA stimulation or the addition of DSCG ( Fig 6C and 6D ) , suggesting that 5-HT release from the jejunum is independent of SEA and mast cells . Taken together , these results strongly suggested that SEA induces degranulation and histamine release from the submucosal mast cells . To clarify whether SEA-induced submucosal mast cell degranulation is associated with emesis , the common marmosets were administered 250 μg/kg of SEA after DSCG injection . Four out of 6 common marmosets in the DSCG-treated group exhibited no vomiting response , and the number of emetic episodes was significantly decreased during observation in comparison with the group without DSCG pre-treatment ( Fig 7A and 7B ) . Next , the effect of histamine H1 blockers , diphenhydramine ( DPH ) and cetirizine , on SEA-induced emesis was investigated . The vomiting response was suppressed in 5 out of 6 common marmosets in DPH-treated and cetirizine-treated groups , and the number of emetic episodes was significantly decreased in comparison with the control group receiving SEA alone ( Fig 7A and 7B ) . These results indicated that SEA-induced degranulation and histamine release evokes emesis . To clarify whether 5-HT is involved in SE-induced emesis , the common marmosets were injected with granisetron ( 40 to 1000 μg/kg ) , a 5-HT3 receptor antagonist , and then administered 250 μg/kg of SEA . All marmosets exhibited vomiting responses following treatment with 40-μg/kg granisetron , whereas treatment with 200-μg/kg and 1000-μg/kg granisetron inhibited the vomiting response in a dose-dependent manner ( Fig 7A and 7B ) . To confirm the effect of 5-HT on SEA-induced emesis , the monkeys were injected with p-chlorophenylalanine ( PCPA ) , a 5-HT synthesis inhibitor , and then administered 250 μg/kg of SEA . Five of 6 marmosets in the PCPA-treated group exhibited no vomiting response and the number of emetic episodes was significantly decreased in comparison with the control group receiving SEA alone ( Fig 7A and 7B ) . Furthermore , we investigated whether SEA-induced emesis is associated with serotonergic afferent nerve . The common marmosets were treated with serotonergic neurotoxin , 5 , 7-dihydroxytryptamine ( 5 , 7-DHT ) , and then administered 250 μg/kg of SEA . None of 6 marmosets in the 5 , 7-DHT-treated group exhibited an emetic response ( Fig 7A and 7B ) . These results suggested that 5-HT is also involved in SEA-induced emesis and that 5-HT acts on the vagus nerve and/or central nervous system . To clarify whether stimulation of vomiting by SEA is transmitted via the vagus nerve to the chemoreceptor trigger zone ( CTZ ) or vomit center , the common marmosets were vagotomized . To confirm whether the vagus nerves were substantially denervated , the monkeys were administered copper sulfate , a control substance that evokes vomiting via vagus nerves . Four out of 6 common marmosets in the copper sulfate-treated group and SEA-treated group exhibited no vomiting response , and the number of emetic episodes was significantly decreased in comparison with the non-vagotomized group ( Fig 8A and 8B ) . Moreover , the difference of susceptibility to SEA-induced emesis between peroral and intravenous SEA administration was investigated to reveal whether SEA is able to act on CTZ directly . All 6 common marmosets exhibited vomiting responses between 45 and 134 min after peroral challenge with 250 μg/kg of SEA; however , 4 out of the 6 monkeys intravenously injected with the same dose of SEA exhibited no vomiting response and the number of emetic episodes was significantly decreased in comparison with the group challenged by peroral administration ( Table 1 ) . In addition , latency periods of emesis in the intravenously injected group were 187 min and 188 min , respectively . These latency periods were prolonged compared with those of peroral administration ( Table 1 ) . Taken together , these results suggested that SEA did not act on the CTZ directly . However , the findings did suggest that SEA-induced emesis was transmitted from GI tract to vomit center via the vagus nerve .
The mechanism of SEA-induced vomiting has not been fully elucidated in primates [2] . To investigate this mechanism , we established the common marmoset as an emetic animal model . In the present study , we showed that vomiting was induced in the common marmosets by orogastric intubation of SEs , including classical SEs and recently identified SEs ( Table 1 ) . However , none of common marmosets administered with TSST-1 or PBS exhibited an emetic reaction ( Table 1 ) . Notably , PBMCs of common marmosets were susceptible to the superantigenic activity of SEs ( Fig 1 ) . These results are consistent with responses of human and other emetic animal models [6 , 11–13 , 18–21] . Although TSST-1 has the highest toxicity to humans among the superantigens , the present study showed that the superantigenic activity of TSST-1 against common marmosets was markedly lower than that against humans . The results suggest the possibility that major histocompatibility complex and/or T cell receptors of common marmosets have low binding affinity to TSST-1 . Furthermore , our results indicated that SEI displayed weak emetic activity but was strongly superantigenic . These SEI properties provide information that there is no complete relationship between emetic activity and superantigenic activity . This observation correlates with the data from Harris et al . and Schlievert et al . which have shown the separation of emesis from superantigenic activity [22 , 23] . Our previous report indicated that 5 out of 10 cynomolgus monkeys administered 10 μg/kg of SEA , and 6 out of 7 cynomolgus monkeys that had ingested 100 μg/kg of SEA exhibited vomiting responses [21] . We also reported that house musk shrews could be used as a small emetic animal model and that shrews were highly sensitive to SEA in the initial report ( 32 μg/kg , orogastric administration ) [19] . However , our recent study showed that house musk shrews required a larger amount of SEA ( 1 . 4–2 . 0 mg/kg of body weight ) to promote emesis , indicating a decreased susceptibility of shrews to SEA [24] . Therefore , it was assumed that the marmosets used in the present study showed almost the same sensitivity to SEA as cynomolgus monkeys . As the body weight of common marmosets was markedly low ( approximately 0 . 3 kg ) , a lower amount of toxins was required compared with cynomolgus monkeys ( 1 . 4–2 . 0 kg ) . Furthermore , the complete genome sequences and transgenic marmosets have been reported [25 , 26] . Hence , the common marmoset may be a useful animal model for detection and analysis of SEs . In 2004 , the International Nomenclature Committee for Staphylococcal Superantigens ( INCSS ) recommended that only toxins that could induce vomiting after oral administration in primates are termed SEs [14] . Our results indicate that SElY has emetic activity in primates ( Table 1 ) . Therefore , in the present study it was proposed that SElY should be renamed SEY , according to INCSS recommendations . Our previous report showed that SEA binds to submucosal mast cells in the GI tract in house musk shrews and can induce submucosal mast cell degranulation , as well as 5-HT release [11] . To clarify the mechanism of SEA-induced emesis in common marmosets , we used histological and pharmacological techniques in the present study . SEA was indicated to bind with submucosal cells in the GI tract , specifically in the stomach , jejunum , ileum and colon of common marmosets ( Fig 2 ) . We identified and characterized cells as submucosal mast cells according to the positive signals of IgE receptor , tryptase and histamine ( Figs 3 and 4 ) . As indicated in Fig 5 , SEA induced submucosal mast cell degranulation in the jejunum 2 h after SEA injection . Interestingly , SEA induced histamine release but not 5-HT release in the ex vivo experiments , and mast cell stabilizer reduced this histamine release ( Fig 6 ) . Furthermore , mast cell stabilizer and histamine H1 blockers reduced SEA-induced emesis induced in common marmosets ( Fig 7 ) . In brief , the degranulation of submucosal mast cells was promoted by SEA , and the inhibition of submucosal mast cell degranulation prevented SEA-induced emesis . These results suggested that submucosal mast cell degranulation is important in SEA-induced emetic responses . In this study , we demonstrated for the first time that histamine release has a pivotal role in the emetic response in the GI tract ( Figs 6 and 7 ) . Conventionally , 5-HT from enterochromaffin cells in the GI mucosa has been considered to be an important mediator for anticancer drugs , chemical substances and vomiting due to food poisoning [27 , 28] . Histamine is a key molecule for transmitting stimuli from the inner ear to the brain during vomiting due to motion sickness and also plays a role in the GI tract with regard to food allergies and histamine fish poisoning [29–31] . However , it has not been considered to be involved in vomiting associated with bacterial food poisoning [29 , 32] . Mast cells in rodents are classified into mucosal mast cells ( MMCs ) and connective tissue mast cells [33 , 34] . Human mast cells are divided into two major groups: MCT ( containing tryptase ) and MCTC ( containing tryptase and chymase ) [35 , 36] . In the human GI tract , MMCs are MCT , whereas submucosal mast cells mostly exhibit MCTC properties [36] . MMCs have been known to be important cells in allergy development [37] . However , a few reports demonstrated that submucosal mast cells of the GI tract are involved in diseases , and their function is still unknown in various respects [32 , 34] . The present study indicated that submucosal mast cells were involved in the emetic response and that SEA specifically affects submucosal mast cells in the GI tract . This study provides a novel insight into the functions of submucosal mast cells in the GI tract . In summary , the results of the present study have shown that the common marmoset is a useful animal for emetic assays and that submucosal mast cells and histamine play critical roles in SEA-induced emesis in common marmosets . Furthermore , 5-HT plays an important role in the transmission of emetic stimulation on the afferent vagus nerve or central nervous system [38] . Taken together , our results suggest that the binding of SEA with mast cells induced histamine release , which acts against the serotonergic nerve and/or other vagus nerve . Furthermore , it was indicated that stimulation could be transmitted to the vomiting center , causing a vomiting reflex . These findings provide a novel function for mast cells and the vomiting pathway , which are considered to be essential in cell biology and neuroscience .
This study was conducted in accordance with the Declaration of Helsinki . All animal experiments were approved by the Animal Research Ethics Committee of Hirosaki University Graduate School of Medicine ( permit number M10037 ) , and followed the Guidelines for Animal Experimentation , Hirosaki University . The Hirosaki University guidelines are in accordance with the guidelines for proper conduct of animal experiments determined by Ministry of the Environment Standard relating to the Care and Keeping and Reducing Pain of Laboratory Animals ( Notice of the Ministry of the Environment No . 88 of 2006 ) and American Veterinary Medical Association ( AVMA ) guidelines for the euthanasia of animals: 2013 edition . Common marmosets ( Callithrix jacchus ) were purchased from CLEA Japan , Inc . ( Tokyo , Japan ) . Common marmosets were housed at 26–30°C in a room lit for 12 h ( from 8:00 a . m . to 8:00 p . m ) . The monkeys had ad libitum access to tap water and food pellets ( CMS-1M , CLEA Japan , Inc . ) . One or two monkeys were housed per cage ( 420x620x670 mm ) . Daily care was provided by the same staff , and the veterinary staff took care of the monkeys in case of any health problems . Eleven sexually mature monkeys ( aged 2–4 years old and weighing 280–360 g ) were used in each experiment . To reduce the number of common marmosets used in this study , each monkey was repeatedly administered different types of toxins . The monkeys were then used for experiments with inhibitors . For tissue sample preparation , common marmosets were euthanized by exsanguination under fully anesthetization using a mixture of medetomizine , midazolam and butorphanol and/or inhaled isoflurane . Cloning and preparation of recombinant SEA , SEB , SEC , SEI , SElY and TSST-1 were performed as described previously [10 , 20 , 21] . Briefly , the toxin genes from S . aureus isolates were amplified by PCR and the PCR products were digested with BamHI and EcoRI or SalI . The fragment of the genes was then cloned into pGEX6P-1 , a glutathione S-transferase ( GST ) fusion expression vector . Expression and purification of the GST-fused recombinant proteins and the cleavage and removal of the GST tag from recombinant SE proteins were performed by the methods described previously [10 , 21] . Recombinant toxins were treated with a ProteoSpin Endotoxin Removal mini kit ( Norgen Biotek Corp . , Thorold , ON , Canada ) . The emetic activity of SEs in common marmosets was estimated using Bergdoll’s monkey feeding assay with some modifications [13] . At 16 h after food deprivation , common marmosets were anesthetized by an intramuscular injection with a mixture of medetomizine ( 80 μg/kg; ZENOAQ , Fukushima , Japan ) and midazolam ( 400 μg/kg; SANDOZ , Tokyo , Japan ) . SEA , SEB , SEC , SEI , SElY or TSST-1 was dissolved in 1 . 5 ml of sterile PBS and fed to common marmosets at a dose of 50 or 250 μg/kg by orogastric intubation . Alternatively , SEA was dissolved in 150 μl of sterile PBS and administered intravenously at a dose of 250 μg/kg into the femoral vein . Following this , the monkeys were intramuscularly administered atipamezole ( 320 μg/kg , ZENOAQ ) for rapid recovery . The emetic responses were recorded using a video camera for 5 h after oral administration of each toxin . The number of emetic responses , the latency period of the first emetic response and behavioral changes were evaluated . In order to avoid the undesirable influences by repeated administration of the same SE , we did not use the same SE in an individual marmoset with the exception of SEA when the responses to various doses were compared . Mitogenic activity of SEs was determined using PBMCs of common marmosets . Blood from 3 healthy monkeys was processed using Ficoll-Paque PLUS ( GE Healthcare Japan , Tokyo , Japan ) density-gradient centrifugation . PBMCs were resuspended in RPMI-1640 medium ( Nissui Pharmaceutical Co . Ltd . , Tokyo , Japan ) containing 10% fetal bovine serum ( FBS , Nichirei Bioscience , Tokyo , Japan ) and penicillin/streptomycin . The PBMCs were then incubated for 72 h in 96-well round-bottomed tissue culture plates ( AGC Techno Glass Co . , Shizuoka , Japan ) with different concentrations of SEA , SEB , SEC , SEI , SEY or TSST-1 and then assayed for the uptake of [3H]-thymidine ( PerkinElmer Japan Co . , Ltd . , Kanagawa , Japan ) . Data ( in counts per min ) were presented as the mean μ standard deviation ( SD ) of triplicate determinations , as previously described [39] . Common marmosets were euthanized by exsanguination under deep anesthesia with a mixture of medetomizine ( 120 μg/kg ) , midazolam ( 600 μg/kg ) and butorphanol ( 600 μg/kg; Meiji Seika Pharma Co . , Ltd . , Tokyo , Japan ) . Specimens of the GI tract were removed , washed with ice-cold PBS and fixed overnight with Mildform 10NM ( Wako Pure Chemical Industries , Ltd . , Osaka , Japan ) . Subsequently , tissue samples were soaked in 30% sucrose in PBS overnight at 40°C and frozen in Optimal Cutting Temperature medium ( Sakura Finetek , Tokyo , Japan ) at -80°C . Cryostat sections ( 10 μm ) were processed for immunofluorescence staining with single or double labeling . The sections were washed in PBS and incubated with 2% normal goat serum ( Jackson ImmunoResearch , West Grove , PA , USA ) in PBS/0 . 05% Tween 20 ( PBST ) for 30 min at room temperature . Afterwards , the sections were rinsed in PBST and incubated with SEA ( 1 . 0 μg/ml ) for 60 min at room temperature . After washing the sections with PBST , rabbit polyclonal anti-SEA antibody was used to detect SEA in the tissue . This rabbit polyclonal anti-SEA antibody ( 0 . 2 μg/ml ) was obtained from sera of SEA-immunized rabbits and purified by affinity chromatography using a HiTrap kit ( GE Healthcare Japan ) , as previously reported [11] . Rat anti-5-HT mAb ( 1:200; EMD Millipore , Billerica , MA , USA ) , rabbit polyclonal anti-histamine antibody ( 1:200; EMD Millipore ) , mouse anti-tryptase mAb ( 1:1000; Agilent Technologies , Inc . , Santa Clara , CA , USA ) and mouse anti-FcεRIα mAb ( clone CRA1 1:1000; BioAcademia , Osaka , Japan ) were also used as primary antibodies . Incubation of the sections with primary antibodies was carried out at 40°C overnight . Then , the sections were rinsed in PBST and incubated with the following secondary antibodies: Alexa 488-conjugated donkey anti-rabbit IgG , Alexa 568-conjugated donkey anti-rat IgG and/or Alexa 568-conjugated donkey anti-mouse IgG ( 1:1000; Life Technologies Japan Ltd . , Tokyo , Japan ) for 60 min at room temperature . All of these antibodies were diluted in Can Get Signal Immunostain Solution A ( Toyobo Life Science , Osaka , Japan ) . After rinsing with PBST , the sections were coverslipped with Prolong Gold antifade reagent ( Life Technologies Japan ) and examined using a fluorescence microscope ( BZ-X700; Keyence , Osaka , Japan ) . Common marmosets were anesthetized by an intramuscular injection with a mixture of medetomizine ( 80 μg/kg ) and midazolam ( 400 μg/kg ) at 16 h after food deprivation . Anesthesia during surgical treatment was maintained using 1 . 0% isoflurane , an inhalational anesthetic . The small intestine was ligated in separate loops of 4 to 5 cm in length [40] . Intestinal loops were injected 0 . 5 ml PBS ( with or without 500 μg of SEA ) using a 27-gauge needle . Specimens of the intestinal loop were removed from common marmosets at 2 h after SEA injection . The specimens were fixed for 2 h with Methanol Carnoy’s solution ( 60% methanol , 30% chloroform and 10% glacial acetic acid ) [41 , 42] . Following this , tissue samples were paraffin-embedded according to the standard procedure . Paraffin-embedded tissue samples were cut in 4- μm-thick sections . Sections were deparaffinized and stained with 0 . 1% toluidine blue solution . The numbers of submucosal metachromatic cells in the intestinal tract were presented as the mean ± SD of n observations per square millimeter . The statistical analysis was performed using the Mann-Whitney U test . For ex vivo culture of the intestine , specimens of the jejunal tract were removed from common marmosets and transferred in RPMI-1640 medium supplemented with 10% FBS and penicillin/streptomycin . The jejunal pieces were incubated with SEA at different concentrations or with 100 μg SEA and different concentrations of DSCG for 2 h at 37°C in 5% CO2 incubator . Histamine and 5-HT in the culture supernatant fluid were measured using a Histamine ELISA kit ( Enzo Life Sciences , Inc . , Farmingdale , NY , USA ) and a Serotonin ELISA kit ( DRG International Inc . , Springfield , NJ , USA ) , respectively . Statistical analysis was performed using ANOVA followed by Tukey’s post hoc test Common marmosets received an intraperitoneal injection of the mast cell stabilizer drug , DSCG ( 200 mg/kg of weight; Wako Chemicals GmbH , Neuss , Germany ) , 1st generation histamine H1 receptor antagonist DPH ( 10 mg/kg of weight; Sigma-Aldrich; Merck KGaA , Darmstadt , Germany ) , 2nd generation histamine H1 receptor antagonist cetirizine ( 400 μg/kg of weight; Sigma-Aldrich; Merck KGaA ) or 5-HT3 receptor antagonist granisetron ( Sigma-Aldrich; Merck KGaA ) , diluted with saline . A total of 30 min later , the animals received SEA ( 250 μg/kg ) diluted with PBS by orogastric intubation . Following this , the emetic response in these monkeys was observed for 5 h . The number of vomiting marmosets , the number of emetic episodes ( frequency of vomiting ) and any behavioral changes during the 5-h observation period were monitored using a video camera recorder . Six marmosets were used in each experiment . The statistical analysis was performed using Fisher’s exact test and Dunnett’s test . The 5-HT synthesis inhibitor PCPA ( Sigma-Aldrich; Merck KGaA ) was suspended in 4% gum arabic ( Wako Chemicals GmbH ) solution . Common marmosets were intraperitoneally injected with PCPA ( 3500 mg/kg of weight ) over 2 consecutive days and administrated SEA ( 250 μg/kg of weight ) by orogastric intubation 30 min after PCPA injection . Then , the emetic response in these monkeys was observed as described above . Common marmosets were injected intraperitoneally with 5 , 7-DHT ( 5 mg/kg of weight ) to deplete 5-HT in peripheral neurons . To inhibit the depletion of norepinephrine , these monkeys were pretreated with desipramine ( 25 mg/kg of weight ) via an intraperitoneal injection for 60 min . Animals received SEA ( 250 μg/kg ) diluted with PBS via orogastric intubation . Then , the emetic response in these monkeys was observed as described above . Common marmosets were anesthetized using an intramuscular injection of medetomizine ( 60 μg/kg of weight ) , midazolam ( 300 μg/kg of weight ) and butorphanol ( 300 μg/kg of weight ) . Surgical denervation was performed by cutting the vagus nerve at the GI level . To confirm whether denervation was successful , the animals were orally administered copper sulfate ( 40 mg/kg of weight ) and the reduction of the emetic response in these monkeys was observed . At the same time , the vagotomized common marmosets were administered 250 μg/kg of SEA by orogastric intubation . The emetic response in these monkeys was observed as described above . Statistical tests undertaken for individual experiments are mentioned in the figure legends . P<0 . 05 was considered to indicate a statistically significant difference . | Staphylococcal enterotoxin A ( SEA ) is a bacterial toxin that has been recognized as a leading causative agent of staphylococcal food poisoning since 1930 . The primary symptoms of staphylococcal food poisoning are nausea and emesis , which develop up to 1–6 h after ingestion of the causative foods contaminated by the bacteria . In the present study , we established the common marmoset as an emetic animal model and investigated the mechanisms of SEA-induced emesis in the primate model . Common marmosets that received SEA showed multiple emetic responses . We observed that SEA bound with submucosal mast cells in the intestinal tract and induced mast cell degranulation . Furthermore , SEA promoted histamine release from mast cells . We also demonstrated that histamine plays an important role in the SEA-induced emetic response in common marmosets . We conclude that SEA induces histamine release from submucosal mast cells in the intestinal tract and that the stimulation is transmitted from intestine to the brain via nerves , causing emesis . Our study provides a novel insight into functions of submucosal mast cells in the digestive tract . | [
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| 2019 | Histamine release from intestinal mast cells induced by staphylococcal enterotoxin A (SEA) evokes vomiting reflex in common marmoset |
Endoreplication is a cell cycle variant that entails cell growth and periodic genome duplication without cell division , and results in large , polyploid cells . Cells switch from mitotic cycles to endoreplication cycles during development , and also in response to conditional stimuli during wound healing , regeneration , aging , and cancer . In this study , we use integrated approaches in Drosophila to determine how mitotic cycles are remodeled into endoreplication cycles , and how similar this remodeling is between induced and developmental endoreplicating cells ( iECs and devECs ) . Our evidence suggests that Cyclin A / CDK directly activates the Myb-MuvB ( MMB ) complex to induce transcription of a battery of genes required for mitosis , and that repression of CDK activity dampens this MMB mitotic transcriptome to promote endoreplication in both iECs and devECs . iECs and devECs differed , however , in that devECs had reduced expression of E2F1-dependent genes that function in S phase , whereas repression of the MMB transcriptome in iECs was sufficient to induce endoreplication without a reduction in S phase gene expression . Among the MMB regulated genes , knockdown of AurB protein and other subunits of the chromosomal passenger complex ( CPC ) induced endoreplication , as did knockdown of CPC-regulated cytokinetic , but not kinetochore , proteins . Together , our results indicate that the status of a CycA—Myb-MuvB—AurB network determines the decision to commit to mitosis or switch to endoreplication in both iECs and devECs , and suggest that regulation of different steps of this network may explain the known diversity of polyploid cycle types in development and disease .
Endoreplication is a common cell cycle variant that entails periodic genome duplication without cell division and results in large polyploid cells [1] . Two variations on endoreplication are the endocycle , a repeated G/S cycle that completely skips mitosis , and endomitosis , wherein cells enter but do not complete mitosis and / or cytokinesis before duplicating their genome again [2] . In a wide array of organisms , specific cell types switch from mitotic cycles to endoreplication cycles as part of normal tissue growth during development [1 , 3] . Cells also can switch to endoreplication in response to conditional inputs , for example during wound healing , tissue regeneration , aging , and cancer [1 , 4–6] . It is still not fully understood , however , how the cell cycle is remodeled when cells switch from mitotic cycles to endoreplication . There are common themes across plants and animals for how cells switch to endoreplication during development . One common theme is that developmental signaling pathways induce endoreplication by inhibiting the mitotic cyclin dependent kinase 1 ( CDK1 ) . After CDK1 activity is repressed , repeated G / S cell cycle phases are controlled by alternating activity of the ubiquitin ligase APC/CCDH1 and Cyclin E / CDK2 [1] . Work in Drosophila has defined mechanisms by which APC/CCDH1 and CycE / Cdk2 regulate G / S progression , and ensure that the genome is duplicated only once per cycle [7–12] . Despite these conserved themes , how endoreplication is regulated can vary among organisms , as well as tissues within an organism . These variations include the identity of the signaling pathways that induce endoreplication , the mechanism of CDK1 inhibition , and the downstream effects on cell cycle remodeling into either an endomitotic cycle ( partial mitosis ) or endocycle ( skip mitosis ) [1 , 7] . In many cases , however , the identity of the developmental signals and the molecular mechanisms of cell cycle remodeling are not known . To gain insight into the regulation of variant polyploid cell cycles , we had previously used two-color microarrays to compare the transcriptomes of endocycling and mitotic cycling cells in Drosophila tissues [13] . We found that endocycling cells of larval fat body and salivary gland have dampened expression of genes that are normally induced by E2F1 , a surprising result for these highly polyploid cells given that many of these genes are required for DNA synthesis . Nonetheless , subsequent studies showed that the expression of the E2F-regulated mouse orthologs of these genes is reduced in endoreplicating cells of mouse liver , megakaryocytes , and trophoblast giant cells [10 , 14 , 15] . Drosophila endocycling cells also had dampened expression of genes regulated by the Myb transcription factor , the ortholog of the human B-Myb oncogene ( MYBL2 ) [13 , 16] . Myb is part of a larger complex called Myb-MuvB ( MMB ) , whose subunit composition and functions are mostly conserved from flies to humans [17–21] . One conserved function of the MMB is the induction of periodic transcription of genes that are required for mitosis and cytokinesis [20 , 22–26] . It was these mitotic and cytokinetic genes whose expression was dampened in Drosophila endocycles , suggesting that this repressed MMB transcriptome may promote the switch to endocycles that skip mitosis . It is not known , however , how E2F1 and Myb activity are repressed during endocycles , nor which of the downregulated genes are key for the remodeling of mitotic cycles into endocycles . In addition to endoreplication during development , there are a growing number of examples of cells switching to endoreplication cycles in response to conditional stresses and environmental inputs [1 , 5 , 6] . We will call these induced endoreplicating cells ( iECs ) to distinguish them from developmental endoreplicating cells ( devECs ) . For example , iECs contribute to tissue regeneration after injury in flies , mice , humans , and the zebrafish heart , and evidence suggests that a transient switch to endoreplication contributes to genome instability in cancer [4 , 6 , 27–33] . Cardiovascular hypertension stress also promotes an endoreplication that increases the size and ploidy of heart muscle cells , and this hypertrophy contributes to cardiac disease [29 , 34 , 35] . It remains little understood how similar the cell cycles of iECs are to devECs . Similar to the developmental repression of CDK1 activity to promote endocycles , we and others had previously shown that experimental inhibition of CDK1 activity is sufficient to induce endoreplication in flies , mouse , and human cells [28 , 36–41] . These experimental iECs in Drosophila are similar to devECs in that they skip mitosis , have oscillating CycE / Cdk2 activity , periodically duplicate their genome during G / S cycles , and repress the apoptotic response to genotoxic stress [13 , 36 , 42 , 43] . In this study , we use these experimental iECs to determine how the cell cycle is remodeled when cells switch from mitotic cycles to endoreplication cycles , and how similar this remodeling is between iECs and devECs . Our findings indicate that the status of a CycA—Myb—AurB network determines the choice between mitotic cycles and endoreplication cycles in both iECs and devECs .
We sought to understand how remodeling of the cell cycle program determines the switch from mitotic cycles to endoreplication cycles , and how similar this remodeling is between iECs and devECs . One challenge to addressing these questions has been obtaining pure populations of cells in different cell cycles , especially for iECs that occur in tissues among a mixed population of cells . As a model for iECs , therefore , we experimentally induced Drosophila S2 cells in culture into endoreplication cycles by knocking down Cyclin A ( CycA ) , which is sufficient to induce endocycles [36 , 38 , 44] . In Drosophila , CycA / CDK2 is not required for S phase , and it is believed that knockdown of CycA promotes endocycles by inhibiting CycA / CDK1 activity required for mitosis , analogous to the common mechanism of CDK1 inhibition during developmental endocycles in multiple organisms [45] . S2 cells were treated with CycA double-stranded RNA ( dsRNA ) , and compared to a negative control population of mitotic cycling S2 cells that were treated in parallel with GFP dsRNA . Importantly , this permitted a comparison of canonical and variant cell cycles in a pure population of cells of the same cell type . Flow profiling 96 hours after treatment with CycA dsRNA indicated that more than 50% of cells had a polyploid DNA content of ≥ 8C , and a commensurate reduced fraction of cells with diploid 2C and 4C DNA contents ( Fig 1A and 1B ) . These cells had genome doublings of 8C , 16C , and 32C that were multiples of the diploid DNA content , suggesting that they were duplicating their genomes through repeated G / S endocycles ( Fig 1A and 1B ) . In contrast , knockdown of the mitotic Cyclin B ( CycB ) did not induce cells to endoreplicate , perhaps because of functional redundancy with CycB3 ( S1 Fig ) [46 , 47] . These results confirm previous findings that inhibition of CDK activity through knockdown of CycA is sufficient to induce endoreplication in S2 cells ( hereafter CycA dsRNA iEC ) [36 , 44] . We had previously shown that endocycling cells ( G / S cycle ) of the Drosophila larval salivary gland and fat body have dampened expression of genes that are normally induced by E2F1 and the MMB transcription factors [13] . To determine if this change in transcriptome signature also occurs in CycA dsRNA iECs , we analyzed the expression of several candidate genes whose expression is induced by E2F1 or MMB . RT-qPCR results indicated that CycA dsRNA iECs had reduced expression of the Myb subunit of the MMB and two genes that are positively regulated by the MMB and essential for mitosis ( aurora B and polo ) ( Fig 1C ) . In contrast , the expression of three genes normally induced by E2F1 at G1 / S ( Cyclin E , PCNA , and dup ( fly Cdt1 ) were similar between CycA dsRNA iECs and mitotic cycling cells ( Fig 1C ) . These results suggest that CycA dsRNA iECs are similar to developmental endocycling cells ( devECs ) in that they both have reduced expression of MMB-dependent M phase genes , but they differ in that iECs do not have reduced expression of E2F1-dependent S phase genes . Although CycA dsRNA iECs had lower expression of two MMB-induced genes that are required for mitosis , it was unclear whether dampened MMB activity contributed to the switch to endoreplication . To address this question , we knocked down expression of the Myb subunit of the MMB , which is required to induce the expression of genes for mitosis and cytokinesis [22–24] . Knockdown of Myb inhibited cell proliferation , and resulted in an increase in polyploid DNA content that was similar to that of CycA dsRNA iECs ( Fig 2A and 2B , S2 Fig ) . We then used fluorescence microscopy to further evaluate ploidy and cell cycle in CycA and Myb knockdown cells . S phase cells were detected by incubating in the nucleotide analog EdU for two hours followed by fluorescent click-it labeling , M phase cells detected with antibodies against phospho-histone H3 ( pH3 ) , and nuclear DNA labeled with DAPI [48–50] . Treatment of cells with either CycA or Myb dsRNA resulted in a similar frequency and size of large polyploid nuclei , indicating that Myb knockdown induced endoreplication ( hereafter Myb dsRNA iEC ) ( Fig 2C–2F ) . There was a higher fraction of multinucleate Myb dsRNA iECs ( ~15% ) than CycA dsRNA iECs ( ~8% ) , suggesting that Myb knockdown results in a somewhat larger fraction of endomitotic cells than does CycA knockdown ( Fig 2G ) . Approximately 30% of CycA dsRNA iECs and Myb dsRNA iECs incorporated EdU , an S phase fraction that was similar in both mononucleate and multinucleate populations , consistent with periodic duplications of the genome during both endocycles and endomitotic cycles ( Fig 2H ) . Despite this evidence for periodic endoreplication , the fraction of total cells with mitotic PH3 labeling was not decreased after CycA knockdown ( ~5% ) , and was slightly increased after Myb knockdown in the mononucleate population ( ~10% ) ( Fig 2H ) . Unlike control mitotic cells , however , the PH3 labeling after CycA and Myb knockdown was diffuse , with little evidence of fully condensed mitotic chromosomes , suggesting that these cells were either arrested or delayed in early mitosis or endomitosis , and are consistent with previous observations of chromosome condensation defects of Myb mutants in vivo [51] ( Fig 2C–2E insets ) . These results indicate that knockdown of Myb is sufficient to induce endoreplication cycles that are similar to those after knockdown of CycA . The similarity between CycA dsRNA and Myb dsRNA iECs suggested that CycA and Myb may have a functional relationship . It had been shown in human cells that CycA / CDK2 phosphorylates Myb and promotes its activity as transcription factor [52 , 53] . These early studies , however , were before the discovery that Myb acts as part of the MMB and the identification of many MMB regulated genes [54 , 55] . Moreover , it is not known whether CycA regulation of Myb is conserved in Drosophila . To begin to address this question , we analyzed iECs by Western blotting . The results showed that CycA and Myb dsRNA treatments resulted in the expected lower levels of their respective proteins ( Fig 3A ) . Importantly , both CycA and Myb dsRNA iECs also had greatly reduced levels of CycB protein , consistent with the known requirement of the MMB for transcriptional induction of CycB during mitotic cycles , and further suggesting that CycA knockdown may compromise MMB activity ( Fig 3A ) [24 , 29 , 56] . To further address this possibility , we used RT-qPCR to quantify mRNA levels for CycB and other known MMB target genes that function in mitosis or cytokinesis . Knockdown of either CycA or Myb reduced the expression of all these MMB target genes to similar extents ( Fig 3B ) . Knockdown of CycA resulted in reduced Myb mRNA , even though the Western results showed that there was no reduction of Myb protein . This result is consistent with previous reports that the periodic proteolysis of Myb , which normally occurs during mitosis , is absent during endoreplication cycles [57] . In contrast , knockdown of Myb did not reduce levels of either CycA mRNA or protein , suggesting that Myb knockdown is sufficient to induce endoreplication cycles even when CycA protein levels are high ( Fig 3A and 3B ) . These results suggest that CycA complexed with either CDK1 or CDK2 , is required for MMB transcriptional activation of M phase genes . To further evaluate CycA regulation of the MMB , we determined if Myb and CycA physically interact . We used the GAL4 / UAS system to express UAS-CycA with either UAS-Myb-RFP or UAS-RFP in mitotic cycling imaginal discs , immunoprecipitated ( IPed ) Myb-RFP or RFP with an anti-RFP nanobody , and then blotted for Cyclin A [25 , 58] . The results indicated that Myb-RFP , but not RFP alone , co-IPs with CycA ( Fig 3C ) . The IP’ed RFP-Myb protein reproducibly migrated as a cluster of four bands , which could be the result of post-translational modification , although lower molecular weight species specifically recognized by an anti-dsRed antibody suggests some proteolysis had occurred ( Fig 3C ) . In the reciprocal experiment , IP of CycA-HA with HA antibodies co-IPed Myb-RFP but not RFP alone ( Fig 3C’ ) . All together , these results are consistent with the hypothesis that during Drosophila mitotic cycles a CycA / CDK complex is directly required for the MMB to induce expression of genes required for M phase , and that in the absence of this activation cells switch to endoreplication cycles . To further evaluate the relationship between CycA and Myb and gain insight into remodeling of mitotic cycles into endoreplication cycles , we analyzed the global transcriptomes of CycA dsRNA and Myb dsRNA iECs by RNA-Seq . The transcriptome of these two iEC populations were compared to control mitotic cycling S2 cells treated in parallel with GFP dsRNA , all in three biological replicates . Genes were defined as differentially expressed ( DE ) in iECs if their normalized steady state mRNA levels differed from mitotic cycling cells with a log2 fold change ( log2FC ) of at least +/- 0 . 5 and a false discovery rate ( FDR ) corrected p-value <0 . 05 [59] . The RNA-Seq results indicated that a switch from mitotic cycles to endoreplication in CycA dsRNA and Myb dsRNA iECs is associated with differential expression of thousands of genes ( Fig 4A and 4A’ , S1 and S2 Tables ) . Comparison of the CycA dsRNA and Myb dsRNA iEC transcriptomes revealed that they shared a total of 966 genes that were differentially expressed compared to mitotic cycling controls ( 698 increased and 268 decreased ) ( Fig 4B , S3 Table ) . Permutation testing indicated that this overlap of DE genes was highly statistically significant , with the overlap in upregulated genes being 4 . 6 fold greater than expected by chance ( p<1 x 10−5 ) , and that of downregulated genes being 5 . 8 fold greater than expected by chance ( p<1 x 10−5 ) ( S3A Fig ) . Analysis of Gene Ontology ( GO ) biological process categories indicated that the upregulated genes shared by CycA dsRNA iEC and Myb dsRNA iECs were significantly enriched in the categories of immunity , metabolism , and development ( q < 5 x 10−4 ) , and that shared down regulated genes also included those for energy metabolism ( q ≤ 1 x 10−9 ) ( S4 and S5 Figs , S3 Table ) [60] . With respect to cell cycle remodeling , the downregulated genes shared by these two iEC types were significantly enriched for multiple GO categories of mitosis and cytokinesis ( q < 1 x 10−9 ) ( Fig 4C , S5 Fig , S4 Table ) . After removing redundant GO categories , we analyzed the genes from the five most significantly enriched categories . These categories comprise 47 genes with functions in mitosis and cytokinesis that were downregulated by up to several fold in both iEC types ( Fig 4C , S4 Table ) . Georlette and colleagues had previously shown that many of these genes require the Myb subunit of the MMB for their expression in Drosophila Kc cells [24] . The common downregulation of these genes in CycA dsRNA iEC and Myb dsRNA iEC further suggests that CycA is required for the MMB to induce transcription of these mitotic genes , and that downregulation of a subset of the MMB transcriptome in these two iEC types may contribute to the switch from mitotic cycles to endoreplication cycles . The RNA-seq results , together with our published analysis of devECs , suggested that iECs are similar to devECs in that both have a dampened Myb transcriptome of mitotic genes [13] . However , our previous analysis of devECs used two-color microarrays that had a limited gene set and sensitivity [13] . Therefore , to more fully compare iEC and devEC transcriptomes , we expanded the analysis of devECs with RNA-Seq . Specifically , we used RNA-Seq to compare the transcriptome of endocycling larval salivary glands ( SG ) to that of mitotic cycling larval brains and discs ( B-D ) from early third instar larvae , all in three biological replicates . The results indicated that 4 , 054 genes were upregulated and 4 , 260 genes downregulated in SG devECs relative to mitotic cycling B-D cells ( log2FC at least +/- 0 . 5 and corrected p-value <0 . 05 ) ( Fig 4D ) . A comparison of SG devEC with CycA dsRNA and Myb dsRNA iECs showed that they had in common 158 genes that are increased and 109 genes that are decreased in expression relative to mitotic cycling cells ( Fig 4E , S5 Table ) . This observed overlap in upregulated genes among all three endoreplicating cell types was 4 . 3 fold greater than expected by chance ( p<1 x 10−5 ) , while the overlap of downregulated genes was 9 . 1 fold greater than expected by chance ( p<1 x 10−5 ) ( S3B Fig ) . Consistent with our previous array analysis , many genes induced by E2F1 and MMB are expressed at lower levels in endocycling SG devECs relative to mitotic cycling B-D cells [13] . Among the 111 genes that are known to depend on E2F1 for their transcriptional induction in S2 cells , 73 were reduced in expression in SG devEC ( Fig 4F , S6 Table ) [61 , 62] . Fewer E2F1-dependent genes ( 59 ) were downregulated in CycA dsRNA iECs , with an overlap of 48 downregulated E2F1-dependent genes in both CycA dsRNA iECs and SG devECs ( Fig 4F , S5 Table ) [61] . 11 of the 25 E2F1-dependent genes that were downregulated in devECs but not in CycA dsRNA iECs have functions in S phase , including CycE , dup ( Cdt1 ) , and PCNA; the three E2F1-regulated genes that RT-qPCR had indicated are not repressed in CycA dsRNA iECs ( Fig 1C , S5 Table ) . Thus , although reduced expression of these E2F1-regulated S phase genes is common in devECs , their repression is not essential for endoreplication [10 , 13–15] . Consistent with this idea , only 12 E2F1-dependent genes were commonly downregulated in both iEC types and devECs , and all have functions in mitosis ( Fig 4F , Table 1 , S6 Table ) . These 12 E2F1-dependent genes are a subset of the 47 Myb-dependent mitotic genes that we had found are downregulated in iEC , and therefore require both E2F1 and the MMB for their full expression ( Fig 4C , S6 Table ) [62] . Considering all downregulated genes , the most significantly enriched GO categories shared by iECs and devECs were mitosis and cytokinesis , including all of the 47 Myb-dependent genes that were commonly downregulated between CycA and Myb dsRNA iECs ( Fig 4C , S6 Fig , S3 and S4 Tables ) . Given that CycA / Cdk1 activity is repressed in both CycA dsRNA iEC and SG devECs , the lower expression of these 47 genes in devECs further suggests that their transcriptional induction by the MMB is dependent on CycA ( S4 Table ) [9 , 11 , 63] . These genomic results show that while iECs and devECs both have a dampened MMB transcriptome of mitotic genes , repression of E2F1-regulated S phase genes is not essential for endoreplication . The findings in S2 cells and tissues suggested that downregulation of an MMB transcriptome of mitotic genes promotes endoreplication . It was unclear , however , which of these downregulated genes are key for the decision to switch to endoreplication cycles . To address this question , we took an integrative genetic approach , using a collection of fly strains with GAL4-inducible UAS-dsRNAs to knock down the expression of genes that RNA-Seq had indicated were downregulated in iECs . We used an inclusive criterion and knocked down genes that were downregulated by log2 fold of at least -0 . 5 in both CycA and Myb dsRNA iECs , but without regard to p value ( 244 available strains representing 240 genes ) [64] ( S7 Table ) . We used dpp-GAL4 to express these dsRNAs along the anterior-posterior compartment boundary of the larval wing disc , and then examined the hair pattern in the central part of adult wings between veins L3 and L4 , the region that is the known fate of cells that express dpp-GAL4 [65 , 66] ( Fig 5A ) . Each hair on the adult wing represents an actin protrusion from a single cell , and it is known that polyploidization of wing cells results in fewer and larger hairs ( Fig 5B ) [67–69] . As proof of principle , expression of a UAS-CycAdsRNA along the A/P boundary resulted in a central stripe of longer hairs on the adult wing surface and wing margin between veins L3 and L4 , with many of these cells producing clusters of multiple hairs ( Fig 5C ) . Analysis of larval wing discs co-expressing a UAS-mRFP reporter showed that dpp-GAL4; UAS-CycAdsRNA cells at the A/P compartment boundary had larger nuclei and increased DNA content compared to control cells outside of this dpp-GAL4 stripe , confirming that the adult wing phenotype is a result of endoreplication ( Fig 5C and 5L , S7B Fig ) . Knockdown of Myb also resulted in an increased DNA content of wing disc cells and a stripe of larger and more widely spaced adult wing hairs ( Fig 5D and 5L , S7C Fig ) . Although both of these Myb knockdown phenotypes were less severe than that of CycA knockdown , this is likely because this Myb dsRNA is inefficient ( S8 Fig ) . Consistent with this , raising dpp-GAL4; UAS-MybdsRNA larvae at 29 C , a temperature at which transcriptional induction by GAL4 is stronger , resulted in a more severe endoreplication phenotype that was similar to CycA ( Fig 5E and 5L ) . Knockdown of either CycA or Myb resulted in a reduced wing surface area between wing veins L3 and L4 , suggesting that growth by an increase in cell size ( hypertrophy ) was not able to completely recapitulate normal tissue growth by cell proliferation ( Fig 5C–5E and 5K ) . Among the 244 strains tested , 26 resulted in lethality before adulthood , suggesting that their functions are essential ( S7 Table ) . Among the other 218 crosses that survived to adults , knockdown of five genes reproducibly resulted in a reduction in the area between the L3 and L4 veins and abnormal wing hairs–aurora B ( aurB ) , Incenp , Spc25 , tumbleweed ( tum ) , and pavarroti ( pav ) . Of these , three reproducibly had enlarged wing hairs and a corresponding increased DNA content of wing disc cells–aurora B ( aurB ) , tumbleweed ( tum ) , and pavarroti ( pav ) ( Fig 5F–5L , S7D–S7I Fig ) [70–74] . Remarkably , all of these genes are either part of the chromosomal passenger complex ( CPC ) or are downstream effectors of it . The AurB kinase and INCENP are two subunits of the four-subunit CPC complex , which phosphorylates downstream targets to regulate multiple processes of mitosis and cytokinesis [75 , 76] . Spc25 is a subunit of the Ndc80 outer kinetochore complex , which is phosphorylated by the CPC to regulate microtubule-kinetochore attachments [77 , 78] . The Tum protein is a Rac-GAP protein that is phosphorylated by the CPC and regulates the kinesin Pav for proper cytokinesis [79] . While knockdown of any of these five genes resulted in longer hairs on the wing margin and surface , knockdown of aurB affected hair length primarily in the anterior half of the L3 / L4 intervein region ( Fig 5F ) . This mild phenotype is not unexpected because the UAS-aurBdsRNA-1 transgene in this strain is based on a series of vectors that are not highly efficient for dsRNA expression . The stronger phenotype in the anterior could reflect the influence of patterning signals on a cells propensity to endoreplicate , although it could also be the result of different levels of dpp-GAL4 expression and aurB knockdown in different cells . Expression of a more efficient UAS-aurBdsRNA-2 had resulted in pupal lethality before adulthood , and RT-qPCR indicated that it knocked down aurB mRNA to lower levels than UAS-aurBdsRNA-1 ( S8 Fig ) . Examination of wing disc cells showed that while UAS-aurBdsRNA-1 induced a low level of polyploidy , UAS-aurBdsRNA-2 resulted in very large polyploid cells , suggesting that a strong knockdown of AurB results in high levels of endoreplication ( Fig 5L , S7D and S7E Fig ) . All five of these MMB-regulated genes were expressed at significantly lower levels in both iECs and devECs . The combined genomic and genetic results suggest , therefore , that a dampened CycA—Myb—AurB network promotes a switch from mitotic cycles to endoreplication cycles . To test whether the status of the CycA—Myb—AurB network determines the decision between mitotic and endoreplication cycles in tissues other than the wing disc , we analyzed the effects in somatic follicle cells of the ovary . These cells have specific advantages for quantifying cell cycle and cell growth . Follicle cells form a regular epithelial sheet that surrounds 15 germline nurse cells and one oocyte in each maturing egg chamber . Their regimented cell cycle programs are well characterized and coupled with stages of oogenesis , dividing mitotically during stages 1–6 , undergoing three endocycles during stages 7-10A , and then selectively re-replicating genes required for eggshell synthesis during stages 10B-14 [80–82] . We induced conditional knockdown of the genes identified in the wing screen using the heat-inducible GAL4 / UAS FLP-On system , which results in clonal activation of GAL4 and induction of a UAS-dsRNA and a UAS-RFP reporter in a subset of cells [83] . This conditional knockdown also permitted an analysis of genes whose knockdown resulted in lethality in the wing screen . Three days after heat induction , we quantified the number of cells in the clone , their nuclear size , and their DNA content by measuring DAPI fluorescence . If a gene knockdown induces a switch from mitotic to endoreplication cycles , it should result in clones with fewer cells that have an increase in nuclear size and DNA content . We had shown previously shown that knockdown of CycA or over-expression of Fzr ( Cdh1 ) induces mitotic follicle cells into precocious endocycles during early oogenesis [36 , 42] . We analyzed clones in stage 6 , the latest stage of oogenesis during which follicle cells mitotically divide . Based on the known rate of egg chamber maturation , patches of RFP+ follicle cells in these stage 6 egg chambers represent the clonal descendants of single founder follicle cells that were either transit amplifying stem cell daughters or in stage 1 egg chambers at the time of induction three days earlier . Wild type , control clones were comprised of ~28 RFP-positive cells , indicating that they had divided ~4–5 times since FLP-On in the original single founder cell ( Fig 6A and 6M ) . FLP-On of UAS-CycAdsRNA resulted in clones with only one to three cells , indicating that cell division was strongly inhibited , and that they had switched to endocycles during the first or second mitotic cycle after CycA knockdown ( Fig 6B and 6M , Table 2 ) . The cells in these clones had a single large nucleus with increased DNA content up to ~16C , indicating that they had endoreplicated , consistent with our previously published results ( Fig 6B and 6N , Table 1 ) [36] . Expression of UAS-MybdsRNA resulted in some clones with reduced cell numbers and larger nuclei , suggesting that they had switched from mitotic divisions to endoreplication , but with variable expressivity among clones ( Fig 6C , 6M and 6N , Table 1 ) . A few Myb knockdown cells had two nuclei that were increased in size and DNA content , suggesting that these cells had failed cytokinesis before replicating their DNA again , a type of endomitosis . This variably expressive phenotype is likely the result of partial Myb knockdown by the inefficient UAS-MybdsRNA transgene ( S8 Fig ) . To address this , we compared these UAS-MybdsRNA clones at 25°C to those grown at 29°C , a higher temperature that increases GAL4 activity . The clones at 29°C had a stronger phenotype , with many Myb knockdown cells having very large polyploid nuclei ( Fig 6D and 6N ) . These results are consistent with the results in S2 cells and wing discs , and indicate that knockdown of CycA or Myb is sufficient to induce endoreplication . The combined RNA-seq and genetic screen results suggested that reduced expression of CPC subunits and other targets downstream of Myb contributes to the switch to endoreplication . Clones expressing the weaker UAS-aurBdsRNA-1 had only two to three cells , indicating that cell proliferation was strongly inhibited , each with variable increases in nuclear size and DNA content ( Fig 6E , 6M and 6N , Table 2 ) . A few of these cells had two nuclei of increased size and DNA content , suggesting that UAS-aurBdsRNA-1 impaired cytokinesis followed by endoreplication . FLP-ON expression of the stronger UAS-aurBdsRNA-2 in follicle cells resulted in clones composed of only one to two cells , each with a single , large , polyploid nucleus ( Fig 6F , 6M and 6N , Table 2 ) . Many of these nuclei were multi-lobed , with connected chromatin masses composed of large chromosomes that appeared polytene ( Fig 6F ) . These results suggest that mild knockdown of AurB results in cytokinesis failure , whereas a stronger knockdown results in a failure to segregate chromosomes and cytokinesis , followed by endoreplication . To further test whether reduced CPC activity induces endoreplication , we knocked down expression of its other three subunits: Incenp , Borealin-related ( Borr ) , and Deterin ( Det ) ( fly Survivin ortholog ) , all of which were expressed at lower levels in iECs and salivary gland devECs ( S3 and S5 Tables ) [72 , 84 , 85] . Expression of UAS-IncenpdsRNA did not reduce the number of cells per clone nor increase DNA content , consistent with its lack of effect in the wing discs , an uninformative negative result because this UAS transgene is optimized for expression in the germline but expressed poorly in the soma ( Table 2 ) . Knockdown of the other CPC subunits , Borr and Det , had resulted in lethality in the wing screen , whereas their conditional knockdown in follicle cells resulted in clones with very few cells , each with large , polyploid nuclei ( Fig 6G , 6H , 6M and 6N , Table 2 ) . As a further test of the importance of the CPC , we knocked down aurB in S2 cells , and compared the effect of its knockdown to another mitotic kinase gene , polo . Similar to wing and ovarian follicle cells , knockdown of aurB in S2 cells resulted in endoreplication , whereas knockdown of polo resulted in a mitotic arrest ( S9A and S9B Fig ) . These results indicate that reduction of CPC activity is sufficient to switch cells from mitotic cycles to endoreplication cycles . We then addressed which processes downstream of the CPC are crucial for the mitotic cycle versus endoreplication cycle decision in follicle cells [76] . The genes Spc25 , tum , and pav encode proteins that function downstream of the CPC [70 , 79 , 86 , 87] . All three of these genes are regulated by the MMB , were expressed at lower levels in iECs and devECs , and were recovered in the wing screen ( Figs 4C and 5H–5J ) . Knockdown of the kinetochore protein Spc25 in follicle cells strongly inhibited cell division and resulted in fewer cells per clone ( Fig 6I , 6M and 6N ) . However , the nuclear size and DNA content of these cells were not increased , indicating that endoreplication was not induced , consistent with results from the wing ( Figs 5L and 6I and 6N , Table 2 ) . To further evaluate kinetochore proteins downstream of the CPC , we knocked down Spc105R , a kinetochore protein important for microtubule attachment and the spindle assembly checkpoint ( SAC ) [88] . Clonal knockdown of Spc105R strongly inhibited cell proliferation , and induced pyknotic nuclei indicative of programmed cell death , but did not result in an increase in DNA content ( Fig 6J , 6M and 6N , Table 2 ) . Growing the Spc25 and Spc105R clones at 29°C to enhance knockdown resulted in fewer cells per clone , and more nuclei that appeared pyknotic , but again did not result in enlarged nuclei ( Fig 6M and 6N ) . Thus , despite a strong mitotic arrest phenotype , knockdown of these two kinetochore proteins downstream of the CPC did not result in endoreplication . Knockdown of the cytokinesis proteins Tum or Pav resulted in many fewer cells per clone , with many binucleate , indicating that cytokinesis was inhibited ( Fig 6K–6M , Table 2 ) . Unlike kinetochore protein knockdown , however , the binucleate Tum knockdown cells had a significant increase in both nuclear size and DNA content per nucleus ( mean ~2 fold , max ~4 fold increase ) , indicating that they had endoreplicated after a failure of cytokinesis , consistent with the results from the wing disc ( Figs 5K , 6K and 6N , Table 2 ) . While some Pav knockdown cells clearly had larger polyploid nuclei ( ~2 fold ) , the average was not significantly different from control cell populations , unlike the results from wings where Pav knockdown induced significant polyploidy ( Figs 5L , 6L , 6M and 6N , Table 2 ) . Stronger knockdown of pav at 29°C , however , did result in a significant increase in nuclear area and DAPI intensity , consistent with the interpretation that these cells have undergone endoreplication ( Fig 6N ) . Thus , inhibition of cytokinetic , but not kinetochore , proteins downstream of the CPC induces an endoreplication cycle . All together , these results suggest that the status of a CycA—MMB—AurB network determines the choice between mitotic and endoreplication cycles .
We have investigated how the cell cycle is remodeled when mitotic cycling cells switch into endoreplication cycles , and how similar this remodeling is between devECs and experimental iECs . We have found that repression of a CycA—Myb—AurB mitotic network promotes a switch to endoreplication in both devECs and iECs . Although a dampened E2F1 transcriptome of S phase genes is a common property of devECs in flies and mice , we found that repression of the Myb transcriptome is sufficient to induce endoreplication in the absence of reduced expression of the E2F1 transcriptome . Knockdown of different components of the CycA-Myb-AurB network resulted in endoreplication cycles that repressed mitosis to different extents , which suggests that regulation of different steps of this pathway may explain the known diversity of endoreplication cycles in vivo . Overall , these findings define how cells either commit to mitosis or switch to different types of endoreplication cycles , with broader relevance to understanding the regulation of these variant cell cycles and their contribution to development , tissue regeneration , and cancer . Our findings indicate that the status of the CycA—Myb—AurB network determines the choice between mitotic or endoreplication cycles ( Fig 7 ) . These proteins are essential for the function of their respective protein complexes: CycA activates CDK1 to regulate mitotic entry , Myb is required for transcriptional activation of mitotic genes by the MMB transcription factor complex , and AurB is the kinase subunit of the four-subunit CPC . While each of these complexes were previously known to have important mitotic functions , our data indicate that they are key nodes of a network whose activity level determines whether cells switch to the alternative growth program of endoreplication ( Fig 7 ) . Our results are consistent with previous evidence in several organisms that lower activity of the Myb transcription factor results in polyploidization , and further shows that repressing the function of the CPC and cytokinetic proteins downstream of Myb also promotes endoreplication [13 , 16 , 23 , 89] . Importantly , our genetic evidence indicates that not all types of mitotic inhibition result in a switch to endoreplication . For example , knockdown of the Spc25 and Spc105R kinetochore proteins or the Polo kinase resulted in a mitotic arrest , not a switch to repeated endoreplication cycles . These observations are consistent with CycA / CDK , MMB , and the CPC playing principal roles in the mitotic network hierarchy and the decision to either commit to mitosis or switch to endoreplication cycles . While knockdown of different proteins in the CycA-Myb-AurB network were each sufficient to induce endoreplication cycles , these iEC populations had different fractions of cells with multiple nuclei diagnostic of an endomitotic cycle . Knockdown of cytokinetic genes pav and tum resulted in the highest fraction of endomitotic cells , followed by the CPC subunits , then Myb , and finally CycA , with knockdown of this cyclin resulting in the fewest endomitotic cells . These results suggest that knocking down genes higher in this branching mitotic network ( e . g . CycA ) inhibits more mitotic functions and preferentially promotes G / S endocycles that skip mitosis , whereas inhibition of functions further downstream in the network promote endomitosis ( Fig 7 ) . Moreover , we found that different levels of CPC function also resulted in different subtypes of endoreplication . Strong knockdown of AurB inhibited chromosome segregation and cytokinesis resulting in cells with a single polyploid nucleus , whereas a mild knockdown resulted in successful chromosome segregation but failed cytokinesis , suggesting that cytokinesis requires more CPC function than chromosome segregation . It thus appears that different thresholds of mitotic function result in different types of endoreplication cycles . This idea that endomitosis and endocycles are points on an endoreplication continuum is consistent with our evidence that treatment of human cells with low concentrations of CDK1 or AurB inhibitors induces endomitosis , whereas higher concentrations induce endocycles [28] . Our results raise the possibility that in tissues of flies and mammals both conditional and developmental inputs may repress different steps of the CycA—Myb—AurB network to induce slightly different types of endoreplication cycles that partially or completely skip mitosis [5 , 90] . Together , our findings show that there are different paths to polyploidy depending on both the types and degree to which different mitotic functions are repressed . Our findings are relevant to the regulation of periodic MMB transcription factor activity during the canonical mitotic cycle . Knockdown of CycA compromised MMB transcriptional activation of mitotic gene expression , and their physical association suggests that the activation of the MMB by CycA may be direct . The MMB-regulated mitotic genes were expressed at lower levels in CycA iECs , even though Myb protein levels were not reduced . This result is consistent with the hypothesis that CycA / CDK phosphorylation of the MMB is required for its induction of mitotic gene expression . Moreover , misexpression of Myb in CycA knockdown follicle cells did not prevent the switch to endoreplication , further evidence that CycA / CDK is required for MMB activity and mitotic cycles ( S10 Fig ) . While the dependency of the MMB on CycA was not previously known in Drosophila , it was previously reported that in human cells CycA / CDK2 phosphorylates and activates human B-Myb in late S phase , and also triggers its degradation [53 , 91] . While further experiments are needed to prove that CycA / CDK regulation of the MMB is direct , interrogation of the results of multiple phosphoproteome studies using iProteinDB indicated that Drosophila Myb protein is phosphorylated at three CDK consensus sites including one , S381 that is of a similar sequence and position to a CDK phosphorylated site on human B-Myb ( T447 ) [92 , 93] . We favor the hypothesis that it is CycA complexed to CDK1 that regulates the MMB because , unlike human cells , in Drosophila CycA / CDK2 is not required for S phase , and Myb is degraded later in the cell cycle during mitosis [45 , 94] . Moreover , it is known that mutations in CDK1 , but not CDK2 , induce endocycles in Drosophila , mouse , and other organisms [37 , 95] . A cogent hypothesis is that CycA / CDK1 phosphorylates Myb , and perhaps other MMB subunits , to stimulate MMB activity as a transcriptional activator of mitotic genes , explaining how pulses of mitotic gene expression are integrated with the master cell cycle control machinery ( Fig 7 ) . It remains formally possible , however , that both CycA / CDK2 and CycA / CDK1 activate the MMB in Drosophila . The early reports that CycA / CDK2 activates B-Myb in human cells were before the discovery that it functions as part of the MMB and the identification of many MMB target genes , and further experiments are needed to fully define how MMB activity is coordinated with the central cell cycle oscillator in fly and human cells [17 , 19 , 24 , 26] . We experimentally induced endocycles by knockdown of CycA to mimic the repression of CDK1 that occurs in devECs . Our data revealed both similarities and differences between these experimental iECs and devECs . Both iECs and SG devECs had a repressed CycA—Myb—AurB network of mitotic genes . In contrast , only devECs had reduced expression of large numbers of E2F1-dependent S phase genes , a conserved property of devECs in fly and mouse [10 , 13–15] . In CycA iECs , many of these key S phase genes were not downregulated , including Cyclin E , PCNA , and subunits of the pre-Replicative complex , among others . This difference between CycA dsRNA iECs and SG devECs indicates that repression of these S phase genes is not essential for endoreplication . In fact , none of the E2F1 -dependent S phase genes were downregulated in Myb dsRNA iEC . Instead , the 12 E2F1-dependent genes that were commonly downregulated in Myb dsRNA iEC , CycA dsRNA iEC , and SG devEC all have functions in mitosis ( Table 1 ) . These 12 mitotic genes are , therefore , dependent on both Myb and E2F1 for their expression , including the cytokinetic gene tum whose knockdown induced endomitotic cycles . This observation leads to the hypothesis that downregulation of the E2F transcriptome in fly and mouse devECs may serve to repress the expression of these mitotic genes , and that the repression of S phase genes is a secondary consequence of this regulation . These genomic data , together with our genetic evidence in S2 cells and tissues , indicates that in Drosophila the repression of the Myb transcriptome is sufficient to induce endoreplication without repression of the E2F1 transcriptome . The observation that both CycAdsRNA iECs and devECs both have lower CycA / CDK activity , but differ in expression of E2F1 regulated S phase genes , also implies that there are CDK-independent mechanisms by which developmental signals repress the E2F1 transcriptome in devECs . Our results have broader relevance to the growing number of biological contexts that induce endoreplication . Endoreplicating cells are induced and contribute to wound healing and regeneration in a number of tissues in fly and mouse , and , depending on cell type , can either inhibit or promote regeneration of the zebrafish heart [27 , 30–32] . An important remaining question is whether these iECs , like experimental iECs and devECs , have a repressed CycA—Myb—AurB network . If so , manipulation of this network may improve regenerative therapies . In the cancer cell , evidence suggests that DNA damage and mitotic stress , including that induced by cancer therapies , can switch cells into an endoreplication cycle [5 , 41 , 96 , 97] . These therapies include CDK and AurB inhibitors , which induce human cells to polyploidize , consistent with our fly data that CycA / CDK and the CPC are key network nodes whose repression promotes the switch to endoreplication [75 , 98] . Upon withdrawal of these inhibitors , transient cancer iECs return to an error-prone mitosis that generates aneuploid cells , which have the potential to contribute to therapy resistance and more aggressive cancer progression [28 , 99–101] . Our finding that the Myb transcriptome is repressed in iECs opens the possibility that these mitotic errors may be due in part to a failure to properly orchestrate a return of mitotic gene expression . Understanding how this and other networks are remodeled in polyploid cancer cells will empower development of new approaches to prevent cancer progression .
Drosophila strains were obtained from the Bloomington Stock Center ( BDSC , Bloomington , IN ) , or the Vienna Drosophila Resource Center ( VDRC , Vienna Austria ) . The UAS-mRFP-Myb strain was kindly provided by Dr . Joe Lipsick . Drosophila were raised on BDSC standard cornmeal medium at 25°C unless otherwise indicated . For the genetic screen of Fig 4 , fly strains with UAS-dsRNA transgenes were made by the Drosophila RNAi Screening Center ( DRSC ) and provided by the BDSC . These strains were crossed to dpp-Gal4 , UAS-mRFP and multiple progeny of each cross were scored for their adult wing phenotype . Specific details about genotypes and strain numbers can be found in S7 and S8 Tables . S2 cells were grown at 25°C in M3 + BPYE medium supplemented with 10% Fetal Bovine Serum as described [102] . iECs were supplemented with an additional 2% Fetal Bovine Serum ( 12% final ) . Cell proliferation in S2 Fig was quantified by counting cells using a hemocytometer . For RNAi , S2 cells were treated with the indicated dsRNA for 1 hour in serum free medium , followed by culturing for 96 hours at 25°C , as indicated above , and then analyzed as indicated below . After dsRNA treatment , S2 cells were harvested in PBS and fixed in ethanol . After fixation , cells were incubated in propidium iodide ( 20 μg/ml ) supplemented with RNaseA ( 250 μg/ml ) at 37°C for 30 minutes . Flow cytometry was performed using an LSRII ( BD Biosciences ) and data were analyzed with Flowjo v7 . 6 . 5 software . Protein extracts were made from S2 cells using a non-denaturing lysis buffer ( 25mM Tris , pH 7 . 5 , 150mM NaCl , 5mM EDTA , 1% IGEPAL ( Sigma-Aldrich ) , 5% glycerol , complete protease inhibitor cocktail ( Sigma-Aldrich ) , PhosSTOP ( Sigma-Aldrich ) ) and homogenizing the cells on ice . Absolute protein levels were determined by Bradford assays . At least 20 μg protein was separated by SDS-PAGE , electrophoretically transferred to PVDF membranes , and blotted using the following antibodies: anti-Cyclin A ( A12 , DSHB , concentrate ) at 1:1000 , anti-Cyclin B ( F2F4 , DSHB supernatant ) at 1:100 , anti-HA ( Y11 , Santa Cruz ) at 1:1000 , anti-Myb ( D3R , provided by J . Lipsick ) at 1:1000 , anti-Tubulin ( E7 , DSHB , concentrate ) at 1:1000 . Blots were labeled with HRP conjugated secondary antibodies and developed using Super Signal West Pico substrate ( Thermo Scientific ) . Hsp70-Gal4 , UAS-mRFP or Hsp70-Gal4 , UAS-mRFP-Myb flies were crossed to UAS-CycA ( Fig 3C ) or UAS-CycA-HA ( Fig 3C’ ) flies . Larvae were heat treated three times at 37°C for 30 minutes over 1 . 5 days beginning in 2nd instar , and protein extracts made from early 3rd instar larvae by homogenizing in non-denaturing lysis buffer ( indicated above ) for 1 hour after the final heat treatment . Lysate was quantified using Bradford assays to normalize total protein content among samples . In Fig 3C , extracts were immunoprecipitated using highly-efficient RFP-Trap ( Chromotek ) single-chain nanobodies made in camelids and conjugated to agarose beads . In Fig 3C’ , extracts were immunoprecipitated with anti-HA ( F7 , Santa Cruz ) or normal mouse serum on Protein G Agarose ( Invitrogen ) . Western blots of input and IP were incubated with antibodies against Drosophila Myb ( gift of J . Lipsick ) , Cyclin A ( DHSB ) , DsRed ( Takara ) , and HA ( Santa Cruz ) . In Fig 2 , S2 cells were treated with dsRNA for 96 hours at 25°C , replated on poly-D-lysine coated chamber slide , and allowed to settle for 16–18 hours . Cells were then incubated in EdU ( 20μM ) for 2 hours at 25°C followed by click-it fluorescent labeling according to the manufacturer’s ( Invitrogen ) protocol . These cells were then labeled with antibodies against ( pH3 ) ( Millipore , 06–570 ) and appropriate fluorescent secondary antibodies . Cells were stained with DAPI ( 0 . 5μg/ml ) and imaged on a Leica SP5 confocal or Leica DMRA2 fluorescent microscope . The fraction of EdU and pH3 labeled cells and nuclear area were quantified using ImageJ v1 . 50b software ( https://imagej . nih . gov/ij/ ) . For S7 Fig , wing imaginal discs were dissected from 3rd instar larvae and labeled with antibodies against DsRed ( Takara ) followed by labelling with anti-rabbit Alexa Fluor 568 ( Thermo Fisher ) . Cells were stained with DAPI ( 0 . 5μg/ml ) and imaged on a Leica SP5 confocal or Leica DMRA2 fluorescent microscope . Nuclear area and DAPI fluorescence was measured with ImageJ . Nuclear area and DAPI fluorescence of GAL4-expressing , DsRed-positive cells within the wing pouch was normalized to that of DsRed-negative cells in the wing pouch of the same discs . Hsp70-FLP;Act>cd2>Gal4 , UAS-mRFP was crossed to different UAS-dsRNA fly strains . Well-fed 3–5 day old adult G1 females were heat induced at 37°C for 30 minutes and allowed to recover for three days before ovaries were dissected , and labeled with anti-dsRed ( Takara ) and counterstained with DAPI as previously described [36] . Cell clones in stage 6 egg chambers were imaged on a Leica SP5 confocal and Leica DMRA widefield epifluorescent microscope . Cell number was quantified by counting RFP+ cells . The area and total DAPI fluorescence of nuclei within individual cells of a clone ( RFP+ ) were measured using ImageJ and normalized to the average of wild type cells outside of the clone ( RFP- ) in the same egg chamber . mRNA for RT-qPCR was isolated by TRIzol ( Invitrogen ) according to the manufacturer’s instructions . cDNA was generated using the Superscript III kit ( Invitrogen ) . qPCR was performed using Brilliant III Ultra-Fast SYBR Green qPCR Master Mix ( Agilent Technologies ) and the primers indicated in S8 Table . Act5C was amplified as an internal reference control . Data were analyzed using LinRegPCR software ( ver . 2016 . 2 ) the Pfaffl method to determine relative transcript levels [103 , 104] . For S2 cell RT-qPCR , RNA was isolated 96 hours after dsRNA knockdown or control GFP dsRNA . Each assay was performed with technical duplicates and biological triplicates . For quantification of knockdown in discs in S8 Fig , hsp70-GAL4; UAS-dsRNA and control hsp70-GAL4 only larvae were heat treated twice at 37 C for ½ hour over one day , and mRNA was isolated from 3rd instar discs ½ hour after the second heat shock and RT-qPCR performed as described above . Reactions were done in technical and biological duplicates . mRNA levels in the knockdown strains were normalized to levels in the hsp70-GAL4 control strain . Statistical analysis of Figs 1B , 1C , 2B , 2G , 2H , 3B and 6M , S9A and S9B Fig were performed using two-tailed Student’s t tests using Microsoft Excel ( version 15 . 0 . 4753 . 1000 ) . For Fig 2F and S10 Fig a two-tailed Welch’s t test was performed using GraphPad Prism ( version 7 . 04 ) , For Figs 5L , and 6N GraphPad Prism ( version 7 . 04 ) was used to perform a one-way ANOVA with a two-stage linear step-up procedure of Benjamini , Krieger and Yekutieli post-hoc test [105] to assess statistical difference between control clones and the indicated dsRNA clones . For RNA-Seq of S2 cells , RNA was prepared from three biological replicates of CycA dsRNA , Myb dsRNA , and GFP dsRNA treated cells . For tissues , RNA was prepared from salivary glands ( SG ) or brains plus imaginal discs ( B-D ) from the same feeding early third instar larvae in three biological replicates , as previously described [13] . TruSeq Stranded mRNA Libraries ( Illumina ) were prepared by the Center for Genomics and Bioinformatics ( CGB ) of Indiana University according to manufacturer’s protocol . Multiplex sequencing barcodes from TruSeq RNA Single Indexes set A or B ( Illumina ) were added to the libraries during construction . The barcoded libraries were cleaned by double side beadcut with AMPure XP beads ( Beckman Coulter ) , verified using Qubit3 fluorometer ( ThermoFisher Scientific ) and 2200 TapeStation bioanalyzer ( Agilent Technologies ) , and then pooled . The pool was sequenced on NextSeq 500 ( Illumina ) with NextSeq75 High Output v2 kit ( Illumina ) . Single-end 75 bp read sequences were generated . The read sequences were de-multiplexed using bcl2fastq ( software versions 1 . 4 . 1 . 2 , 1 . 4 . 1 . 2 , and 2 . 1 . 0 . 31 for GSF1389 , GSF1471 , GSF1611 ) . Read quality was checked with FastQC v0 . 11 . 5 [106] , and reads were then mapped against the Dmel R6 . 23 genome assembly and annotation using STAR v2 . 6 . 1a [107] . Mapped fragments were assigned to exons via the featureCounts function of the Rsubread v1 . 24 . 2 bioconductor package [108] , and various pseudogenes and ncRNAs were excluded . Differential gene expression between samples was calculated using DESeq2 v1 . 14 . 1 [109] . Gene lists derived from RNA-Seq data sets were categorized as upregulated ( Log2 fold-change ≥ 0 . 5 with an FDR adjusted p ≤ 0 . 05 ) or downregulated ( Log2 fold-change ≤ -0 . 5 with an FDR adjusted p ≤ 0 . 05 ) [59] . Human ortholog information and DIOPT scores were downloaded from FlyBase on 09-11-2018 [110] and GO terms were retrieved using the Bioconductor package AnnotationHub v2 . 12 . 0 with a snapshot date of 04-30-2018 [111] . GO enrichment analysis was performed and plots were generated using clusterProfiler v3 . 8 . 1 [112] . The comparisons between the differentially expressed genes in the RNA-seq and the accompanying Venn diagrams were created using custom scripts and the R library VennDiagram [113] . Permutation testing was used to calculate p-values and fold enrichment of the DE gene double overlap between CycA iEC and Myb iEC or triple overlap among CycA iEC , Myb iEC and Sg devEC relative to chance ( S3 Fig ) [114] . Briefly , either two or three random gene sets were sampled ( for the double and triple overlap sets respectively ) , with total genes sampled equal to the number of DE genes observed for those samples , and the number of overlapping genes between the sampled sets was recorded . This randomization sampling process was repeated 100 , 000 times . The p-values were calculated by finding the number of permutation samples that resulted in an overlapping number of genes greater than or equal to the observed number of overlapping genes plus one , over the number of permutation samples plus one . For the enrichment plot of S3 Fig , each observed overlap value was converted to fold difference relative to the sampled overlap values , and the median , 5% and 95% quantiles are shown . | Endoreplication is a cell cycle variant that entails cell growth and periodic genome duplication without cell division , and results in large , polyploid cells . Cells switch from mitotic division cycles to endoreplication cycles during development , and also in response to conditional stimuli during wound healing , regeneration , aging , and cancer . Much remains unknown , however , about how mitotic cycles are remodeled into endoreplication cycles , and how similar this remodeling is between induced and developmental endoreplicating cells ( iECs and devECs ) . In the present work , we define a Cyclin A regulated mitotic network in Drosophila whose downregulation promotes the switch from mitotic cycles to endoreplication cycles in both iECs and devECS . Repression of this network in iECs was sufficient to induce endoreplication without reduced expression of E2F-regulated S phase genes that is common among devECs in both flies and mice . Knockdown of downstream cytokinetic proteins , but not kinetochore proteins , were sufficient to induce different types of endoreplication . Altogether our results define a CycA—Myb—AurB network as a key determinant of alternative cell cycles , and provide insight into the regulation of a diversity of polyploid cycle types in development and disease . | [
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| 2019 | A Cyclin A—Myb-MuvB—Aurora B network regulates the choice between mitotic cycles and polyploid endoreplication cycles |
Dengue is a growing problem both in its geographical spread and in its intensity , and yet current global distribution remains highly uncertain . Challenges in diagnosis and diagnostic methods as well as highly variable national health systems mean no single data source can reliably estimate the distribution of this disease . As such , there is a lack of agreement on national dengue status among international health organisations . Here we bring together all available information on dengue occurrence using a novel approach to produce an evidence consensus map of the disease range that highlights nations with an uncertain dengue status . A baseline methodology was used to assess a range of evidence for each country . In regions where dengue status was uncertain , additional evidence types were included to either clarify dengue status or confirm that it is unknown at this time . An algorithm was developed that assesses evidence quality and consistency , giving each country an evidence consensus score . Using this approach , we were able to generate a contemporary global map of national-level dengue status that assigns a relative measure of certainty and identifies gaps in the available evidence . The map produced here provides a list of 128 countries for which there is good evidence of dengue occurrence , including 36 countries that have previously been classified as dengue-free by the World Health Organization and/or the US Centers for Disease Control . It also identifies disease surveillance needs , which we list in full . The disease extents and limits determined here using evidence consensus , marks the beginning of a five-year study to advance the mapping of dengue virus transmission and disease risk . Completion of this first step has allowed us to produce a preliminary estimate of population at risk with an upper bound of 3 . 97 billion people . This figure will be refined in future work .
Despite increased interest in dengue in recent years , the global distribution of dengue remains highly uncertain . Estimates for the population at risk range from 30% [1] to 54 . 7% [2] of the world's population ( 2 . 05–3 . 74 billion ) while the Centers for Disease Control ( CDC ) and the World Health Organization ( WHO ) currently disagree on dengue presence in 34 countries across five continents ( Table S1 ) . Clinical features of dengue virus infection include fever , rash and joint pain [3] , which ensure the disease's misdiagnosis and mis-reporting among many other febrile illnesses . The diagnostic methods available also have limitations and a full complement of tests is not feasible in many healthcare settings . There is consensus , however , that dengue is a growing problem both geographically and in its intensity [4] , [5] , [6] . There is an urgent need to compile more extensive occurrence records of dengue virus transmission and assess them for contemporariness and accuracy . Evidence on dengue transmission comes in a wide variety of forms , with varying levels of spatial coverage and reliability . A global audit of dengue distribution therefore requires a transparent methodology to compile these disparate data types and synthesise an output map summarising the current consensus for each country . Such a methodology for compiling and assessing evidence must be robust , repeatable , able to evaluate a large variety of evidence types and incorporate expert opinion . An ideal output metric is a summary statistic ( hereafter referred to as evidence consensus ) that quantifies certainty on dengue virus transmission presence or absence given the accuracy and contemporariness of the evidence available . An evidence-based map of the current distribution of dengue virus transmission will have direct implications for design and implementation of dengue surveillance and , by showing gaps in contemporary knowledge , provide an advocacy platform for improved data . Existing approaches to mapping the global limits of vector-borne diseases have used estimates of biological suitability of local environments , which have proved informative in the cases of some pathogens , such as Plasmodium falciparum [7] , [8] and P . vivax [9] . Several approaches have been used to map biological suitability for dengue using non-dengue-specific variables such as temperature , rainfall and satellite-derived environmental variables [1] , [10] , [11] . Although successive attempts have each increased predictive capacity and resolution , this approach produces variable results in Africa due to a scarcity of confirmed occurrence points across extensive geographic areas . An alternative approach has been to map evidence of dengue occurrence making no assumptions about biological suitability , as in Van Kleef et al . , who reviewed published literature to contrast historic , current and future limits of dengue [5] . To date dengue mapping has focussed on future scenarios , yet understanding of the current distribution of dengue virus transmission is far from complete and needs to be better evaluated before we can make predictions about forthcoming patterns and trends . In this study we combine evidence from large occurrence-point style databases used in biological suitability mapping approaches with a wider systematic review of various sources of evidence to create a more comprehensive dengue database . Using this database we then use the novel method of defining evidence consensus to evaluate the current level of certainty on dengue virus transmission presence or absence at national ( and some sub-national ) levels using a weighted evidence scoring system . Finally , we present these results as a series of global maps that explicitly identify surveillance gaps . This study is the initial part of a five year project to collect , analyse and publicise global dengue virus transmission data . While the map presented here is the most extensive display of current dengue evidence available , we hope that continual data acquisition will result in more evidence from uncertain areas , increasing the resolution at which we can map evidence consensus in future advances .
Evidence for indigenous dengue virus transmission was obtained from four evidence categories: health organisations , peer-reviewed evidence , case data and supplementary evidence ( Figure 1 ) . The first three categories were used for all countries . For countries where some of these categories were not available and/or did not provide good consensus , the fourth category of supplementary evidence was used . Evidence was initially collected at a country level ( Admin0 ) , but resolution was improved to a state/province level ( Admin1 ) or district level ( Admin2 ) at the fringes of the distribution of detectable virus transmission when sufficient data were available . Country dengue status as defined by health organisations was determined by consulting the WHO [12] and CDC [13] dengue distribution maps as well as the Global Infectious Diseases and Epidemiology Online Network ( GIDEON ) database [14] . GIDEON provides a collection of literature and case reports for a range of tropical and infectious diseases in 224 countries . Dengue status by country was recorded as present or absent . The peer-reviewed evidence category contained evidence of dengue occurrence as determined by peer-reviewed sources where details of diagnostic techniques were given . Peer-reviewed journal ( Google Scholar , PubMed , ISI Web of Science ) and disease surveillance network ( ProMED archives , Eurosurveillance archives ) searches were conducted with search terms “country” or “Admin1/2” and “dengue” . Sources were included for the period 1960–2012 and only if cases were confirmed as resulting from indigenous ( i . e . not imported ) transmission . The specialist regional journal collections African Journals Online ( http://www . ajol . info/ ) and China National Knowledge Infrastructure ( http://en . cnki . com . cn/ ) were also searched . Extra publications were found by searching using the location term in Genbank nucleotide records for dengue viruses isolated from human hosts . The search of peer-reviewed sources of evidence resulted in a total of 285 articles being selected for 123 countries where positive dengue occurrence records were identified . This included evidence from returning travellers who were diagnosed upon return to their often non-endemic home countries as opposed to the transmission setting . For these cases , evidence was attributed to the place to which they had travelled . The added value of returning traveller reports is that the travellers are often more immunologically naïve to dengue infections , and also that diagnosis is often pursued more rigorously . Therefore , the sensitivity of detecting an infection is increased . The results of our search were then cross-referenced against a dengue occurrence-point database compiled internally , in a separate exercise . Unlike our country-specific searches , this database of 2836 articles results from searches simply for “dengue” , which were then geo-referenced using the article text . Full details are available in Protocol S1 and the geographic location of the occurrence points are displayed in Figures S1 , S2 , S3 , S4 , S5 , S6 . This cross-referencing resulted in the inclusion of an additional 16 articles in the current analysis and also provided increased justification for our choice of countries to evaluate at Admin1 level . The case data category contained evidence of dengue outbreaks ( minimum 50 infections ) where evidence contained less diagnostic detail , but was more informative about the magnitude of dengue transmission occurring . Case data from the most recent outbreak were obtained from the Program for Monitoring Emerging Diseases ( ProMED ) archive search , WHO DengueNet data query [15] and from GIDEON which holds a detailed record of government-reported case numbers . This resulted in 100 countries with useful dengue case data . In many resource-poor countries , both surveillance and researcher-generated reports are rare . Therefore , in countries where other evidence categories were sparse , we looked for supplemental evidence that suggested possible dengue virus presence . Supplemental evidence types included: presence of an established mosquito vector population of public health significance ( Aedes aegypti , Ae . albopictus or Ae . polynesiensis ) as documented by peer-reviewed literature , confirmed presence of multiple other rarely diagnosed arboviral diseases as documented by peer-reviewed literature , news reports of dengue epidemics found using GoogleNews archives ( http://news . google . co . uk/archivesearch ) and travel advisories from the National Travel Health Network and Centre ( http://www . nathnac . org/ds/map_world . aspx ) issued at a country-level . We included evidence of multiple other rarely diagnosed arboviral diseases , as these are informative about the ability of a country to detect any possible dengue infection . If other arboviral diseases are poorly reported , but documented by peer-reviewed literature as present , then it is possible that dengue is also underreported . In addition to this , we cross-referenced our dataset with the HealthMap database ( www . healthmap . org/dengue/ ) . This website-based application automatically geo-positions cases from websites with news reports and outbreak alerts related to dengue and contains data from a wide variety of sources dating back to 2007 [16] , [17] . This extensive database contributed important evidence especially at smaller spatial scales and in areas where translated articles are not so easily obtained . Supplementary evidence was used in evaluating dengue consensus in 45 countries . While the categories are clearly defined here and in Figure 1 , some overlap of evidence sources did occur , depending on the information content of each source . This meant evidence sources such as ProMED reports could be included twice , in both the peer-reviewed evidence and case data categories , if they contained information about diagnostic tests used for confirmation as well as overall outbreak case numbers . In this section we outline the main sources used for each category , but it should be noted that if evidence from a particular source fitted the criteria for a different evidence category , it was not excluded , but rather included in that category . In order to quantify evidence consensus , a weighted scoring system was developed that attributed positive values to evidence of presence and negative values to evidence of a lack of presence . The aim here was to use an optimal subset of evidence to accurately assess dengue status within a given area . By scoring the evidence categories mentioned above individually and then combining their respective scores , we were able to calculate “evidence consensus , ” a measure of how strongly the combined evidence collection supports a dengue-present or dengue-absent status ( Figure 2 ) . We defined a country as having “complete consensus” on dengue presence when the evidence base was comprised of contemporary forms of most or all of the following evidence types: 1 ) unanimous health organisations agreement , 2 ) a seroprevalence survey , 3 ) Polymerase Chain Reaction ( PCR ) typing of dengue virus or dengue viral RNA , 4 ) a foreign visitor to the area with a confirmed dengue infection upon returning to their home country , and 5 ) records of an epidemic of greater than 50 infections . Such a country has a consensus score of between 80% and 100% . A country with a complete consensus on dengue virus absence is characterised by all health organisations agreeing on dengue absence and high healthcare expenditure ( as an approximate proxy for surveillance capability ) , therefore accounting for both the observed absence of dengue and the minimised possibility of any undetected dengue infections . Such a country scores between −80% and −100% on our scale . A country with no consensus on dengue virus status is characterised by conflicting evidence from different categories and scores close to 0% . Each evidence category was scored independently and category weights applied to reflect the level of detail each category provides: health organisation status ( maximum score 6 ) , peer-reviewed evidence ( maximum 9 ) , case data ( maximum 9 ) and supplementary evidence ( maximum 6 ) . To support the choice of assigned category weights we performed a sensitivity analysis in which two alternative evidence weighting scenarios were applied to the same sources of data: 1 ) neutral ( all categories hold the same weight ) and 2 ) reversed ( health organisation status and supplementary evidence hold weight 9 , peer-reviewed evidence and case data hold weight 6 ) . We then checked for any major deviations in overall country score resulting from such alternative scenarios . In countries where evidence consensus was at best moderate , we attempted to increase consensus through targeted questionnaires . The questionnaire asked about endogenous surveillance and data collection . If available , diagnostic method ( s ) and summary results were requested . Any returned data or reports were then entered into their relevant evidence categories and scored in combination with existing evidence . Questionnaires were distributed to healthcare officials in the country of interest as well as selected offices of the Institut Pasteur . Questionnaire responses and expert comments are part of an on-going process that will lead to future modifications of this map . To map public awareness of dengue worldwide , we searched the ministry of health websites of each of the 128 countries identified as dengue-present ( evidence consensus positive but not indeterminate ) . A country was indicated as publicly displaying dengue data if national dengue case numbers were displayed annually or during epidemic years at a minimum . To calculate the maximum possible population at risk for dengue virus transmission we obtained total population counts from the Global Rural Urban Mapping Project ( GRUMP ) for the 128 countries identified as dengue-present . The GRUMP beta version provides gridded population count estimates at a 1×1 km spatial resolution for the year 2000 [26] , [27] . Population counts for the year 2000 were projected to 2010 by applying country-specific urban and rural national growth rates [28] using methods described previously [29] . As 2010 forms a landmark year for many national censuses , we were able to adjust these expanded population counts using the United Nations 2010 population estimates [30] .
The global distribution of dengue virus transmission as defined by evidence consensus is shown in Figures 3–7 . The mapped colour scale ranges from complete consensus on dengue presence ( dark red ) to indeterminate consensus on dengue status ( yellow ) then through to complete consensus on dengue absence ( dark green ) . A full list of the evidence used for each area and their scoring is available in Table S1 and Figure S7 . In total we identified 128 countries as dengue-present ( i . e . positive values outside the indeterminate range ) , compared to 100 from the WHO , 104 from the CDC and 118 from GIDEON . Compared to the lists produced by the WHO and CDC , we identified 41 additional countries where evidence consensus for presence was outside the indeterminate range yet dengue-absent status was assigned by at least one of these health organisations . Even after performing the sensitivity analysis described earlier , the number of countries defined by our methodology as dengue-present but defined by WHO/CDC as absent never dropped below 36 ( Table 1 ) . We therefore suggest that this list of 36 countries be subject to a review regarding their current health organisation dengue-absent classification . Of these countries , 31 had at least moderate consensus on dengue presence in our final analysis . The majority of these newly identified dengue-present countries were in Africa and the evidence type that allowed greatest identification was returning traveller reports . These sporadic reports established preliminary evidence , which we improved with supplementary evidence and questionnaire retrieval to clarify dengue status if possible ( Table 2 ) . Outside of Africa , the remaining newly identified countries were almost exclusively islands in the Indian and Pacific Oceans and in the Caribbean . The reason for a lack of dengue presence identification by health organisations here is likely the longer interval between epidemics in small isolated nations , resulting in sparse data which different health organisations have interpreted inconsistently . Inclusion of less official surveillance evidence , such as ProMED reports , that detected background case loads alongside officially reported outbreaks allowed our distinction of these areas as in fact dengue-present . A total of 3 . 97 billion people live in these 128 countries outside the indeterminate consensus class . Of these , 824 million live in urban and 763 million in peri-urban areas . These numbers therefore constitute plausible preliminary estimates for the maximum possible population at any risk of dengue transmission . We expect more comprehensive population at risk calculations to refine this figure and quantify levels of risk in our future work , allowing us to give a more accurate estimate . Public display of dengue data varied by continent ( Figure 8 ) . In total , 46 of 128 dengue-present countries displayed annual dengue case numbers . Of these , the highest reporting coverage was observed in Asia and the Americas where 55% and 57% of countries respectively reported dengue publically . This figure was comparably worse in the Pacific ( 29% ) and Africa , Saudi Arabia , Yemen and the western Indian Ocean islands ( Africa+ ) where just 7% of dengue-present countries publicly report dengue and none on mainland continental Africa . There were no regional patterns in the level of dengue case data provided , although the publicising of epidemiological weeks in some Central and South American countries tended to provide higher levels of detail . Deaths due to DHF/DSS/severe dengue were far less commonly reported , although the data are available for some Central American countries . Even allowing for variable internet usage and endogenous public health systems , we highlight the magnitude of disparity in countries' provision of freely available dengue data . Dengue presence is well documented in the Americas with a continuous set of good- or complete- consensus countries from southern Brazil to the Mexico-U . S . A . border ( Figure 3 ) . However , a general regional classification was not producible as in some cases such as Montserrat and Saint Vincent and the Grenadines , where moderate rather than good consensus was found . With only 22% of dengue-present Caribbean countries displaying dengue data publically , dengue status in these small island nations that are characterised by longer inter-epidemic periods proved considerably more heterogeneous . This was mainly due to a lack of confirmed indigenous cases during recent epidemics . Other regions of uncertainty reflect dynamic dengue status at the limits of the disease distribution . Lower consensus estimates in areas of Florida and Argentina result from reliance on smaller amounts of evidence from recent epidemics . Although the disease extent is better described in Florida ( both in terms of resolution and consensus ) due to greater data availability , uncertainty is still present due to the unknown persistence of recent events . A similar pattern of uncertainty exists in Texas but for different reasons , being that the occurrence evidence is older and six of seven counties have no record of occurrence since the late 1980s . A total of 58% of Africa+ countries had a good consensus or better but Africa still showed the highest levels of uncertainty in countries with poor consensus . Concentrations of higher consensus were identified in East and West Africa ( Figure 4 ) . Multiple seroprevalence surveys over several years [31] , [32] , [33] , [34] , [35] made the most significant contribution in defining East Africa's higher-consensus cluster which ranges from Sudan to Tanzania with only Uganda , Rwanda and Burundi exhibiting poor or worse evidence consensus . In addition to this , evidence of outbreaks in coastal areas of Yemen , Saudi Arabia and some evidence of spill-over into Egypt added certainty to the definition of the East Africa high-consensus cluster . Although not as contiguous a tract of countries , a higher-consensus region also exists in West Africa from Senegal to Gabon . Inclusion of reported dengue cases in travellers and soldiers returning from West Africa was available for 13 countries and proved the most useful information in this region . Outside of these higher-consensus regions , evidence consensus is low and a series of countries with moderate or worse consensus can be identified from Chad to Mozambique with only the Democratic Republic of Congo exhibiting good evidence consensus . For many of these countries , there are sporadic reports of dengue occurrence combined with poor disease surveillance and a general lack of data . Dated seroprevalence surveys in areas where many other arboviruses are circulating did little to increase certainty . These factors result in a positive evidence consensus that is nevertheless highly uncertain in large portions of Africa . Even where evidence was available from contemporary epidemics , such as in the case of the western Indian Ocean islands , it was often devalued because there was a lack of clinical differentiation between dengue and chikungunya despite epidemics coinciding . The lack of clear clinical distinction between the two diseases [36] makes the scale of dengue here difficult to identify and as a result , some countries ( such as Reunion ) were identified as having low consensus . Despite the widespread uncertainty in dengue status in many African countries , we were able to differentiate multiple levels of uncertainty . Angola and Mozambique both show lower consensus due to dated evidence forms , yet they are still distinguishable from countries with no evidence or just sporadic occurrences such as Zambia or Congo . A wide variety of contemporary evidence allowed us to display a near continuous distribution of good or complete evidence consensus countries from Indonesia to as far north as Pakistan and Zhejiang , China ( Figure 5 ) . Within this dengue-present area , 58% of countries publicly displayed dengue data ( Figure 8 ) and many reported dengue case data with a high spatial resolution . Minor exceptions to this continuous distribution occur in southern China and North-East India largely due to a lack of contemporary evidence . In Gunagxi and Hainan there is little research interest or case data in recent years despite occurrences in urban centres further along the Chinese coast [37] , [38] , [39] . In North-East India , lower consensus was observed due to a lack of reported cases in recent years combined with the arrival of chikungunya in the area which complicates any potential dengue reporting [40] . Evidence consensus in Asia is lowest in central Asia where contemporary dengue occurrence records combined with low surveillance capacity results in an unclear boundary to the disease . While evidence for dengue presence in the lowland urban centres of Pakistan is accurate and contemporary , reports from the more remote north-west provinces are contemporary , but not accurate [41] , [42] , [43] . This makes determining the extent further north into remote and data-deficient areas of Afghanistan and central Asia difficult to assess . We also found serologic evidence consistent with dengue presence in Turkey [44] and Kuwait [45] , reducing evidence consensus for absence in these countries despite not belonging to any known cluster of dengue-present countries . Although no countries in Europe were defined as dengue-present , sporadic indigenous transmission events have lowered consensus in some countries ( Figure 6 ) . Since the invasion and spread of Ae . albopictus along the Mediterranean coast [46] , indigenous dengue transmission has been detected in Marseilles , France and Korčula , Croatia ( both regions have moderate consensus on dengue absence ) and chikungunya has been found in Italy ( having good consensus on dengue absence ) [47] , [48] , [49] . These isolated events do not in themselves confer dengue presence , but increased surveillance will be required in light of the Ae . albopictus invasion to maintain this status . This , combined with the lower levels of healthcare expenditure , has led to an observed greater uncertainty in some eastern European states . In general , consensus on dengue presence and absence was well defined across Australia and the Pacific islands , with 85% of countries showing good or complete evidence consensus ( Figure 7 ) . Where low consensus was observed , it was largely due to a lack of contemporary evidence despite Pacific-wide dengue epidemics such as in Niue , Nauru , Tuvalu and Papua New Guinea . The duration between epidemics is typically longer in the Pacific and consensus is subject to continual change; for example , in the Marshall islands evidence consensus was upgraded from moderate to complete in the wake of the December 2011 epidemic , which came two decades after the last reported epidemic [50] . Such fluctuation is not entirely unexpected from remote , isolated communities , however . Even though evidence consensus decreases with time , it still remains positive , allowing for potential re-occurrence . Lower evidence consensus was observed for Papua New Guinea due to a lack of reported case data since the 1980's , yet multiple literature sources suggest that dengue is still widespread [51] , [52] , [53] . While dengue occurrence is closely documented in some counties on the Australian coast , the serologic results from Charters Towers has contributed to uncertainty over the inland extent of the disease in Queensland [54] . Only the governments of Australia , New Caledonia and the Solomon Islands report dengue case numbers publicly . Considering the long intervals between epidemics in the Pacific , it is perhaps unsurprising that this is not a priority .
Here we present the distribution of dengue virus transmission as assessed by evidence-based consensus . By emphasising the need for accurate , contemporary evidence through a weighted scoring system , we were able to identify areas where dengue status was more uncertain , particularly in Africa and Central Asia , and identify evidence gaps where surveillance might be better targeted to more accurately assess dengue status . By including a wide variety of evidence we were able to cast doubt on dengue status in countries previously described by health organisations as dengue-absent . While many studies have focussed on the future threat of dengue as a result of range expansion or climate change , this is the first to assess the entirety of knowledge regarding the extent of current virus transmission . We have found that evidence of dengue virus transmission is temporally dynamic and that a contemporary map must emphasise evidence by weighting it appropriately . By increasing temporal resolution to one inter-epidemic period , we have extended the approach of Van Kleef et al . [5] who used evidence from literature searches to produce distribution maps pre- and post- 1975 . Focussing on a higher resolution timescale for dengue evidence is necessary if we are to infer changes in the evidence-based distribution of dengue . The suggestion that dengue is an under-recognised problem in Africa is not a new one [55] , [56] , [57] , but here we present a detailed summary of the specific gaps in evidence that exist in different regions . We show that consensus mapping is flexible to regional differences in evidence availability and as such can produce meaningful outputs in resource-high and low settings . The evidence that dengue is widespread in Africa implies that the continent is underrepresented by occurrence points in the model-based approaches that have been used to investigate the distribution of dengue so far [1] , [10] , [11] . If we are to estimate the burden of dengue in Africa with any fidelity , available data and their underlying assumptions need to be reassessed . Evidence consensus maps provide a more informative alternative to existing country-level maps , such as those provided by the WHO [12] and CDC [58] . As presence or absence exists on a continuous scale of certainty , evidence consensus approaches are more adaptable to incorporating diverse forms of dengue evidence ignored by these organisations in producing their estimates . While we show that different evidence weightings in our scoring system do not significantly alter the result , we were unable to formalise a statistical validation of these weightings due to lack of a training dataset . Our results provide the best estimate thus far of where such data are most needed and comparisons with higher-consensus countries in similar settings should form the first step in directing regional surveillance . Development of methodologies to make approaches such as consensus mapping more reliable is needed as dengue status will increasingly rely on harder-to-quantify evidence types , such as internet search engine terms [59] and multi-language internet text-mining systems [60] , [61] . The success of automated disease surveillance systems such as HealthMap [16] , [17] , and Biocaster [60] , [63] have already been demonstrated . We believe evidence consensus provides the best platform for integrating these diverse forms of information now available for disease occurrence to create an up-to-date , high-resolution map of dengue evidence , whilst retaining important assessments of certainty . We also intend to extend our own data collection and accessibility with a new website linked to the Global Health Network ( http://globalhealthtrials . tghn . org/ ) that will allow evidence contribution from members and will provide a key platform for display of dengue data and consensus maps . Although the current approach was used to map the distribution of dengue , minor modifications to the scoring system would allow it to be utilised for a variety of diseases for which the quality of presence evidence is spatially variable . In this work , our aim was to produce a standardised methodology that used the largest variety of evidence to assess country dengue status , whilst still being applicable in diverse healthcare settings and suitable at multiple spatial scales . We considered the stark contrast in evidence available in Africa as compared to the rest of the world . Our results show that the inclusion of supplementary evidence ( used in 44% of African countries but only 11% of the rest ) , healthcare expenditure information ( for case data absences ) and questionnaires increased evidence consensus in these countries without impacting the methodology applied to the rest of the world . Similarly , we are aware that increasing resolution to Admin1 or Admin2 level may well reduce the evidence available for calculating evidence consensus in each area compared to country-level calculations . As a result , we carefully chose which countries should have increased spatial resolution based on whether sufficient evidence was available in smaller administrative units . We also limited the selection of these countries to those at the limits of the disease's distribution , as data deficiencies in these regions more accurately represent the uncertainty on dengue status given the dynamic nature of global dengue spread . Here we present the most flexible methodology available , to date , for overcoming these problems . We have demonstrated that a systemic approach with relevant optional categories has allowed us to utilise the maximum variety of evidence available for assessing dengue status in the widest variety of situations . We also openly provide a full list of evidence for each country by category ( Table S1 ) . We intend to continue data acquisition by including more endogenous , local evidence through questionnaires and local language search methods , which we expect will allow us to further customise our methodology and assess dengue status in places where we are currently uncertain . Mapping by evidence consensus is a useful approach to quantifying contemporary disease evidence and can be further integrated with geo-spatial modelling to produce worldwide continuous surfaces of dengue risk [64] . Current mapping approaches use presence/absence expert opinion maps to sample pseudo-presence or pseudo-absence points to increase the number of data points on which to base their prediction [65] , [66] , [67] , [68] . Pseudo-sampling could be improved by using the continuous scale of evidence consensus to either affect sample number or point weight within the geo-spatial model . This will lead to more robust , higher resolution dengue maps which are currently in progress [69] . By combining uncertainty assessment from consensus mapping with high-resolution predictions using geo-spatial modelling , we will be able to make more accurate predictions of disease burden with associated confidence intervals made explicit . This will then provide a series of up-to-date assessments of global dengue distribution , thus providing key information to assess dengue spread and the impact of control measures . | Previous attempts to map the current global distribution of dengue virus transmission have produced variable results , particularly in Africa , reflecting the lack of accuracy in both diagnostic and locational information of reported dengue cases . In this study , instead of excluding these less informed points we included them with appropriate uncertainty alongside other diverse evidence forms . After assembling a comprehensive database of different evidence types , a weighted scoring system calculated “evidence consensus” for each country a continuous measure of the certainty of dengue presence or absence when considering the full aggregate of evidence . The resulting map and analysis helped highlight important evidence gaps that underlie uncertainties in the current distribution of dengue . We also show the importance of local knowledge through incorporating questionnairebased responses that can help add clarity in uncertain regions . This analysis showed that presence/absence maps do not sufficiently highlight the uncertainties in the evidence base used to construct them . Mapping by evidence consensus not only encourages greater data inclusion , but it also better illustrates the current global distribution of dengue . Consensus mapping is thus ideal for a range of neglected tropical diseases where the evidence base is incomplete or less diagnostically reliable . | [
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| 2012 | Refining the Global Spatial Limits of Dengue Virus Transmission by Evidence-Based Consensus |
The assembly of the mitotic centromere has been extensively studied in recent years , revealing the sequence and regulation of protein loading to this chromosome domain . However , few studies have analyzed centromere assembly during mammalian meiosis . This study specifically targets this approach on mouse spermatocytes . We have found that during prophase I , the proteins of the chromosomal passenger complex Borealin , INCENP , and Aurora-B load sequentially to the inner centromere before Shugoshin 2 and MCAK . The last proteins to be assembled are the outer kinetochore proteins BubR1 and CENP-E . All these proteins are not detected at the centromere during anaphase/telophase I and are then reloaded during interkinesis . The loading sequence of the analyzed proteins is similar during prophase I and interkinesis . These findings demonstrate that the interkinesis stage , regularly overlooked , is essential for centromere and kinetochore maturation and reorganization previous to the second meiotic division . We also demonstrate that Shugoshin 2 is necessary for the loading of MCAK at the inner centromere , but is dispensable for the loading of the outer kinetochore proteins BubR1 and CENP-E .
Accurate chromosome segregation in mitosis is crucial to maintain a diploid chromosome number . Errors in chromosome segregation result in aneuploid daughter cells which are prone to become malignant . The centromere is the chromosome domain that directs this segregation process since it is involved in relevant events such as sister-chromatid cohesion , the spindle assembly checkpoint ( SAC ) , the attachment to spindle microtubules ( MTs ) and chromosome movements [1]–[4] . The centromere is structurally divided into the kinetochore and the inner centromere domains . The kinetochore is a proteinaceus structure at the centromere surface mostly involved in the attachment of spindle MTs , chromosome movements and SAC regulation [2] , [3] , [5] . In vertebrates , this domain is subdivided into three distinct regions: the inner , the central and the outer kinetochore plates [2] , [3] , [6] . The inner kinetochore plate is formed by the chromatin subjacent to the kinetochore , in which histone H3 is replaced by CENP-A [7] , [8] , and additional constitutive proteins that appear at kinetochores throughout the cell cycle . On the other hand , the outer kinetochore plate and the fibrous corona , detected only in prometaphase , are mainly composed of MT motor proteins , such as CENP-E and cytoplasmic dynein , as well as SAC proteins , as for instance Bub1 , BubR1 , Mad1 and Mad2 [2] , [3] , [5] , [6] . The inner centromere domain is the region spanning between sister kinetochores . Several proteins with different functions have been localized in this region [9] . Some proteins like CENP-B are constitutive , while many others are incorporated to the inner centromere at specific cell cycle stages . This is the case for the chromosomal passenger complex ( CPC ) proteins INCENP , the kinase Aurora-B , Survivin and Borealin/Dasra . The CPC has been involved in many different functions such as chromatin modifications ( through the phosphorylation of histone H3 ) , correction of kinetochore attachment errors , the SAC , the assembly of a stable bipolar spindle , and completion of cytokinesis [9] , [10] . Another group of inner centromere proteins are the cohesin complexes that maintain sister chromatids tightly associated until their segregation in anaphase [11] , [12] . Cohesin complexes are located between sister chromatids along their entire length , but interestingly , during mitosis most of them are released from chromosome arms during prophase/prometaphase [13] , while the centromeric ones are protected until the metaphase/anaphase transition [14] . It has been proposed that two proteins placed at the inner centromere , shugoshins SGOL1 and/or SGOL2 , protect centromeric cohesin complexes from cleavage by separase until the onset of anaphase [15] , [16] . Additionally , the MT depolymerizing kinesin MCAK is also found at this domain [17]–[19] and is involved in the correction of improper kinetochore-MTs attachments [17] , [20] . The precise sequence of loading of proteins to the centromere and the kinetochore is largely unknown . While constitutive proteins of the inner kinetochore are present all along the cell cycle , some of the inner centromere proteins are loaded during prophase and most outer kinetochore proteins are assembled during prometaphase after nuclear envelope breakdown . Thereafter , most of these proteins are released from the centromere and the kinetochore after the inactivation of the SAC during anaphase [21] , [22] . CENP-A has a crucial role in centromere specification and kinetochore assembly since most kinetochore proteins need , either directly or indirectly , its presence to be properly incorporated to the kinetochore [3] , [22] , [23] . In this context , CENP-A is needed for the incorporation of the constitutive inner kinetochore proteins CENP-C , -H and -I , at least in mouse and Caenorhabditis elegans [24] . These constitutive proteins are in turn needed for the loading of the outer kinetochore proteins CENP-E , CENP-F and SAC proteins [22] , [24] , [25] . These outer kinetochore proteins also present a precise loading sequence . For instance , Bub1 and BubR1 are necessary for the correct loading and function of CENP-E and other SAC proteins [26] , [27] . Kinetochore proteins are also needed to recruit proteins to the inner centromere . There is an ongoing controversy over whether CENP-A is required for the localization of Aurora-B . While some studies support that Aurora-B needs CENP-A for its loading [28]–[30] , other reports suggest that this loading is independent of CENP-A [31] . CENP-A also seems to condition the localization of MCAK [30] . Additionally , Bub1 is necessary for the accurate localization of MCAK [30] , the stability and correct positioning of CPC , and the binding of Shugoshin to the inner centromere [32] . Regarding the CPC , different studies have revealed that Borealin , INCENP , Aurora-B and Survivin form a complex in which each subunit seems to be necessary for the loading of the others [9] , and the immunodepletion of any of them caused the removal of the others [33] . Borealin might be the key protein , since it can bind DNA in vitro [31] and Dasra-A , the Borealin-related protein in Xenopus , is necessary for the loading of the other CPC proteins [34] . However , other proteins like INCENP , which can bind the histone variant H2Az [35] , or Aurora-B , which may be recruited at the centromere through the phosphorylation of CENP-A by Aurora-A [28] , may be also important for CPC loading . Alternatively , it has also been suggested that SGOL2 is needed for the correct loading of the CPC proteins in yeast since , in its absence , Aurora-B can not be targeted to the centromeres [36] , [37] . Moreover , SGOL2 is needed for the loading of MCAK at centromeres in human cells [38] . The diverse pathways leading to the recruitment of outer kinetochore and inner centromere proteins have been mainly studied in mitotic chromosomes , whereas little information is available regarding the assembly of centromeres and kinetochores during mammalian meiosis . Previous reports have shown that in male mouse meiosis , INCENP is loaded at the pericentromeric chromatin before Aurora-B [39] , and that MCAK is loaded after Aurora-B [19] . In this study , we have analyzed the loading sequence of the inner centromere proteins Borealin , INCENP , Aurora-B , SGOL2 , and MCAK , and the outer kinetochore proteins CENP-E and BubR1 , during both male mouse meiotic divisions . Additionally , we have used a knockout mouse for Sgol2 to analyze the influence of this protein in the loading of MCAK and the outer kinetochore proteins CENP-E and BubR1 . Our results lead us to present a working model for the sequential assembly of centromere and kinetochore proteins during meiosis .
The constitutive kinetochore proteins revealed by an anti-centromere autoantibody are located at kinetochores from the beginning of meiosis [40] . However , most of the inner centromere and outer kinetochore proteins are loaded at different times during both meiotic divisions . In order to delineate the loading sequence of the CPC proteins Borealin , INCENP , and Aurora-B we made double immunolabelings on spermatocytes . Unfortunately , we were unable to detect Survivin even though we used several antibodies . The double immunolabeling of INCENP and SYCP3 , a structural component of synaptonemal complex lateral elements , allowed us to determine previously that INCENP labels the synaptonemal complex central element from zygotene up to mid/late pachytene when it begins to relocalize to heterochromatic chromocenters , while Aurora-B appears at chromocenters later in diplotene [39] . In this study we compared the relative loading of these two proteins with Borealin . We found that Borealin appeared at chromocenters during pachytene when INCENP was still only present at synaptonemal complexes ( Figure 1A and 1B ) . The chromocenters represent clustered centromere heterochromatic regions that are clearly discerned after DAPI staining , and located at the nuclear periphery . However , since we have projected different focal planes through the spermatocytes , some chromocenters appear in the middle of the nuclei ( Figure 1A and 1B ) . In other pachytene spermatocytes , Borealin and INCENP colocalized at chromocenters whereas INCENP was also visualized at synaptonemal complexes ( Figure 1C and 1D ) . Taking into account these results we considered that Borealin first appeared at early pachytene , while INCENP began to redistribute from synaptonemal complexes to chromocenters in mid pachytene . These proteins colocalized at centromeres from mid pachytene up to late anaphase I . The labeling of INCENP at synaptonemal complexes became undetectable at late pachytene ( Figure 1E and 1F ) as previously reported [39] . Although INCENP was present at chromocenters in late pachytene , Aurora-B was not detected at this stage ( Figure 1E and 1F ) . Aurora-B became first detectable at chromocenters later , during early diplotene , colocalizing with INCENP ( Figure 1G and 1H ) . From diplotene onwards , the three CPC proteins colocalized . We next studied the timing of centromere loading of SGOL2 and MCAK , which are present at the inner centromere in metaphase I [19] , [41] . We have previously analyzed the time of appearance of SGOL2 at centromeres by double immunolabeling with the cohesin subunit RAD21 , which labels cohesin axes that are coincident with the synaptonemal complex lateral elements , and can then be used to accurately stage prophase I spermatocytes [42] . Likewise , we have already analyzed the loading time of MCAK by double immunolabeling with SYCP3 . These studies showed that SGOL2 and MCAK were loaded at centromeres by late diplotene [19] , [41] . However , we did not know the relative loading sequence of these two proteins . Since we had found that Aurora-B was the last CPC protein loaded at centromeres , we used it as a marker to ascertain the loading time of SGOL2 and MCAK . During early diplotene , when Aurora-B labeled the chromocenters , no labeling was found for SGOL2 ( Figure 2A and 2B ) or MCAK ( data not shown ) . However , SGOL2 became detectable at centromeres later , by late diplotene , as dotted signals close to or inside the Aurora-B labeled chromocenters ( Figure 2C and 2D ) . On the other hand , when spermatocytes where double immunolabeled for SGOL2 and MCAK , some of them only showed SGOL2 labeling ( Figure 2E and 2F ) , while in other ones both proteins colocalized at centromeres ( Figure 2G and 2H ) . Thus , SGOL2 is loaded at centromeres during late diplotene , and MCAK loads later at very late diplotene or in early diakinesis . The location of SGOL2 and MCAK was identical , indicating that these two proteins colocalized at the inner centromere . Interestingly , at the time SGOL2 and MCAK were loaded to the centromere , the CPC proteins , which in previous stages occupied the entire chromocenters , had changed their distribution . Thus , from late diplotene up to early diakinesis , and concomitantly with ongoing chromosome condensation , the CPC proteins appeared as more discrete signals that colocalized with MCAK and SGOL2 ( Figure 3 ) . We also analyzed the loading of the outer kinetochore proteins CENP-E and BubR1 [43] , [44] . Taking into account bivalent condensation , we had reported that during male mouse meiosis CENP-E was first detectable at kinetochores during late diakinesis/early prometaphase I [39] , [40] . However , there are not data about the relative loading sequence of CENP-E and BubR1 . These proteins were first found at kinetochores once the CPC proteins and SGOL2 and MCAK had been recruited to the inner centromere . In early diakinesis spermatocytes , when MCAK was already loaded at the inner centromere , neither CENP-E nor BubR1 could be detected at kinetochores ( Figure 2I and 2J ) . During zygotene and pachytene , BubR1 was detected as large nucleoplasmic masses ( Figure S1A–S1D ) . A double immunolabeling of BubR1 and fibrillarin , a nucleolar protein , demonstrated that the BubR1 nuclear masses corresponded to nucleoli lying in the nucleoplasm or associated to the sex body ( Figure S2A and S2B ) . From diplotene up to early diakinesis , BubR1 was visualized at the disintegrating nucleoli and numerous smaller aggregates in the nucleoplasm ( Figures S1E–S1H and S2C and S2D ) . However , these smaller BubR1 aggregates did not colocalize with either the kinetochores , as revealed by an ACA serum ( Figure S1E–S1H ) , or the inner centromere protein MCAK ( Figure 2I and 2J ) . BubR1 was first detected onto kinetochores at late diakinesis . During this stage , identified by the absence of a sex body typical of the pachytene and diplotene stages , and showing condensed bivalents , BubR1 was no longer detected at small nucleoplasmic aggregates ( Figure S1J and S1K ) . BubR1 appeared as plates or dots near the larger MCAK signals ( Figure 2K and 2L ) . Following BubR1 incorporation , CENP-E became loaded to kinetochores . CENP-E was not detectable at late diakinesis ( Figure 2M and 2N ) , but was clearly found later at early prometaphase I kinetochores colocalizing with BubR1 after nuclear envelope breakdown ( Figure 2O and 2P ) . A recent study proposes that SGOL2 is needed for the loading of MCAK at the inner centromere of mitotic chromosomes [38] . We then analyzed whether MCAK , as well as BubR1 and CENP-E , were loaded at the inner centromere and outer kinetochore , respectively , in the absence of SGOL2 in male knockout mice for Sgol2 [45] . Our results showed that MCAK , that is found at the inner domain of wild-type metaphase I centromeres ( Figure 4A and 4B ) [19] , was not present at centromeres in metaphase I Sgol2−/− spermatocytes . Instead , MCAK only appeared at one or two round cytoplasmic aggregates ( Figure 4C and 4D ) . By contrast , BubR1 ( Figure 4E–4H ) and CENP-E ( Figure 4I–4L ) loaded accurately to be present at the outer kinetochore in metaphase I Sgol2−/− spermatocytes . Moreover , these two proteins , that are involved in the regulation of the SAC , appeared enriched at kinetochores of unaligned bivalents in both wild-type ( Figure 4E , 4F , 4I , and 4J ) and Sgol2−/− ( Figure 4G , 4H , 4K , and 4L ) metaphase I spermatocytes . During male mouse meiosis , the CPC proteins INCENP and Aurora-B , and also CENP-E , relocalize from the centromeres to the spindle midzone during late anaphase I [39] , whereas SGOL2 and MCAK disappear from centromeres during the telophase I/early interkinesis transition [19] , [41] , and BubR1 is lost from kinetochores during anaphase I/telophase I ( Figure S1M–S1T ) . Accordingly , all these proteins were not present at the centromeres in early interkinesis nuclei and need to be reloaded to the centromere in preparation for the second meiotic division . Interkinesis nuclei are characterized by the presence of a variable number of chromocenters at their internal regions ( Figure 5 ) . As occurred during prophase I , the first detectable proteins in interkinesis were the CPC ones . In this sense , we found that INCENP was loaded at chromocenters before Aurora-B ( Figure 5A–5D ) . Afterwards , these two proteins colocalized at the heterochromatic chromocenters ( Figure 5C and 5D ) . Then , we compared the labelings of Aurora-B and SGOL2 . SGOL2 was targeted to the centromere after the loading of Aurora-B as during prophase I ( Figure 5E–5H ) . Interestingly , Aurora-B labeled the entire chromocenters , while the SGOL2 signals were inside them ( Figure 5G ) . Following the SGOL2 loading ( Figure 5I and 5J ) , MCAK was detected at the centromeric regions as small spots inside the chromocenters and colocalizing with SGOL2 ( Figure 5K and 5L ) . As during prophase I , we observed that the distribution of the CPC proteins dramatically changed once MCAK was detected at the chromocenters . Thus , they concentrated inside the chromocenters to appear as small spots that colocalize with MCAK and SGOL2 ( data not shown ) . The last proteins loaded to the centromeric region after MCAK were the outer kinetochore proteins BubR1 and CENP-E ( Figure 5M–5P ) . Interestingly , BubR1 appeared only at nucleoli in most interkinesis nuclei , but during the late interkinesis/prophase II transition it relocalized onto kinetochores ( Figure S3 ) .
In this study we have analyzed the loading sequence of different inner centromere and outer kinetochore proteins in male mouse meiosis . Our observations lead us to propose a sequence of assembly for those proteins ( Figure 6 ) . The first group of proteins that we have detected at the centromeric region , excluding the kinetochoric constitutive ones detected by the ACA serum , are the CPC proteins . During prophase I , these proteins are loaded at the inner centromere between early pachytene and early diplotene . Although it has been previously established that during mitosis the presence of all CPC subunits is necessary for the assembly of the complex at the inner centromere [9] , we have detected that during meiosis Borealin , Aurora-B and INCENP are loaded in a precise sequence . Thus , Borealin was the first CPC protein that we found at the heterochromatic chromocenters during early pachytene , followed by INCENP and Aurora-B during mid pachytene and early diplotene , respectively . It has been described that Borealin can bind DNA in vitro [31] , but it is still unknown if it has any affinity for centromeric DNA . However , Borealin could initiate the sequence of CPC assembly to the inner centromere during meiosis . In this sense , it might bind to the pericentromeric DNA and trigger the loading of the remaining CPC proteins . The next CPC protein in the meiosis assembly sequence , INCENP , presents an N-terminal domain that binds Borealin and Survivin that when depleted prevents their association to the inner centromere [31] , [46] . Thus , during meiosis INCENP could also interact with Borealin , and presumably also Survivin , by its N-terminal domain . We have to highlight that INCENP is initially detected at the central element of the synaptonemal complex during zygotene , and relocalizes to the centromeric region by mid pachytene [39] . We do not know whether INCENP may play any specific role at the synaptonemal complex or is just waiting to be assembled at the inner centromere . Finally , the last CPC protein to be loaded at the pericentromeric chromatin is Aurora-B which is described to bind the C-terminal domain of INCENP [33] . This fact suggests that once INCENP is targeted to the inner centromere , Aurora-B would then be loaded . The sequential loading of the CPC proteins that we have observed during meiosis contrasts with the proposed simultaneous presence of the four CPC proteins for assembling the complex during mitosis [9] . These apparent differences might be due to the long duration of prophase I , in relation to the relatively shorter mitotic one , which allows to accurately analyze the sequential loading of the three CPC proteins . Nevertheless , it is likely that the entire CPC complex is assembled once all the subunits have been accurately loaded . During male mouse meiosis SGOL2 appears at the inner centromere during late diplotene [19] , [41] to protect centromeric cohesin complexes from cleavage by separase during the metaphase I/anaphase I transition [45] , [47] . We have found that during late diplotene SGOL2 appears inside the chromocenters where Borealin , INCENP and Aurora-B are present . This result demonstrates that the interaction area of SGOL2 within the centromeric region is smaller than the targeting zone for the CPC proteins . Interestingly , after the SGOL2 loading , the CPC proteins then change their distribution to become restricted to a smaller area that at that time colocalizes with SGOL2 . It has been recently proposed that during fission yeast mitosis and budding yeast meiosis Sgo2 and Sgo1 are required , respectively , for the recruitment of some CPC components to the centromere [36] , [37] , [48] . By contrast , during Drosophila meiosis and in Xenopus egg extracts , the CPC proteins promote the loading of the single Shugoshin MEI-S322 and xSgo to the inner centromere , respectively [49] . Likewise , in HeLa cells the localization of SGOL2 is dependent on Aurora-B [38] . In this sense , our results indicate that during mouse meiosis the CPC proteins are loaded to centromeric heterochromatin without the participation of SGOL2 , but their redistribution from the centromeric heterochromatin to the inner centromere occurs after the loading of SGOL2 . After the loading of SGOL2 , and concomitantly with the relocalization of the CPC proteins , we detected the incorporation of MCAK to centromeres . MCAK , Aurora-B and SGOL2 have been involved in the correction of inaccurate merotelic attachments in mitosis [38] and meiosis [19] , [41] . Thus , the relocalization of the CPC and the loading of MCAK could involve a reorganization of the centromere in preparation for microtubule interactions . The temporal localization of SGOL2 may indicate its key role in such centromere reorganization , and/or in the recruitment of other inner centromere proteins like MCAK . Indeed , it has been reported that in HeLa cells SGOL2 recruits MCAK to the inner centromere [38] . Our results on Sgol2−/− spermatocytes support that SGOL2 also recruits MCAK during mouse meiosis since in mutant spermatocytes MCAK never localizes to the inner domain , as occurs in Sgol2−/− mouse embryonic fibroblasts [45] . In this respect , we have found that in the absence of SGOL2 and MCAK , bivalents align accurately at the metaphase I plate and meiosis progression is not blocked . Consequently , it is uncertain whether MCAK has an essential role during at least meiosis I . We have found that BubR1 is recruited at kinetochores at late diakinesis after the loading of all the studied inner centromere proteins . This is followed by the incorporation of CENP-E onto kinetochores during prometaphase I . These results thus suggest that during mouse meiosis , as occurs in mitosis , the outer kinetochore proteins are loaded on maturing kinetochores once the inner centromere has been completely organized . Indeed , the inhibition of Aurora-B function during mitosis impairs the loading of BubR1 , MAD2 and CENP-E to the outer kinetochore [50] . Furthermore , it has been demonstrated that the Aurora-B/INCENP complex induces the localization of MPS1 , BUB1 , BUB3 , and CENP-E to the kinetochores in CSF Xenopus egg extracts [51] . The sequence of loading that we found for BubR1 and CENP-E in meiosis seems to be consistent with previous reports in mitosis . Thus , although CENP-E is required to enhance the recruitment and the activity of BubR1 [52] , the previous presence of Bub1 and BubR1 is necessary for CENP-E to be properly loaded to the outer kinetochore [26] , [27] . All the proteins which we have tested in this study are released from the inner centromere and the kinetochore at the end of meiosis I between late anaphase I and the end of telophase I . During this period , INCENP , Aurora-B and CENP-E , relocalize to the spindle midzone and finally disappear [39] , [40] , and SGOL2 , MCAK and BubR1 become undetectable [19] , [41] . We have previously shown that SYCP3 , a structural component of the lateral elements of the synaptonemal complex , colocalizes with the cohesin subunit RAD21 at the inner domain of metaphase I centromeres . These proteins are released from the inner centromere during interkinesis and are not visualized during meiosis II , thus suggesting that they are not essential for centromere behavior during meiosis II [42] . By contrast , all the proteins analyzed in this study are incorporated again at the centromere during the interkinesis stage with the same loading sequence as during meiosis I . This fact strongly suggests that the structural assembly of the centromere follows a pattern that is conserved in both meiotic divisions . However , important differences may be highlighted , since the assembly of the centromere during meiosis I is initiated during late prophase I , while the assembly for meiosis II takes place during interkinesis . This reveals that the underestimated interkinesis is not just a resting stage between the two meiotic divisions , but a crucial period during mammalian male meiosis , for , at least , chromosome and centromere reorganization for the second meiotic division .
Testes from adult normal C57BL/6 and Sgol2−/− [45] male mice were used . All animals were handled in strict accordance with good animal practice as defined by the relevant national and/or local animal welfare bodies , and all animal work was approved by the UAM committee . Testes were removed , detunicated and seminiferous tubules fixed for squashing and subsequent immunofluorescence as previously described [40] , [53] . Seminiferous tubules were fixed for 10 min in freshly prepared 2% formaldehyde in PBS ( 137 mM NaCl , 2 . 7 mM KCl , 10 . 1 mM Na2HPO4 , 1 . 7 mM KH2PO4 , pH 7 . 4 ) containing 0 . 1% Triton X-100 ( Sigma ) . After 5 min , several seminiferous tubules fragments were placed on a slide coated with 1 mg/ml poly-L-lysine ( Sigma ) with a small drop of fixative , and gently minced with tweezers . The tubules were then squashed and the coverslip removed after freezing in liquid nitrogen . The slides were later rinsed three times for 5 min in PBS , and incubated for 45 min at room temperature or 12 h at 4°C with primary antibodies diluted in PBS . In double labeling experiments , primary antibodies from different host species were incubated simultaneously . Following three washes in PBS for 5 min , the slides were incubated for 30 min at room temperature with secondary antibodies . The slides were subsequently rinsed in PBS and counterstained for 3 min with 5 µg/ml DAPI ( 4′ , 6-diamidino-2-phenylindole ) . After a final rinse in PBS , the slides were mounted with Vectashield ( Vector Laboratories ) and sealed with nail polish . Immunofluorescence image stacks were collected on an Olympus BX61 microscope equipped with epifluorescence optics , a motorized z-drive , and an Olympus DP70 digital camera controlled by analySIS software ( Soft Imaging System ) . Stacks were analyzed and processed using the public domain ImageJ software ( National Institutes of Health , USA; http://rsb . info . nih . gov/ij ) . Final images were processed with Adobe Photoshop 7 . 0 software . Kinetochores were detected with a purified human anti-centromere autoantibody ( ACA ) ( Antibodies Incorporated , cat . no . 15-235 ) at a 1∶50 dilution . Borealin was detected with a rabbit affinity-purified antibody against human Borealin ( 1647 ) kindly provided by Dr . W . C . Earnshaw [46] at a 1∶30 dilution . To detect INCENP we used a polyclonal rabbit serum ( pAb1186 ) raised against chicken INCENP kindly provided by Dr . W . C . Earnshaw [54] , which also recognizes mouse INCENP [39] , at a 1∶100 dilution . Aurora-B kinase was detected with the mouse monoclonal AIM-1 antibody ( Transduction Labs ) at a 1∶30 dilution . SGOL2 was detected with a rabbit polyclonal serum ( K1059 ) against the C-terminus of mouse SGOL2 kindly provided by Dr . J . L . Barbero [19] , [41] at a 1∶20 dilution . To detect MCAK we used affinity-purified sheep and rabbit polyclonal antibodies against human MCAK , kindly provided by Dr . L . Wordeman [18] , [55] at 1∶40 and 1∶200 dilutions , respectively . An affinity purified sheep polyclonal antibody against human BubR1 ( SBR1 . 1 ) kindly provided by Dr . S . S . Taylor [56] was used at a 1∶50 dilution . CENP-E was detected using a polyclonal rabbit serum ( pAb1 . 6 ) that recognizes the neck region ( amino acids 256–817 ) of human CENP-E kindly provided by Dr . T . Yen [57] , at a 1∶100 dilution . Fibrillarin was detected with a human anti-fibrillarin autoantibody ( S4 ) kindly provided by Dr . R . Benavente at a 1∶200 dilution . The secondary antibodies used were: donkey anti-human IgG ( Jackson ) at a 1∶150 dilution , donkey anti-rabbit IgG ( Jackson ) at a 1∶150 dilution , donkey anti-mouse IgG ( Jackson ) at a 1∶150 dilution , donkey anti-sheep IgG ( Jackson ) at a 1∶40 dilution . All of them were conjugated with either Texas Red or fluorescein isothiocyanate ( FITC ) . | The centromere is a chromosome domain essential for the correct partitioning of chromosomes during mitotic and meiotic cell divisions . The characterization of the centromeric proteins and their sequential assembly have been extensively studied in mammalian mitosis , since defective chromosome segregation is associated with birth defects and cancer . However , few studies have analyzed the centromere assembly during meiosis , a special cell division leading to the production of haploid gametes . Here , we analyze the sequence of loading of several centromeric and kinetochoric proteins during male mouse meiosis . We show that during both meiotic divisions , the proteins of the chromosomal passenger complex Borealin , INCENP , and Aurora-B load sequentially to the inner centromere before Shugoshin 2 and MCAK . The outer kinetochore proteins BubR1 and CENP-E are the last ones to be assembled . We also demonstrate , using a knockout mouse for Sgol2 , that the inner centromeric protein Shugoshin 2 is dispensable for the loading of the outer kinetochore proteins BubR1 and CENP-E , but necessary for the assembly of MCAK . This study shows that the analysis of the behavior of different centromere proteins during meiosis can offer new insights concerning centromere organization . | [
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| 2009 | Sequential Assembly of Centromeric Proteins in Male Mouse Meiosis |
Cell stress and infection promote the formation of ubiquitinated aggregates in both non-immune and immune cells . These structures are recognised by the autophagy receptor p62/sequestosome 1 and are substrates for selective autophagy . The intracellular growth of Salmonella enterica occurs in a membranous compartment , the Salmonella-containing vacuole ( SCV ) , and is dependent on effectors translocated to the host cytoplasm by the Salmonella pathogenicity island-2 ( SPI-2 ) encoded type III secretion system ( T3SS ) . Here , we show that bacterial replication is accompanied by the formation of ubiquitinated structures in infected cells . Analysis of bacterial strains carrying mutations in genes encoding SPI-2 T3SS effectors revealed that in epithelial cells , formation of these ubiquitinated structures is dependent on SPI-2 T3SS effector translocation , but is counteracted by the SPI-2 T3SS deubiquitinase SseL . In macrophages , both SPI-2 T3SS-dependent aggregates and aggresome-like induced structures ( ALIS ) are deubiquitinated by SseL . In the absence of SseL activity , ubiquitinated structures are recognized by the autophagy receptor p62 , which recruits LC3 and targets them for autophagic degradation . We found that SseL activity lowers autophagic flux and favours intracellular Salmonella replication . Our data therefore show that there is a host selective autophagy response to intracellular Salmonella infection , which is counteracted by the deubiquitinase SseL .
Salmonella enterica is a facultative intracellular pathogen that survives and replicates in a variety of hosts . The virulence traits of Salmonella include the Salmonella pathogenicity-island ( SPI ) -1- and -2-encoded type 3 secretion systems ( T3SSs ) , which are important for invasion of host cells [1] and intracellular replication [2] , [3] , respectively . Intracellular replication occurs in a membrane-enclosed compartment , the Salmonella-containing vacuole ( SCV ) , and requires the delivery of an extensive repertoire of effectors to the host cytoplasm by the SPI-2 T3SS [4] . Ubiquitination is an essential process in eukaryotic cells that regulates protein degradation , localization and function . The ability to manipulate the ubiquitin system is a feature common to many intracellular pathogens , including Salmonella , which delivers several effectors to the cell cytosol that interfere with the host cell ubiquitination [5] , [6] . Absence of the SPI-2 T3SS-delivered deubiquitinase SseL leads to an accumulation of ubiquitinated proteins within infected cells and attenuates Salmonella virulence in mice [7] . Intracellular bacteria generate a diverse array of substrates that are ubiquitinated during infection . These include vacuolar membrane remnants produced after intracellular lysis of bacterial vacuoles [8] , bacterial cell wall products [9] and protein aggregates or aggresome-like induced structures ( ALIS ) [10]–[12] . In addition , bacterial LPS , cell stress and toll-like receptor ( TLR ) signalling can trigger the formation of ALIS in several cell types , including macrophages [13] , [14] . ALIS and other ubiquitinated protein aggregates are targeted by ubiquitin binding proteins such as p62/sequestosome 1 ( p62 hereafter ) , which can lead to receptor-mediated selective macroautophagy ( hereafter referred to as selective autophagy ) . In response to this cellular defence pathway , bacteria have evolved different ways to interfere with selective autophagy . Listeria ActA recruits the Arp2/3 complex and Ena/VASP to the surface of cytosolic bacteria to prevent recognition by ubiquitin and p62 [15] . Likewise , Shigella camouflages its surface through binding of the T3SS effector protein IcsB to the bacterial surface protein IcsA/VirG , thereby preventing the recognition of VirG by the autophagy-related protein , Atg5 [16] , and avoiding recruitment of ubiquitin and p62 [17] . The ubiquitination and selective autophagy of cytosolic Salmonella has been studied extensively by others [18]–[20] . In this work , we demonstrate that Salmonella within vacuoles induces a cellular response leading to the formation of SPI-2 T3SS-dependent ubiquitinated aggregates and ALIS during infection . We show that a SPI-2 T3SS effector , SseL , deubiquitinates these aggregates and prevents the recruitment of the autophagy markers p62 and LC3 . SseL deubiquitinase activity leads to a reduction in autophagic flux during infection and promotes intracellular bacterial replication .
In epithelial cells , replicating Salmonella form a cluster of SCVs ( referred to as a microcolony ) close to the microtubule organizing centre and Golgi apparatus [21] . Bacterial replication is accompanied by dramatic reorganization of late endosomal compartments [22] , and condensation of actin [23] , microtubules [24] and intermediate filament networks [25] , in close proximity to SCVs . These processes could perturb cellular homeostasis and cause cell stress . Since cell stress often leads to the appearance of inclusions containing ubiquitinated proteins [11] , [13] confocal microscopy was used to analyse the localization of mono- and poly-ubiquitinated proteins in relation to bacterial microcolonies in HeLa cells infected with Salmonella enterica serovar Typhimurium ( S . Typhimurium ) strains . At 10 h after invasion , in addition to characteristic labelling of the nucleus , punctate ubiquitin labelling was observed close to bacterial microcolonies in approximately 40% of cells infected with wild-type bacteria ( Fig . 1A and 1B ) . In contrast , less than 10% of cells infected with an ssaV null mutant ( ΔssaV ) strain ( lacking a functional SPI-2 T3SS ) had such structures ( Fig . 1A and 1B ) , even after infection for 14 h , when the numbers of intracellular ΔssaV bacteria were similar to those of the wild-type strain at 10 h ( Fig . 1D ) . This suggested that translocation of SPI-2 T3SS effectors is required for the formation of the majority of these ubiquitinated aggregates . Several SPI-2 T3SS effectors directly interfere with ubiquitin pathways by acting as E3 ligases ( SspH1 , SspH2 and SlrP ) [26] , [27] or as a deubiquitinase ( SseL ) [6] . SCV-associated ubiquitinated aggregates in cells infected with strains lacking the E3 ligases were similar to those in cells infected with wild-type bacteria ( Fig . 1B ) and were also present following infection with a triple mutant strain lacking all three E3 ligases ( Fig . S1 ) . In contrast , 75% of cells infected with ΔsseL mutant bacteria showed striking SCV-associated ubiquitin labelling ( Fig . 1A and 1B ) , indicating that the presence of SseL results in the loss of ubiquitinated structures near SCVs . These results suggest that SPI-2 T3SS effector translocation induces the accumulation of aggregates close to SCVs that are ubiquitinated by unknown E3 ligase ( s ) and are deubiquitinated by SseL . To establish that the deubiquitinase activity of SseL was required to reduce the frequency of SCV-associated ubiquitinated aggregates , HeLa cells were infected with a Salmonella ΔsseL mutant strain complemented with plasmid-expressed epitope-tagged versions of SseL: SseL or SseLC262A ( SseLC/A ) , a mutant that lacks a catalytic cysteine required for its activity [7] . Complementation with wild-type SseL dramatically reduced the frequency of cells containing ubiquitinated structures , whereas translocated SseLC/A had no noticeable effect , despite extensive co-localization with ubiquitin ( Fig . 1B and S2A ) . Ubiquitin structures within clusters of at least five bacteria were detected from approximately 6 h after bacterial invasion , coinciding with the onset of SPI-2 T3SS effector translocation [28] , [29] ( Fig . 1C ) ; infections with bacteria lacking SseL resulted in a consistent increase in their frequency from 8 h onwards ( Fig . 1C ) . Deletion of sseL had no major effect on the number of ubiquitinated cytosolic bacteria occurring at early time-points after invasion ( Fig . S2C ) , indicating that the accumulation of ubiquitin near SCVs is distinct from the ubiquitination of the small proportion of cytosolic Salmonella . Colocalization between SCV-associated ubiquitin labelling and the lysosomal membrane glycoprotein LAMP1 ( Fig . 1A ) suggested that manipulation of host vesicular trafficking pathways might be involved in the accumulation of ubiquitin . Salmonella SPI-2 T3SS effectors , including SifA , induce and regulate extensive tubular networks called Salmonella-induced filaments ( sifs ) that are enriched in late endosomal markers such as LAMP1 [30] and cause the collapse of endosomal compartments around SCVs [31] . To investigate whether there was a link between the accumulation of the ubiquitinated aggregates and these effects we analysed HeLa cells infected with additional S . Typhimurium mutant strains . Deletion of sifA alone results in instability of the SCV membrane and bacterial escape [32] , [33] , therefore we analysed HeLa cells infected with double mutant strains containing mutations in either sifA and sseJ or sifA and sopD2 , as these strains have increased membrane stability [34] , [35] . Because the sifA , sseJ double mutant strain has a reduced replication rate [36] , cells were examined 14 h after invasion , when it was possible to compare cells with similar numbers of bacteria as cells infected with wild-type bacteria at 10 h after invasion ( Fig . 1D ) . At this time point only 25% of the cells infected with sifA , sseJ double mutant strain contained SCV-associated ubiquitinated aggregates ( Fig . 1D ) . However , cells infected with a sifA , sopD2 double mutant or sseJ mutant strain contained the same frequency of ubiquitinated structures observed with wild-type Salmonella ( Fig . 1D ) . Salmonella also induces extensive rearrangement of actin and microtubules around SCVs [23] , [24] . To determine whether these processes are required for the formation of ubiquitinated aggregates , HeLa cells were infected with single mutant strains lacking SseG , which is required for formation of bundles of microtubules [37] or SteC , which causes assembly of an SCV-associated F-actin meshwork [38] . Both these strains induced ubiquitinated aggregates to similar levels as seen in cells infected with wild-type bacteria ( data not shown ) . Hence , accumulation of these aggregates is not linked to positioning of SCVs or actin polymerization , but is at least in part due to the combined actions of SifA and SseJ . The ultrastructure of these ubiquitinated aggregates was examined by transmission immunoelectron microscopy ( IEM ) . Ubiquitin labelling was observed in electron-dense structures in both wild-type and ΔsseL infected cells ( Fig . 2A ) . In agreement with our observations by confocal microscopy , ubiquitin labelling was found more frequently in ΔsseL than wild-type infected cells and was found in very close proximity to SCVs . No labelling was found within the luminal space or on bacteria by IEM . Further immunofluorescence analysis of these structures using antibodies recognizing either lysine 48-linked ( K48 ) or lysine 63-linked ( K63 ) ubiquitin chains revealed that K63 but not K48 ubiquitin chains were present in these structures ( Fig . 2B ) , consistent with the known preference of SseL for K63 chains [7] . However , co-localization between the FK2 antibody ( which labels all ubiquitin chains ) and the K63 antibody was incomplete; therefore it is possible that other ubiquitin topologies might also be present . Autophagy is required for the clearance of various types of ubiquitinated aggregates induced following bacterial infection [39] . This clearance can involve p62 , which interacts with both ubiquitin chains and LC3 to promote the autophagic degradation of protein inclusions [40] . We analysed the distribution of p62 and LC3 in HeLa cells infected with wild-type or ΔsseL mutant bacteria . Dense punctate labelling of both p62 and LC3 was detected close to SCVs , and this partly co-localized with ubiquitin ( Fig . 3A and 3B ) . To examine the influence of SseL on these proteins , confocal microscopy was used to measure the relative fluorescence intensity of ubiquitin , p62 and LC3 labelling in SCV-associated structures . The absence of SseL significantly increased the intensity of ubiquitin , p62 and LC3 labelling , compared to cells infected with wild-type bacteria ( Fig . 3C–E ) . This was complemented by a plasmid expressing wild-type SseL , but not by expression of SseLC/A ( Fig . 3C–E ) . The presence of ubiquitin and p62 within the same aggregates was confirmed by IEM analysis of infected HeLa cells , which showed dense structures in the vicinity of SCVs co-labelled with p62 and ubiquitin ( Fig . S3A ) . Likewise , a HeLa cell line stably expressing GFP-LC3 also contained ubiquitin and GFP-LC3 positive structures close to SCVs ( Fig . S3B ) . Together , these results demonstrate that the deubiquitinase activity of SseL reduces the presence of ubiquitin , p62 and LC3 in SCV-associated aggregates . To examine the role of autophagy in the regulation of these structures , HeLa cells infected with ΔsseL mutant bacteria were subjected to conditions that either inhibit or stimulate autophagy and examined for the presence of SCV-associated ubiquitinated aggregates ( Fig . 4 ) . 3-methyladenine ( 3-MA ) , ammonium chloride ( NH4Cl ) or bafilomycin A1 ( BafA1 ) were used to inhibit autophagy at early ( 3-MA ) and late ( NH4Cl and BafA1 ) stages of the pathway; starvation and rapamycin were used to stimulate autophagy . To avoid any interference with the maturation of the SCV and early targeting of cytosolic bacteria , infected cells were maintained in normal medium until 8 h after invasion . Cells were then exposed to autophagy-altering conditions for 4 h before processing for confocal microscopy . The frequency of ubiquitinated aggregates was enhanced significantly upon inhibition of autophagosome formation ( 3-MA ) or autophagic degradation ( BafA1 and NH4Cl ) , whereas stimulation of autophagy by starvation or rapamycin strongly reduced their presence ( Fig . 4A and 4B ) . The alterations in autophagic flux were confirmed by an increased presence of LC3-positive compartments around SCVs in starved cells ( Fig . 4B ) ; LC3-positive compartments were almost undetectable in cells exposed to 3-MA despite the increase in ubiquitin labelling ( Fig . 4B ) . BafA1 , which inhibits degradation of lysosomal contents , enhanced both ubiquitin and LC3 labelling of these compartments ( Fig . 4B ) . These results show that the ubiquitinated aggregates induced by SPI-2 T3SS effectors are substrates of the autophagy pathway . Stimulation of macrophages and dendritic cells with LPS induces the formation of cytosolic ubiquitinated aggregates that are subject to autophagy . These are referred to as aggresome-like induced structures ( ALIS ) [11] , [13] , [40] . We showed previously that lysates from mouse macrophages infected with ΔsseL mutant bacteria are enriched in ubiquitinated proteins [7] . Immunofluorescence and IEM analysis of infected RAW264 . 7 macrophages revealed the presence of small ubiquitin puncta similar to those seen in epithelial cells , as well as larger , more spherical aggregates that were particularly evident in cells infected with the ΔsseL mutant strain ( Fig . 5 ) . In RAW264 . 7 macrophages , these larger structures were up to approximately 0 . 5 µm in diameter , similar to the size of ALIS [14] ( Fig . 5C ) , and they co-labelled with p62 ( Fig . 5C and 5D ) and LC3 ( Fig . 5E ) . Fluorescence microscopy revealed that HA-tagged SseLC/A co-localized to approximately 50% of ALIS in infected RAW264 . 7 macrophages ( data not shown ) and the effector was also visualised on cytosolic structures co-labelled with ubiquitin by IEM ( Fig . S2B ) . As there was a continuum of different sized puncta in RAW264 . 7 macrophages , the percentage of infected cells with any ubiquitin-positive aggregates was quantified . Approximately 35% of macrophages infected with the wild-type strain contained ALIS and other aggregates 10 h after bacterial uptake ( Fig . 5A and 5B ) . 24% of cells infected with ΔssaV mutant strain also contained these structures , indicating that the majority of aggregates in macrophages are induced independently of SPI-2 T3SS function . Indeed , a similar proportion of uninfected cells that had been exposed to bacteria contained ubiquitinated aggregates ( Fig . 5B ) . Since it is known that E . coli and S . Typhimurium LPS induce ALIS in macrophages [13] , [14] , LPS is likely to account for the SPI-2-independent induction of ubiquitinated aggregates . Approximately 70% of RAW264 . 7 macrophages infected with ΔsseL mutant bacteria contained ubiquitinated aggregates ( Fig . 5B ) . SseL-dependent deubiquitination of aggregates was also observed in infected mouse bone marrow-derived primary macrophages ( Fig . S4 ) . These cells contained large ALIS that were readily distinguishable from SCV-associated aggregates . Their quantitation revealed that wild-type Salmonella-infected cells had fewer ALIS than uninfected cells in the same well , whereas cells infected with ΔsseL mutant bacteria had a far greater number ( Fig . 6A ) . Uninfected cells not exposed to extracellular bacteria did not contain any ALIS ( Fig . S5 ) . To establish whether SseL was sufficient to deubiquitinate independently-generated ALIS , we transiently transfected HeLa cells with vectors encoding myc-tagged SseL or myc-tagged SseLC/A . LPS does not induce ALIS in HeLa cells , but puromycin ( which causes premature chain termination during translation ) leads to the accumulation of defective ribosomal products ( DRiPs ) that are ubiquitinated and incorporated into inclusion bodies . These aggregates also contain p62 and are indistinguishable from LPS-induced ALIS [11] , [14] , [40] . HeLa cells were either mock transfected or transfected with a vector expressing myc-tagged SseL or SseLC/A , then treated with puromycin for 4 h and analysed for ALIS . There were approximately 5-fold fewer ALIS in cells expressing SseL compared to mock-transfected or SseLC/A-expressing cells ( Fig . 6B and 6C ) . Collectively , these experiments show that SseL is sufficient to deubiquitinate proteins in Salmonella- or puromycin-induced ALIS . The catalytically inactive SseL mutant ( SseLC/A ) binds stably to ubiquitinated substrates and allows their immunoprecipitation [7] . To determine if SseL can co-immunoprecipitate autophagic substrates , macrophages were infected with the sseL deletion mutant expressing either wild-type SseL-HA or SseLC/A-HA from a plasmid , and after 10 h of infection , lysates were immunoprecipitated with an anti-HA antibody ( Fig . 7A ) . Immunoblotting revealed that p62 was specifically co-immunoprecipitated by SseLC/A-HA ( Fig . 7A ) . A similar result was obtained using infected HeLa cells ( data not shown ) . This shows that SseL and p62 might bind to the same ubiquitinated substrates . Furthermore , since p62 was immunoprecipitated by SseLC/A but not wild-type SseL ( Fig . 7A ) , it is likely that this interaction occurs indirectly through the ubiquitinated substrates . Next we examined the effects of autophagy on the accumulation of SseL-interacting ubiquitinated substrates . Infected macrophages were subjected to 3 h of 3-MA or starvation treatment to inhibit or stimulate autophagic flux respectively before cell lysis . 3-MA markedly increased the levels of ubiquitinated proteins interacting with SseLC/A ( Fig . 7B ) . In contrast , starvation conditions virtually eliminated these substrates ( Fig . 7B ) . Therefore , SseL deubiquitinates substrates targeted by p62 and destined for autophagic degradation . Accumulation of protein aggregates has been shown to induce autophagy in models of Huntington's disease [41] . In addition , LPS-induced ALIS formation increases autophagic flux in macrophages [11] , [42] . Since SseL deubiquitinates ALIS , we analysed autophagic flux by determining LC3-II levels in infected macrophages . When LC3 is conjugated with phosphatidylethanolamine ( LC3-II ) it localizes to autophagosomal membranes [43] and the amount of LC3-II correlates with the number of autophagosomes present in cells . Exposure of cells to NH4Cl to inhibit lysosomal degradation allows differentiation between an increase in autophagic flux and impaired autophagic degradation [43] , [44] . Cells infected with sseL mutant bacteria had increased levels of LC3-II compared to wild-type infected cells and this was complemented when bacteria expressed wild-type SseL but not SseLC/A ( Fig . 8A and B ) . Following exposure to NH4Cl , the levels of LC3-II were still higher in the absence of SseL ( Fig . 8A and B ) , indicating that the increase in LC3-II observed in cells infected with bacteria lacking SseL did not result from a block in autophagosomal degradation . Therefore , the absence of SseL deubiquitinase activity stimulates autophagic flux , presumably by increasing the accumulation of ubiquitinated aggregates . Previous studies have demonstrated that SseL contributes to the virulence of S . Typhimurium in mice , but failed to show an intracellular growth defect of sseL mutant strains [7] , [45] . In view of its effect on ALIS , we reassessed the contribution of SseL to intracellular bacterial replication using fluorescence dilution , a technique which enables direct measurement of bacterial replication at the single cell level [3] . The replication of wild-type and ΔsseL mutant bacteria was indistinguishable 10 h after uptake into RAW264 . 7 cells ( Fig . 8C ) . The mean replication of both strains was approximately 40-fold at 10 h , as calculated by fluorescence dilution [3] . However , ΔsseL mutant bacteria complemented with a plasmid overexpressing SseL underwent a ∼35% increase in bacterial replication . This increase was not observed for isogenic bacteria expressing the catalytically inactive SseLC/A ( Fig . 8C ) . RAW264 . 7 cells are relatively permissive for replication of S . Typhimurium [3] . To examine the replication in a more physiological and restrictive environment , primary macrophages were also used to compare replication of the sseL mutant ( Fig . 8D ) . Mouse bone marrow-derived macrophages were infected for 16 h and bacterial replication was determined by fluorescence dilution . The replication of sseL mutant Salmonella at 16 h ( 10 . 5-fold ) was significantly lower than that of wild-type bacteria ( 17-fold ) ( Fig . 8D ) , indicating that SseL contributes to replication in this more restrictive cellular environment .
We have shown that intravacuolar Salmonella induces the formation of ubiquitinated aggregates containing autophagic markers in both epithelial cells and macrophages . In epithelial cells , rather diffuse ubiquitin puncta were found close to SCVs , and these partially co-localized with LAMP1 and electron-dense material . These structures were SPI-2 T3SS-dependent and required the combined activities of SifA and SseJ , effectors that regulate vacuolar membrane dynamics and which perturb the endosomal network [35] . Infected macrophages contained similar structures and also dense , more spherical aggregates that resemble ALIS . Although such ALIS have not been characterized previously in Salmonella-infected cells , they are similar to those induced in macrophages following bacterial LPS stimulation [13] , [14] . The aggregates found in Salmonella-infected macrophages resembled LPS-induced ALIS in their size , shape and recruitment of p62 and LC3 [14] , suggesting that LPS signalling through TLR4 might trigger their formation . Remarkably , both aggregates and ALIS in epithelial cells and macrophages are targets for the SPI-2 T3SS effector , SseL , which deubiquitinates as yet unidentified p62-bound proteins and reduces the recruitment of autophagic machinery . It was reported recently that knockout of the deubiquitinase AMSH ( associated molecule with the SH3 domain of STAM ) in mice caused accumulation of ubiquitinated inclusions containing p62 in neurons [46] . In addition , LPS-induced autophagic signalling can be reduced by the deubiquitinase A20 which deubiquitinates K63-linked chains from Beclin-1 [47] . However , it is not known whether A20 or AMSH act directly on substrates of autophagy or whether their effects are indirect . Therefore , to our knowledge SseL is the first deubiquitinase to be described that targets material destined for autophagic degradation , and this represents a new mechanism by which Salmonella counteracts a cellular response induced by an innate immune signalling pathway . It is likely that a proportion of the ubiquitinated aggregates found in macrophages form in response to LPS-TLR4 signalling , as S . Typhimurium LPS stimulates ALIS formation in RAW 264 . 7 macrophages [13] and LPS-induced ALIS in macrophages have been shown to require TLR4 signalling [14] . However other bacterial products may also be involved; Listeria monocytogenes culture broth , zymosan beads as well as heat treatment and cell stress have been shown to induce ALIS formation in several cell types [11] , [13] . The presence of ubiquitinated aggregates in Salmonella-infected macrophages might also be inhibited by other effectors that interfere with TLR-dependent immune signalling , such as the phosphothreonine lyase SpvC , which acts on MAP kinases downstream of MyD88 [48] . Ubiquitinated aggregates that are targeted by selective autophagy have been described in cells exposed to other bacterial pathogens , but these have mainly been cytosolic or extracellular bacteria [8] , [9] , [49] , [50] . However , expression of the Legionella pneumophila Dot/Icm type IV secretion system in macrophages was shown to reduce ALIS formation during infection [12] . Although a Legionella deubiquitinase has not yet been identified , these observations together with our results suggest that such an activity might exist . Indeed it is possible that deubiquitinases of other pathogens might target ubiquitinated aggregates in a similar way , and their activity could mask the presence of these aggregates . An inability to degrade misfolded or aggregated proteins can lead to cell death , as seen in neurodegenerative disorders [51] . Autophagy ameliorates the cellular stress induced by the presence of protein aggregates [52] . Therefore it is possible that the reduction in autophagic flux mediated by SseL accounts for its ability to induce delayed cytotoxicity in macrophages [7] . While the possible functions of ALIS remain unclear , they serve to sequester cytosolic proteins under a variety of stress conditions and are suggested to be involved in host immune defence [14] , [53] , [54] . The ubiquitinated proteins in ALIS are considered as a source of antigenic peptides for presentation on MHC class I molecules in dendritic cells [14] , [53] , [54]; therefore the deubiquitination of aggregates by SseL might regulate antigen presentation in infected macrophages . It will be important to determine the fate of proteins contained within Salmonella-induced aggregates following deubiquitination by SseL . As ubiquitin , p62 and LC3 are currently the only markers of these structures , we are unable to assess whether they persist in cells following their deubiquitination . The presence of non-ubiquitinated protein inclusions may also adversely affect the cell and impede bacterial replication . The identity of the substrates within aggregates that are targeted for deubiquitination by SseL remains elusive . The ability of exogenously expressed SseL to target puromycin-induced ALIS suggests that it may be a promiscuous deubiquitinase that can target a number of substrates and that its activity is regulated by its localization to the SCV during infection . It is known that the activity of SseL contributes to Salmonella growth in vivo [7] . In the present study we found that SseL also contributes to intracellular replication in macrophages . In the permissive RAW264 . 7 macrophage cell line , no differences in replication were observed between wild-type bacteria and the ΔsseL mutant , but an increase in replication occurred upon over-expression of active SseL . However , in the more restrictive bone-marrow derived macrophages , which better represent the cells that Salmonella encounters in vivo , replication was reduced in the absence of SseL . Recent work has proposed that SseL binds to oxysterol-binding protein ( OSBP ) [55] , and interferes with lipid metabolism , leading to the clearance of lipid droplets , which otherwise accumulate during infection of mice gallbladder epithelial cells [56] . Lipid droplet metabolism has been associated with autophagy [57]; therefore it would be interesting to study the possible relationship between the inhibition of selective autophagy by SseL and its interference with lipid droplet metabolism . Understanding how SseL interferes with selective autophagy and contributes to bacterial replication and cytotoxicity will be helped by the identification of molecules present in the ubiquitinated aggregates and the ubiquitin-conjugated proteins that are targeted by SseL . Many intracellular bacteria produce effectors that are known to interfere with ubiquitin-related processes , suggesting that modulating this pathway is important for bacterial growth and survival inside cells . During and following cell invasion , the ubiquitination machinery is used by Salmonella SPI-1 T3SS effectors to regulate both their degradation and cellular localization [58] , [59] . Our work has shown that ubiquitinated aggregates are formed at late time-points in infected cells , in response to both SPI-2 T3SS-dependent rearrangement of endocytic compartments and to SPI-2 T3SS-independent signals . We found that Salmonella uses the SPI-2 T3SS effector SseL to deubiquitinate proteins in these aggregates , thereby impeding their autophagic degradation . The possible links between this interference of autophagy and SseL-associated cytotoxicity and replication will be the subject of future research .
Standard microbial techniques were used for construction of strains and plasmids . Detailed information on the S . Typhimurium strains and plasmids used in this study are provided in Tables S1 and S2 respectively [60] , [61] . Strains harbouring the pDiGc plasmid were cultured in MgM-MES minimal medium supplemented with 0 . 2% L-arabinose at 37°C with aeration [3] . All other strains were grown in Luria Bertani ( LB ) medium , at 37°C with aeration . When appropriate , cultures were supplemented with kanamycin ( 50 µg/ml ) and carbenicillin ( 50 µg/ml ) . The chromosomal deletions of avrA , sspH1 and sspH2 in S . Typhimurium were performed by one-step gene-disruption method [62] . sspH2 and slrP mutations were transduced into ΔsspH1 and ΔsspH1ΔsspH2 mutant strains of S . Typhimurium respectively by P22 phage transduction . HeLa ( cloneHtA1 ) and RAW264 . 7 cells were obtained from the European Collection of Animal and Cell Cultures , Salisbury , UK . Cells were grown in Dulbecco's modified Eagle medium ( PAA laboratories ) supplemented with 10% foetal calf serum ( PAA laboratories ) at 37°C in 5% CO2 . Primary bone marrow-derived murine macrophages ( BMM ) were obtained from C57BL/6 mice ( Charles River ) extracted from tibia and femur [63] and grown as described [3] . HeLa cells were transfected using Lipofectamine 2000 ( Invitrogen ) transfection reagent according to the manufacturer's instructions . HeLa and RAW264 . 7 macrophage infections were performed as previously described [32] except for infections carried out with strains harbouring pDiGc , which were grown overnight in MgM-MES supplemented with 0 . 2% L-arabinose [3] . Primary BMM were infected as described [3] . For all experiments , coverslips from 1 h post-inoculation were labelled to verify that there were similar levels of infection between strains and conditions . In BMM , approximately 50% of cells were infected . For confocal microscopy , FK2 mouse anti-mono and polyubiquitinated proteins ( ENZO ) was used at 1∶10 , 000; CSA-1 goat anti-Salmonella ( Kirkegaard and Perry Laboratories ) at 1∶400; 931A rabbit anti-LAMP1 [64] at 1∶1000; Clone Apu3 rabbit anti-K63 ubiquitin chains ( Millipore ) at 1∶1000; Clone Apu2 rabbit anti-K48 ubiquitin chains ( Millipore ) at 1∶1000; rabbit anti-p62 ( ENZO ) at 1∶1000; rabbit anti-LC3B ( MBL ) at 1∶1000; 3H603 rat anti-myc ( Santa Cruz ) at 1∶200; rat anti-HA ( Roche ) at 1∶200 . Secondary antibodies were obtained from Invitrogen: Alexa 488- , Alexa 555- and Alexa 633-conjugated donkey anti-goat , anti-rabbit , anti-mouse , or anti-rat antibodies were used for immunofluorescence at a dilution of 1∶400 . For immunoblotting , antibodies were used at following dilutions: P4D1 mouse anti-ubiquitinated proteins ( Santa Cruz ) 1∶1000; HA11 mouse anti-HA ( Covance ) 1∶5000; rabbit anti-actin ( Sigma ) 1∶5000; rabbit anti-p62 at 1∶1000 ( MBL ) ; anti-mouse ( IgG ) and anti-rabbit ( IgG ) horseradish peroxidase secondary antibodies ( GE Healthcare ) at 1∶10 , 000 . For immunoelectron microscopy the following antibodies and reagents were used: rabbit anti-GFP ( Rockland ) at 1∶100; mouse anti-rabbit ( Dako ) at 1∶300; rabbit anti-p62 ( ENZO ) at 1∶30; and Protein A gold from the Cell Microscopy Center , UMC Utrecht , The Netherlands . All drugs were prepared in stock solutions and stored as indicated by the manufacturer . 3-Methyladenine ( 3-MA ) ( Sigma ) was used at 10 mM [65]; bafilomycin A1 ( BafA1 ) ( Sigma ) at 200 nM [40]; rapamycin ( Sigma ) at 0 . 2 mg/ml [66]; puromycin ( Sigma ) at 5 µg/ml [40] . For starvation treatments cells were incubated in Hank's buffer salt solution ( HBSS ) . NH4Cl was used at 10 mM . For confocal microscopy , samples were fixed in 4% formaldehyde and permeabilized in 0 . 2% Triton X-100 for 5 min . All antibodies were diluted to the appropriate concentration in PBS containing 10% horse serum . The coverslips were washed twice in PBS , incubated with primary antibodies for 1 h , washed 3 times in PBS and incubated with secondary antibodies for 30 min . Coverslips were washed and mounted onto glass slides using Mowiol mounting medium . Cells were observed with a confocal laser scanning microscope ( Zeiss Axiovert LSM510 ) or an epifluorescence microscope ( BX50 Olympus Optical ) . Images were processed using Zeiss LSM510 image software and Adobe Photoshop software . In all figures apart from S2A , single confocal sections are shown . In Fig . S2A a projection of stacked confocal sections is shown . HeLa cells were transfected with 500 ng of pRK5::myc-SseL [67] , pRK5::myc-SseLC/A or empty pRK5 for 16 h and cells were subjected to puromycin treatment for additional 4 h until processing for confocal microscopy . The number of ubiquitin inclusions per cell was quantified for at least 50 cells for each sample . To quantify the percentage of cells displaying ubiquitinated structures around bacterial microcolonies ( a cluster of at least five SCVs with overlapping fluorescence signals ) HeLa cells , RAW264 . 7 macrophages or BMM were infected with strains of S . Typhimurium and immunolabelled for mono and poly-ubiquitinated proteins . Detection of the bacteria was done by using GFP-expressing strains of S . Typhimurium [68] or by immunolabelling with anti-Salmonella antibodies . Cells were scored positive if the bacterial fluorescence signal overlapped ubiquitin labelling . At least 50 infected cells were counted for each sample in at least three independent experiments . Only cells containing a minimum of five bacteria and a maximum of 20 bacteria were counted . For quantification of the numbers of ubiquitinated bacteria , at least 100 bacterial cells were counted for each sample and analysed for pixel to pixel colocalization between ubiquitin and GFP-expressing bacteria . The percentage of cells containing at least one singular ubiquitinated bacterium was obtained by counting 50 infected cells for each sample for each time-point . All quantifications were done in a blind manner for at least 3 independent experiments . For the quantification of ubiquitin , p62 or LC3 on bacterial microcolonies , HeLa cells were infected with strains S . Typhimurium immunolabelled for mono and polyubiquitinated proteins , p62 , LC3 and Salmonella . 3D confocal stacks were acquired for each sample using a slice increment of 0 . 4 µm . These stacks were then analysed as 3D images using the Volocity software . A protocol in the Volocity software was created and identical settings used for analysis of all experiments . Intracellular bacteria were selected according to an arbitrary threshold of fluorescence intensity of anti-Salmonella labelling for all images . Manual manipulation ensured that only cells with at least 5 bacteria clustered in a perinuclear position were selected . All intracellular bacteria were then grouped as one individual object containing the volume of cellular cytoplasm . The mean values of fluorescence intensity of ubiquitin , p62 or LC3 labelling present within individual microcolonies were quantified and divided by the mean values of fluorescence intensity of bacterial labelling . Data were expressed as the ratio of mean fluorescence of ubiquitin , p62 or LC3 to the fluorescence of bacteria . At least 30 individual microcolonies were quantified for each sample per individual experiment in a minimum of 3 independent experiments ( minimum of 90 microcolonies in total ) . For IEM of HeLa cells or RAW264 . 7 macrophages , cells were infected with different strains of S . Typhimurium for 12 h , then fixed , immunolabelled and embedded as previously described [9] . Single labeling to reveal FK2 antibody and sequential double labeling were performed according to the protein A gold method [69] . For immunoblot analysis of the levels of LC3B , RAW264 . 7 macrophages ( 2×106 cells ) infected with strains of S . Typhimurium were left untreated or incubated with 10 mM NH4Cl for 2 h before harvesting . To ensure equivalent bacterial loads , coverslips were examined at 1 h post-uptake and for all experiments , approximately 70% of RAW264 . 7 macrophages were infected by all strains . 12 h after bacterial uptake cells were washed and harvested in ice cold PBS and centrifuged at 14 , 000× g for 2 min . Cells were lysed in TBS ( 20 mM Tris-Cl , pH 7 . 5 150 mM NaCl ) containing 1% Triton X-100 , 2 . 5 mM MgCl2 , 1 mM PMSF and 1× protease inhibitor cocktail ( Roche ) and lysates were centrifuged at 14 , 000× g at 4°C for 10 min . Supernatants were resuspended in sample buffer ( 0 . 25 mM Tris-Cl pH 6 . 8 , 10% SDS , 50% glycerol , 5% β-mercaptoethanol ) and subjected to SDS-PAGE followed by immunoblot analysis . Immunoblots were developed using HRP-conjugated secondary antibodies and visualised using ECL ( GE healthcare ) . Densitometry was performed on film by high-resolution scanning and analysis using ImageJ software ( NIH ) . For HA immunoprecipitations , RAW264 . 7 macrophages ( 6×106 cells ) infected with strains of S . Typhimurium for 12 h were washed , harvested in cold PBS and centrifuged at 200× g at 4°C . Cells were lysed in RIPA buffer ( Sigma ) containing 1 mM PMSF and 1× protease inhibitor cocktail ( Roche ) and left on ice for 30 min . The lysates were centrifuged at 14 , 000× g for 10 min and the supernatants were pre-cleared with protein G-immobilized beads ( Pierce ) . After a brief centrifugation supernatants were incubated overnight at 4°C with 30 µl of protein G-immobilized beads conjugated to 1 µg of HA11 mouse anti-HA antibody overnight at 4°C . Samples were then centrifuged for 30 sec at 14 , 000× g , and washed twice in lysis buffer . Immunoprecipitated proteins were eluted from beads in sample buffer and subjected to SDS-PAGE followed by immunoblot analysis . Infections were carried out with strains harbouring pDiGc as described . Briefly , bacteria were grown overnight in MgM-MES supplemented with 0 . 2% L-arabinose prior to infection . At appropriate time points after infection , intracellular bacteria released from macrophages were analysed by flow cytometry on a two-laser , four colour FACS Calibur™ flow cytometer . Data were analysed with FlowJo software version 8 . 1 . 1 ( TreeStar ) . Fold replication values from 2 h to the indicated time points were calculated and normalised to wt . Results represent the means ± SEM of at least 3 independent experiments . All statistical analyses were performed with Prism 5 software ( GraphPad ) using one-wayANOVA with Dunnett post hoc analyses to compare different means in relation to a control sample , with Newman-Keuls post hoc analyses for pairwise comparisons of more than 2 different means or using two-tailed un-paired Student's t-test for comparison of means between 2 samples . For p values<0 . 05 , the differences between samples were considered as statistically significant . In all figures , * p<0 . 05; ** p<0 . 01; *** p<0 . 001 . | Ubiquitination can target substrates to a number of fates , including autophagy , the essential cellular process that allows cells to degrade cytosolic material . Although Salmonella enterica resides in a vacuolar compartment during infection , it translocates several virulence proteins into the host cell cytoplasm . We have found that intracellular Salmonella induces the formation of ubiquitinated aggregates near the Salmonella-containing vacuole and that these aggregates are recognised by the autophagy machinery . Salmonella inhibits this response through the action of a translocated enzyme , SseL , which deubiquitinates the aggregates and thereby decreases the recruitment of autophagy markers . We show that SseL alone can deubiquitinate known substrates that are degraded by autophagy , that it reduces autophagy in infected cells and that its activity can increase intracellular Salmonella replication . This is a new example of how a bacterium counteracts a cellular defence pathway through the action of a translocated virulence protein . | [
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| 2012 | The Salmonella Deubiquitinase SseL Inhibits Selective Autophagy of Cytosolic Aggregates |
Children affected by Specific Language Impairment ( SLI ) fail to acquire age appropriate language skills despite adequate intelligence and opportunity . SLI is highly heritable , but the understanding of underlying genetic mechanisms has proved challenging . In this study , we use molecular genetic techniques to investigate an admixed isolated founder population from the Robinson Crusoe Island ( Chile ) , who are affected by a high incidence of SLI , increasing the power to discover contributory genetic factors . We utilize exome sequencing in selected individuals from this population to identify eight coding variants that are of putative significance . We then apply association analyses across the wider population to highlight a single rare coding variant ( rs144169475 , Minor Allele Frequency of 4 . 1% in admixed South American populations ) in the NFXL1 gene that confers a nonsynonymous change ( N150K ) and is significantly associated with language impairment in the Robinson Crusoe population ( p = 2 . 04 × 10–4 , 8 variants tested ) . Subsequent sequencing of NFXL1 in 117 UK SLI cases identified four individuals with heterozygous variants predicted to be of functional consequence . We conclude that coding variants within NFXL1 confer an increased risk of SLI within a complex genetic model .
Language deficits form a central feature of many developmental disorders and account for a high number of pediatric referrals and statements of special educational need [1] . These language impairments often represent a secondary clinical feature of a more pertinent developmental disability such as Down syndrome , Autistic Spectrum Disorder or intellectual disability . However , in a proportion of cases , the primary clinical concern is the language difficulties , which occur in the absence of any other developmental deficit or neurological impairment and in the presence of normal non-verbal IQ . In such cases , the diagnosis is Specific Language Impairment ( SLI ) [2] . SLI affects between 5 and 7% of children in the UK [3] and significantly more boys than girls [4] . The disorder is highly heritable [5] but genetic contributions are expected to be complex in nature with significant heterogeneity between individuals [6] . Common risk variants within ATP2C2 ( OMIM#613082 ) , CMIP ( OMIM#610112 ) [7] , ABCC13 ( OMIM#608835 ) [8] , FLNC ( OMIM#102565 ) , RBFOX2 ( OMIM#612149 ) [9] and ROBO2 ( OMIM#602431 ) [10] have been associated with quantitative measures of language skills . Genome-wide association studies of language-impaired probands have also highlighted potential risk variants in NDST4 ( OMIM#615039 ) , ZNF385D , COL4A2 ( OMIM#120090 ) [11] and NOP9 [12] . Other studies implicate rare genetic events which may have higher penetrance [13 , 14] . However , it is clear that the contributions of these various genetic effects are complex . Some may be specific to individuals with certain forms of language deficits , others may contribute across the range of ability [7 , 8 , 11 , 15 , 16] . The functional impact of these candidate genes has yet to be elucidated and further candidates need to be identified before we can properly understand the molecular pathways underlying SLI . Clearer links have been made between the presence of language deficits and disruption of the FOXP2 gene ( OMIM#605317 ) , a forkhead/winged-helix transcription factor [17 , 18] . Reduced functional dosage of FOXP2 , caused by mutation or chromosomal rearrangements , leads to characteristic deficits in coordinating sequences of orofacial movements , impairing speech , producing a disorder known as developmental verbal dyspraxia ( DVD ) or childhood apraxia of speech ( CAS ) [18–22] . Typically the DVD/CAS features of FOXP2 mutation cases are accompanied by wide-ranging problems with spoken and written language [23] . Whilst FOXP2 disruptions are rare and account for only a small proportion of DVD/CAS cases , the investigation of this gene , its expression patterns and interactions , have led to the elucidation of genetic networks that are important to language development and contribute to more common forms of language impairment [23–25] . One of the transcriptional targets of FOXP2 is CNTNAP2 ( OMIM#604569 ) , a member of the neurexin family which mediates interactions between neurons and glia during nervous system development [26] . Genetic variation across CNTNAP2 has been associated both with language deficits [15 , 27–29] and language ability in the general population [30–32] . Variations in , and disruptions of , this gene have also been implicated across a range of neurodevelopmental disorders such as autism , epilepsy and schizophrenia [26] , indicating that it is likely to be crucial for brain development . These investigations demonstrate how the identification of genetic mutations underlying a distinct severe form of disorder provide entry points into mechanisms that are relevant to the wider processes underlying the initial deficit . In 2008 , Villanueva et al described a population who are affected by an unusually high prevalence of language impairment [33] . This admixed population inhabits the Robinson Crusoe Island which forms part of the Juan Fernandez Archipelago in the South Pacific Ocean , approximately 400 miles off the coast of Chile . The Island was last colonized in 1876 by 64 individuals of European and South American descent . In the 2002 census , the Island population was 633 , the majority of whom were descendants of the founder families . More than 70% of the current population has a surname from the colonizing families and 14% of marriages involve consanguineous unions [34] . In their 2008 study , Villanueva et al completed psychometric profiling of 66 island children aged between 3 and 9 years of age , of whom 40 were descendants of the founder party . They found that 35% of the founder-related children ( 14 of 40 ) were affected by specific language impairment . No evidence for a male bias was observed in this group . A further 27 . 5% of the founder-related child population ( 11 of 40 ) had language abilities below that expected for their age but presented with additional developmental concerns or low non-verbal IQ , precluding a diagnosis of SLI . The remaining 37 . 5% of founder-related children ( 15 of 40 ) had typical language development . In contrast , only one of 26 children whose parents are not related to the founder families ( 3 . 8% ) had evidence of language impairment , a frequency of language impairment that coincided with that seen in mainland Chile ( 3% ) [33] . Furthermore , when the genealogical records of the islanders were recompiled , 90% of the individuals affected by SLI were direct descendants of a single pair of founder brothers who formed part of the founder party [33 , 35] . Given the clear phenotypic differences between founder-related and non-founder-related children on the Island , we postulated that the founder brothers may have carried a rare causative genetic mutation or , alternatively , combinations of common genetic variations that together confer a high risk of language impairment . A previous genome-wide linkage study of 34 families from the Robinson Crusoe Island identified significant linkage to several chromosome regions , the most consistent of which included a large section ( 48Mb ) of chromosome 7q ( SLI4 – OMIM#612514 ) that included many genes which represent good candidates for language impairment , including FOXP2 and CNTNAP2 [35] . However , in depth genomic profiling has yet to be performed within this population . In this study , we make use of this admixed isolated population and assess the possibility of a founder mutation , by completing exome sequencing of five individuals from the Robinson Crusoe population affected by SLI . We substantiate the findings of the exome screen by performing association analyses of selected putative functional variants in the wider Robinson Crusoe population . The contribution of identified risk variants is subsequently validated by performing targeted sequencing of candidate genes in a UK-based cohort of individuals affected by SLI .
On average , 47 , 276 ( median = 49 , 543 , range = 43 , 075–50 , 112 ) genic variants were identified in each of the five exomes . This included an average of 17 , 405 ( median = 17 , 326 , range = 15 , 200–19 , 837 ) exonic variants , 8 , 379 ( median = 8 , 089 , range = 7 , 258–9 , 629 ) missense variants and 106 ( median = 90 , range = 72–157 ) nonsense ( including indels ) variants per individual . Across all five samples , 90 . 0% of targeted exome sequencing had coverage of at least 10-fold . The average coverage of targeted sequence was 56 . 5-fold and 21% of the reads reached this level . Sequencing metrics can be found in S1 Table . To test the hypothesis that the founder brothers carried a rare causative genetic mutation , we focused upon novel variants that caused nonsynonymous protein substitutions or altered canonical splice sites for our downstream analyses . Comparisons between individuals found that no such variants were shared by all five individuals . However , allowing for potential genetic heterogeneity between affected individuals , we identified nine novel nonsynonymous or splice-site variants that were shared by at least 3 of the 5 children sequenced ( Table 1 ) . Eight novel nonsynonymous or splice-site variants were validated in the five exome samples by Sanger sequencing . None of these variants overlapped with the regions of suggestive linkage ( P<7 . 3×10−4 , chromosomes 2 , 6 , 7 , 8 , 9 , 12 , 13 and 17 , as listed in S2 Table ) previously identified in this population [35] . S3 Table provides a full list of all shared , high-quality variants that fell within the previously identified regions of linkage . All of these had previously been reported in dbSNP ( 138 ) and many were non-genic , intronic or synonymous ( see notes column in S3 Table ) . All shared novel nonsynonymous or splice-site variants identified in the exome screen were subsequently genotyped in 111 members of the Robinson Crusoe population ( 49 individuals with language-impairment and 62 individuals with typical language ability ) . This validation cohort was ascertained via 35 children living on the Robinson Crusoe Island who had been diagnosed with SLI or who showed typical language development ( as described in methods ) and included the five children used in the exome sequencing . All children were descendants of the founder families of the Robinson Crusoe Island and , as such , the cases and controls used in these association analyses were inter-related ( Fig . 1 ) . We therefore employed an association algorithm that allowed for relatedness between cases ( MQLS , [36] ) , and that took into account the shared ancestry of the Robinson Crusoe validation cohort ( 288 individuals over 5 generations ) . These analyses highlighted one particular coding variant ( chr4:g . 47 , 907 , 320A>T , hg19 ) that was present at a significantly higher frequency in Islanders with language impairment than in Islanders with typical language ability ( Table 1 ) . Thirty nine percent of Islanders with language impairment were found to carry this variant compared to ten percent of Islanders with typical language skills ( p = 2 . 04 × 10−4 ) ( Table 1 ) . Across the Robinson Crusoe validation cohort , the minor allele frequency was 11 . 3% ( 25 of 222 chromosomes sampled ) ( Table 1 ) . Chr4:g . 47 , 907 , 320A>T ( hg19 ) falls in exon 4 of the Homo sapiens nuclear transcription factor , X-box binding-like 1 ( NFXL1 ) gene ( Fig . 2 ) . The variant causes a nonsynonymous change yielding an asparagine to lysine substitution in the encoded protein ( p . N150K , uncharged amino acid to positively charged amino acid ) . This change is predicted to be “disease-causing” by MutationTaster with a confidence probability of 0 . 98 ( SIFT = 0 . 67 , PolyPhen-2 = 0 . 178 ) . The position is conserved at both the amino acid and nucleotide level ( PhyloP = 0 . 66 , PhastCons = 1 ) ; the amino acid N150 is invariant across 36 of the 38 vertebrate species in which an alignment could be made and the thymine nucleotide at this position is conserved across all six ENSEMBL primate species investigated ( Human , chimp , gorilla , orangutan , macque and marmoset ) ( Fig . 2 ) . The variant at chr4:47 , 907 , 320 was not observed in 127 independent European population controls that were genotyped ( Table 2 ) . We therefore went on to genotype an additional 320 independent individuals from a Colombian population cohort and 121 independent individuals from a Chilean control population cohort . In these cohorts , the variant was present with a minor allele frequency of 4 . 2% ( 27 of 640 chromosome sampled ) and 7 . 4% ( 18 of 242 chromosome sampled ) respectively ( Table 2 ) . Subsequent data released by the 1000 genomes project confirmed that this variant is specific to admixed American populations ( AMR ) with an average minor allele frequency of 4 . 1% . In the sub-populations of the AMR grouping , the minor allele frequency is reported as 0 . 9% in Puerto Ricans ( PUR – 1 in 110 chromosomes sampled ) , 3 . 3% in Colombians in Medellin ( CLM – 4 in 120 chromosomes sampled ) and 7 . 6% in individuals of Mexican ancestry in Los Angeles ( MXL – 10 of 132 chromosomes sampled ) ( Table 2 ) . The variant has recently been designated as rs144169475 accordingly . Parametric and nonparametric linkage analyses were performed for 55 SNPs across the NFXL1 region of chromosome 4 ( 46–49Mb , hg19 ) within seven extended pedigrees from the Robinson Crusoe validation cohort ( S2 Fig . ) . In these analyses , we did not observe evidence of linkage ( maximum LOD score = 0 . 62 , S3 Fig . ) . We sequenced the entire coding region of the NFXL1 gene in 117 unrelated probands affected by SLI ( from the UK SLI Consortium ( SLIC ) cohort [7 , 37–39] ) , to assess whether we could replicate a role for NFXL1 in SLI etiology . In total , we identified 166 high-quality sequence variants across the NFXL1 gene . 155 of the variants detected were intronic , 4 were in the 3’UTR and 7 affected the coding region . Of the coding variants , three were nonsynonymous and four were synonymous substitutions ( Table 3 ) . Nonsynonymous variants and those with estimated allele frequencies of <5% were verified across all the pools of DNA in which they were observed using Sanger sequencing . This allowed the derivation of accurate allele frequencies within the SLIC cohort . One of the synonymous variants ( chr4:g . 47 , 916 , 008G>A , hg19 ) was found in a heterozygous state in one SLIC proband ( allele frequency of 0 . 43% ) but had not been documented in any European individuals in the 1000 genomes project [40] or the NHLBI GO ESP Exome Variant Server ( EVS ) , which together consist of data from 4679 control individuals and therefore have the ability to detect rare variants with a population frequency of 0 . 0001 . A comparison of allele frequencies between SLIC probands ( 1 of 234 chromosomes tested ) and controls ( 0 of 9358 chromosomes tested ) yielded a significant P-value of 0 . 0244 . Intriguingly , although it is synonymous , this variant was predicted to be “disease-causing” by MutationTaster with a confidence probability of 0 . 98 ( SIFT = 1 . 0 ) . This variant falls in the most 5’ coding exon of NFXL1 and is part of a CpG island , indicating that it may be important for the regulation of gene expression . Furthermore , ENCODE data shows that it is part of a H3K4Me3 mark ( which is often associated with promoters ) and binds multiple transcription factors , particularly POLR2A c-MYC and PHF8 ( www . genome . ucsc . edu , accessed April 2014 ) . The remaining three synonymous variants ( rs2053404 , rs6818556 and rs35139099 ) found in SLIC probands were also found at similar frequencies in control databases . All had allele frequencies of >5% and are therefore thought to represent common polymorphisms ( Table 3 ) . One nonsynonymous substitution ( chr4:g . 47 , 887 , 652T>C , hg19 – rs151113647 ) was found in a heterozygous state in a single SLIC proband ( allele frequency of 0 . 43% ) and again , was not observed in 4679 independent European individuals in the control public databases ( Table 3 ) , yielding a significant P-value of 0 . 024 ( 1 of 234 SLIC chromosomes tested vs 0 of 9358 control chromosomes tested ) . Further investigations found that this variant had been observed in a heterozygous state in a single African American individual from the EVS . Principal components analysis of genome-wide SNP data in the SLIC proband against the hapmap-3 populations did not detect any African ancestry . The rarity of the rs151113647 variant and its position within a zinc-finger motif ( Fig . 2 ) indicates that it may confer negative effects upon protein function . Nonetheless , because the nucleotide is not highly conserved across species ( phyloP = −0 . 418 , phastCons = 0 . 925 ) , the change was predicted to be a polymorphism by MutationTaster with a confidence probability of 0 . 99 ( SIFT = 0 . 68 , polyphen-2 = 0 . 00 ) ( Fig . 2 ) . A second nonsynonymous substitution ( chr4:47 , 898 , 575G>A , hg19 - rs35139099 ) was observed in a heterozygous state in two independent SLIC probands ( allele frequency of 0 . 85% ) . This variant was also found in 44 of 4679 independent European control individuals from public databases ( allele frequency of 0 . 47% , Table 3 ) yielding a P value of 0 . 3097 . Although , it was not observed to occur at a significantly increased frequency in SLIC probands , the rs35139099 variant occurs at a conserved residue ( phyloP = 1 . 466 , phastCons = 1 ) within a zinc-finger motif ( Fig . 2 ) and is therefore predicted to be damaging by MutationTaster with a confidence probability of 0 . 99 ( SIFT = 0 . 00 , Polyphen-2 = 1 . 00 ) ( Fig . 2 ) . The remaining nonsynonymous variant ( chr4:g . 47 , 901 , 476G>A , hg19 - rs12651301 ) was observed to occur across all the sequence pools with an estimated allele frequency of 32% ( Table 3 ) . This common variant was also observed in independent European controls from public databases with a frequency of 31% ( Table 3 ) and falls outside of any protein motifs and is thus likely to represent a polymorphism . The three rare variants identified ( rs151113647 , rs35139099 and chr4:g . 47 , 916 , 008G>A , hg19 ) were sequenced in all available family members of the SLIC proband in whom they were observed ( Fig . 3 ) . The chr4:g . 47 , 916 , 008 variant was inherited from an affected father by two affected children and one child with typical language development ( Fig . 3 ) . The rs151113647 variant was inherited from a father , who reports a history of language and literacy problems , by the proband , who attends a special language unit , and his sibling , who also has SLI . The middle child in this family , who also showed evidence of expressive and receptive language deficits , did not inherit the variant ( Fig . 3 ) . Two SLIC families carried the rs35139099 variant; in the first , the variant is present in the father , who self-reports a history of dyspraxia , and passed onto both the proband and her elder sib , each of whom has expressive and receptive language problems . The youngest daughter in this family , who was observed to have a similar pattern of language deficits , did not inherit the variant ( Fig . 3 ) . In the second family carrying the rs35139099 variant , the change was present in both the proband and his younger sib , who had expressive and receptive language scores ∼1SD below that expected for his age , indicating that it is inherited ( Fig . 3 ) . The variant was not present in the mother and we did not have a DNA sample , or phenotypic data , from the father . Nonetheless , haplotype analyses of genome-wide SNP data indicated that the two children shared the same paternal chromosome in this region indicating that the rs35139099 variant was likely inherited from the father .
A natural limitation of all studies of founder or isolated populations is the restricted size of the cohort . Although our study represents a comprehensive profiling of the Robinson Crusoe child population , the total sample consisted of only 111 individuals , 100 of whom were founder-related and 49 of whom had language impairment . Although it should be noted that the power of this particular sample lies in the close relationships between individuals rather than the absolute number of samples , the issue of sample sizes is especially pertinent when one is considering rare variations . Thus it is of particular importance that we observed independent evidence implicating NFXL1 rare variants in another cohort . However , in the absence of a large South American cohort of language-impaired individuals , we were unable to include the rs144169475 variant in our replication investigations ( since this SNP is particular to South American populations ) . Thus , further studies of larger sample sizes that include language-selected controls and South American individuals will be required to fully evaluate the role of rs144169475 and rare NFXL1 coding variants in SLI susceptibility . Of note , none of the shared variants identified through exome sequencing co-incided with regions of suggestive linkage reported in a previous genomewide linkage study of the Robinson Crusoe population ( S2 and S3 Tables ) [54] . Nor did we find evidence for linkage to the NFXL1 region of chromosome 4 ( S4 Fig . ) . We must therefore acknowledge that the increased frequency of rs144169475 in language-impaired individuals of the Robinson Crusoe validation cohort does not directly indicate pathogenicity . The result may represent a chance finding or , alternatively , rs144169475 may be a proxy for the causal variant . Since the exome sequencing performed did not capture 100% of the exome , it is possible that the causal variant was not detected here . Full genome sequencing would be required to fully investigate this possibility . However , it is also important to note that a lack of linkage does not preclude the presence of a causal variant and may instead reflect the complexities of analyzing a pedigree of this size and complexity [55] . The pedigree , which explained the known relationships between the founder brothers and the Robinson Crusoe validation cohort , included 288 individuals ( 321 bits , where a bit is defined as twice the number of non-founders—the number of founders ) and so had to be broken into smaller sets for linkage analyses . This segmentation process discards information and can reduce the power to detect linkage if individuals sharing the linked chromosome segment are split between sub-pedigrees [56] . Lastly , since we hypothesize that SLI in this population has a complex genetic basis and involves incomplete and a high phenocopy frequency , it is possible that the power to detect linkage is insufficient . We observed reduced penetrance at the NFXL1 locus ( of 25 variant carriers , 19 were diagnosed with SLI , penetrance of 76% ) in combination with evidence of a high phenocopy rate in our cohort ( of 49 individuals with language impairment , 19 carried the variant , phenocopy rate of 61% ) . In combination , these factors break down the correspondence between genotype and phenotype , compromising the ability to detect linkage [57] . In summary , the Robinson Crusoe admixed founder population represents a rare resource which may assist in the identification of genetic variants that contribute to SLI susceptibility . Exome sequencing of five individuals from this population identified eight shared coding variants . One of these variants ( rs144169475 ) was found to be significantly associated ( P = 0 . 0002 ) with language impairment in the wider Robinson Crusoe population . rs144169475 confers a nonsynonymous change ( N150K ) in the NFXL1 gene at a highly conserved residue . Subsequent sequencing of the NFXL1 coding regions in 117 independent UK SLI cases identified four individuals with rare heterozygous variants predicted to be of functional consequence . We conclude that coding variants within NFXL1 confer an increased risk of SLI within a complex genetic model .
The work on the Robinson Crusoe Island was approved by the ethics department of the University of Chile . Ethical permission for each SLIC collection was granted by local ethics committees . Guys Hospital Research Ethics Committee approved the collection of families from the Newcomen Centre to identify families from the South East of England with specific language disorder . Ref No . 96/7/11 . Cambridge Local Research Ethics Committee approved the CLASP project “Genome Search for susceptibility loci to language disorders” Ref No . LREC96/212 . Ethical approval for the Manchester Language Study was given by the University of Manchester Committee on the Ethics of Research on Human Beings . Ref No . 03061 The Lothian Research Ethics Committee approved the project “Genetics of specific language impairment in children in Scotland” for the use of the Edinburgh samples . Ref . No . LREC/1999/6/20 . The ethics department of the University of Chile approved the project “Genetic analysis of language-impaired individuals from the Robinson Crusoe Island” . Project Number 001-2010 . Informed consent was given by all participants and/or , where applicable , their parents . The Robinson Crusoe cohort was ascertained on the basis of phenotypic data from 61 children , between the ages of 3 years and 8 years , 11 months ( i . e . the child cohort , described below ) all of whom were descendants of the founder families and represents an extended cohort ( including children who have turned 3 years of age since 2008 ) of that described in [33] . First-degree relatives of founder-related children found to meet criteria for SLI or typical language development were then also assessed for language performance ( i . e . the family cohort , described below ) . Age constraints of available standardized tests meant that different language batteries were employed within the child and family cohorts . The language ability of 61 children , all of whom were related to a founder individual , was assessed by tests of expressive and receptive language ( Toronto Spanish Grammar Exploratory test , TEGE [58] ) and phonology ( Phonological simplification test ( Test para Evaluar Procesos de Simplificación Fonológica—TEPROSIF [59] ) . Nonverbal IQ was tested using the Colombia Mental Maturity Scale [60] . In addition , all children were subjected to an auditory screen and oral motor exam [61] . All tests were validated and normalized in Chilean populations . On the basis of these tests , all children were classified into one of the three following categories: “Specific Language Impairment ) ” ( N = 16 , 7 male , 9 female , 26 . 2% ) defined as ( i ) performance >2SD below expected on TEPROSIF ( for children aged 6 years or less ) or performance >2 years below expected for chronological age on TEPROSIF ( for children aged over 6 years ) and/or performance below the 10th percentile on either the receptive or expressive scales of the TEGE , ( ii ) nonverbal IQ not below the 10th percentile , ( iii ) normal hearing , oral motor skills and neurological development . “Typical language development” ( N = 23 , 8 male , 15 female , 37 . 7% ) defined as ( i ) performance not >2SD below expected on TEPROSIF or performance >2 years below expected for chronological age on TEPROSIF ( for children aged over 6 years ) and performance above the 10th percentile on both the receptive and expressive scales of the TEGE . “Nonspecific language impairment” ( N = 22 , 13 male , 9 female , 36 . 1% ) defined as ( i ) performance >2SD below expected on TEPROSIF or performance >2 years below expected for chronological age on TEPROSIF ( for children aged over 6 years ) and/or performance below the 10th percentile on either the receptive or expressive scales of the TEGE , and ( ii ) nonverbal IQ >1SD below age-expected , and/or ( iii ) evidence of hearing loss or oral motor disability ( e . g cleft lip ) or abnormal neurological development . The observed language deficits in the individuals diagnosed with SLI were typical of those described in other SLI cohorts and involved varied deficits across grammatical , morphosyntactical and receptive aspects of language , but not dialectic variations in intonation , vocabulary or phonology . Since we were particularly interested in genetic contributions to SLI , our family cohort consisted of the first-degree relatives of the 39 founder-related children presenting with SLI or typical language development . All available first-degree family members ( 92 parents and siblings , 47 male , 45 female ) were assessed for language difficulties using tests of verbal fluency ( Barcelona test [62] ) and verbal comprehension ( Token test [63] ) . These family members included 11 parents who were not related to a founder member of the Island ( referred to as non-founder-related parents ) . In addition to these formal language assessments , all individuals ( or their parents or spouses ) completed a family history interview ( provided by P Tallal ) [64] , which specifically asks questions regarding language difficulties . On the basis of these data individuals were classified as either: “Language-impaired” ( N = 34 , 15 male , 19 female , 37 . 0% , including 4 non-founder-related parents ) if they scored below the 10th percentile on either the Barcelona test or the token test or they self-reported a need for writing or reading support at school or a history of language support in the family history questionnaire . “Typical language ability” ( N = 58 , 32 male , 26 female , 63 . 0% , including 7 non-founder-related parents ) if they scored above the 10th percentile on both the Barcelona test and the token test and they indicated no requirement for writing , reading or language support in the family history questionnaire . Five Islanders ( 3 male , 2 female ) from the child cohort who had been diagnosed with SLI were selected for exome sequencing . The selection of individuals for sequencing was based upon the amount and quality of DNA available , the severity of observed language impairment and their known relationships with other affected individuals . The five children were selected to cover the different branches of the founder pedigree and were descendants of the founder families ( Fig . 1 ) . Exome capture was performed using 10μg of genomic DNA with a first generation ( v1 ) Agilent SureSelect human exome kit ( Agilent , Santa Clara , CA , USA ) , which provide an average target coverage of 80% of the exome at 56-fold across all samples . Sequencing of the generated fragments was performed on the SOLiD 4 sequencer ( Life Technologies , Carlsbad , CA , USA ) . Color space reads were mapped to the human reference genome ( hg18 ) in the SOLiD bioscope software ( v1 . 2 ) , which applies an iterative mapping approach . Variants were called using a diBayes algorithm [65] using high stringency settings , requiring calls on each strand . Small insertions and deletions were detected using the SOLiD Small Indel Tool . We assumed a binomial distribution with a probability of 0 . 5 of sequencing the variant allele at a heterozygous position . Given such a distribution , a minimum of ten reads would be required to provide a 99% probability that two or more reads contain an allele variant call . We filtered variant calls to have at least four unique ( i . e . different start sites ) variant reads with the variant being present in at least 15% of all reads . To test the hypothesis that the founder brothers carried a rare causative genetic mutation , for our downstream analyses , we focused upon novel variants that were potentially deleterious . Each exome file was individually filtered to exclude nongenic , intronic ( other than canonical splice sites ) and synonymous variants . The remaining nonsynonymous and splice-site mutations were further filtered to exclude known sites of variation ( as described in dbSNP , ( build 130 ) , publically available genome sequences and an in-house sequencing database ) . The remaining variants were then compared across exome samples to allow the selection of variants that occurred in 3 or more of the 5 children sequenced . A flow diagram of the methodology can be found in S1 Fig . . Shared novel , potentially deleterious variants discovered in the exome data were verified by Sanger sequencing . Primers for Sanger sequencing were designed in primer3 [66] . Primer sequences are available on request . All novel nonsynonymous or canonical splice-site variants found to occur in 3 or more of the 5 exome samples were also genotyped in the wider child and family cohorts from the Robinson Crusoe population . We were able to obtain DNA samples for 35 founder-related children ( from the SLI and typical language development child groups described above ) and their family members ( from the family cohort described above ) . Forty nine of these individuals ( 16 children , 22 parents ( 4 of whom were non-founder-related ) , 7 siblings and 4 half-siblings ) were language impaired and 62 ( 19 children , 32 parents ( 7 of whom were non-founder-related ) , 9 siblings and 2 half-siblings ) had language ability in the normal range . These families included the five children used in the exome sequencing . DNA was extracted from EDTA whole blood samples using a standard chloroform extraction protocol . All novel nonsynonymous or canonical splice-site variants identified from the exome screen were sequenced using a standard Sanger protocol in these 111 individuals . The resultant genotype data were used to perform a family-based test of association within the MQLS-XM package [36 , 67] . This algorithm calculates a quasi-likelihood score which corrects the Chi-square statistic for relationships between individuals , providing accurate type I error rates [68] . The MQLS-XM extension allows for the accurate application of this statistic to X-linked markers [67] . The MQLS algorithm distinguishes between unaffected controls and controls of unknown phenotype , can incorporate phenotypic data from individuals who have not been genotyped [36] and is robust to the mis-specification of prevalence . It allows for the presence of both linkage and association effects in the test statistic and is computationally straightforward making it particularly suitable for large complex pedigrees in which cases and controls may be inter-related , as is the case in this study [36] . A full pedigree structure was generated that accounted for all known relationships between 111 individuals from the child and family cohorts and the two identified , shared , founder brothers . This pedigree included 288 individuals ( 141 males , 144 females and 85 founders ( i . e . individuals with no parental information available—both original founders and incoming ) , 203 non-founders ) over 5 generations . As described above , 111 individuals ( including 11 non-founder-related parents ) had full genotype and phenotype data , 11 individuals were also included who had phenotype data but no genotype data and the remaining 166 individuals had no phenotype or genotype data but defined relationships between the 111 genotyped individuals and the founder brothers . In the MQLS-XM analyses , the expected prevalence of SLI was set at 0 . 25 for males and 0 . 27 for females . These figures were derived from the child cohort described above . Any variant that was significantly associated with language impairment in the population cohort was genotyped in 127 independent European population controls ( ECACC , HRC-1 DNA Panel ) , 441 independent South American controls; 320 individuals of Colombian descent and 121 individuals of Chilean origin . The Colombian controls were collected as part of a genetic demography study in the Colombian population , where all participants had to have four grandparents of local origin ( provided by Luis Carvajal-Carmona and Maria Magdalena Echeverry ) . The Chilean controls were ascertained from the Santiago area and consisted of DNA from 30 male Chilean students ( provided by P Villanueva ) and from 91 female adult controls from a breast cancer study ( provided by L Jara , University of Chile ) . Genome-wide SNP data indicated that these samples were of Amerindian and European ancestry . Note that both the European and South American control populations were unselected and , as such , were not screened for language ability . Genome-wide linkage data for the Robinson Crusoe validation cohort have previously been reported [35] . These previous analyses included 6 , 090 SNPs and reported suggestive linkage ( P<7 . 3×10−4 ) between SLI and chromosomes 2 , 6 , 7 , 8 , 9 , 12 , 13 and 17 . In the current study , we had access to a new set of denser genotypes from the Robinson Crusoe population , generated with the Affymetrix Axiom GW-LAT 1 array ( Affymetrix Inc , Santa Clara , CA , www . affymetrix . com ) , supplemented with a custom array designed to cover South American-specific variants which together included 1 , 141 , 741 SNPs . 929 SNPs across chromosome region chr4:46 , 000 , 000–49 , 000 , 000 ( hg19 ) were selected to cover the chromosome region surrounding the NFXL1 gene ( reported transcript—chr4:47 , 849 , 258–47 , 916 , 680 , hg19 ) . SNP data were filtered within PLINK [69] to remove markers in close linkage disequilibrium ( r2>0 . 5 ) resulting in a linkage dataset of 54 independent SNPs that were appended with rs144169475 genotype data and analysed for linkage in MERLIN [70] . Linkage disequilibrium between these SNPs and rs144169475 are provided in S3 Fig . Since linkage packages were unable to analyse genome-wide data for the 321-bit Robinson Crusoe validation pedigree as a whole , it was broken into sub-pedigrees manually selected on the basis of closest shared ancestor . We employed linkage sub-pedigrees and linkage methods analogous to those described in the previous linage study [35]; Seven extended families of 20–24 bits ( where a bit is defined as twice the number of non-founders—the number of founders ) were analysed for linkage under parametric and nonparametric models with MERLIN ( S2 Fig . ) Parametric linkage analyses were performed under a model which reflected the observed nature of rs144169475 ( assuming a disease frequency of 26 . 2% ( as observed in the Robinson Crusoe children ) and penetrance of 0 . 76 ( as observed in the Robinson Crusoe validation cohort ) . Nonparametric linkage results are reported as P-values derived from the Kong and Cox exponential model , which can be more powerful in large pedigrees [71] . Expected allele frequencies were derived from the 1000 Genomes AMR super-population ( integrated phase 1 , accessed March 2014 ) which includes 181 independent South American individuals ( 60 Colombians from Medellin , Colombia ( CLM ) , 66 individuals with Mexican ancestry in Los Angeles ( MXL ) and 55 Puerto Ricans from Puerto Rico ( PUR ) ) [40] . Putative functional effects of associated variants were evaluated using MutationTaster [72] . MutationTaster uses a Bayes classifier which integrates information from various biomedical databases and analysis tools to evaluate the possible pathogenicity of coding variants . MutationTaster considers evolutionary conservation at both the nucleotide and amino acid level , splice-site changes , loss of protein motifs or features and changes that might affect the level of mRNA expression and stability within a single tool to classify variants as a “disease mutation” or a “polymorphism” . A p-value is given to indicate “the security” of the prediction [72] . The MutationTaster algorithm was trained using more than 390 , 000 known disease mutations from HGMD and more than 6 , 800 , 000 SNPs and Indel polymorphisms from the 1000 Genomes Project . For each of the variants highlighted , we also present the SIFT and polyphen-2 scores . In contrast to MutationTaster , the SIFT and PolyPhen algorithms primarily consider protein sequences , motifs and structures to assign pathogenicity and therefore can only be applied to coding changes . SIFT performs a multiple alignment of closely related protein sequences to identify conserved motifs and assign a probability that a given amino acid substitution is pathogenic [73] . PolyPhen-2 uses a Bayes classifier to consider the property of the reference and variant amino acids , the amino acid conservation , protein motifs and 3D protein structure to derive a probability that a mutation is damaging [74] . SIFT scores vary between 0 and 1 . Amino acid substitutions are classified as “deleterious” for scores ≤0 . 05 and “tolerated” for scores >0 . 05 . In Polyphen-2 , two training models are available—HumDiv , which is more appropriate for the identification of fully penetrant Mendelian mutations and HumVar , which is more appropriate for the classification of rare alleles at loci potentially involved in complex phenotypes . PolyPhen scores from both of these models vary from 0 to 1 , where 0 represents a variant with no functional effect . Functional effects are classified as “benign” , “possibly damaging” , or “probably damaging” , depending on whether the posterior probability falls above or below the appropriate false positive thresholds . In order to further investigate the role of NFXL1 variants in SLI , the coding regions of the NFXL1 gene were subsequently sequenced in 117 unrelated British children affected by SLI . These children formed part of the SLI Consortium ( SLIC ) collection , which has previously been described in detail [7 , 37 , 39] . In short , the probands were collected from four sites across the UK ( The Newcomen Centre at Guy’s Hospital , London , the Cambridge Language and Speech Project ( CLASP ) [75] , the Child Life and Health Department at the University of Edinburgh [76] and the Manchester Language Study [77] ) . All probands were selected to have receptive and/or expressive language skills ( as assessed by the Clinical Evaluation of Language Fundamentals ( CELF-IV-R ) [78] ) more than 1 . 5SD below the normative mean for his or her age and non-verbal IQ ( as measured by the Wechsler Intelligence Scale for Children [79] ) in the “normal” range ( >80 ) . The concentration of genomic DNA samples from 117 independent SLIC probands was quantified by picogreen and each sample normalized to 10ng/μl . Individual DNAs were pooled prior to PCR amplification . Following PCR , the amplicons were fragmented , end-repaired and adapter-ligated . The prepared and tagged libraries were then multiplexed before paired-end sequencing in a single lane of flow-cell on an Illumina HiSeq 2000 ( Illumina Inc , SanDiego , CA , www . illumina . com ) . Sequences were aligned against human reference sequence ( 37d5 ) using STAMPY [80] and variants called by the Syzygy ( 1 . 2 . 6 ) algorithm to create a VCF file . Syzygy implements a Bayes likelihood calculation to allow a base calling strategy that is particularly suited to the calling of variants in pooled samples , in which the frequency of reads containing a rare variant will be lower than expected [81] . Identified sequence variants were annotated within the SNPeff package allowing the identification of coding variants [82] . Individual DNAs from all pools that contained a nonsynonymous coding variant with an expected frequency of <5% were resequenced by Sanger sequencing using primers designed with the primer3 software [66] . This allowed the verification of the variants , the derivation of true variant frequencies across pools and the identification of the individuals who carried the variant . The allele frequencies of coding variants discovered in SLIC probands were compared to those observed in 4679 individuals of European ancestry across publically available control databases; the 1000 genomes project ( the European ( EUR ) super-population from integrated phase 1 , accessed March 2014 ) [40] which includes 379 independent European individuals ( 89 British in England and Scotland , 93 Finnish in Finland , 14 Iberian populations in Spain , 98 Toscani in Italy and 85 Utah residents with Northern and Western European ancestry ) and the European American ( EA ) cohort from the exome variant server ( ESP6500 SI-V2 , accessed March 2014 ) ( http://evs . gs . washington . edu/EVS/ ) which includes data from 4300 independent individuals of European American ancestry . The 1000 genomes samples are unselected controls while the EVS samples are selected to include controls , extremes of specific traits ( LDL and blood pressure ) and specific diseases ( early onset myocardial infarction and early onset stroke ) . Allele frequencies were compared between SLIC probands and controls using a two-tailed Fisher’s exact test with 1 degree of freedom . Calculations were performed in the graphpad online calculator ( http://www . graphpad . com/ ) . Where given variants were observed in alternative populations , these data are reported but were not included in the statistical analyses since population admixture and stratification can lead to false positives , especially when investigating rare variants [83] . | Children affected by Specific Language Impairment ( SLI ) have unexpected problems learning to talk and understand language , despite developing normally in all other areas . This disorder runs in families but we do not understand how the genetic contributions work , or which genetic mechanisms might be important . In this paper , we study a Chilean population who are affected by a high incidence of SLI . Such populations may provide increased power to discover contributory genetic factors , under appropriate conditions . We identify a genetic change in the population that causes a change to a protein called NFXL1 . This change is usually very rare but is found at a higher frequency than expected in our population , particularly in those people affected by SLI . We then looked at this gene in over 100 individuals from the UK affected by SLI and found four more changes that probably affect the protein . This is a higher number than we would expect by chance . We therefore propose that the NFXL1 gene and the protein it encodes might be important in risk of SLI . | [
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| 2015 | Exome Sequencing in an Admixed Isolated Population Indicates NFXL1 Variants Confer a Risk for Specific Language Impairment |
It has been proposed that in wild ecosystems viruses are often plant mutualists , whereas agroecosystems favour pathogenicity . We seek evidence for virus pathogenicity in wild ecosystems through the analysis of plant-virus coevolution , which requires a negative effect of infection on the host fitness . We focus on the interaction between Arabidopsis thaliana and Cucumber mosaic virus ( CMV ) , which is significant in nature . We studied the genetic diversity of A . thaliana for two defence traits , resistance and tolerance , to CMV . A set of 185 individuals collected in 76 A . thaliana Iberian wild populations were inoculated with different CMV strains . Resistance was estimated from the level of virus multiplication in infected plants , and tolerance from the effect of infection on host progeny production . Resistance and tolerance to CMV showed substantial genetic variation within and between host populations , and depended on the virus x host genotype interaction , two conditions for coevolution . Resistance and tolerance were co-occurring independent traits that have evolved independently from related life-history traits involved in adaptation to climate . The comparison of the genetic structure for resistance and tolerance with that for neutral traits ( QST/FST analyses ) indicated that both defence traits are likely under uniform selection . These results strongly suggest that CMV infection selects for defence on A . thaliana populations , and support plant-virus coevolution . Thus , we propose that CMV infection reduces host fitness under the field conditions of the wild A . thaliana populations studied .
It is commonly accepted that hosts and pathogens coevolve [1] . This concept rests on the assumption that pathogens are virulent parasites , defining virulence as the negative impact of infection on the host fitness . As a consequence , hosts will evolve defences to limit pathogen infection , or to compensate for its costs [2] . In plants , the two major defences against pathogens are resistance , defined as the ability of the host to limit infection and/or parasite multiplication and tolerance , which limits the fitness effect of a given parasite burden , i . e . , specifically decreases virulence [3 , 4] . As host defences may reduce the parasite’s fitness , hosts and parasites may coevolve , coevolution being the process of reciprocally adaptive genetic change in two or more species [1] . Evidence for host pathogen coevolution is not abundant . For plants it derives mostly from studies of agroecosystems in which the pathogen evolves in response to the deployment of resistance in the host population [5] . These studies have provided the bases for theory on host-pathogen coevolution , including the gene-for-gene model of host-pathogen interaction [6 , 7] . However , coevolution requires certain conditions to be met [1]: i ) genetic variation in the relevant host ( e . g . , resistance , tolerance ) and pathogen ( e . g . , infectivity , virulence ) traits; ii ) reciprocal effects of the relevant traits of the interaction on the fitness of host and pathogen; iii ) dependence of the outcome of the host-pathogen interaction on the combination of host and pathogen genotypes involved . These conditions must be analysed in wild systems , in which the host may evolve in response to environmental pressures , including pathogen infection , at odds with agricultural systems . Evidence for plant-pathogen coevolution from wild pathosystems is limited to a few instances , all involving fungal , oomycete or bacterial pathogens [8 , 9] . To our knowledge , plant-virus coevolution has not been demonstrated in any wild system . In fact , it has been proposed that viruses often would be mutualistic symbionts , rather than pathogens , in wild plant ecosystems [10–12] , and it has been shown that virus infection may be detrimental or positive for the host depending on the environment [13 , 14] . Hence the interest in seeking evidence about whether viruses are plant pathogens in wild ecosystems and viruses and plants co-evolve , or if virus virulence is the result of the specific conditions of agroecosystems . Reports of negative effects of virus infection in wild plants in their natural habitats are not abundant [e . g . , [15–22] and indicate that effects may largely depend on site or host population [23] , but the genetic variation of defence and virulence has not been analysed in these systems . To analyse plant-virus coevolution in wild ecosystems we have chosen the system Arabidopsis thaliana L . Heynh . ( Brassicaceae ) -Cucumber mosaic virus ( Cucumovirus , Bromoviridae ) , ( CMV ) . A . thaliana is an annual semelparous species with two distinct developmental periods: the vegetative growth period , producing a rosette of leaves , and the reproductive period in which the inflorescence grows , new flowers are produced continuously , and older flowers develop into siliques [24] . It is a cosmopolitan species , with a broad native distribution in Eurasia and Africa [25–27] . The Iberian Peninsula has been shown to contain the largest A . thaliana diversity in Eurasia due to its colonization from different refugia after the last glaciations [26–29] . In Iberia , A . thaliana occurs in a variety of habitats , and substantial genetic variation has been described , within and among populations , for relevant adaptive traits including phenological traits like flowering time and seed dormancy [30–33] . Although wild populations of A . thaliana have been shown to contain ample genetic and phenotypic diversity for responses to herbivores and pathogens [34–38] , the diversity for traits related to plant-virus interactions has not been systematically analysed . CMV is an RNA virus with the broadest host range including about 1 , 200 species in more than 100 plant families . CMV is horizontally transmitted by many species of aphids in a non-persistent manner , and through the seed with efficiencies that depend on the genotypes of CMV and the plant species [39] . In A . thaliana , seed transmission rates vary between 2 and 8% [40 , 41] . CMV isolates are highly diverse and have been classified into subgroups IA , IB and II , based on the nucleotide sequence similarity of their genomic RNAs [39 , 42] . Analyses of the incidence of five viruses in six wild A . thaliana populations from central Spain during 10 years showed that CMV was most prevalent , up to 80% according to population and year [43 , 44] , indicating that the A . thaliana–CMV interaction is significant in nature . As in other hosts monitored in the Iberian Peninsula , Subgroup IA isolates are most prevalent [44–46] . Our group has analysed the role of resistance and tolerance in this interaction . The infection of 21 wild genotypes of A . thaliana representing the variation of the species in Eurasia with three CMV strains , showed that quantitative resistance to CMV depended on the interaction between host genotype x virus strain , and was a host trait with moderate to high heritability [47] . Virulence , estimated as the effect of infection on viable seed production , did not correlate with virus load , due to host genotype x virus strain-specific tolerance and , again , tolerance was a host trait with moderate to high heritability [47] . Interestingly , tolerance was positively correlated with the length of post-embryonic development ( life span ) of the host genotypes [47] , and was due , at least in part , to host life-history trait modification upon infection: long life span genotypes delayed flowering upon infection and re-allocated resources from vegetative growth to reproduction , thus decreasing the effects of infection on progeny production , i . e . , attaining tolerance [48] . It remains to be shown that defence polymorphisms result from the selection applied by CMV infection , and not by any other environmental factor known to modulate plant developmental architecture and phenology , such as life span , flowering time and plant size , which are known to have a role in adaptation of A . thaliana to the abiotic environment [49–51] . In this work we study the genetic diversity of A . thaliana for resistance and tolerance to CMV at a regional scale in the Iberian Peninsula . To this end , we exploit a collection of 76 natural populations covering the wide ecological , environmental and genetic diversities of A . thaliana in this region [32 , 49] . We address the following questions: i ) Which is the amount of genetic diversity for resistance and tolerance to different CMV genotypes ? ii ) Are the geographic and environmental climatic patterns of resistance and tolerance similar or different from those of related adaptive life history traits of A . thaliana ? iii ) Are resistance and tolerance traits under natural selection ? Addressing these questions is crucial to determine if CMV infection has a negative impact on its host fitness under natural field conditions and , consequently , if there is coevolution between A . thaliana and CMV .
To estimate the genetic diversity for resistance and tolerance to CMV , 76 A . thaliana wild genotypes collected in different natural populations from the Iberian Peninsula ( Fig 1 and S1 Table ) were assayed . We consider here a population as the set of A . thaliana plants growing in a specific geographical site . Plants were inoculated after eight-week vernalisation , using two CMV isolates from Iberian A . thaliana populations , Cdc-CMV and Lro-CMV . All 76 genotypes were systemically infected by both CMV isolates , no immunity or hypersensitive resistance reaction being detected . Resistance was estimated from the levels of virus multiplication , quantified as virus RNA accumulation . RNA accumulation varied between 0 . 26 and 56 . 28 μg of virus RNA g fwt-1 ( Table 1 , Fig 2 and S2 and S3 Tables ) , variation significantly depending on the A . thaliana genotype ( F75 , 816 = 2 . 98 , P<10−4 ) , the virus isolate ( F1 , 816 = 113 . 30 , P<10−4 ) and the interaction virus isolate x host genotype ( F75 , 816 = 75 . 82 , P<10−4 ) , which together explained 88% of the variance ( 24 . 6% for A . thaliana genotype , 38 . 9% for virus isolate and 24 . 5% for their interaction ) . On average , Cdc-CMV accumulated to higher levels than Lro-CMV ( 19 . 28±1 . 37 and 7 . 53±0 . 71 μg virus RNA g fwt-1 , respectively ) ( Fig 2 and Table 1 ) . Virus accumulation in the host plant showed high heritability for both CMV isolates , with an average of 0 . 8 ( Table 1 ) , heritability being defined as the genetic component of the variance of the trait . Tolerance was estimated from the effect of virus infection on progeny production . Since CMV infection does not affect seed viability or the weight of single seeds in a large number of A . thaliana genotypes [14 , 47 , 52] , tolerance was estimated as the ratio of seed weight in infected to mock-inoculated plants ( SWi/SWm ) , which varied between 0 . 03 and 0 . 79 ( Table 1 , Fig 2 , S2 and S3 Tables ) . As for resistance , tolerance significantly depended on A . thaliana genotype ( F76 , 816 = 3 . 10 , P<10−4 ) , virus isolate ( F1 , 816 = 71 . 40 , P<10−4 ) and their interaction ( F75 , 816 = 5 . 18 , P<10−4 ) , which together explained 73% of the variance ( 26 . 9% for A . thaliana genotype , 25 . 0% for virus isolate and 21 . 2% for their interaction ) . Tolerance to Cdc-CMV ( 0 . 35±0 . 02 ) was lower than tolerance to Lro-CMV ( 0 . 51±0 . 02 ) ( Fig 2 and Table 1 ) . Tolerance in the host showed medium to high heritability , between 0 . 70 and 0 . 54 for Cdc-CMV and Lro-CMV , respectively . No significant relationship was detected between virus RNA accumulation and SWi/SWm across genotypes nor within genotypes ( r≤-0 . 11 , P≥0 . 358 ) . Together , results show that natural populations of A . thaliana contain substantial but independent genetic variation for resistance and tolerance to CMV . To determine if there is a geographic pattern for the genetic diversity for CMV resistance or tolerance , we first analysed the spatial autocorrelation of both variables . Neither virus accumulation nor SWi/SWm showed significant spatial autocorrelation at any geographic scale ( P>0 . 050 ) ( Table 1 ) . Second , to find if these defence traits might be associated with the climate , we analysed their relationship with climatic variables from the original local populations . Neither the accumulation of any of the two CMV isolates , nor the SWi/SWm ratio in plants infected by any of them , significantly correlated with any of the analysed abiotic variables ( see Methods and S4 Table ) according to Dutilleul’s t-tests ( r≤-0 . 30 , P≥0 . 039 ) , SARs ( F≤7 . 185 , P≥0 . 009 ) , or Mantel tests ( r≤-0 . 12 , P≥0 . 019 ) ( S4 Table ) . Finally , we analysed if there is a relationship between the genetic diversity for CMV defence traits and the overall genetic diversity of the A . thaliana wild genotypes estimated from neutral markers ( 250 SNPs ) . Mantel tests between pair-wise genetic distances estimated from neutral markers and pair-wise differences for virus RNA accumulation or SWi/SWm did not detect any significant correlation in relation to Cdc-CMV or Lro-CMV ( r≤-0 . 04 , P≥0 . 403 ) . Tolerance to CMV in A . thaliana is related to the host life history traits , as tolerance is due in part to a reallocation of resources from growth to reproduction , which depends on the allometry of the vegetative to reproduction organs , ( SW+IW ) /RW [48] . In the 76 wild genotypes , resource reallocation upon infection by both CMV isolates also depended on plant allometry , being more efficient in genotypes with lower ( SW+IW ) /RW ( F1 , 76>14 . 58 , P<10−4 ) , and the effect of infection by both CMV isolates on RW correlated positively with LP and RW of mock-inoculated plants ( r≤0 . 29 , P≥0 . 022 ) . Neither resistance nor tolerance correlated with viable seed production of mock-inoculated controls ( r≤0 . 02 , P≥0 . 540 ) . We then analysed several life history traits related with growth and phenology in eight-week vernalised mock-inoculated plants of the 76 wild genotypes . Rosette weight ( RW ) , inflorescence without seeds weight ( IW ) and seed weight ( SW ) , growth period ( GP ) and life-span ( LP ) significantly differed between genotypes ( F75 , 399≥9 . 23 , P<10−4 ) ( S2 and S3 Tables ) . Heritability of these traits was high , between 0 . 61 and 0 . 96 ( Table 1 ) . Overall , A . thaliana populations display a large genetic variation for the analysed life history traits , as in previous works [33 , 49 , 50] . Rosette weight ( RW ) , inflorescence without seeds weight ( IW ) and seed weight ( SW ) significantly differed between genotypes ( F75 , 399≥9 . 23 , P<10−4 ) ( S2 Table ) . To determine if life history traits related to tolerance showed similar or different geographic patterns than CMV defences , we analysed their autocorrelation . All growth and phenological traits showed significant spatial autocorrelation up to 153 km ( Table 1 ) . Furthermore , we analysed the relationship between these life history traits and climate using Dutilleul’s t-test , univariate SAR models and Mantel tests ( S4 Table ) . Overall , RW , IW , SW and GP , but not LP , were positively correlated with altitude and negatively correlated with most climatic variables , including annual mean , minimal and maximal temperature , and precipitation seasonality ( Fig 3 and S4 Table ) . These analyses showed A . thaliana genotypes from higher altitude , lower temperatures and higher precipitation seasonality flowered later , developed larger rosettes and inflorescences and produced more seeds . Moreover , Mantel tests showed that genetic distance was positively correlated with IW , SW , GP ( r>0 . 15 , P<0 . 008 ) and marginally with RW ( r = 0 . 08 , P = 0 . 078 ) but not with LP ( r<-0 . 02 , P>0 . 700 ) . In contrast to CMV defence traits , life history traits related with resource allocation vary according to the climatic environment where populations evolved . Therefore , resistance and tolerance to CMV show different evolutionary histories than life history traits , likely reflecting distinct abiotic and biotic environmental selective forces acting on each group of traits . To quantify the distribution of genetic diversity for CMV defence traits within and among A . thaliana populations we analysed ten randomly sampled individual plants ( henceforth named as “individuals” ) from 10 or 12 Iberian populations , which were tested for their resistance and tolerance to two CMV isolates . One isolate from an Iberian population of A . thaliana ( Cdc-CMV ) and a reference isolate ( Fny-CMV ) were chosen because they had been used in previous work [43 , 47 , 48] . Since CMV resistance and tolerance depend on the environment [14 , 48 , 53] , two experiments were performed with different vernalisation period lengths , as vernalisation affects life history traits relevant to tolerance such as rosette size , rosette leaf number , flowering time [33 , 49 , 54] , and seed germination [55] . An eight-week vernalisation treatment simulated a cold winter , whereas a four-week vernalisation simulated a mild winter , as often occur across years in the original population locations . In both experiments , all individuals were systemically infected by both CMV isolates . The two experiments yielded similar results . For clarity only results of the long vernalisation treatment experiment are presented in the text , but results of the short-vernalisation are shown in S5 Table . Virus accumulation varied considerably among individuals within populations ( Fig 4 , Table 2 and S6 Table ) . The average virus accumulation in each population ranged from 2 . 62 to 32 . 02 μg virus RNA g fwt-1 . The heritability of virus accumulation varied between 0 . 23 and 0 . 90 depending on CMV isolate and host population ( Table 2 ) . Virus accumulation significantly depended on the virus isolate ( F1 , 884 = 67 . 88 , P<104 ) , the A . thaliana population ( F9 , 884 = 2 . 40 , P = 0 . 059 ) ( Fig 4 and Table 2 ) , the A . thaliana individual nested to population ( F88 , 884 = 2 . 54 , P<10-4 ) , and on the interactions CMV isolate x A . thaliana population ( F9 , 884 = 4 . 11 , P<10-4 ) and CMV isolate x A . thaliana individual nested to population ( F88 , 884 = 7 . 08 , P<10-4 ) . CMV isolate , A . thaliana individual nested to population and their interaction explained 49 . 7 , 3 . 3 and 14 . 6% of the variance , respectively , while A . thaliana population and the interaction CMV isolate x population explained 7 . 0 and 5 . 5% . The average accumulation was higher for Cdc-CMV than for Fny-CMV ( 18 . 61±2 . 76 and 7 . 37±1 . 74 μg virus RNA g fwt-1 , respectively ) . Besides , average values of Cdc-CMV accumulation over individuals and populations correlated significantly , or marginally , with the corresponding values of Fny-CMV accumulation ( rs = 0 . 32 , P = 0 . 001; rs = 0 . 60 , P = 0 . 067 , respectively ) , indicating that , in general , individuals and populations that were more resistant to Cdc-CMV were also more resistant to Fny-CMV . CMV tolerance also showed substantial variation among individuals within populations , SWi/SWm values ranging between 0 . 02 and 0 . 93 for Cdc-CMV , and between 0 . 03 and 0 . 81 , for Fny-CMV-infected plants ( Fig 4 , Table 2 and S6 Table ) . Average SWi/SWm values in each population varied from 0 . 16 to 0 . 47 ( Fig 4 and Table 2 ) , indicating a lower range of variation among than within populations . Heritability of tolerance varied between 0 . 10 and 0 . 80 depending on CMV isolate and host population ( Table 2 ) . SWi/SWm varied significantly depending on virus isolate ( F1 , 884 = 5 . 30 , P = 0 . 046 ) , A . thaliana population ( F9 , 884 = 2 . 40 , P = 0 . 059 ) ( Fig 4 and Table 2 ) , A . thaliana individual nested within population ( F88 , 884 = 2 . 445 , P = 0 . 023 ) and the interaction CMV isolate x A . thaliana individual ( F88 , 884 = 2 . 65 , P<10−4 ) . A . thaliana individual and the interaction CMV isolate x A . thaliana individual explained a larger proportion of SWi/SWm variance ( 32 . 2% , 16 . 1% , respectively ) than CMV isolate or A . thaliana population ( 1 . 6% , 6 . 4% , respectively ) . When averaged over all individuals , tolerance to Cdc-CMV ( 0 . 36±0 . 06 ) and Fny-CMV ( 0 . 32±0 . 06 ) were similar . Average values of SWi/SWm in Cdc-CMV-infected plants correlated across individuals and populations with those of Fny-CMV-infected plants ( rs = 0 . 73 , P = 0 . 016; rs = 0 . 60 , P<10−4 , respectively ) . Thus , as for resistance , the individuals and populations more tolerant to Cdc-CMV were also , in general , more tolerant to Fny-CMV . However , values of virus accumulation and SWi/SWm did not correlate over individuals for any CMV isolate ( rs≤0 . 09 , P≥0 . 129 ) . Together , these results indicate that A . thaliana defences against CMV infection depend on the host genetic variation determining the specific defences . To find out if CMV defence traits of A . thaliana might be under selection , we compared the genetic differentiation among populations for CMV resistance and tolerance , with that for neutral genetic variation ( Fig 5 ) . Two-hundred and fifty genome-wide SNPs , distinguished 74 different genotypes among the 120 individuals analysed ( S7 Table ) and were used to calculate FST values . As in the previous section , analyses are based on the results of the long vernalisation treatment experiment , analyses based on the short-vernalisation treatments gave similar results and are shown in S5 Table . The estimated average genetic differentiation among populations for neutral markers was 0 . 58 ( 95%CI = 0 . 54–0 . 62 ) , which is presumed to reflect the demographic history of the populations . Genetic differentiation among populations for quantitative traits was estimated by QST values , leading to a similar average value of 0 . 31 ( 95%CI = 0 . 22–0 . 52 ) for accumulation of both CMV isolates . The average QST estimated for SWi/SWm was 0 . 18 ( 95%CI = 0 . 12–0 . 35 ) for Cdc-CMV and 0 . 10 ( 95%CI = 0 . 06–0 . 21 ) for Fny-CMV-infected plants . Therefore , the genetic variation of A . thaliana for both defence traits is distributed mostly within populations . QST values for resistance and tolerance to Cdc-CMV and Fny-CMV were significantly smaller than FST values ( Fig 5 ) , thus indicating that A . thaliana populations are genetically less differentiated for resistance and tolerance to two CMV isolates than for neutral markers . Furthermore , we analysed if FST and QST values followed a pattern of isolation-by-distance . Mantel tests detected a significant correlation between FST values and geographic distance between pairs of populations ( r = 0 . 50 , P<10−4 ) , a likely result of their demographic history . By contrast , no significant correlation was found between the pair-wise QST values for virus accumulation or SWi/SWm , for any CMV isolate , and their geographic distances ( r≤0 . 24 , P≥0 . 258 ) . Therefore , factors other than demography contribute to the population differentiation patterns observed for resistance and tolerance to CMV .
Host-pathogen coevolution determines the dynamics and genetics of infectious disease , and may shape the genetic structure of host and pathogen populations [1] . Understanding this topic , central in pathology and evolutionary biology , requires knowledge on the genetics of defence and pathogenicity , and the dynamics of their change in populations [1] . The abundance of theoretical analyses of host-pathogen coevolution ( e . g . , [1 , 56 , 57] ) is not matched by a similar amount of empirical and experimental studies . While there is abundant information compatible with host-pathogen coevolution in plant systems ( e . g . [58] ) , it mostly derives from crops , in which the genetic composition of the host plant is manipulated by humans . Studies from wild systems , in which the genetic composition of host and pathogen populations may be determined by reciprocal selection , are much scarcer , particularly for plant-virus interactions [5 , 59] . To address this question we challenged A . thaliana individuals collected from a high number of local populations with different CMV isolates . We chose to inoculate plants mechanically rather than by aphid transmission , which is the natural means of horizontal transmission [39] . Mechanical inoculation ensures a high rate of infection and minimises inoculum dose effects on virus accumulation , as opposed to aphid transmission , which is highly inefficient for CMV [39 , 40] . Moreover , there is no information on the aphid species that transmit CMV in A . thaliana populations in central Spain . Also , A . thaliana genotypes were assayed under common controlled conditions , which are not necessarily the same as in the field . The challenge with different CMV strains of 185 individuals collected in 76 A . thaliana local populations from the Iberian Peninsula , showed large differences in quantitative resistance , as estimated from the level of virus multiplication in infected plants . Similarly , large differences were found for tolerance to CMV , estimated as the effect of infection on host progeny production . A large part of the variation of resistance and tolerance was explained by the virus isolate , in agreement with previous results showing that CMV isolates vary largely in multiplication rate and virulence in A . thaliana [41 , 47 , 48 , 53] . Variation for resistance and tolerance occurred at all analysed spatial scales: among individuals from a local population , among local populations , and within the whole Iberian Peninsula region . These conclusions held for assays conducted in different environments , a result to be underscored , as resistance and tolerance of A . thaliana to CMV can be modulated by the abiotic environment [14] . The observed variation in resistance and tolerance had a significant genetic component , as they showed medium to high heritability values ( 0 . 23–0 . 81 for resistance , and 0 . 10–0 . 70 for tolerance ) depending on the spatial scale of the analysis and the isolate of CMV . Thus , our results show genetic variation for presumably defence traits in the host population , a condition for host-pathogen coevolution . Regardless of spatial scales , our data also show that resistance and tolerance significantly depend on the interactions between virus genotype and host genotype , another condition for host-pathogen coevolution . It has been reported that A . thaliana genotypes showing high tolerance to CMV have a long life span , and that tolerance is , at least in part , the result of resource re-allocation from vegetative growth to reproduction , which is more efficient in long-lived genotypes [14 , 47 , 48 , 52] . It also has been shown that these life history and phenological traits have evolved as ( direct or indirect ) responses to climatic conditions [33 , 49 , 50 , 60] . Accordingly , the analysis of 76 A . thaliana genotypes from different Iberian populations showed that resource reallocation upon infection depended on the allometric ratio ( SW+IW ) /RW , and the effect of CMV infection on vegetative growth correlated positively with LP and RW of mock-inoculated plants . Life span , vegetative growth and seed production in non-infected plants were correlated with climatic variables . In contrast , the genetic variation for CMV multiplication in the infected host , and for the effect of CMV infection on seed production , was unrelated to those climatic factors . Therefore , the CMV defence traits have evolved , at least partly , independently from those other adaptive traits which have evolved in response to climate . Accordingly , the evolution of defence traits is not the result of A . thaliana responses to climate . These differential evolutionary histories strongly suggest that these traits are true resistance and tolerance defence responses that may have evolved in response to CMV infection ( see below ) . This conclusion also agrees with the fact that resistance and tolerance are virus-specific traits of A . thaliana , and not unspecific responses to the stress of virus infection [52] . Thus , our study shows substantial genetic variation for resistance and tolerance to CMV within and between populations of A . thaliana . Genetic variation within or/and between populations for resistance to a variety of pathogens has been reported for a limited number of wild plants , including Amphicarpaea bracteata , Eucalyptus globulus , Podophyllum peltatum , Linum marginale , Silene latifolia , Phaseolus vulgaris , Plantago laceolata or A . thaliana [61–78] . Analyses of the variation for plant tolerance to pathogens are much rarer [47 , 75 , 79] , and none of them has analysed within population variation . All these studies refer to resistance or tolerance to fungi , oomycetes or bacteria , and the only report we are aware of on variation for resistance to a virus , is our previous analysis in the A . thaliana-CMV system [43] , which involved a much more limited sample of host populations . The analyses in this study also showed that resistance and tolerance display different evolutionary histories than neutral genetic variation , since QST values for resistance and tolerance are lower than FST values . Accordingly , resistance and tolerance are traits likely under uniform selection , i . e . , a selection which favours a higher diversity of traits within than among populations . This conclusion is supported by results for two CMV isolates and in different environmental conditions . Also , neutral genetic differentiation follows a pattern of isolation by distance , which is not detected for the genetic differentiation for resistance or tolerance . By contrast , similar analyses have previously suggested diversifying selection on the genetic variation for quantitative traits such as flowering time , leaf number , specific leaf area or leaf succulence in A . thaliana [32 , 80 , 81] . The most parsimonious explanation for uniform selection on CMV defence traits is that CMV infection plays a role as selective factor . Although we cannot discard that the analysed defence traits may have evolved in response to selection from other pathogens or pests , several arguments make a strong case for selection pressure due to CMV infection: i ) the high prevalence of CMV ( up to 80% ) in wild A . thaliana populations in the Iberian peninsula [43 , 44]; ii ) the fact that the analysed defence traits are virus-specific in A . thaliana [52]; and iii ) the lack of correlation between CMV multiplication and virulence [47] , virus multiplication being highly dependent on virus genotype and environment [14 , 47] . Selection for resistance to pathogens has been best documented in populations of Linum marginale in response to the fungus Melampsora lini , of Plantago lanceolata in response to the fungus Podosphaera plantaginis , or of A . thaliana in response to the oomycete Hyaloperonospora arabidopsidis or the bacterium Pseudomonas syringae [37 , 68 , 69 , 72–74 , 78 , 82 , 83] . Our results extend these observations to plant-virus interactions . If defence in A . thaliana against CMV is under selection , a corollary is that CMV is a virulent pathogen of this plant under natural conditions , a relevant conclusion that contributes significantly to understanding plant-virus interactions . The observed pattern of genetic diversity for resistance and tolerance to CMV , higher within than between populations , can be explained by the features of the pathosystem . CMV is ubiquitous in Iberia [45 , 84] and has been found in all monitored wild populations of A . thaliana , prevalence differing among populations and years [43] . Also , our present and past results [47 , 48] show that CMV isolates differ in virulence to A . thaliana , and that virulence is modulated by environmental factors as diverse as temperature , light intensity or host plant density [14 , 53] . Variation in the genetic composition of CMV populations would also result in variation for CMV infection-associated selection , as the outcome of the interaction depends on the A . thaliana and CMV genotypes involved . These factors would explain the maintenance of genetic variation in defence traits within host populations and the limited differentiation among populations . Furthermore , this explanation suggests that resistance and tolerance to CMV involve fitness penalties for A . thaliana , which would hinder fixation of resistance/tolerance alleles and would contribute to the maintenance of defence polymorphisms within populations [85] . We have not found evidence for such costs under the assayed conditions , as neither resistance nor tolerance were negatively correlated with viable seed production of mock-inoculated controls . However , fitness costs might not be detectable under our experimental conditions , and/or might be unveiled under the less favourable environment of the field . Another interesting result is that resistance and tolerance to CMV co-occur in wild A . thaliana populations . Theory predicts that resources being limited , hosts would not invest in both resistance and tolerance , which would be mutually exclusive defences . The conditions that should favour the evolution of resistance or tolerance have been much modelled , and a negative correlation between both traits across host genotypes is expected [4 , 86 , 87] . We found no correlation between resistance and tolerance in any experiment , indicating that they evolve as independent traits . It has been proposed that resistance and tolerance could coexist if costs and benefits of each defence were different and non-additive [88–91] . Models also propose that tolerance alleles should become fixed in host populations , which would not be polymorphic for this trait under most assumptions [91–93] . A report on the tolerance to CMV in Mimulus guttatus conforms to these predictions , as tolerance had no costs , but showed little genetic variation [94 , 95] . On the contrary , our results do not agree with model predictions , as we found large genetic variation for tolerance and evidence for coexistence of resistance and tolerance . In conclusion , this work shows that in A . thaliana there is genetic variation , within and among populations , for defences to CMV that result in lower virus multiplication or in lower impact of infection on the plant fitness . Genetic variation for defence to CMV is not associated with variation for climatic factors , in contrast to variation for other adaptive life-history traits of A . thaliana . In addition , we found evidence that these two defence traits are under uniform selection . The results of this study are compatible with CMV infection acting as a selection pressure for defence on populations of A . thaliana and , hence , we propose that CMV infection likely reduces the host fitness under the field conditions of the analysed wild A . thaliana populations , although field experiments would be required to prove this fact . The results presented here also show that some of the conditions for coevolution are met in the system A . thaliana-CMV , but more work on the virus side is necessary to prove if coevolution occurs . These results raise two challenging questions: what are the mechanisms that maintain polymorphisms for resistance and tolerance within A . thaliana populations , and what is the negative impact of CMV infection on the host in nature and how does such an impact vary according to field conditions .
Two different sets of A . thaliana samples from the Iberian Peninsula were analysed . First , 76 accessions or wild genotypes collected from different populations were selected to cover the genetic and environmental diversity of the species in that region [30 , 49] ( Fig 1 and S1 Table ) . This collection spanned 800 x 700 km , populations being spaced in the average 384 . 9±3 . 7 ( 20 . 2–1 , 038 . 1 km ) . Altitudes ranged from 123 to 1 , 670 m above sea level . Each sample was genetically different based on previous SNP genotyping and genome sequences [51 , 96] . Second , ten individuals plants ( individuals ) randomly sampled from 12 of these populations were selected for intra and inter-population analyses ( Fig 1 and S1 Table ) . Samples from eight of these populations have been previously genotyped for 250 genome-wide SNPs that were segregating in these populations [30 , 32] . For the remaining four populations ( Bis , Mer , Moc , Pob ) 10 individuals/population were genotyped in this study for the same set of SNPs . The climatic information from the locations of A . thaliana populations was obtained from the digital climatic atlas of the Iberian Peninsula at 1 km2 resolution [50 , 97] . Thirty-three variables were used , related to temperature , precipitation and solar radiation ( S3 Table ) . In addition , 19 bioclimatic variables derived by combination of annual trends , seasonality and extreme conditions were also included ( www . worldclim . org ) . Altitude was also analysed as a proxy for climate . Annual mean temperature of the populations ranged 6 . 1–17 . 4°C ( 12 . 5±0 . 3 ) and annual precipitation ranged 405 . 7–1695 . 8 mm ( 753 . 8±33 . 9 ) ( S8 Table ) . All accessions or individuals used in this study were propagated by selfing during two generations by the single seed descent procedure , in a glasshouse supplemented with lamps to provide a long-day photoperiod . This allowed reducing residual heterozygosity that might contain some wild individuals but also removing any potential maternal and grand-mother effects . Seeds were stratified ( darkness , 4ºC ) for 7 days before germination at 25/20ºC day/night , 16 h light . Ten day-old seedlings were transferred to 4ºC , 8 h light , for vernalisation during 4 or 8 weeks , depending on the experiment . After vernalisation , plants were transplanted to 0 . 43 L pots and returned to the greenhouse , where they were kept ( 25/20ºC day/night , 16 h light ) until the end of the experiment . Three subgroup IA CMV isolates were used , Fny-CMV , Cdc-CMV and Lro-CMV , which differ in the sequence of their genomic RNAs in about 1% of positions . Fny-CMV is a well-characterized reference isolate [98] . Cdc-CMV and Lro-CMV were isolated from field-infected A . thaliana plants of the Cdc and Cho populations , respectively , in 2008 and 2011 , Cdc-CMV was named At-CMV in [43] . Isolates were multiplied in Nicotiana clevelandii , Fny-CMV from transcripts of cDNA clones and Cdc-CMV and Lro-CMV from biological clones derived from local lesions in Chenopodium quinoa . Virions were purified as in [99] . A . thaliana plants were mechanically inoculated at the five-leaf stage ( stage 1 . 05 , [100] ) with 15 μl of sap from infected N . clevelandii leaves in 0 . 01 M phosphate buffer pH 7 . 0 , 0 . 2% sodium diethyldithiocarbamate . Fifteen μl of buffer were applied to mock-inoculated controls . The ( unkown ) virus concentration in leaf sap ensured infection of 100% of inoculated plants . Each treatment ( virus-inoculated or buffer mock-inoculated ) involved at least five replicated plants from each original sample , that is at least five plants derived from the same genotype or individual . All plants in each experiment were grown in a completely randomized design . Virus multiplication in plants was estimated from virus RNA accumulation as described in Pagán et al . , ( 2014 ) [41] . Briefly , at fifteen days post-inoculation 0 . 01 g fresh weight ( fwt ) of leaf tissue was harvested from four different systemically infected leaves . Nucleic acids were extracted from the pooled leaf tissue using TRI-reagent ( Sigma-Aldrich , St Louis , MO , USA ) . Virus RNA was then quantified by dot-blot hybridization with 32P-labelled RNA probes complementary to nucleotides 1933–2215 of Fny-CMV RNA3 ( GeneBank Acc . No . D10538 ) . In each blot , internal standards for Fny-CMV , Cdc-CMV or Lro-CMV RNA were included as a two-fold dilution series ( 1–0 . 001 μg ) of purified virion RNA in nucleic acid extracts from non-inoculated plants . Mock-inoculated samples served as negative controls . Nucleic acid extracts were blotted at different dilutions to ensure that hybridization signal was on the linear portion of the RNA concentration-hybridization curve . As loading controls , parallel membranes were hybridized with a cDNA probe of β-tubulin chain 2 ( TUB2 ) mRNA of A . thaliana ( 1086–1568 nt , GeneBank Acc . No . NM_125664 . 4 ) . Rosette weight was used to estimate vegetative growth effort , inflorescence plus seed weight to estimate total reproductive effort , and seed weight to estimate progeny production [101] . Previous work has shown that CMV infection does not affect seed viability , nor the weight of individual seeds , in a broad range of A . thaliana genotypes [14 , 47 , 52] . Plants were harvested at complete senescence and dry weight of rosettes ( rosette weight , RW ) , inflorescence structures without seeds ( inflorescence weight , IW ) and seeds ( seed weight , SW ) were measured separately ( g ) . Two phenological parameters of A . thaliana life cycle were quantified: Growth period ( GP ) and life-span ( LP ) were measured as the time ( days ) between planting seedlings in soil and opening of the first flower ( GP ) , or complete senescence ( LP ) . Tolerance was measured by the effect of virus infection on progeny production: SWi/SWm , where i and m denote infected and mock inoculated plants , respectively [48] . Broad sense heritability of each trait was estimated as h2b = VG/ ( VG+VE ) , where VG is the among-genotypes or among-populations variance component and VE is the residual variance . Variance components were determined using the REML method [102] of SPSS 20 package ( SPSS Inc . , Chicago , USA ) . Genetic differentiation between populations for quantitative traits was measured by QST values [103] , estimated as VB/ ( VB+VW ) [104 , 105] , where VB is the between-population variance and VW is the within-population variance . VB and VW were estimated by the REML method from a nested analysis of variance performed using population and individual or genotype ( nested within populations ) as random factors . The 95% confidence intervals ( 95%CI ) for QST values were estimated as P[S2 ( n−1 ) χn−12≤σ2≤S2 ( n−1 ) χn−12]=0 . 95 [106] . Genetic differentiation for neutral markers was estimated as FST [107] using the analysis of molecular variance ( AMOVA ) as implemented in ARLEQUIN v3 . 5 . 1 . 2 [108] . AMOVA were performed using multilocus genotypes for 250 segregating SNPs [30 , 32] and their significances were estimated from 1 , 000 permutations . The relationships between Euclidean geographical distance and FST or QST values among population pairs were determined by Mantel correlation test using PASSaGE v . 2 [109] with 1 , 000 permutations . Genetic distances between individuals were calculated as the proportion of allelic differences over the total number of alleles in the corresponding set of polymorphic loci , using GGT v . 2 . 0 [110] . Differences in RW , IW , SW , GP , LP , virus accumulation or tolerance to CMV , according to host individual/genotype and virus isolate , were analysed by general linear models ( GLM ) considering host individual/genotype as a random factor , and virus isolate as a fixed factor . Differences in RW , IW , SW , GP , LP , virus accumulation or tolerance to CMV according to population , host individual/genotype , and virus isolate , were analysed by GLM considering isolate as a fixed factor , and population and individual/genotype nested to population , as random factors . Relationships between values of different traits were tested using Spearman’s correlation test . GLMs and Spearman’s correlation tests were performed using SPSS 20 software package . Spatial autocorrelation patterns of environmental variables , life-history traits , virus accumulation and tolerance to virus , were analysed using correlograms [111] generated with PASSaGE v . 2 . For each variable , Moran’s I autocorrelation coefficients [112] were calculated and their significance tested from 1 , 000 permutations . Correlation between pairs of environmental variables , between pairs of different traits and between environmental variable and different traits were tested with Dutilleul’s modified t-test using SAM v . 4 [113 , 114] . Simultaneous autoregressive models ( SAR ) [115] were performed to test the relationship between environmental variables and different traits using SAM v . 4 . Bonferroni correction was applied for multiple comparisons . The relationships between environmental variables and life-history or defence traits were analysed by partial Mantel tests controlling for the location of populations given by the geographic distance matrix using PASSaGE v . 2 . For that , matrices of euclidean distances were derived for each environmental variable and phenotypic trait and significance was evaluated from 1 , 000 permutations . | Plant-virus coevolution has not been demonstrated in any wild system , and it has been proposed that viruses often would be mutualistic symbionts , rather than pathogens , in wild plant ecosystems . We analyse if viruses are virulent pathogens of plants in wild ecosystems and , consequently , plants have evolved defences against virus infection . To test this hypothesis , we studied the genetic diversity of Arabidopsis thaliana for two defence traits , resistance and tolerance , to Cucumber mosaic virus ( CMV ) at a regional scale in the Iberian Peninsula . Resistance and tolerance to CMV showed substantial genetic variation within and between host populations , and depended on the virus x host genotype interaction , two conditions for coevolution . Resistance and tolerance were independent traits that co-occurred at the population and regional scales , and that have evolved independently from other adaptive life-history traits . Analyses also indicated that resistance and tolerance are likely under selection , most likely due to virus infection . These results support a hypothesis of plant-virus coevolution and contribute to demonstrate that plant viruses may be virulent parasites of plants in wild ecosystems . | [
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| 2019 | Cucumber mosaic virus infection as a potential selective pressure on Arabidopsis thaliana populations |
The maternally inherited intracellular bacteria Wolbachia can manipulate host reproduction in various ways that foster frequency increases within and among host populations . Manipulations involving cytoplasmic incompatibility ( CI ) , where matings between infected males and uninfected females produce non-viable embryos , are common in arthropods and produce a reproductive advantage for infected females . CI was associated with the spread of Wolbachia variant wRi in Californian populations of Drosophila simulans , which was interpreted as a bistable wave , in which local infection frequencies tend to increase only once the infection becomes sufficiently common to offset imperfect maternal transmission and infection costs . However , maternally inherited Wolbachia are expected to evolve towards mutualism , and they are known to increase host fitness by protecting against infectious microbes or increasing fecundity . We describe the sequential spread over approximately 20 years in natural populations of D . simulans on the east coast of Australia of two Wolbachia variants ( wAu and wRi ) , only one of which causes significant CI , with wRi displacing wAu since 2004 . Wolbachia and mtDNA frequency data and analyses suggest that these dynamics , as well as the earlier spread in California , are best understood as Fisherian waves of favourable variants , in which local spread tends to occur from arbitrarily low frequencies . We discuss implications for Wolbachia-host dynamics and coevolution and for applications of Wolbachia to disease control .
The vertically-transmitted intracellular bacterium Wolbachia may be the most widespread [1]–[3] and evolutionarily significant endosymbiont [4] , [5] of insects and other arthropods . Wolbachia induce many reproductive manipulations within hosts that increase their chance of spreading through females . Their ability to suppress other microbes in their hosts provides a novel method to control human vector-borne diseases such as dengue [6] , [7] and malaria [8] , [9] . Yet despite their ubiquity and potential importance in vector-borne disease control , there are few documented examples of Wolbachia infections spreading in natural host populations [10] , [11] . The most commonly documented host reproductive manipulation by Wolbachia is cytoplasmic incompatibility ( CI ) [12] , which reduces hatch rates for embryos produced when sperm from an infected male fertilizes uninfected ova or ova that carry an incompatible Wolbachia strain . CI provides a reproductive advantage to infected female hosts ( whose infected eggs are protected from CI ) , and it can drive the spread of a Wolbachia infection within and among host populations [11] , [13]–[15] . Several factors can affect the spread of CI-inducing Wolbachia infections , including the strength of incompatibility ( quantified by H , the relative hatch rate of embryos from incompatible fertilizations ) , the maternal transmission frequency ( 1−μ , where μ is the frequency of uninfected ova produced by an infected female ) , and the fecundity of infected females relative to uninfected females ( F , which can also approximate viability effects ) [12] . Mathematical analyses [14]–[16] show that irrespective of the level of CI , as measured by H ( 0≤H≤1 ) , if a CI-inducing infection satisfies F ( 1−μ ) <1 , it will tend to decrease in frequency when very rare . The reason is that the CI-induced reproductive advantage for infected females depends on the frequency of infected males , whereas disadvantages attributable to fitness costs , F<1 , or imperfect maternal transmission , μ>0 , are frequency independent . If the level of CI is sufficient that F ( 1−μ ) >H ( i . e . , when the CI-inducing infection is very common , more infected offspring are produced by infected mothers than viable uninfected offspring are produced by uninfected mothers ) , the infection will tend to spread locally once it becomes sufficiently common . Hence for Wolbachia strains that satisfy H<F ( 1−μ ) <1 , we expect “bistable” dynamics [13]–[15] , where there is an unstable equilibrium infection frequency , denoted pu , that satisfies 0<pu<1; pu separates two stable equilibria , one at 0 , the other a high frequency denoted ps ( which always exceeds ½ , [17] ) . If maternal transmission is imperfect ( μ>0 ) , then ps<1 because uninfected individuals are continuously introduced . The unstable equilibrium frequency , pu , plays a central role in both local dynamics and spatial spread . Local dynamics depend on the initial infection frequency , denoted p0 . If p0>pu , the infection frequency tends to increase towards ps; conversely if p0<pu , the frequency tends to decrease to 0 . Spatial dynamics are more complex , and predictions depend on the initial frequencies over an extended area . Nevertheless spatial spread from a localized introduction cannot occur if pu is too large; roughly , bistable infections do not spread unless pu<½ ( the exact condition is described in [13] and [18] ) . If a CI-inducing infection with pu<½ becomes established in a sufficiently large region ( see Fig . 3 of [13] ) , it will tend to spread spatially at a rate determined by average dispersal distance of females and the intensity of CI . These bistable waves can be stopped by regional variation in population density or dispersal barriers [19] . In contrast to bistable infections , Wolbachia strains that provide a frequency-independent fitness advantage to infected hosts , such that F ( 1−μ ) >1 , would increase locally , even from low initial frequencies and regardless of whether they cause CI . Local spread would be followed by a “Fisherian” spatial wave [20] , [21] that , unlike a bistable wave , is unlikely to be halted . For such infections , 0 is the unstable equilibrium and the unique stable equilibrium satisfies ps<1 if maternal transmission is imperfect [12] . As in [13] , we use “Fisherian” to describe the spatial dynamics of variants that tend to increase even when locally very rare , unlike bistable variants that tend to increase only once they exceed a critical frequency pu>0 . The mechanism that reduces the unstable equilibrium to zero may involve interactions between Wolbachia , D . simulans and various pathogenic microbes , as postulated by Fenton et al . [22] . Alternatively it may involve nutritional provisioning [23] , [24] or other effects as yet unknown . In the absence of relevant field data , we approximate the effects of Wolbachia on D . simulans by a frequency-independent advantage that produces F ( 1−μ ) >1 . Nothing about our analyses or conclusions , which rest on the rapid sequential spread of two Wolbachia variants , requires a more detailed model . The key feature of Fisherian variants is that they spread much more rapidly than bistable variants , because small advance propagules can catalyze invasions of new areas [25 , Ch . 5] , in contrast to the steadily moving wave fronts expected with bistable dynamics . Unfortunately , the temporal and spatial resolutions of our data are insufficient for fitting mechanistic models of spatial spread . Turelli & Hoffmann [11] , [17] first documented a Wolbachia infection spreading within and among natural populations . A strain of Wolbachia ( wRi ) was initially found infecting Drosophila simulans populations in southern California . Turelli and Hoffmann monitored the northward spread of wRi over ten years ( 1985–1994 ) . Because they repeatedly found both imperfect maternal transmission in nature and reduced relative fecundity for infected females in the laboratory [15] , [17] , they assumed F ( 1−μ ) <1 and inferred bistable dynamics ( i . e . , H<F ( 1−μ ) <1 ) . Using field-based estimates of μ , F and H , their mathematical analyses implied ps≈0 . 94 , in close agreement with relatively constant infection frequencies observed in several natural populations for over 20 years [26] . However , as emphasized by Jaenike [27] and elaborated by Carrington et al . [26] , the CI-based prediction for ps is quite insensitive to variation in the relative fecundity of infected females , F . Based on field estimates of μ near 0 . 045 , F ( 1−μ ) >1 requires only that infected females have a fecundity advantage on the order of 5% . We present new mtDNA data from recent California samples of D . simulans and from a 1961 southern California collection which suggest that wRi invaded California less than 25 years before Hoffmann et al . [28] found it . This supports our new interpretation of Fisherian versus bistable spread in both California and Australia . Other studies of Wolbachia in natural host populations have also assumed bistable dynamics [10] , [29] , in one case [29] based on observing very rare imperfect maternal transmission ( μ≈0 . 014 ) , in the other [10] based on an indication of spatial spread analogous to that seen in D . simulans . A definitive example of bistable dynamics comes from recent field releases of Wolbachia-infected Aedes aegypti mosquitoes . The wMel Wolbachia infection was transferred in the laboratory from its native host D . melanogaster to Ae . aegypti as part of a novel strategy for blocking transmission of the human dengue virus [6] , [7] . The observed rate of infection frequency increase within populations was consistent with significant fitness costs , and hence bistable dynamics; and low-frequency introductions into neighbouring populations have not led to Wolbachia establishment outside the release areas , as predicted with bistability ( Eliminate Dengue Team , unpubl . data ) . These mosquito data provide experimental support for bistable Wolbachia dynamics in nature . However , other infections including wMel in D . melanogaster cause minimal CI and persist despite incomplete maternal transmission . These infections may show Fisherian dynamics [12] , [30] , [31] , consistent with the apparent global spread of alternative forms of wMel , none of which causes appreciable CI [32] . Here we present Wolbachia and mtDNA data that document the sequential recent spread of two Wolbachia infections in eastern Australian D . simulans populations . Previous research [33] indicated that D . simulans were polymorphic for a novel Wolbachia infection ( wAu ) present at a low frequency that induced no detectable CI . The persistence of this infection is most easily understood if it increases fitness consistent with F ( 1−μ ) >1 . Although various potential fitness advantages have been associated with Wolbachia infections in the laboratory , including virus protection [34] , [35] and fecundity increases [36] , [37] , they have not been demonstrated in nature for wAu or any other Wolbachia strain [38] . We show that the temporal and spatial data for wAu , and for wRi that has recently invaded Australia [39] , are best explained by postulating that each variant increases host fitness in nature . We also present a new observation relevant to the history of wRi spread in Californian populations of D . simulans . These new data suggest that wRi invasion may be consistent with a Fisherian rather than a bistable wave , increasing our understanding of Wolbachia infection dynamics in nature and supporting theoretical analyses [22] suggesting that the global distribution of Wolbachia throughout insects and other arthropods is most easily understood if these infections tend to spread deterministically from low initial frequencies .
We assayed individual D . simulans samples for Wolbachia infection status and strain type using a real-time PCR/high resolution melt ( RT/HTM ) method designed to amplify a fragment of the wsp gene [39] . Samples which successfully amplified using this method yielded amplicons with melting temperature peak rates ( Tm ) that clustered into two distinct groups which showed a consistent difference of ∼0 . 5°C . Wolbachia strain type for positively infected D . simulans individuals was assigned on this basis . Positive controls where DNA extracted from both a wAu-infected and a wRi-infected individual fly was combined and amplified in the same reaction produced intermediate and less distinct Tm peaks . This pattern was never observed for amplicons derived from individual specimens , suggesting that double infections are unlikely to occur amongst Australian field populations of D . simulans at any appreciable frequency . Sequencing of the amplicons derived from 13 of these samples using a standard PCR method with primers wsp_81F_Fwd: 5′-TGGTCCAATAAGTGATGAAGAAAC-3′ and wsp_691_Rev: 5′-AAAAATTAAACGCTACTCCA-3′ [40] confirmed that 9 samples from the high-Tm cluster were the wRi haplotype ( cf . GI: 225591853 ) , whilst the remaining 4 from the low-Tm cluster were wAu ( cf . GI: 2687519 ) . For a further 5 high-Tm and 5 low-Tm cluster individuals we also obtained sequence data using standard PCR methods for each of the Wolbachia MLST genes gatB , coxA , hcpA , ftsZ and fbpA [41] , and for the sucB gene [42] . These samples were drawn from isofemale lines derived from diverse geographic locations including , for the high-Tm cluster , lines established in 2011 from Brisbane , Melbourne and Gosford , New South Wales; and for the low-Tm cluster , recently sourced lines from Perth and Geraldton ( Western Australia ) , Y6 originally from Cameroon , West Africa ( E . A . McGraw , pers . comm . ) , and Coff1 sourced from Coffs Harbour , New South Wales in the mid-1990s bearing the originally designated wAu infection [33] . A single haplotype was obtained for the high-Tm samples which showed 100% sequence identity with the wRi strain of Wolbachia ( cf . GI:225629872 ) . A single distinct haplotype was also obtained for the low-Tm samples which was identical to previous data for the wAu strain at the sucB locus ( cf . GI:84028372 ) . Further validation of these results was obtained using the RT/HTM method with two sets of strain-specific primers ( wRi_wsp_Fwd: 5′-TGATGTTGAAGGGCTTTATTCACAG-3′; wRi_wsp_Rev: 5′-GTATCTGGGTTAAATGCTGCACCTG and wAu_wsp_Fwd: 5′-TGATGTTGAAGGAGTTTATTCATAC-3′; wAu_wsp_Rev: 5′-TTTGCTGGGTCAAATGTTACATCTT-3′ ) . 34 of the original samples from the high-Tm cluster each amplified successfully using the wRi-specific primers but did not amplify with the wAu primers , whilst the reverse was true for 27 samples from the original low-Tm cluster . Results for samples collected in 2004 ( n = 162 ) , 2008 ( n = 499 ) , and 2011/12 ( n = 799 ) are summarised in Figure 1 ( full details in Table S1 ) . In contrast to the infection frequencies found for samples collected in 1993/94 [33; Figure 1] , a second Wolbachia strain ( wRi ) was already prevalent amongst some east Australian D . simulans populations by 2004 , both in the far north ( Cairns ) and the south ( Melbourne ) ; it occurred at a lower frequency further down the Queensland coast ( Maryborough ) and in northern Tasmania , but was not yet present in the central coastal area of northern New South Wales or in southern Tasmania . Additionally , for three central populations ( Maryborough , Kingscliff and Red Rock ) , wAu infection frequencies increased significantly compared to geographically equivalent populations sampled previously ( P<0 . 001 , G-test ) . Ballard [43] also reports significantly higher wAu frequencies for samples collected in December 1999 from both Coffs Harbour and Brisbane than those previously found by Hoffmann et al . [33] . The 2008 data show wRi spreading considerably since 2004 , being at high frequency throughout Queensland , and now detected in both southern Tasmania and 6 of 7 populations from New South Wales . By 2011/12 , wRi was at high frequency at every location we sampled in mainland eastern Australia and Tasmania ( n = 794 ) with an overall frequency of 0 . 929 ( 0 . 909 , 0 . 946 ) ; whilst the wAu infection was only detected from an island ( Hayman Island ) ∼29 kms offshore from Airlie Beach in Queensland . We did not detect wRi infection from two sites in Western Australia sampled in late 2011 and early 2012 . Samples from Perth ( n = 105 ) and Geraldton ( n = 40 ) , 370 km to the north , were polymorphic for the wAu infection , with infection frequencies of 0 . 648 ( 0 . 548 , 0 . 738 ) and 0 . 500 ( 0 . 338 , 0 . 662 ) respectively or 0 . 607 ( 0 . 522 , 0 . 687 ) when pooled . Populations of D . simulans in Western Australia are separated from those in the east by ∼2000 km of dry habitat , preventing direct migration . Laboratory crosses indicate that the wRi-infected Australian flies have a CI phenotype indistinguishable from California wRi . Hatch rates ( Table 1 ) from crosses between infected females from four north Queensland lines established from females collected in 2011 and five-day-old males from a wRi-infected laboratory line provided no evidence for CI and were similar to the average hatch rate when females from these lines were crossed to males from an uninfected and a wAu-infected laboratory line . Females from these lines therefore behaved like wRi lines in crossing type . In addition , when males from these lines were crossed , they were compatible with the wRi line females , but generated incompatibility in crosses with females from both the uninfected and wAu-infected lines . All differences between reciprocal crosses were significant ( Mann-Whitney U-tests ) at the table wide α' = 0 . 05 level after corrections for multiple comparisons . To test maternal transmission of wAu in D . simulans in Western Australia , we established 55 isofemale lines from field-caught females obtained from two locations near Perth and from Geraldton in Western Australia in 2011/12 . We assayed the Wolbachia infection status of the mothers and several F1 progeny , including between 10 and 12 progeny for each of 34 lines infected with wAu . From these lines , 350 progeny were tested , and all but 8 were found to be infected . This yields a field estimate for μ of 0 . 023 with 95% bootstrapped confidence interval ( bCI ) of 0 . 003 , 0 . 049 based on 10 , 000 replicates ) . The bCI is broader than the 95% binomial confidence interval based on total numbers ( 0 . 01 , 0 . 045 ) , but is more appropriate because of heterogeneity in transmission frequencies across females [cf . 26] . We also tested maternal transmission of wRi in eastern Australia using isofemale lines established from two locations near Melbourne in 2013 . Ten F1 progeny were tested for each of 23 of these lines where the mother was found to be infected . All but 7 of the F1s were also infected , yielding a field estimate of μ of 0 . 026 ( 95% bCI 0 . 004 , 0 . 057 ) which is consistent with previous field estimates of μ for wRi in California [17] , [26] . The wRi infection has previously been found associated with significant fecundity increases in D . simulans lines from California [37] . We performed a three-way comparison for fecundity using Australian wRi-infected , wAu-infected and uninfected laboratory lines which had been outcrossed to a common genetic background for 5 generations . Results ( Figure 2 ) indicate that females from the wRi-infected line showed a marginally significant fecundity advantage relative to the uninfected line ( p = 0 . 045 , t-test ) but strain differences were marginally non-significant in the three-way comparison ( F2 , 37 = 2 . 7 , p = 0 . 082 ) . To examine the association between Australian wAu and wRi infections across time , we obtained sequence data for a 1256 bp mtDNA fragment from 7 wAu-infected and 11 wRi-infected samples , and found no variation apart from a single G to A base substitution previously identified by Ballard [43] ( position 457 on our fragment , position 8201 in [43] , see Table 2 ) with the former sequence ( here denoted “A haplotype” ) found only in wAu-positive samples and the latter ( denoted “R haplotype” ) only in wRi-positive samples . Additional mtDNA haplotype sequence data obtained for 45 Wolbachia uninfected ( w– ) samples from 2004 , 39 w– samples from 2008 and 6 w– samples from 2011 were similarly invariant , comprising only A or R haplotypes . The mtDNA haplotype for a further 9 w- samples from 2004 , and 55 w- samples from 2008 , was determined by treatment of PCR amplicons with the restriction enzyme HinfI . Full details of the mtDNA haplotypes determined for w- samples are summarised in Table S1 . Both A and R haplotypes were detected amongst the 2004 and 2008 eastern Australian w- samples ( simultaneously for some locations ) , whereas sequence data for w- samples collected in 2011 ( n = 5 ) all confirmed the presence of the R haplotype . A single 2011 w– sample from Perth , Western Australia had the A haplotype . To better understand the history of association of wRi with D . simulans , we also examined mtDNA variation in California D . simulans stocks by sequencing two regions which included those identified in Table 3 of Ballard ( 2004 ) as showing the greatest variation among siII mtDNA haplotypes , positions 1175 to 1626 and 7838 to 8287 . We examined mtDNA from a stock founded in 1961 in Nueva , California , ∼25 km SE of Riverside , the type locality for wRi [28] . This stock is not infected with Wolbachia , and its origin pre-dates the initial 1985 Wolbachia analyses . It yielded a novel haplotype , denoted CA61 , different from those described in Ballard ( 2004 ) by two base pairs from both the canonical “R haplotype , ” which Ballard calls DSR , and the canonical haplotype ( DSW ) associated with northern California D . simulans prior to the invasion of wRi . To better understand the novel CA61 haplotype , we examined mtDNA from two reference wRi stocks , Riv84 and Riv88 , collected in Riverside in 1984 and 1988 , respectively , and from seven stocks ( denoted Y26 , Y35 , Y36 , Y42 , Y46 , Y54 and Y62 ) collected from an orchard approximately 5 km east of Winters , California in August 2011 . All stocks apart from Y35 and Y62 were wRi infected . The Y54 stock produced a novel haplotype which differs from the canonical DSR by one nucleotide ( Table 1 ) . As expected , the mtDNA from Riv84 , Riv88 , and all the other stocks matched DSR . Given the paucity of sequence variation in siII D . simulans mtDNA sequences , and in the mtDNA associated with wRi [43] , [44] , there is little statistical power to infer phylogenetic relationships among these sequences , apart from the fact that the sequences from the Indian Ocean seem to be part of a distinct clade ( see Fig . 3 of [43] ) . However , with only one exception , all wRi-infected stocks share the nucleotide A at position 8201 , in contrast to the G found in DSW and CA61 ( as well as AU23 and all other wAu-infected stocks examined by Ballard [43] . This substitution corresponds to the HinfI polymorphism described by Hale and Hoffmann [45] differentiating mtDNA of wRi-infected stocks sampled worldwide from DSW . Nucleotide A was found at position 8201 in all wRi-infected stocks examined worldwide ( Ballard [43] , 92 wRi-infected stocks from California [46] and 29 of 30 additional wRi-infected stocks [17] . The only exception may correspond to rare paternal transmission of mtDNA [cf . 47] . Given that uninfected flies from wRi-infected populations quickly become associated with the mtDNA from their infected maternal ancestors [46] , it seems unlikely that wRi was present at an appreciable frequency near Riverside in 1961 . As noted by Hoffmann and Turelli [12] , for a Wolbachia variant such as wAu to persist despite imperfect maternal transmission and little CI , it must increase fitness sufficiently to offset imperfect transmission , i . e . , F ( 1−μ ) >1 . Let IA ( IR ) denote individuals infected with wAu ( wRi ) , and let U denote uninfected individuals . We measure fitness of IA and IR females relative to uninfected females , and denote their relative fitnesses FA and FR . Let μA ( μR ) denote the frequency of U ova produced by IA ( IR ) females . The condition for wAu to increase when rare is FA ( 1−μA ) >1 . When this is satisfied , wAu will reach a stable equilibrium frequency of ( 1 ) The wAu frequencies observed in southern Queensland and northern New South Wales in 1999 and 2004 , as well as the 2011/12 wAu frequencies in Western Australia , all suggest an equilibrium frequency for wAu of roughly 0 . 6 . Using ( 1 ) and our estimate μA≈0 . 023 implies FA≈1 . 061 . Given the wide confidence interval for μA ( 0 . 003 , 0 . 049 ) , ( 1 ) implies that plausible values for FA range from 1 . 008 to 1 . 140 . However , estimates of FA are further constrained by the observed wAu frequency changes . A pooled frequency estimate ( n = 1641 ) for mainland eastern Australia in 1993/1994 [33] is 0 . 054 , with 95% confidence interval ( 0 . 044 , 0 . 066 ) ; by December 1999 , the estimated frequency ( n = 92 ) had increased to 0 . 565 ( 0 . 458 , 0 . 669 ) . This increase occurred over about 120 generations , assuming roughly 20 generations per year for 6 years . With FA = 1 . 061 and μA = 0 . 023 , pA would increase from 0 . 054 to 0 . 529 in 120 generations , consistent with our data . If μA were significantly smaller , say 0 . 01 , ( 1 ) with = 0 . 6 implies FA = 1 . 026 . Assuming μA = 0 . 01 and FA = 1 . 026 , pA would increase in 120 generations from 0 . 054 to only 0 . 229 , far below our 1999 estimate . However , if μA were appreciably larger , say 0 . 04 , ( 1 ) with = 0 . 6 implies FA = 1 . 11 . With those values , pA would increase from 0 . 054 to 0 . 597 in 120 generations . Thus , our frequency estimates and maternal transmission data are consistent with a positive fitness effect for wAu on the order of 5–10% , but inconsistent with appreciably smaller fitness advantages of 3% or less ( Figure 3 ) . Given the uncertainty of our estimates for both the apparent stable equilibrium frequency , , and the maternal transmission rate , μA , for wAu , our data documenting the frequency increase of wAu from 1993 to 1999 provide the most robust estimate for the fitness advantage it seems to induce . As shown in Materials and Methods , once wAu is established , wRi will tend to increase when very rare only if ( 2 ) The roughly simultaneous spread of wRi from three geographically disparate foci in north Queensland , Victoria and Tasmania ( Figure 1 ) indicates that condition ( 2 ) was met . Moreover , the observed temporal displacement of wAu by wRi is consistent with ( 2 ) and the levels of CI and maternal transmission for wRi observed in California [17] , [26] . For instance , the wRi and wAu infection frequencies estimated in March–May 2008 from seven coastal cities from New South Wales were statistically consistent ( G test; G = 20 . 39 , P = 0 . 06 ) with pooled frequencies ( n = 253 ) of 0 . 09 and 0 . 54 for wRi and wAu respectively ( and 37% uninfected ) . In 2011 , seven New South Wales coastal populations spanning the same range of latitudes were sampled ( n = 302 ) ; again their infection frequencies were statistically consistent ( G-test; G = 3 . 74 , P>0 . 68 ) , but wAu had been eliminated and the pooled frequency estimate for wRi had risen to 0 . 91 ( 0 . 88 , 0 . 94 ) . We conjecture that there may be on the order of 15 generations per year on average in this area of coastal New South Wales ( comparable to the Central Valley of California , versus 20 for sub-tropical coastal populations farther north ) . To compare these data to the predictions of our simple model ( see Eqs . 3 below ) , we need estimates of fitness effects , maternal transmission rates and CI . As above , we assume FA = 1 . 061 and μA = 0 . 023 . Assuming that H , the relative hatch rate from incompatible fertilizations ( i . e . , sperm from wRi-infected males fertilizing either uninfected or wAu-infected ova ) , and the fidelity of maternal transmission were as observed in California , e . g . , H = 0 . 55 and μR = 0 . 045 [17] , [26] , we predict that starting with pA = 0 . 54 and pR = 0 . 09 , pA will fall below 0 . 01 within 40 generations only if FR is on the order of 1 . 08 ( Figure 4 ) . After 45 generations , these parameter values imply that pR should rise from 0 . 09 to very near its predicted stable equilibrium value of 0 . 94 , consistent with our data . The stable equilibrium frequency of wRi is very insensitive to FR [27] . For instance , with H = 0 . 55 and μR = 0 . 045 , as FR increases from 0 . 95 to 1 . 1 , increases from 0 . 93 to only 0 . 94 . In contrast , the unstable equilibrium for wRi ( when entering an uninfected population ) plummets from 0 . 22 to 0 , converting the predicted spatial dynamics from bistable to Fisherian . Our temporal data are too coarse to approximate accurately the speed of wRi's spatial spread . However , between 2008 and 2011 , wRi spread southward from Coffs Harbour and northward from Bega ( Figure 1 ) , filling in roughly 1000 km of the New South Wales coast in three years . The speed of this bidirectional spread is comparable to the northward spread of wRi observed in California , roughly 100 km/year [11] . Such rapid spatial spread can be explained by human-mediated , long-distance dispersal and Fisherian local dynamics that allow rare long-distance migrants to greatly accelerate spatial advance [25 , Ch . 5] .
Despite the ubiquity of Wolbachia in natural populations of arthropods , there are very few documented examples of Wolbachia spread [10] , [11] . This has limited our understanding of Wolbachia spatial dynamics , with bistable spread for CI-causing Wolbachia suggested by imperfect maternal Wolbachia transmission by wild-caught females [15] , [29] and demonstrated fecundity costs for infected females in the laboratory [17] , [48] , [49] . However , bistable dynamics cannot explain the persistence of Wolbachia that cause little or no reproductive manipulation [12] . Moreover , as emphasized by Fenton et al . [22] , bistable dynamics are difficult to reconcile with molecular data indicating that the widespread occurrence of Wolbachia is attributable to rare horizontal transmission events involving one or very few infected founders [3] , [44] , [50] , [51] . These phenomena are more easily explained by Fisherian dynamics [22] , in which the frequency dynamics of rare Wolbachia infections are dominated by positive fitness effects . Our temporal and spatial data , describing the sequential spread of two Wolbachia strains ( wAu and wRi ) through natural populations of D . simulans along the east coast of Australia over the last two decades , support the view that both strains may have spread under Fisherian dynamics . The recent spread of wRi is particularly compelling , with apparently independent introductions in both the north and south of the country between 1994 and 2004 , leading to near-fixation of wRi in all populations sampled on the east coast mainland of Australia by late 2011/early 2012 . The appearance of wRi in three geographically separated locations – Queensland , Victoria and Tasmania – in 2004 cannot be explained as a single bistable wave that originated and spread from elsewhere . In 1994 , before the arrival of wRi , wAu was rare in several populations . Ballard's ( 2000 ) data from 1999 and our samples from 2004 and 2008 ( Figure 1 ) document wAu frequency increases across the Australian east coast . Given that wAu does not induce CI [33] , its spread from low frequency in multiple populations implies that it confers a fitness benefit . The rapid displacement of wAu by wRi suggests that wRi must enhance fitness even more than wAu . Both wAu and wRi have been detected in D . simulans populations around the world [43] , [52] , [53] , and these infections have co-occurred previously ( e . g . Sangoqui and Rocafuerta , Ecuador in 2000; Brooksville , FL , USA in 2002 [43] , and possibly also Japan [17] ) . The wAu infection in D . simulans may have a Neotropical origin as a result of a relatively recent horizontal transfer event from Drosophila willistoni [54] . The origin of wRi in D . simulans is less certain , but very closely related Wolbachia have been found in various Drosophila that co-occur with this cosmopolitan species [e . g . ] , [ 54] , [55 , 56] . It is not known how either strain was introduced into Australian D . simulans populations , but our data suggest that both introductions occurred within the last few decades . The pooled frequency of wRi ( 92 . 7% , Table S1 ) in our 2011/12 eastern Australian samples ( including Tasmania ) is consistent with the stable equilibrium frequency found in California over the past two decades [∼93%]; [ 17] , [26 , 37] . This equilibrium frequency is determined primarily by significant CI and imperfect maternal transmission . Our data from crossing experiments , and mtDNA and Wolbachia sequencing suggest that the wRi strain now found throughout eastern Australia is essentially identical to that initially identified in California [28] and subsequently found in Africa , Asia , Europe and South America [17] , [43] . Genome comparisons support this ( M . Turelli , unpublished data ) . Our mtDNA data from uninfected individuals in 2011/12 indicate that the R haplotype is being swept to fixation in eastern Australia as a consequence of the spread ( with imperfect maternal transmission ) of wRi [cf . 46] . A previous mitochondrial sweep of the A haplotype associated with wAu may have also occurred , but the ancestral haplotypes in eastern Australian D . simulans are not known . Wolbachia and mtDNA are expected to be co-inherited maternally . Even though Wolbachia exhibit imperfect maternal transmission ( μ>0 ) , mtDNA haplotypes associated with a given infection will be driven to fixation , by CI or other Wolbachia-associated positive fitness effects , if all offspring of infected females inherit maternal mtDNA [46] . Eventually , all individuals in the population have infected maternal ancestors who passed on their mtDNA even if not their Wolbachia . Although rare paternal transmission of Wolbachia has been detected in laboratory D . simulans [48] , [49] , Turelli et al . [46] and Turelli & Hoffmann [17] inferred that paternal transmission or horizontal transfer must be rare in nature . Similarly , there is evidence for rare paternal inheritance of mtDNA in D . simulans [47] , [57] , [58] , but it is apparently too rare to create incongruity between Wolbachia and mtDNA lineages [43] . Drosophila simulans populations worldwide carry three distinct mtDNA haplotype groups , with very little intra-haplogroup variation [44] , [59] , [60] . Ballard [43] found both wAu and wRi only in flies carrying siII haplogroup mtDNA . He identified a single ( synonymous ) G to A base substitution ( see Table 2 ) that was consistently different between wAu-infected versus wRi-infected flies respectively , and matched a restriction enzyme polymorphism identified by Hale and Hoffmann [45] . In our analyses , all wRi flies had the same base substitution as identified by these researchers , consistent with the hypothesis of a unique origin for wRi in D . simulans . Turelli and Hoffmann [11] , [17] had previously suggested bistable spatial dynamics of wRi in California populations of D . simulans . However , the rate at which wRi spread spatially , on the order of 100 km per year , is more consistent with Fisherian dynamics whose deterministic spread from very low frequencies allows rare long-distance dispersal to greatly accelerate spatial advance . Assuming that the mtDNA haplotype of the 1961 strain we characterized was widespread in southern California , our new analysis suggests that wRi was not yet at appreciable frequency at this time , and that the invasion detected by Turelli and Hoffmann [11] may reflect a rapid spatial spread from populations farther south . The speed of wRi spread throughout eastern Australia is comparable to the spread of wRi in California , on the order of 100 km/year [11] . The infection was common in two populations sampled in 2004 ( Melbourne and Cairns ) , then swept to near fixation in all east Australian mainland populations and Tasmania by 2011/12 . Although some genes and traits exhibit clinal variation over this range [61] , [62] , several molecular markers indicate that D . simulans is effectively a single panmictic unit throughout eastern Australia , including Tasmania [63] . Thus , there are no apparent barriers to dispersal that would isolate populations from the spread of wRi . We did not , however , detect wRi in Western Australia , with the wAu infection persisting at relatively high frequency ( ≈60% ) . Western Australian populations of D . simulans may be geographically isolated from eastern Australian populations , although once wRi is introduced it is likely to sweep through this area in the coming decades . The Drosophila-Wolbachia dynamics have been used to interpret and model the spread of Wolbachia-infected Aedes aegypti mosquitoes in Australia [7] . In contrast to the apparent Fisherian spatial dynamics of wAu and wRi , these releases seem to follow bistable dynamics , with strong evidence for a non-trivial unstable equilibrium frequency , on the order of 10–20% [6] , [7] . After wMel was driven to a high frequency in two relatively isolated populations by 10 weeks of releases , it increased to near-fixation , and has remained at over 90% frequency for the past 2 years [7; unpublished data] . Although the infection was detected in disjunct residential areas near the release sites soon after the initial releases , it has not persisted or spread in these areas [7; unpublished data] , as would be expected under Fisherian dynamics . Given the evolutionary pressure for Wolbachia to evolve towards mutualism with its hosts [37] , [64] , a decreasing unstable point may alter these dynamics in the future [13] particularly if fitness costs produced in novel hosts are overcome . In summary , our results suggest that the wAu and wRi Wolbachia infections of D . simulans spread at least part of the time in a Fisherian manner . The wAu infection increased in frequency despite having imperfect transmission and not causing CI . Its spread and apparent equilibrium make sense only if it increased host fitness . Over the past decade , wAu was rapidly displaced by wRi throughout eastern Australia . The rapidity of this replacement starting from three locations in 2004 indicates that wRi also increases host fitness in nature , at least under some circumstances . These data plus evidence that wRi entered southern California after 1960 lead us to reinterpret that canonical example of Wolbachia spread in California as Fisherian rather than bistable . Depending on the rapidity with which Wolbachia adapt to novel hosts , its spread after artificial introductions to target species may be greatly accelerated .
Field-caught D . simulans samples were collected from various locations along the east coast of Australia including Tasmania during February–March 2004 [61] and March–May 2008 [see 39] . Further samples were collected from mainland eastern Australia at similar coastal and some inland localities , and from the Perth region of Western Australia during 2011 , and from both Geraldton ( Western Australia ) and Tasmania in early 2012 . All samples were preserved in 100% ethanol and stored at −20°C . Specimens subsequently used in PCR analysis were males except where a few isofemale lines had been established prior to preservation of the mother . Species identity was established by male genital arch morphology and confirmed by molecular methods [39] . The new California samples were from isofemale lines ( Y26 , Y35 , Y36 , Y42 , Y46 , Y54 and Y62 ) established in August 2011 . The collection site was a peach orchard approximately 5 km east of Winters , CA . The stocks were maintained as isofemale lines until analysed . The 1961 Nueva , California stock was obtained from the Drosophila Species Stock Center at University of California , San Diego ( stock #14021-0251 . 006 ) . DNA extractions were performed using a standard Chelex based method , and assays for Wolbachia infection status and strain type were performed with a RT/HRM method using the Roche LightCycler® 480 system as previously described [39] . Briefly , a conserved set of primers ( wsp_validation_Fwd: 5′-TTGGTTACAAAATGGACGACATCAG-3′ and wsp_validation_Rev: 5′-CGAAATAACGAGCTCCAGCATAAAG-3′ ) were used to target a variable ∼340 bp region of the wsp gene . The wRi and wAu alleles are expected to differ by 22 SNPs and a single 3 bp indel over this wsp region , potentially yielding a significant difference in observed Tm for the respective PCR amplicons , with the wRi allele expected to have the higher average Tm [39] . Samples from D . simulans isofemale lines previously identified as wRi- or wAu-infected respectively , were included on each PCR plate as Wolbachia strain-type positive controls . Further positive controls entailed equal volumes of DNA extracted from an individual fly of each type being combined in a single reaction . Separate D . simulans specific primers ( Dsim_RpS6_Fwd: 5′-CCAGATCGCTTCCAAGGAGGCTGCT-3′; Dsim_RpS6_Rev: 5′-GCCTCCTCGCGCTTGGCCTTAGAT-3′ ) were used as a host species-specific control for each sample . The RT-PCR/HRM conditions were as follows: 10 minutes at 95°C; 45 cycles of 10 seconds at 95°C , 15 seconds at 58°C , 30 seconds at 72°C; 1 minute at 95°C; 1 minute at 40°C . High resolution melting; temperature ramped up to 95°C at 0 . 02°/second with continuous fluorescence acquisition; 30 seconds at 40°C . Individual samples were scored as Wolbachia infected , uninfected or inconclusive based on machine reported wsp and RpS6 crossing point ( Cp ) relative and absolute value criteria and examination of individual Tm profiles for evidence of non-specific products . A relatively small number of samples , which initially yielded inconclusive results , were retested . Additional standard PCR amplification of wsp , gatB , coxA , hcpA , ftsZ , fbpA and sucB gene fragments for selected individual samples followed [41] with annealing temperatures of 54°C for gatB , coxA , hcpA , ftsZ and sucB , and 59°C for wsp and fbpA . PCR products were checked using a 2% agarose gel for presence of an unambiguous single band of expected size . Amplified DNA for selected samples which yielded a clear and positive PCR result were sent to Macrogen ( Korea ) for purification and sequencing . Sequence data obtained was analyzed using Geneious v6 . 1 ( Biomatters , Auckland , NZ ) . Assays for mitochondrial DNA haplotype for a total of 172 individual Australian samples were performed using primers ( Dsim_siII_7765_Fwd: 5′-ATTTAATATTCAAGCAATAGC-3′; Dsim_siII_8981_Rev: 5′-TTCTGGTTCTATAATTTTAGC-3′ ) designed to target a 1256 bp region of the D . simulans mitochondrial genome previously identified as containing at least one ( synonymous ) G to A base substitution at position 457 ( outer strand ) that was a characteristic difference between the haplotypes found to be associated with either the wAu or wRi strains of Wolbachia , respectively [43] . Amplification of mtDNA was performed using a standard PCR method with the following conditions: 5 minutes at 94°C; 38 cycles of 30 seconds at 94°C , 1 minute at 55°C , 1 minute at 72°C; 5 minutes at 72°C; hold at 4°C . PCR products were checked using a 2% agarose gel for the presence of an unambiguous single band of expected size . Amplified DNA for 108 samples that yielded a clear and positive PCR result were sent to Macrogen ( Korea ) for purification and sequencing . Sequence data obtained was inspected using Sequencher v4 . 5 ( Gene Codes , Ann Arbor , Mi ) . Subsequently PCR amplicons for a further 64 samples were checked on a 2% agarose gel after digestion with the restriction enzyme HinfI [45] . For each California line , DNA was extracted from 30 flies as described in [65] . Following the position descriptions in [43] , two sets of primers were used to amplify two mtDNA regions from positions 1034 to 1718 and from 7797 to 8425 . The second region contains the G to A base substitution at position 8201 that distinguished the R haplotype from the A haplotype , associated with wRi and wAu infection , respectively . ( The primers used were: region 1: DSRmt1033+: 5′-CCAAAATGACTTGTAATCCA-3′ and DSRmt1719−: 5′-GCACCTAATATTAAAGGCACT-3′ , region 2: DSRmt7796+: 5′-GCTACATCTCCAATTCGATTA-3′ and DSRmt8426−: 5′-TTTATATTCTTTTAGACAACATGG-3′ . ) The PCR conditions were: 3 minutes at 94°C; 34 cycles of 30 seconds at 94°C , 30 seconds at 55°C , 75 seconds at 75°C; 8 minutes at 72°C; hold at 10°C . The PCR products were checked on a 2% agarose gel for an unambiguous band of the correct size . The PCR products were purified using the QIAquick PCR purification kit . The amplified fragments were Sanger sequenced by the UC Davis College of Biological Sciences UCDNA Sequencing Facility , using the same primers used for the PCR reaction . The sequencing results were analysed using ApE alignment tool ( A plasmid Editor v2 . 0 . 45 , by M . Wayne Davis ) . To confirm the sequence coordinates we realigned the mtDNA genomes described by Ballard in [43] , [44] , [66] also including the D . yakuba mtDNA reference genome [67] , and confirmed the positions of polymorphisms described in Table 3 of [43] . We then aligned our new sequences against this reference . Three separate laboratory lines previously confirmed to be wRi-infected ( Riv88 ) , wAu-infected ( Coffs 1 ) and uninfected ( W88 ) respectively were used in reciprocal crosses with each of four separate lines ( CBQ40 , CBQ46 , CBQ72 and CBQ80 ) established from field caught females collected in 2011 from north Queensland to test for CI . The level of CI was determined by mating virgin 5 d-old males to virgin females ( 5–8 d old ) . Males were mated once , and females were placed after mating in a vial with a spoon containing 5 ml of agar-treacle-yeast medium and left for 24 h at 25°C . The number of unhatched eggs was counted 24–32 h later . CI data ( egg hatch rates ) were angular transformed prior to analysis . Mann-Whitney U-tests were used to compare CI levels between the different lines . Multiple comparisons were corrected at the table wide α' = 0 . 05 level using the Dunn-Sidak method [68] . Separate mass bred wAu-infected and uninfected fly lines were established from 20 to 30 Western Australian isofemales lines for which infection status had previously been determined . Virgin females from each of these mass bred lines were then mated with a similar number of field caught Brisbane males which had been aged in the laboratory for at least 5 d to reduce the impact of CI . An equivalent mass bred wRi-infected line was established from virgin female F1 progeny of field collected Brisbane females mated with males from the uninfected mass bred line . Each mass bred line was then outcrossed to field collected aged Brisbane males which were also aged for at least 5 d for a further 4 generations to homogenise the genetic backgrounds . Lines were then retested to confirm infection status . Flies were reared at low densities by transferring 25 eggs into vials on ∼20 ml of agar-cornmeal-yeast medium . Pairs of virgin females and males were transferred to fresh medium vials for 2 d , then females were transferred to vials with spoons containing 5 ml of medium with 20% fresh live yeast paste added to the medium surface . Spoons were replaced every 24 h for 5 d and eggs counted . Twelve to 13 females were assayed for each line . Model I ANOVA ( analysis of variance ) and t-tests were used to compare fecundity between lines . We address two issues: first , the conditions for the initial spread and maintenance of wAu in an isolated population , then the conditions for its displacement by wRi . Let IA ( IR ) denote an individual infected with wAu ( wRi ) , and let U denote an uninfected individual . Assuming discrete generations , we denote the frequency of these three types of adults in generation t by pA , t , pR , t and pØ , t . No individuals doubly infected for wAu and wRi have been found ( and each infection is associated with distinct mtDNA haplotypes , [43] ) , hence we assume pA , t+pR , t+pØ , t = 1 . We measure fitness of IA and IR females relative to uninfected females , and denote their relative fitnesses FA and FR . In principle , these Wolbachia may affect viability or fecundity . Given that our data indicate relatively small fitness effects ( see Results ) , we can simplify the analysis by approximating fitness differences as female fecundity variation with no viability effects . Field-collected IA and IR females have both been shown to produce uninfected offspring . Let μA ( μR ) denote the frequency of U ova produced by IA ( IR ) females . Before wRi arrives in the population , pA , t+pØ , t = 1; so we need only keep track of pA , t . Because wAu causes no CI , ( 3 ) Assuming FA ( 1−μA ) >1 , this produces the stable equilibrium ( 1 ) in Results . ( Equilibrium ( 1 ) is equivalent to the standard mutation-selection equilibrium for haploids , in which the uninfected , less-fit type is maintained at frequency μ/s , where μ = μA is the fraction of uninfected ova produced by infected mothers and 1−s = 1/FA is the relative fitness of U females . ) Equilibrium ( 1 ) implies that if and μA<<1 are known , FA≈1+μA/ ( 1− ) . To understand the dynamics of wRi entering a population with wAu , note that both Hoffmann et al . [33] , and James and Ballard [53] demonstrated that wAu infection is not able to rescue CI caused by wRi . Indeed , wRi induces the same level of CI in matings with both uninfected ( U ) and wAu-infected ( IA ) females . The joint dynamics of pA , t , pR , t and pØ , t follow a simplified version of the recursions for double and single infections derived by Hoffmann and Turelli [12 , Eqs . 2 . 5] . Let H = 1−sh<1 denote the relative hatch rate of embryos produced by IA or U ova fertilized by sperm from IR males . The relevant recursions are ( 4a ) ( 4b ) ( 4c ) ( 4d ) Note that in ( 4d ) is just the sum of the right hand sides of ( 4a–c ) . Each of these three expressions has a simple interpretation as the product of two terms , the first ( in square brackets ) proportional to the fraction of ova of each type , the second proportional to the fraction of fertilized ova that hatch . ( In ( 4b ) this second term is one , because wRi-infected ova are compatible with all sperm . ) Given that the three frequencies sum to 1 , there are only two independent variables . Now consider the conditions for wRi to increase when it is extremely rare and wAu is at equilibrium with U , as described by Eq . ( 1 ) . When pR , t≈0 , ≈pØ , t+FApA , t . When wAu and U are at equilibrium , Eq . ( 3 ) implies that pØ , t+FApA , t = FA ( 1−μA ) . Hence , from ( 4b ) , we see that the condition for wRi to increase when very rare is condition ( 2 ) provided in Results . This is just the condition for spread of a Wolbachia variant that is completely compatible with the existing type [64] . Because essentially no CI occurs when wRi is very rare , the incompatibility of wRi with wAu does not enter condition ( 2 ) . When ( 2 ) is not met , pR , t increases only once it exceeds a frequency threshold so that the fitness advantage IR individuals receive from CI overcomes their frequency-independent disadvantage relative to variant A . This condition is a simple generalization of the unstable equilibrium that arises for a single Wolbachia infection that satisfies H<F ( 1−μ ) <1 . Sequences for the MLST genes gatB , coxA , hcpA , ftsZ and fbpA , and the sucB gene obtained in this study have been deposited in GenBank ( http://www . ncbi . nlm . nih . gov/Genbank ) under accession numbers KF278668–KF278673 . | Wolbachia are bacteria that live within the cells of arthropod hosts and are widespread in many groups of insects . These bacteria can rapidly spread through a population through a process of cytoplasmic incompatibility whereby females uninfected by Wolbachia show embryo death when they mate with males carrying the bacteria . Because the infected females pass on Wolbachia to their offspring , this places them at a reproductive advantage , ensuring that the infection spreads through insect populations once it reaches a high enough frequency to overcome any negative fitness effects on its host . Yet while such a rapid spread has been predicted , it has rarely been observed in nature . Here we show that a Wolbachia infection of Drosophila simulans flies has spread very rapidly in eastern Australia , replacing another Wolbachia infection that has also spread in recent years . These invasions appear to have taken place from a very low frequency , implying that both infections are likely to have had a benefit to their hosts rather than a cost . These results have implications for the spread of Wolbachia infections currently being introduced into populations of mosquitoes and other insects for disease suppression . | [
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| 2013 | Rapid Sequential Spread of Two Wolbachia Variants in Drosophila simulans |
Regulation of cell cycle progression is fundamental to cell health and reproduction , and failures in this process are associated with many human diseases . Much of our knowledge of cell cycle regulators derives from loss-of-function studies . To reveal new cell cycle regulatory genes that are difficult to identify in loss-of-function studies , we performed a near-genome-wide flow cytometry assay of yeast gene overexpression-induced cell cycle delay phenotypes . We identified 108 genes whose overexpression significantly delayed the progression of the yeast cell cycle at a specific stage . Many of the genes are newly implicated in cell cycle progression , for example SKO1 , RFA1 , and YPR015C . The overexpression of RFA1 or YPR015C delayed the cell cycle at G2/M phases by disrupting spindle attachment to chromosomes and activating the DNA damage checkpoint , respectively . In contrast , overexpression of the transcription factor SKO1 arrests cells at G1 phase by activating the pheromone response pathway , revealing new cross-talk between osmotic sensing and mating . More generally , 92%–94% of the genes exhibit distinct phenotypes when overexpressed as compared to their corresponding deletion mutants , supporting the notion that many genes may gain functions upon overexpression . This work thus implicates new genes in cell cycle progression , complements previous screens , and lays the foundation for future experiments to define more precisely roles for these genes in cell cycle progression .
The budding yeast Saccharomyces cerevisiae undergoes a cell cycle similar to other eukaryotic organisms except for the lack of nuclear envelope dissolution during mitosis and the production of daughter cells via budding , and thus budding yeast has become a model system for studying eukaryotic cell cycle progression [1] due to its rapid division , the availability of genetic tools , and homology to higher eukaryotic cell cycle processes . Numerous genes and proteins are involved in directing cells through the 4 major cell cycle phases , the growth gap phase G1 , the DNA synthesis ( S ) phase , a second growth gap phase G2 , and the mitotic ( M ) cell division phase [2] , [3] . Extensive effort has been made to decipher the mechanisms of cell cycle control . However , given the extreme complexity of the cell cycle , with ∼300–800 genes regulated in a cell cycle-dependent manner [4]–[6] , the complete set of cell cycle regulators , effectors , and helper proteins has yet to be determined . Classically , conditional temperature-sensitive mutants have been very effective for studying yeast cell cycle division . Hartwell and colleagues identified more than 50 cell division cycle ( CDC ) genes required at specific stages in cell cycle division , by identifying conditional temperature-sensitive mutants with specific arrest points [7]–[10] . Gene dosage has been another powerful approach to study gene function . Either increasing ( overexpression ) or decreasing gene dosage ( gene deletion or gene knockdown ) can influence the activity of genes and lead to detectable phenotypes . Most large-scale cell cycle screens have focused on studying cell cycle progression by employing loss-of-function approaches such as gene deletion , RNAi , and promoter shutoff [11]–[13] and have successfully identified many cell cycle genes . However , loss-of-function mutations can often be masked , such as in the cases of genes acting as negative regulators or genes compensated for by redundant functions [14]–[16] . In contrast , overexpression of a gene product can potentially overcome such effects and often leads to a more detectable effect on cellular function [16] . Overexpression also offers the opportunity to identify and study gain-of-function mutations . In order to identify additional cell cycle genes , especially those difficult to identify in loss-of-function studies , large-scale screens focusing on the effects of overexpression-induced gain-of-function of genes in cell-cycle progression are needed . Stevenson et al . performed the first such large-scale overexpression screen for cell cycle genes by expressing a moderated GAL promoter-driven cDNA library and sheared genomic DNA pool in ARS-CEN vectors [17] . Although 113 genes , including those causing only slight effects on the cell cycle , were identified from this screen , this screen was unsaturated due to the coverage of the cDNA library and incomplete gene annotation . Therefore , completion of the S . cerevisiae genome sequence and the systematic cloning of all genes into overexpression vectors now allow a more comprehensive analysis of the set of genes . Analysis of overexpression phenotypes using cell sorting to assay the distribution of cells in different cell cycle stages has the advantage of being more quantitative and discerning than simple growth screens . However , flow cytometry has not been carried out comprehensively to cover all genes in the genome . In the present work , we performed a near-saturating screen for yeast genes having overexpression-induced defects in cell cycle progression , taking advantage of the availability of a yeast open reading frame ( ORF ) clone collection covering 91% of the yeast complete ORF set , including dubious ORFs [14] . After measuring the fraction of cells in different phases of the cell cycle via high-throughput flow cytometry for each of 5 , 556 individual ORFs and performing secondary validation assays , we identified 108 genes whose overexpression leads to significant changes in the timing of passage through the G1 or G2/M stages of the cell cycle . 82 of these genes are newly implicated in the cell cycle , with the majority likely to affect cell cycle progression via gain-of-function mechanisms .
The yeast ORF collection was obtained from Open Biosystems , in which each ORF was cloned into a 2μ plasmid under control of the GAL1 promoter in order to provide highly elevated expression when supplemented with galactose [14] . Control strains were constructed by transforming the empty precursor vector BG1766 to the ORF host strain Y258 ( MATa pep4-3 , his4-580 , ura3-53 , leu2-3 , 112 ) and plating on synthetic complete medium lacking uracil . The plasmid PGAL1-SKO1 was also transformed into ste2Δ , ste4Δ , ste5Δ , ste20Δ , ste11Δ , fus3Δ , far1Δ , fus1Δ , kar4Δ , sst2Δ , dig2Δ deletion strains [18] ( ResGen/Invitrogen ) and a Fus1-GFP strain [19] ( Invitrogen ) , as well as their parent strain BY4741 ( MATa his3Δ leu2Δ met15Δ ura3 and then plated on synthetic complete medium lacking uracil . Yeast ORF strains were induced in parallel with the corresponding empty vector ( BG1766 ) control strain . Cells were initially grown in 96-well plates ( Corning 3595 ) with 170 µl SD-URA medium for 1–2 days at 30°C , and then 5 µl cells were inoculated into fresh 96-well plates with 170 µl SC-URA , 2% raffinose medium . After 12 hours growth in raffinose medium , cells were re-inoculated to fresh plates with 100 µl SC-URA , 2% raffinose medium at a final O . D . 600nm of 0 . 15 and grown for 1 hour . 70 µl SC-URA medium with 5% galactose ( final concentration 2% ) was added , and cells were grown for 8–10 hours at 30°C . Flow cytometry analyses were performed as in [20] . Briefly , ∼2×106 cells were harvested and fixed in 200 µl 70% ethanol , treated with 1mg/ml RNAse A ( Sigma ) for 4 hours at 37°C , then incubated with 1mg/ml Proteinase K ( Sigma ) for 1 hour at 50°C . ∼8×105 cells were then resuspended in 200 µl 50 mM sodium citrate with Sytox green ( Invitrogen ) at a final concentration of 1 . 5 µM , performing the above liquid transfers using a Biomek FX robot ( Beckman Coulter ) . Samples were analyzed by flow cytometry , using a Becton Dickinson FACSCalibur with BD HTS auto sampler , controlled by Plate Manager and Cellquest pro software ( BD Biosciences ) . Well-to-well contamination was minimized by flushing with ddH2O between each pair of samples . In order to maximize measured events while minimizing data collection time for 5 , 556 strains , we collected the shorter of either 20 , 000 events/strain or 30-seconds acquisition time/strain . Thus , for the extremely slow growing strains , the number of events collected in 30 seconds may drop below 20 , 000 events . Analysis of DNA profiles was automated using ModFit 3 . 0 software ( Verify Software house , Inc ) , fitting the histograms of 1C and 2C cells with Gaussian distributions ( Figure 1C ) and calculating the goodness-of-fit via the Reduced Chi Square ( RCS ) method . For quality control , DNA profiles with RCS>5 and event number<5000 were discarded . Empirically , we observed the resolution of the S phase cell distribution to not be of sufficiently high quality to merit systematic analysis; we thus focused instead on the well-resolved G1 and G2/M phase cells . The percentage of cells under each DNA peak ( 1C peak or 2C peak ) was calculated by dividing the number of events under each peak by the total number of events under all peaks , and the ratio ( 1C/2C ) of the percentage of cells under the 1C peak to that under the 2C peak was calculated for each strain . The base 2 logarithm of the 1C/2C ratio was calculated for each strain; the distribution of Log2 ( 1C/2C ) values ( abbreviated LR below ) was fit well by a Gaussian distribution ( R2 = 0 . 97 ) ( Figure 2A ) , allowing each ORF strain i to be assigned a Z-score , calculated as ( LRi−<LR> ) /σLR . Additionally , we manually categorized strains as diploid and 3C: 208 strains appeared diploid ( e . g . , had 2C and 4C peaks , rather than 1C and 2C ) based upon the flow cytometry data and 56 strains showed notable 3C peaks and were assigned into the 3C category . Follow-up validation of these trends showed that the DNA content of these strains did not change upon galactose induction , suggesting these to be artifacts of these strains rather than an inducible effect of gene overexpression , and thus these strains were not studied further . These strains are listed in Table S6 . 108 ORF strains showing reproducible cell cycle arrest were grown and induced as described above . After induction , cells were fixed in 70% ethanol and treated with 1mg/ml RNAse A ( Sigma ) , and then stained with 1 µM Sytox green ( Invitrogen ) . Cells were examined via phase contrast microscopy and fluorescence microscopy using a Nikon Eclipse 800 fluorescence microscope . From differential interference contrast ( DIC ) images , we used ImageJ software ( National Institute of Mental Health ) to measure the length of the bud and mother cell for an average of 100 cells for each of the 108 strains . Bud size was assigned by dividing the bud length by the length of mother cell . Cells with a ratio of 0 were classified as ‘no bud’; cells were categorized into ‘small bud’ when the ratio was between 0 and 0 . 4 , and ‘large bud’ when the ratio was higher than 0 . 4 [7] . We further examined the large-budded cells and counted three types of nuclear morphology: an undivided nucleus in one cell body ( class I ) , an undivided nucleus in the bud neck ( class II ) , and divided nuclei in two cell bodies ( class III ) [21]–[23] . An average of 50 cells was counted for each of 87 G2/M strains . The 77 of 82 genes not previously implicated in cell cycle defects ( and 3 positive controls , TUB2 , PAC2 , and CST9 ) were assayed for growth defects in three conditions: SC-URA , 2% galactose; SC-URA , 2% galactose plus 15 µg/ml nocodazole , and SC-URA , 2% galactose plus 50 µM hydroxyurea [15] , [24] . 4 dubious ORFs ( YLL066W-B , YBR131C-A , YLR123C , YJL077W-A ) were not included in the growth assays , as well as one gene ( PPZ1 ) in the G2/M category . Cells were grown overnight in SD-URA medium , and then washed with SC-URA , 2% raffinose medium and grown in SC-URA , 2% raffinose medium for one hour at 30°C before being spotted onto agar plates . Six 10-fold serial dilutions were made for each strain , with the O . D . 600nm of the first series at 0 . 2 . 10 µl of each series was spotted onto SC-URA , 2% galactose plates and SC-URA 2% galactose plates containing the appropriate drugs , and grown at 30°C . Plates were photographed after 2–3 days growth in SC-URA , 2% galactose plates , or 5–8 days in the plates supplemented with drugs . The plasmids PGAL1-YPR015C and pRS412::ADE2 [cir+] were transformed into the strain Cry1 ( MATa ade2-1 , ura3-1 , leu2-3 , 112 , trp1 , his3-11 ) , plating transformants on synthetic complete medium lacking uracil and adenine . A single colony was picked and diluted in ddH2O . ∼104 cells were inoculated into SC-URA , 2% galactose medium and grown for 10 generations at 30°C , before plating ∼200 cells on a YPD plate . After growing 2–3 days at 30°C , plates were shifted to 4°C to maximize the color changes . Red and white colonies were counted , where red colonies have lost the centromere-containing plasmid and white colonies have retained it . The SKO1 overexpression strain was induced in parallel with the corresponding empty vector ( BG1766 ) control strain with 2% galactose in selective medium for 8 hours , as described above . Total RNA isolation and processing , microarray hybridization , and data analysis were performed as described previously [25] , hybridizing RNA isolated from the SKO1 ORF strain against RNA from the empty vector control strain . For each strain , two biological replicates were analyzed , each by two technical ( array ) replicates . Differentially expressed genes were selected as having a minimum expression ratio ( corresponding to the absolute value of Log ( base2 ) of R/G normalized ratio ( Median ) ) > = 1 . 5 for at least 2 arrays . The significance of differential expression was calculated using the error model of Hughes et al . [26] . Yeast cells were induced 8 hours , then fixed in growth medium with 1/10 volume 37% formaldehyde for 1 hour at 30°C . Fixed cell cultures were spheroplasted with 0 . 025 mg/ml zymolyase 20T ( Seikagaku corporation ) for 1 hour at 30°C . Cells were then spotted onto poly-L-lysine coated microscope slides . Cells on the slide were permeablized in −20°C methanol for 6 minutes , followed by −20°C acetone for 30 seconds . Cells were blocked with 3%BSA in PBS for 30 minutes at 30°C in a humid chamber , followed by incubation with 4 µg/ml mouse anti alpha-tubulin monoclonal primary antibodies ( Invitrogen ) for 1 hour and 4 µg/ml Texas Red conjugated goat anti-mouse secondary antibody ( Invitrogen ) for 2 hours at 30°C . After washing three times with PBS , cells were mounted with 60 µl VECTASHIELD hard set mounting medium with 1 . 5 µg/ml DAPI ( Vector Laboratories , Inc ) , and imaged at 100x magnification with a Nikon Eclipse 800 microscope .
To analyze the effect of overexpression of yeast genes on cell cycle progression , we applied high-throughput flow cytometry to screen 5 , 556 strains of a yeast ORF collection [14] for genes that induce delay or arrest at particular cell cycle stages when overexpressed . Figure 1 outlines the overall approach . Excess accumulation of cells with either one copy ( 1C ) or two copies ( 2C ) of DNA content indicates a defect in progression through a particular cell cycle stage ( G1 or G2/M , respectively ) . Thus , in order to search for such defects induced by overexpression of a particular yeast gene , we analyzed asynchronous cell cultures and determined the distributions of DNA content , assaying if cells from each given ORF overexpression strain exhibited a skewed distribution relative to control cells . In all , ∼5 , 700 DNA histograms were acquired and quantitatively analyzed , measuring the ratio of 1C/2C cells for each strain , i . e . , the ratio of cells in the G1 phase to cells in the G2/M phase . We observed the Log2 ( 1C/2C ) ratios of the 5 , 556 ORF strains and of 139 replicate analyses of control strains to be approximately normally distributed and well-fit by a Gaussian distribution ( R2∼0 . 97 ) ( Figure 2A ) . Therefore , for each strain , we calculated a Z-score for its distribution of DNA content across cells and could thus identify the ORF strains with significantly higher accumulations of cells in the G1 or G2/M growth phases . Based on this Z-score , 2 categories were assigned: ORF strains with Log2 ( 1C/2C ) ratios in the left tail of the Gaussian distribution were considered to have significant G2/M delays , in which cells accumulated with two copies of DNA . Similarly , ORF strains with Log2 ( 1C/2C ) ratios in the right tail of the distribution showed significantly higher proportions of cells with one copy of DNA , and were considered to exhibit G1 delays . Examples are shown in Figure 1C . We could assign genes to the G1 and G2/M categories using different confidence levels ( Figure 2B ) . At the 95% confidence level , 198 genes were identified whose overexpression caused cell cycle detects; only 3 of 139 control strains exceeded this threshold . As the large-scale screen was based upon only a single culture per ORF strain , we further selected those strains with reproducible defects . Of the 198 strains , 108 were validated at least twice by manual flow cytometry analysis ( DNA histograms are shown in Figure S1 ) . Additionally , we tested that all 108 genes identified showed cell cycle delay phenotypes only upon induction in galactose , and that the phenotype for each hit therefore derived specifically from the GAL-promoter-driven gene . Of the 108 genes , 21 caused a significant accumulation of cells in the G1 phase , 87 genes in the G2/M phase . These genes are listed in full in Table S1 . The size of the bud relative to the size of the mother cell is the most notable morphological landmark of the cell cycle stages in budding yeast . Bud size was the basis of classical cell cycle screens [7]–[10] , [27] , [28] , allowing the identification of mutants blocked at specific stages of the cell cycle: DNA replication occurs when bud size is small , nuclear division occurs when the bud is about three-fourths the size of the mother cell , and cell separation when the bud is approximately equal in size to the mother cell . In order to independently validate genes in the G1 and G2/M categories using bud size , we measured the ratio of bud size to mother cell size for the 108 ORF strains identified by flow cytometry as having cell cycle defects . Genes in the G1 category caused clearly elevated populations of unbudded cells when overexpressed , and the 20 of 21 genes in the G1 category tested for bud size all exhibited a higher percentage of unbudded cells than control strains ( Figure 3A ) , with 12 being more than 2 standard deviations higher than controls , as shown in Figure 3A . For example , 92% of cells were unbudded and only 2% of cells were large-budded when TRM5 was overexpressed . In contrast , only 57% of wild type cells were unbudded , and 28% were large-budded ( Figure 4B , Table S1 ) . Of 87 strains in the G2/M category , 85 exhibited a higher percentage of large-budded cells than control strains ( Figure 3B ) . For instance , at least 60% of cells had large buds when TUB2 and SPC97 were overexpressed ( Table S1 ) . Consistent with previous observations , TRM5 , TUB2 and SPC97 are known to cause cell cycle delays when their normal function is perturbed [13] , [21] , [29] , [30] . SPC97 is an example of the successful recovery of genes known to be important for the cell cycle; it encodes a structural constituent of the spindle pole body , and performs a key role in mitotic spindle formation . 47 strains in the G2/M category had proportions of large-budded cells more than two standard deviations higher than controls , as shown in Figure 3B . Bud size analysis thus provided a useful independent validation of the DNA content observations , with genes validated by both flow cytometry analysis and bud size distributions being the most likely to affect cell cycle progression . One major expected cause of defective cell cycle progression is chromosome instability , especially chromosome loss and non-disjunction . Chromosome loss is characteristic of defects in DNA metabolism , while non-disjunction typically reflects defects in mitotic segregation [15] . To help address which chromosomal functions were primarily affected by the overproduction of the identified ORFs , we examined the strains' sensitivities to hydroxyurea and nocodazole . Hydroxyurea ( HU ) is an inhibitor of ribonucleotide reductase , an enzyme necessary for DNA synthesis . Nocodazole ( NOC ) is a microtubule depolymerizing drug that prevents formation of the mitotic spindle . Genes involved in DNA metabolism and the DNA replication checkpoint are often sensitive to HU , whereas genes sensitive to microtubule drugs are often involved with the mitotic checkpoint and mitotic spindle formation [15] . Due to the presence of the spindle checkpoint control , yeast mutants affecting spindle structure normally show cell-cycle arrest in mitosis [31] . We tested the 77 genes potentially newly implicated in the cell cycle for their sensitivity to HU and NOC separately . In the absence of the drugs , we observed all but 4 tested strains ( all but IMG1 , DHR2 , GPT2 , and YGR109W-A ) to show strong growth defects indicative of toxicity of the overexpressed proteins . A semiquantitative score for growth defects , from 0 ( no defect ) to 3 ( strong defect ) , shows the 77 strains have an average defect of 2 . 5 . Beyond this intrinsic toxicity , we observed 22 strains to be specifically sensitive to NOC , 6 to be specifically sensitive to HU , and 13 strains to show sensitivity to both ( Table S4 and Figure S2 ) . As expected , TUB2 and PAC2 exhibited the non-disjunction-relevant phenotype , sensitivity to nocodazole but not hydroxyurea; TUB2 and PAC2 are required for normal microtubule function and mitotic sister chromatid segregation [21] , [24] . We might expect that genes in the same category as TUB2 and PAC2 might be directly or indirectly involved in microtubule function or functions related to chromosome segregation , consistent with nearly all ( 21 of 22 ) genes having increased sensitivity specifically to NOC arresting at the G2/M phase when overexpressed . We examined in more detail the functions for the 108 genes that caused cell cycle defects when overexpressed . Among these genes , 26 are known to be involved in different aspects of cell cycle progression , 21 are essential ORFs , 17 are transcription factors , 20 ORFs are uncharacterized , and 4 are dubious ORFs ( Table S2 and Figure 2D ) . Importantly , of the 26 genes identified in the screen that were previously known for having cell cycle defects , 24 were consistent with the previously observed phenotypes . Of 8 Cdc28p cyclins included in the ORF collection , we recovered 5 ( CLN1 , CLB2 , CLB3 , CLB5 , and CLB6 ) . A number of known essential genes cause cell cycle defects when down-regulated [13]; we recovered 67% of these genes in this screen . These observations validate the general quality of the current screen by indicating that cell cycle defects caused by overexpression of these 108 genes do not generally result from random effects of overexpression , but rather the 108 genes are strongly enriched for known regulators of the cell cycle . We tested to see if the 108 genes were cell cycle regulated or showed obvious expression level biases . They do not appear to be cell cycle regulated , as the set of 108 hits is not significantly enriched for cell-cycle regulated genes as measured by Spellman et al . [6] ( p>0 . 05 , hypergeometric probability ) . Analysis of the overexpression levels of the genes show typical induction by 5- to >15-fold over the native expression levels , for proteins of both low and high native levels ( Figure S3 ) . We analyzed the distribution of steady state native expression levels of proteins identified in this screen , and do not observe a significant bias in the native levels of the hits; the median expression level of the proteins we identified , measured in rich medium [32] , is 2025 copies per cell , versus 2250 copies per cell expected ( for all proteins ) . We also compared the 108 genes with those previously identified by Sopko et al . [16] and Stevenson et al . [17] and observe a significant ( p<0 . 05 , hypergeometric probability ) but small overlap , with 15 of the 108 genes observed previously and 93 new to this study ( Figure S4 ) . Genes observed in at least two of the three assays are strongly statistically enriched for direct regulators of the cell cycle ( e . g . , the cylins CLB3 , CLB2 , and CLB5 , and components of the spindle pole body BIM1 , TUB2 , SPC42 , SPC98 , KAR1 ) . Analysis of enriched functions ( using Funspec [33] ) among genes observed in ≥2 assays reveals the most strongly enriched functions also relate to the cell cycle , with the strongest enrichment observed for the MIPS annotations “cell cycle and DNA processing” ( p<10−7 ) , “cell cycle” ( p<10−6 ) , and “mitotic cell cycle and cell cycle control” ( p<10−6 ) . In the next two sections , we describe the G1 and G2/M genes in more detail . The 87 G2/M genes showed dramatic enrichment in cell cycle-related Gene Ontology ( GO ) biological process annotations , including regulation of CDK activity [GO:0000079] ( p<9×10−7 ) , microtubule-based process [GO:0007017] ( p<2×10−6 ) , cell cycle [GO:0007049] ( p<4×10−6 ) , cytoskeleton organization and biogenesis [GO:0007010] ( p<8×10−6 ) , microtubule cytoskeleton organization and biogenesis [GO:0000226] ( p<8×10−6 ) , G2/M transition of mitotic cell cycle [GO:0000086] ( p<5×10−5 ) , DNA replication and chromosome cycle [GO:0000067] ( p<5×10−5 ) , and related processes . These genes include CLB2 , CLB3 , CLB5 , CDC31 , KAR1 , SPC97 , PAC2 , TUB2 , NIP100 , SLK19 , ASK1 , AME1 , MAD2 , and ACT1 , which have direct roles in regulating the G2/M transition and related processes such as microtubule nucleation , chromosome segregation , and mitotic spindle checkpoint control . Additionally , 7 genes identified in previous large-scale studies [13] , [16] , [17] ( SPO13 , SEC17 , MYO2 , PRP31 , ARF1 , TFG2 , and SHE1 ) , although not directly involved in mitotic cell cycle control , were also observed in this study . Of 63 genes newly identified in this screen ( 3 were not tested for growth phenotype ) , 56 caused slow growth upon induction and the overexpression of 21 genes lead to specific sensitivity to nocodazole . In order to better classify the genes by the nature of their overexpression defects , i . e . , as to whether the cells exhibited M phase arrest or whether chromosome segregation defects led to G2/M arrest , 3 classes of nuclear morphology were assigned based on the patterns of DNA staining , as shown in Figure 4 D–F: an undivided nucleus in one cell body ( class I , pre-M ) , an undivided nucleus in the bud neck ( class II , early-M ) , and divided nuclei in two cell bodies ( class III , late-M ) [17] . In control strains , 60% of the cells exhibited class III nuclear morphology , with chromosomes in these cells successfully segregated , while only 11% of cells showed class I morphology , and 26% of cells class II morphology . We observed 20 ORF strains to have significantly elevated percentages ( 95% confidence level ) of cells with class I morphology , 13 ORF strains with class II , and 17 ORF strains with class III ( Figure 5 ) . Among the 33 genes in the Class I and II , 9 have direct roles in regulating G2/M transition ( CLB2 , CLB3 and CLB5 ) , or related important events in the mitotic cell division phase ( ACT1 , TUB2 , NIP100 , PAC2 , CDC31 , SPC97 ) . For example , Spc97p is a component of the microtubule-nucleating Tub4p ( gamma-tubulin ) complex and overproduction of SPC97 causes microtubule defects , which in turn gives rise to a failure of chromosome segregation and a early M phase arrest ( Figure 5B ) [29] . We therefore reasoned that 24 newly implicated Class I and II genes causing a similar phenotype to that of SPC97 might play direct or indirect roles in chromosome segregation , especially for genes whose overexpression also leads to hyper sensitivity to nocodazole ( GEA2 , RFA1 , HOS3 , YPR015C , AVO2 , CBF1 , SHE1 , and TEA1; Figure S2 ) . We characterized two of these genes , RFA1 and YPR015C , in more detail . YPR015C encodes an uncharacterized putative transcription factor known to exhibit synthetic lethality with and be functionally linked to CTF4 [34] , [35]; both genes have zinc finger motifs . CTF4 encodes a chromatin-associated protein required for sister chromatid cohesion , which in turn regulates high-fidelity chromosome segregation ( Hanna et al . , 2001 ) . Deletion of CTF4 increases chromosome instability and causes early mitotic delay [36]–[38] . We observe overexpression of YPR015C to give rise to a very similar phenotype to deletion of CTF4 . YPR015C overexpression causes hyper sensitivity to nocodazole and slight sensitivity to hydroxyurea ( Figure S2 ) , and an elevated population of large-budded cells with the nucleus in the bud neck ( Figure 6B ) . In order to test whether the overexpression of YPR015C also leads to chromosome instability , we overexpressed YPR015C in the strain Cry1 ( MAT a ade2-1 , ura3-1 , leu2-3 , 112 , trp1 , his3-11 ) carrying the low copy centromere-containing plasmid pRS412::ADE2 [cir+] . Overexpression of YPR015C doubled the rate of loss of centromere plasmids: 36% in the YPR015C overexpressing strain vs . 16% in the wild type control strain , indicating chromosome instability and mis-segregation . Bud size and nuclear morphology indicated that cells arrested in early mitosis phase when YPR015C was overexpressed ( Figure 6B ) . To test whether the early mitotic delay caused by the overexpression of YPR015C is due to activation of the DNA damage checkpoint or the spindle assembly checkpoint , we overexpressed YPR015C in the background of rad9Δ or mad2Δ mutants in which the DNA damage or spindle assembly checkpoints were removed , respectively . Cell cycle progression in these mutants was measured by DNA content analysis of galactose-induced cultures ( Figure 6E ) . We observed that the YPR015C-induced early mitotic delay was dependent on the DNA damage checkpoint and not the spindle assembly checkpoint , in contrast to the early mitotic delay caused by deletion of CTF4 , which is dependent on the spindle checkpoint [37] . Interestingly , three ribonucleotide reductases ( RNR2 , RNR3 , RNR4 ) are the most significantly up-regulated genes following overexpression of YPR015C [39] , and these three ribonucleotide reductases are regulated by the DNA replication and DNA damage checkpoint pathways [40] . Since transcriptional response , DNA replication , DNA repair , and chromosome condensation are the major chromatin restructuring events in cohesin operation [37] , it appears that overexpression of YPR015C may interfere with chromosome cohesion , inducing defects in mitotic chromosome segregation via a different mechanism than CTF4 . RFA1 is another gene involved in DNA replication whose overexpression leads to G2/M delay . The Rfa1p protein is a subunit of the heterotrimeric replication protein A ( RPA ) , which is involved in DNA replication , repair , and the DNA damage checkpoint [41] , [42] . RFA1 is essential for yeast viability , an RFA1 null mutant is inviable [18] . However , several point mutations of RFA1 caused accumulation of large-budded [43] or dumb-bell shaped cells with a single nucleus in the bud neck [42] at the nonpermissive temperature and had defects in DNA replication and DNA repair [42]–[45] . We observe ∼73% of large-budded cells of the RFA1 overexpression strain showed a butterfly-shaped nucleus in their bud necks , similar to phenotype of SPC97 overexpression ( i . e . , asymmetric chromosome segregation ) and fewer than 10% of large-budded cells had chromosomes segregated into two cell bodies ( Figure 7B ) , suggestive of chromosome mis-segregation . In contrast , 63% of large-budded cells of the parental control strain had the chromosomes successfully segregated into two cell bodies . Furthermore , we observed that the RFA1 overexpression strain had short mitotic spindles , with spindle pole bodies not clearly attached to the nucleus ( Figure 7C , lower row ) . This defect is distinct from the spindle morphology caused by overexpression of SPC97 ( Figure 7C , middle row ) ; Spc97p is a component of the microtubule-nucleating Tub4p ( gamma-tubulin ) complex and is involved in spindle pole body separation and mitotic spindle formation . Cells either carrying point mutations [29] or overexpressing SPC97 ( Figure 7C , middle row ) had short spindles and elongated cytoplasmic microtubules , but the spindle pole appeared normally attached to the nucleus . Given that Rfa1p is a single-stranded DNA binding protein involved in DNA replication , it seems likely that overexpression of RFA1 disrupts DNA replication and leads to the observed spindle morphology defects , giving rise to the observed early mitotic delay . Such a role would also be consistent with the observation that DNA replication proteins can act as cohesion proteins and play important roles in regulating spindle integrity and maintaining the tension on chromosomes exerted by spindle microtubules [37] , [46] , [47] . While the strains arresting in G2/M phase were strongly enriched for cell cycle associated functions , diverse mechanisms are known to induce G1 arrests [16] , [17] . This diversity was reflected in the enrichment of GO biological process annotations among the G1 arresting ORFs: no pathway was enriched at p<0 . 001 when calculated by the method of [33] , consistent with previous overexpression studies [16] , [17] . When calculated as in [25] , the strongest enrichment consisted of negative regulators of transcription from RNA polymerase II promoters ( GO:0000122; p<4×10−4 ) . Among the 21 genes inducing G1 delays , 6 ( 29% ) are uncharacterized or dubious ORFs . The only functional information available for YOR131C and YDR493W is localization: YOR131C is localized in the nucleus and cytoplasm , and YDR493W is localized in mitochondria [18] , [19] , [48] . Our data further associate these two genes with cell cycle progression , either directly or indirectly . Tma64p is another protein of unknown function , previously identified in a mass spectrometry-based proteomic screen of yeast ribosomal complexes [49] . Tma64p associates with ribosomes , has a RNA binding domain and interacts with Rps4bp , a component of the small ( 40S ) ribosomal subunit [50] . Moreover , it has been suggested that there might be a strong connection between ribosomal biogenesis and G1 transit [11] , [13] . Therefore , the G1 delay caused by overexpression of TMA64 may suggest a role in ribosomal biogenesis . The weak enrichment observed for transcriptional regulators derives from 4 transcription factors involved in responding to environmental stress that were observed in the G1 category . Three are transcriptional repressors ( MIG3 , NCB2 , and SKO1 ) , and the fourth ( GAT4 ) is unclear as to mode of action . We observed unusual cellular morphology upon overexpression of SKO1 , and examined this repressor in more detail . We observed overproduction of SKO1 to strongly inhibit cell growth and arrest cells at the G1 phase ( Figure 8A ) . Bud size analysis showed that 90% of cells had no bud when SKO1 was overexpressed ( Table S1 ) . SKO1 is a basic leucine zipper ( bZIP ) transcription factor of the ATF/CREB family , involved in osmotic and oxidative stress responses . The Sko1p protein forms a complex with Tup1p and Ssn6p to both activate and repress transcription [51]–[53] . Surprisingly , overproduction of SKO1 resulted in formation of shmoos , cell morphology changes that are normally seen in mating yeast in response to mating pheromone ( Figure 8B ) . We reasoned that the elevated expression of SKO1 might activate the pheromone response pathway either directly or indirectly , causing shmoo formation and a mating-associated G1 arrest . Since Fus1p is a marker protein induced during shmoo formation that localizes to the shmoo tip when the pheromone response pathway is activated [54] , we tested SKO1 activation of the pheromone response pathway by examining the localization of Fus1p when SKO1 was overexpressed . We transformed PGAL1-SKO1 plasmids into a MATa strain in which FUS1 was C-terminally tagged with green fluorescent protein ( GFP ) [19] . Upon SKO1 overexpression , Fus1-GFP localized to the shmoo tip ( Figure 8B ) , resembling its localization pattern upon alpha factor treatment , demonstrating that the morphological changes are accompanied by general activation of the mating pathway , thus explaining the G1 cell cycle arrest phenotype of the SKO1 ORF strains . To further explore which genes involved in the pheromone MAP kinase pathway were activated by the overexpression of SKO1 , we performed cDNA microarray profiling and found that the activated genes were highly enriched in pheromone response and mating genes . Significantly upregulated genes ( p<0 . 01 ) included MFA1 , STE2 , BAR1 , FAR1 , FUS1 , KAR4 , FIG1 , FIG2 , GIC2 , PRM4 , PRM5 , PRM8 , AGA1 , and AGA2 , as listed in Table S5 . To establish direct genetic interactions between SKO1 and pheromone response pathway , we overexpressed SKO1 strains in ste2Δ , ste4Δ , ste20Δ , ste11Δ , ste5Δ , kar4Δ , fus3Δ , far1Δ , fus1Δ , sst2Δ , and dig2Δ strains , and examined whether or not SKO1 overexpression induced shmoo formation in these deletion strains . We did not observe SKO1-induced shmoo formation in ste2Δ , ste4Δ , ste20Δ , ste11Δ , ste5Δ , kar4Δ , and far1Δ strains ( Figure 8C ) , indicating that these genes are required for shmoo induction by SKO1 overexpression . FUS3 is functionally compensated by KSS1 , FUS1 is downstream of the pheromone response signal transduction pathway , SST2 and DIG2 are inhibitors in the pathway; deletion of these genes affects neither pheromone nor SKO1-dependent shmoo induction . The observed effects of SKO1 overexpression on cell cycle progression thus appear to be indirect , activating the pheromone response pathway in a manner dependent upon the pheromone receptor ( STE2 ) and MAP kinase signal transduction pathway , and this activation in turn results in G1 arrest through the normal mating pheromone-mediated pathway . Overexpression of a normal gene product can result in gain-of-function , but may also mimic loss-of-function phenotypes [16] , such as in cases where precise levels of a protein are required , with either too much or too little equally disruptive . In order to systematically assess the extent of these phenomena amongst the phenotypes of the overexpression strains , we took advantage of quantitative cell morphology data ( bud count data ) for deletion strains collected in the Saccharomyces cerevisiae Morphology Database ( SCMD ) [55] and compared them to our quantitative bud count data . Of 108 genes from this screen , 77 also appear in SCMD ( 21 essential genes and 10 additional genes are not included in SCMD ) ( Figure 9 ) . We selected genes from our screen with significantly elevated populations ( p<0 . 05 ) of unbudded cells or large-budded cells . In the G1 category , there were 12 strains from our screen whose percentages of cells without buds were significantly higher than that of wild type . Of these 12 G1 genes , only one also led to a significantly elevated population of unbudded cells when deleted , as measured by SCMD . Therefore , our rough estimate is that 11/12 ( 92% ) of genes in the G1 category exhibit an overexpression phenotype distinct from the loss-of-function phenotype , at least as measured with regard to proportions of unbudded cells . Similarly , 44 ( 94% ) genes in the G2/M category caused a significantly elevated proportion of large-budded cells when overexpressed but not when deleted , versus 3 that resembled the loss-of-function phenotype ( Figure 9 , Table S3 ) . Thus , the majority of the overexpressed genes in this paper appear to exhibit a phenotype distinct from the loss-of-function case , supporting the previously hypothesized notion that gain-of-function may be common amongst the overexpression phenotypes [16] . SKO1 appears to represent such an example of a gain-of-function leading to differences between the overexpression phenotype and the corresponding deletion phenotype . When overexpressed , SKO1 , which encodes a transcription repressor responsive to salt and osmotic stresses , activates the pheromone response pathway and leads to a strong G1 arrest , but the deletion of SKO1 has no detectable arrest or mating phenotype ( Figure 8B ) . Moreover , transcriptional profiling of cells overexpressing SKO1 revealed that genes involved in the pheromone response pathway are significantly upregulated . However , genes involved in the pheromone response pathway do not appear to be regulated by SKO1 under normal culture conditions , at least as measured by chromatin-immunoprecipitation of SKO1 [56] . Therefore , our results suggest that SKO1 regulates genes in the pheromone response pathway through a gain-of-function mechanism , e . g . , such as by enabling binding to a cryptic or lower affinity promoter when overexpressed . In this paper , we describe a near-saturating screen for yeast genes whose overexpression causes cell cycle delays and which are thus likely to function in cell cycle progression . We individually examined the effects of overexpression on cell cycle progression for each of ∼5 , 556 yeast ORFs , and report the 108 genes with the most significant and reproducible cell cycle defects . 82 of these genes have not been reported in previous large-scale screens [13] , [16] , [17] , probably due to different overexpression conditions and strain backgrounds , false positives in large-scale screens [11] , or more likely , false negatives , e . g . , such as might derive from variable 2 micron plasmid copy numbers [57] increasing phenotypic variability and thus allowing cell cycle defects to escape detection . Our analysis thus complements previous screens . These results lay the foundation for future experiments to elucidate the precise roles of these genes in cell cycle progression , such as the mechanisms of RFA1 and YPR015C . Overexpression screens such as we have described here provide complementary information to loss-of-function studies and therefore offer new opportunities for discovery of genetic interactions , such as by systematically testing the overexpression plasmids in deletion strains to screen for phenotype suppression or synthetic interactions . Finally , since overexpression is an efficient technique in human cell culture and since regulation of cell proliferation is an important aspect of studying human diseases , we anticipate that a similar effort to this work in human cell lines could accelerate our understanding of cell cycle control in mammalian systems and help to further clarify the many connections between cell cycle control and cancer . | All cells require proper cell cycle regulation; failure leads to numerous human diseases . Cell cycle mechanisms are broadly conserved across eukaryotes , with many key regulatory genes known . Nonetheless , our knowledge of regulators is incomplete . Many classic studies have analyzed yeast loss-of-function mutants to identify cell cycle genes . Studies have also implicated genes based upon their overexpression phenotypes , but the effects of gene overexpression on the cell cycle have not been quantified for all yeast genes . We individually quantified the effect of overexpression on cell cycle progression for nearly all ( 91% ) of yeast genes , and we report the 108 genes causing the most significant and reproducible cell cycle defects , most of which have not been previously observed . We characterize three genes in more detail , implicating one in chromosomal segregation and mitotic spindle formation . A second affects mitotic stability and the DNA damage checkpoint . Curiously , overexpression of a third gene , SKO1 , arrests the cell cycle by activating the pheromone response pathway , with cells mistakenly behaving as if mating pheromone is present . These results establish a basis for future experiments elucidating precise cell cycle roles for these genes . Similar assays in human cells could help further clarify the many connections between cell cycle control and cancers . | [
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"expression"
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| 2008 | Mechanisms of Cell Cycle Control Revealed by a Systematic and Quantitative Overexpression Screen in S. cerevisiae |
After infection with T . brucei AnTat 1 . 1 , C57BL/6 mice lost splenic B2 B cells and lymphoid follicles , developed poor parasite-specific antibody responses , lost weight , became anemic and died with fulminating parasitemia within 35 days . In contrast , infected C57BL/6 mice lacking the cytotoxic granule pore-forming protein perforin ( Prf1-/- ) retained splenic B2 B cells and lymphoid follicles , developed high-titer antibody responses against many trypanosome polypeptides , rapidly suppressed parasitemia and did not develop anemia or lose weight for at least 60 days . Several lines of evidence show that T . brucei infection-induced splenic B cell depletion results from natural killer ( NK ) cell-mediated cytotoxicity: i ) B2 B cells were depleted from the spleens of infected intact , T cell deficient ( TCR-/- ) and FcγRIIIa deficient ( CD16-/- ) C57BL/6 mice excluding a requirement for T cells , NKT cell , or antibody-dependent cell-mediated cytotoxicity; ii ) administration of NK1 . 1 specific IgG2a ( mAb PK136 ) but not irrelevant IgG2a ( myeloma M9144 ) prevented infection-induced B cell depletion consistent with a requirement for NK cells; iii ) splenic NK cells but not T cells or NKT cells degranulated in infected C57BL/6 mice co-incident with B cell depletion evidenced by increased surface expression of CD107a; iv ) purified NK cells from naïve C57BL/6 mice killed purified splenic B cells from T . brucei infected but not uninfected mice in vitro indicating acquisition of an NK cell activating phenotype by the post-infection B cells; v ) adoptively transferred C57BL/6 NK cells prevented infection-induced B cell population growth in infected Prf1-/- mice consistent with in vivo B cell killing; vi ) degranulated NK cells in infected mice had altered gene and differentiation antigen expression and lost cytotoxic activity consistent with functional exhaustion , but increased in number as infection progressed indicating continued generation . We conclude that NK cells in T . brucei infected mice kill B cells , suppress humoral immunity and expedite early mortality .
Trypanosoma brucei brucei , T . congolense and T . vivax cause Animal African Trypanosomiasis , which severely constrains cattle ranching and integrated agriculture in sub-Saharan Africa [1] . In addition , two sub-species of T . brucei , namely T . b . gambiense and T . b . rhodesiense , infect humans causing human African trypanosomiasis [2] , which threatens about 60 million people in sub-Saharan Africa . These tsetse-transmitted protozoan parasites inhabit the blood plasma , and in the case of T . brucei ( sensu lato ) and T . vivax , also the tissue fluids of their mammal hosts . African trypanosomes evade immune elimination by antigenic variation of their expressed variable surface glycoprotein ( VSG ) coat , which in T . brucei occurs in “about 0 . 1% of trypanosome divisions” , and results from “differential expression of a VSG gene from an archive of hundreds of silent VSG genes and pseudogenes” [3] . As a result of VSG antigenic variation , African trypanosomiasis is characterized by recurring waves of parasitemia in which dominant VSG types are cleared by VSG specific antibody and the surviving antigenic variants seed the next parasitemic wave . Peak levels of trypanosome parasitemia in trypanosomiasis-susceptible mammals , which include humans , domestic ruminants , domestic ungulates , dogs and laboratory rodents , range from <106 to >108 trypanosomes per ml blood depending on host species and parasite virulence . In each parasitemic wave dominant antigenic variants are tagged for phagocytosis , and in some species antibody and complement dependent lysis by IgM and IgG antibodies that are specific for VSGs expressed by dominant antigenic variants [4–6] . However , infected trypanosomiasis-susceptible hosts typically develop only short-lived , low-titer , IgG antibody responses against VSGs and other trypanosome polypeptides [7] , eventually become immunosuppressed and less able to control new trypanosome antigenic variants and secondary infections [8–10] , develop anemia , cachexia and reproductive disorders [11–14] , and often die . In contrast , infected trypanosomiasis-resistant mammals , e . g . , African Cape buffalo , rapidly constrain trypanosome parasitemia to a level of <102 trypanosomes/ml blood , do not show signs of disease [15–17] , and accumulate trypanostatic/trypanocidal IgG antibodies against previously expressed VSGs in their blood [17] . This raises the possibility that preservation of immune competence in infected trypanosomiasis-susceptible hosts , particularly the sustained ability to make VSG-specific IgG antibodies and associated T helper cell responses , would increase their ability to control parasitemia and decrease trypanosomiasis pathology . B cell clonal exhaustion has been proposed as a cause of trypanosomiasis-induced suppression of humoral immune responses [18] . This view is supported by the development of lymphopenia in cattle and sheep with animal African trypanosomiasis [11 , 19 , 20] and with ablation of bone marrow lymphopoiesis and depletion of splenic transitional , marginal zone ( MZ ) cells and follicular ( Fo ) B cells in mice infected with T . brucei [10 , 21 , 22] . The loss of these B cells , which are from the B2 B cell lineage [23 , 24] , severely compromises trypanosome- and vaccine-specific antibody production [10] . Splenic B cell depletion also occurs in mice infected with T . congolense [25] and T . vivax [26] . Splenic B2 B cell dynamics have not been studied in cattle although an infection-induced increase in splenic B1-like B cells has been reported [27] . Analyses of peripheral blood B cell dynamics in human patients with T . b . gambiense show a significant increase in T cell-independent B cells and a significant decrease in T cell-dependent B cells as a percentage of total blood lymphocytes accompanied by a significant decrease in anti-measles antibody in serum relative to age- , gender- and habitat-matched uninfected individuals , although not of a level to compromise immunity [28] . It remains possible that there is a link between the infection-induced mechanism that causes the depletion of splenic B2 B cells in trypanosome-infected mice , and those that prevent the development of trypanosome-specific IgG antibodies in infected cattle [29] and cause the proportional decrease in T cell-dependent relative to T cell-independent B cells in the peripheral blood of T . b . gambiense infected people . We have previously shown that depletion of splenic B2 B cells in T . brucei infected mice results from their apoptosis [10] , which occurs independently of tumor necrosis factor ( TNF ) - , FasL- and prostaglandin-induced death pathways [22] and is thus distinct from B cell fratricide which arises in mice infected with the South American trypanosome T . cruzi [30] . Here we dissect the cellular basis of T . brucei infection-induced B2 B cell depletion using a combination of mutant mouse strains on the C57BL/6 background that lack various defined immune functions , as well as antibody-mediated depletion of putative effector cells , effector cell phenotype analysis , fate-mapping of fluorochrome-labeled cells after adoptive transfer , and in vitro and in vivo B cell killing assays . These studies show that splenic B2 B cells in T . brucei-infected mice are killed by NK cell-mediated cytotoxicity . The killing requires perforin , a pore-forming protein , which is found in cytotoxic granules of killer cells and which together with granzymes induces target cell apoptosis [31–34] . Although cytotoxic T cells and NKT cells also mediate perforin-dependent killing we show that they are not involved in killing the B2 B cells in T . brucei infected mice . NK cells are lymphocyte-like components of the innate immune system . They monitor cell surfaces using an array of receptors and kill target cells on which ligands for NK cell activating receptors predominate over ligands for inhibitory receptors [35 , 36] . We show here that the B cells in T . brucei-infected mice acquire an NK cell activating phenotype . This paper does not address the nature of the infection-induced NK cell activating ligand ( s ) on B cells , but does address the fate of the activated NK cells .
Splenic B2 B cells ( Table 1—mAb profiles; S1 Fig—gating profiles ) were quantified in uninfected ( Table 2 ) and infected ( 5 x 103 T . b . brucei Antat 1 . 1 i . p ( Table 3 ) intact C57BL/6 mice and mice lacking genes that modulate leukocyte activation and effector functions . Irrespective of the mutation , infected mice became parasitemic ( about 106 T . brucei/ml blood ) by 4 days after infection , developed peak parasitemias of between 8x107 and 2x108 T . brucei/ml blood depending on mutant mouse strain on day 6 after infection and , with the exception of mice lacking the gene encoding complement factor 3 ( C3-/- ) , remitted first wave parasitemia to a level of <106 T . brucei/ml blood between 6 and 7 days after infection . In infected C3-/- mice the level of T . brucei parasitemia declined in a gradual fashion between 7 and 10 days after infection to reach a trough level of <106 T . brucei/ml blood indicating that C3 may facilitate immune clearance of T . brucei , as previously shown for T . congolense [37] . Because numbers of splenic transitional , marginal zone and follicular B cells varied among mutant C57BL/6 mice ( Table 2 ) , post infection changes in spleen cells are presented as the log2 fold change relative to uninfected mice of the same strain ( Table 3 ) . By 10 days after infection , splenic transitional , MZ and Fo B cells ( B2 B cells ) were severely depleted from C57BL/6 mice that: ( i ) were unmodified ( Table 3 , line 1; representative Facs plot—Fig 1A–1D ) consistent with earlier studies [10 , 22] , ( ii ) were homozygous for Nu gene expression ( nu+/+ ) ( Table 3 , line 2 ) and thus athymic and largely deficient in T cells; ( iii ) lacked genes encoding the β and δ chains of the T cell antigen specific receptor ( Table 3 , line 3 ) and thus lacked all αβ and γδ T cells and NKT cells; ( iv ) lacked the gene encoding complement factor 3 ( C3 , Table 3 , line 4 ) and were thus unable to mediate antibody- and complement-dependent cell lysis , and C3b- and iC3b-dependent opsonization , ( v ) lacked the gene encoding CD16 ( Table 3 , line 5 ) and thus lacked the low-affinity Fcγ receptor which is required for antibody-dependent cell-mediated cytotoxicity; ( vi ) lacked genes encoding Toll like receptor adaptor proteins MyD88 ( Table 3 , line 6 ) and TRIFF ( Table 3 line 7 ) and thus had defective TLR signaling , ( vii ) had a disrupted gene encoding interferon gamma ( INFγ ) and made little or no interferon gamma [38] ( Table 3 , line 8; spleen cell numbers and representative Facs plots—S2 Fig ) , ( viii ) lacked the gene encoding TNFR1 ( Table 3 , line 9 ) and thus lacked the lymphotoxin α-induced death pathway , and ( ix ) lacked the gene encoding galectin 3 ( Table 3 , line 10 ) , which is implicated in trypanosomiasis-associated inflammation and anemia [39] . In contrast , by 10 days after infection of Prf1-/- mice with T . b . brucei Antat 1 . 1 , splenic transitional and marginal zone B cells increased slightly while numbers of follicular B cells remained similar to those in uninfected mice ( table 3 , line 11; representative Facs plots Fig 1E–1H ) . Thus , of the immune components studied , only perforin , which is encoded by Prf1 , had a non-redundant function required for the early ( day 10 ) depletion of transitional , MZ and Fo B cells from the spleens of T . brucei infected mice . Perforin is found in cytotoxic granules of cytotoxic T cells ( CTL ) , NKT cells and NK cells , any or all of which might therefore mediate depletion of splenic B2 B cells in T . brucei infected mice . However numbers of transitional , marginal zone and follicular B cells were significantly decreased in TCR-/- mice infected for 10 days with T . b . brucei AnTat 1 . 1 compared to uninfected TCR-/- mice ( Table 3 line 3 , Fig 5A–5G ) , showing that splenic B2 B cell depletion did not require TCR+ cells , ie . , T cells or NKT cells . To determine whether NK cells in T . brucei infected mice express their cytolytic function , these cells were examined for surface expression of CD107a , which is a marker of secretory lysosomes including cytotoxic granules . CD107a is required for delivery of perforin to NK cell cytotoxic granules from trans-Golgi transport vesicles , and is exported onto the surface of cytotoxic cells when they degranulate during execution of their cytotoxic function [45–49] . The portion of splenic NK cells expressing CD107a on their surface increased from <5% in uninfected mice to almost 100% at 10 days after infection with T . b . brucei AnTat 1 . 1 ( day 10 only shown in Fig 7A ) . This was accompanied by only a minimal increase in expression of CD107a by splenic CD8 T cells and NKT cells . Binding of the CD107a-specific antibody to T and NKT cells was constitutively high in C57BL/6 mice relative to binding to the NK cells ( Fig 7B & 7C ) ; this was not an experimental artifact because the splenic NK , NKT and T cells were stained in the same tube . CD107a expression also increased on NK cells harvested from the liver and lymph nodes after parasite wave remission ( day 10 post infection data shown in S5 Fig ) indicating that NK cells expressed their cytotoxic function , in multiple organs . Fig 7 also shows changes in expression of several other antigens by NK cells from infected mice , and these are discussed later in the text . An adoptive transfer system was used to determine whether NK cells from C57BL/6 mice cause splenic B cell depletion in T . brucei-infected Prf1-/- mice . The C57BL/6 NK cells were isolated using negative selection and magnetic bead sorting ( Miltenyi Corp . , MACS micro beads ) from cell suspensions prepared from pooled spleens of uninfected C57BL/6 mice . A total of 2 x 106 viable NK cells were recovered/processed spleen . The isolated NK cells were labeled with e-Fluor 670 in vitro washed in physiologic phosphate buffered saline ( PBS ) and injected intravenously ( iv ) ( 5 x 106 NK cells/recipient mouse ) into uninfected Prf1-/- mice or Prf1-/- mice that had been infected 6 days earlier with T . b . brucei AnTat 1 . 1 which is 1 day prior to parasite wave remission ( Fig 2 ) . Spleens were harvested from recipient mice 48 and 96 hours later ( corresponding to 8 and 10 days after infection ) , cell suspensions made and analyzed for content of eFluor 670 labeled NK cells and B2 B cells using multicolor flow cytometry . Between 8% and 10% ( 4 to 5 x 105 cells ) of transferred eFluor 670-labeled NK cells that had been isolated from the spleens of uninfected mice were recovered from each recipient’s spleen 48hr after injection . A similar number of eFluor 670-labeled C57BL/6 NK cells were recovered from the spleens of uninfected Prf1-/- recipients 96 hours after transfer . However no eFluor 670-labeled C57BL/6 NK cells were recovered from the infected Prf1-/- mice 96 hours after transfer . It is unclear whether the labeled cells hat were transferred into infected recipients lost their efluor 670 label or died in , or migrated out of , the infected spleen between 48 and 96 hours after transfer . Similar adoptive studies were attempted with efluor 670 labeled NK cells that had been isolated from spleens of day 10 infected C57BL/6 mice . These were not recovered from the spleens of either uninfected or day 6 infected recipient C57BL/6 mice at 24 or 48 hours after iv injection; we did not determine whether these previously activated NK cells fail to localize to the recipients’ spleens , or localize and rapidly die . The iv injection of naïve C57BL/6 NK cells into uninfected Prf1-/- recipients did not affect numbers of splenic B2 B cells up to 4 days after injection . Intravenous injection of naïve C57BL/6 NK cells into Prf1-/- mice that had been infected 6 days earlier with T . brucei had no effect on splenic B cell numbers at day 8 after infection relative to infected mice that had been injected iv with PBS . However , splenic B2 B cell numbers were significantly decreased at day 10 after infection relative to those in infected mice injected iv with PBS , but not relative to uninfected Prf1-/- mice ( Fig 8 ) . The results are consistent with modest depletion of splenic B2 B cells in infected Prf1-/- mice by the C57BL/6 NK cells . To determine whether NK cells directly kill B cells from infected mice , NK cells and resting B2 B cells were isolated using negative selection and magnetic bead sorting ( Miltenyi Corp . , MACS micro beads ) from the spleens of uninfected mice , and from mice infected with T . b . brucei AnTat1 . 1 either 8 days earlier ( for post-infection B cells ) or 10 days earlier ( for post-infection NK cells ) . The B cells were labeled with eFluor 670 in vitro , washed in PBS and the populations mixed at NK to B cell ratios ranging from 50:1 to 6 . 25:1 holding the number of target cells ( B cells ) constant , which is a standard killing protocol [50] . B cells were also incubated in the absence of NK cells . The cell mixtures , in duplicate , were incubated in vitro at 37°C in a humid atmosphere of 5% CO2 in air for 3 hours after which the vital dye 7AAD and a fixed number of fluorescent beads was added to each culture to facilitate quantitation of viable B cells ( 7AAD- IgM+ eFluor 670+ ) by flow cytometry . NK cells from uninfected or T . brucei infected mice did not kill B cells from uninfected mice even at a ratio of 50 NK cells to 1 B cell ( Fig 9 ) . However during 3 hours of incubation in vitro and at all NK cell to B cell ratios tested , NK cells from the uninfected mice killed >50% splenic B cells isolated from mice that had been infected 8 days earlier with T . brucei AnTat 1 . 1 ( Fig 9 ) . Interestingly , NK cells from the infected mice showed some B cell killing at a ratio of 50 NK cells to 1 B cell but lost killing activity at lower NK cell to B cell ratios . In contrast , NK cells from uninfected mice maximally killed B cells from infected mice even at a ratio of 6 . 5 NK cells/ B cell and in repeat experiments maximal killing was observed at 1 or fewer NK cells/B cell , consistent with serial killing of a magnitude similar to that shown by IL-2 activated human NK cells [51] . Results presented in Fig 9 show that NK cells collected from the spleens of mice that had been infected with T . brucei for 10 days had much lower B cell killing activity compared to NK cells from uninfected mice consistent with exhaustion of cytolytic function by prior cytotoxic activity as denoted by increased levels of surface CD107a ( Fig 7A ) . Day 10 post infection splenic NK cells also differed from splenic NK cells of naïve mice in several other ways . Whereas NK cells from uninfected mice expressed the natural cytotoxicity receptor NKp46 ( Fig 7D blue ) and the integrin alpha subunit CD49b ( Fig 7E blue ) , expression of these differentiation antigens was greatly decreased on NK cells from day 10 infected mice ( Fig 7D & 7E red ) . In addition , while about 40% of the NK cells from uninfected mice cells had high expression of receptors for MHC class I detected by a Ly49 I F C H pan specific mAb and for m157 MCMV protein detected by an Ly49 H specific mAb ( Fig 7F & 7G blue ) as previously reported [52] , expression of these receptors was greatly decreased on NK cells from day 10 infected mice ( Fig 7F & 7G red ) . In contrast , whereas few NK cells from uninfected mice expressed the common β chain of the IL-2R/IL-15R , CD122 ( Fig 7H blue ) , and death ligands TRAIL ( Fig 7J blue ) and Fas L ( Fig 7K blue ) , their expression , particularly that of FASL was increased on NK cells from day 10 infected mice ( Fig 7H , 7J and 7K red ) . Furthermore , expression of CD11c was weak on NK cells from uninfected mice but substantially greater on NK cells from day 10 infected mice ( Fig 7I ) , a characteristic that is shared with NK dendritic cells although dendritic NK cells also express CD49b [53] distinguishing them from the splenic NK cells of day 10 infected mice . NKp46- NK cells accumulated in the spleens of infected mice throughout 30 days of infection ( Fig 10; representative FACs plots S6A–S6H Fig ) and showed characteristic loss of CD49b as well as NKp46 expression , and gain of CD107a , FasL and TRAIL expression ( S7 Fig , representative day 20 infected mouse ) . It is noteworthy that a small population of NKp46- CD49b- CD107a+ NK cells is also present in the spleens of naïve mice ( S8 Fig ) . Thus the NKp46- NK cells that accumulate in the spleens of T . brucei infected mice might derive from these cells . To determine whether that is the case , purified splenic NK cells ( >99% NKp46+ ) were labeled with efluor 670 and 5x106 injected iv into uninfected C57BL/6 mice or C57BL/6 mice that had been infected 7 days earlier with T . brucei AnTat 1 . 1 . Recipient splenic efluor 670+ NK cells were analyzed 48 hours later for expression of NKp46 and CD107a ( Fig 9 ) . Similar numbers ( 10% of input ) of efluor 670+ NK cells were recovered from the spleens of uninfected and infected recipients . Those in uninfected recipients retained expression of NKp46+ and did not acquire expression of CD107a- , whereas those in infected recipients had decreased levels of NKp46 and increased expression of CD107a , both of which were similar in level of expression to endogenous splenic NK cells in the infected mice ( S9 Fig ) . These data suggest that NKp46- CD49b- CD107a+ NK cells derive from NKp46+ CD49b+ CD107a- NK cells .
Severe depletion of splenic B2 B cells within 10 days after infection with T . b . brucei Antat 1 . 1 is a property of infected C57BL/6 mice as well as other mouse strains . Several strains of mutant C57BL/6 mice ( TCR-/- , C3-/- , CD16-/- , MyD88-/- , TRIF-/- , INFγ-/- , TNFR1-/- , Gal3-/- , Prf1-/- ) with defects in innate and adaptive immunity were examined for expression of this infection-induced , immunosuppressive trait . Only the gene encoding perforin ( Prf1 ) had a non-redundant function required for rapid depletion of the splenic B2 B cells and loss of humoral immune function . Analyses over several weeks after infection showed that unlike T . b . brucei AnTat 1 . 1-infected C57BL/6 mice , infected Prf1-/- mice retained B cell follicles , developed high-titer IgM and IgG , particularly IgG1 , antibody responses against a wide range of trypanosome polypeptides , controlled trypanosome parasitemia , maintained their body weight and blood packed cell volume , and survived at least twice as long as similarly infected intact mice , which jointly are characteristics of trypanosomiasis resistance [7 , 15 , 29 , 55 , 56] . Despite the dramatic decrease in trypanosomiasis pathology in infected Prf1-/- mice compared to infected intact mice , perforin gene deletion is not proposed as a practical approach to generate trypanosomiasis-resistant mammals . The mutation leaves cytotoxic T cells , NKT cells and NK cells reliant solely on death ligand-dependent killing of target cells and consequently would decrease their cytotoxic arsenal and increase host vulnerability to infections with intracellular pathogens . In our view , elucidation of the mechanism of T . brucei infection-induced , perforin-dependent , B cell killing is required before considering strategies to prevent this without compromising cell-mediated immunity . Four complementary lines of evidence suggest that NK cells are solely responsible for the perforin-dependent depletion of splenic B cells in intact T . brucei-infected mice . Firstly , transitional and mature B2 B cell populations were depleted from the spleens of intact mice and mice lacking genes encoding both the beta and delta chains of the T cell antigen specific receptor and thus lacking all T cells and NKT cells , but were not depleted from infected intact and TCR-/- mice from which NK cells were removed by repeated administration of the NK1 . 1 specific IgG2a mAb PK136 . In contrast the B2 B cells were depleted from infected mice that were administered an irrelevant myeloma IgG2a ( done with intact C57BL/6 only ) . Secondly , splenic , liver and lymph node NK cells , but not T cells or NKT cells showed greatly increased surface expression of the cytotoxic granule degranulation marker CD107a during the period of B cell depletion consistent with selective execution of their cytolytic function . Thirdly , NK cells from uninfected and to a much lesser extent T . brucei-infected C57BL/6 mice killed splenic B cells during co-culture in vitro . Importantly , NK cells from uninfected or infected C57BL/6 mice did not kill B2 B cells from uninfected mice in vitro but did kill B2 B cells from infected mice indicating acquisition by these B cells of an NK cell activating phenotype . Fourthly , naïve NK cells that were transferred from intact C57BL/6 mice into T . brucei–infected Prf1-/- mice prevented infection-induced expansion of splenic B2 B cells consistent with in vivo B cell depletion . NK cells typically kill virus-infected or transformed cells but do not kill healthy lymphocytes or other self-cells [57] . However , there are cases where this rule seems to be relaxed . Thus IL-2 activated human NK cells have been shown to kill stromal cells and antigen presenting cells in vitro [58] . In addition , NK cells have been shown to deplete virus-specific CD4+ T cells in mice with acute systemic LCMV infection with resultant suppression of adaptive immunity [59 , 60] . They have also been shown to exacerbate pathology in mice exposed to high dose influenza virus infection although the mechanism is not resolved [61] . In addition , NK cells have been shown to regulate auto- and allo-reactive lymphocytes [62 , 63] by pathways that include depletion of activated T cells [64 , 65] and in which perforin-dependent cytolysis is implicated [66 , 67] . Furthermore , several publications show that NK cells inhibit the development of antibody responses [68–70] and can inhibit the generation of virus-specific antibody and long-lived T- and B memory cells in a perforin-dependent manner [71] . These studies are consistent with a role for NK cells in down-regulation of adaptive immune responses per se , and with their more profound role in depletion of splenic B2 B cell and generalized immunosuppression in T . brucei-infected mice documented here . Splenic NK cells from uninfected mice had a substantially higher capacity to kill T . brucei infection-modified B cells in vitro compared to NK cells isolated from the spleens of day 10 infected C57BL/6 mice . A similar T . brucei infection-induced loss of NK cell killing activity has been reported for tumor cell targets [72] . The decreased cytotoxic activity of the day 10 post-infection NK cells may result from their functional exhaustion following expression of cytotoxic activity during B cell depletion , evidenced by their greatly increased plasma membrane expression of the cytotoxic granule degranulation marker CD107a . In addition , NK cells that accumulated in the spleens of T . brucei infected mice after parasite wave remission had decreased expression of NKp46 , CD49b and Ly49 receptors , an inability to populate the spleen of adoptive recipients , and decreased expression of genes that regulate NK cell development and function relative to mature splenic NK cells of uninfected mice including EOMES , TBX21 , Prf1 , GZMA , KLRA18 , NCR1 , KLRC1 , KLRK1 , IL-5Rβ , IL-18R1 , IL-18Rap . Overall the data suggest , that the NKp46-CD107a+ NK cells that accumulate in the spleens of T . brucei infected mice have exhausted their cytotoxic potential , undergone partial reprogramming and will die rather than revert to normal mature splenic NK cells . If so , newly arising precursor cells would be required to sustain B2 B cell deletion throughout infection . In this regard adoptive transfer studies of efluor 670-labelled splenic NKp46+NK cells into naïve and T . brucei infected recipients indicated they were the precursors of the putative “spent NK cells” elicited by T . brucei infection , and further studies showed that NKp46+CD107a- NK cells are sustained in the spleen throughout infection and hence may be responsible for on-going deletion of newly arising B2 B cells . NKp46 is an NK cell activating receptor that is important in tumor killing [73–75] . It has been reported that aberrant CD56Dim human NK cells with decreased surface expression of NKp46 , NKp30 , NKG2D and decreased cytotoxic function relative to CD56Dim NK cells of uninfected newborns arise in the cord blood of newborns congenitally infected with T . cruzi [76] . Thus decreased expression of NKp46 by NK cells might be an outcome of exposure to infection-induced cytokine environments and consequently not limited to the T . brucei infected splenic environment . NKp46- NK cells have also been shown to arise in the lungs of mice infected with a high dose of influenza virus and to exacerbate pathology leading to weight loss and death [61] . In addition , a loss of function mutation in the gene encoding NKp46 has been shown to result in NKp46- NK cells that are hyper-responsive to various stimuli [77] . While it is possible that loss of NKp46 facilitates lymphocyte-destructive NK cell activity in influenza virus infected mice , that is unlikely to be the case in T . brucei infected mice because NK cells from naïve mice , which for the most part express NKp46 , killed B cells from infected mice in vitro and had much higher cytotoxic activity than NKp46- CD107a+ NK cells from the infected mice consistent with loss of cytolytic function of NK cells that had already degranulated . NK cells integrate receptor and ligand interactions that are either activating or inhibitory [36] . Activated NK cells release cytotoxic granules at an immunological synapse [78] which forms between the NK cell and target cell after an activating threshold is achieved by an increase in activating ligands relative to inhibitory ligands on the target cell [79 , 80] . MHC class I is a major inhibitory ligand for NK cells [36 , 81] , which consequently kill virus-infected and tumor cells that have decreased levels of MHC class I on their plasma membrane . In the case of T . brucei-infected mice , MHC class I expression is substantially increased , not decreased , on IgM+ cells ( S11 Fig ) . However this does not automatically exclude loss of negative signaling through MHC class I receptors as a cause of NK cell-mediated B2 B cell destruction because splenic NK cells from day 10 T . brucei infected mice had decreased gene and protein expression of the Ly49 family of MHC class I receptors . Despite this , there is no compelling reason to consider that decreased expression of Ly49 receptors on NK cells is a requirement for T . brucei infection-induced B cell depletion . Thus NK cells from uninfected C57BL/6 mice , the majority of which express moderate or high levels of the Ly49 family of receptors , selectively killed B cells from T . brucei infected mice in vitro . Instead , we propose that the splenic B2 B cells from T . brucei-infected mice acquire potent NK cell activating ligand ( s ) , which co-opt ( s ) NK cells to kill them . Many NK cell activating and co-stimulatory ligands have been reported [82] providing insights into possible infection-induced changes that would result in an NK cell activating phenotype . The identity of the ligand on post-infection B cells that tags the cells for NK cell mediated cytotoxicity is being addressed in on-going studies . The studies presented above show that NK cells kill splenic B2 B cells in T . brucei infected mice by cell-mediated cytotoxicity and thus have a central role in infection-induced loss of humoral immune competence . Furthermore , the studies show that perforin-dependent cytotoxicity , possibly NK cell-mediated cytotoxicity , is required for development of anemia , weight loss and early mortality consistent with greatly increased NK cell degranulation in the spleen , liver and lymph nodes of the infected mice shown here . Further investigation is needed to identify the NK cell receptor ( s ) and B cell associated ligand ( s ) that mediate trypanosome infection-induced B cell killing , to determine how NK cell-mediated cytotoxicity interacts with other processes that contribute to trypanosomiasis-induced pathology [83–87] , and importantly , to determine the contribution , if any , of NK cells to trypanosomiasis-pathology in host species other than mice , and in pathology associated with diseases in addition to African trypanosomiasis .
The study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health and Guidelines for the Use of Laboratory Animals in Research , Teaching and Testing of the International Council for Laboratory Animal Science . All animal studies were approved by the Institutional Animal Care and Use Committee , University of Massachusetts , Amherst , MA01003 USA , as documented in protocol #s 2010–0028 , 2013–0049 and 2013–0050 . Male C57BL/6 ( Taconic , Germantown , NY ) , B6-129S4-C3<tm1Crr>/j ( C3-/- ) , B6-129P2-Fcgr3tm1Sjv/J ( CD16-/- ) , B6-Cg-Lgals3tm1/poi/J ( Gal3-/- ) , B6-prf1tm1sdz/1 ( Prf1-/- ) , B6-129P2-Tcrbtm1momTcrdtm1mom/J ( TCR-/- ) , B6-Tnfrs1atm1/mx/J ( TNF R1-/- ) , B6 . 129S7-Ifngtm1ts/J ( INFγ-/- ) mice were purchased from Jackson Laboratory ( Bar Harbor , ME ) . All mice were housed under barrier conditions for at least 1 week after arrival at the university and were used at 7–9 week of age . Breeding pairs of B6-MyD88-/- and B6-TRIF-/- mice were a gift from Dr . Fitzgerald , University of Massachusetts School of Medicine ( Worcester MA ) , were bred in the animal facilities at UMass Amherst and were used at 7 to 9 weeks of age . Mice were infected by intraperitoneal ( i . p . ) injection of 5000 exponentially growing pleomorphic Trypanosoma brucei Antat 1 . 1 or sham infected by i . p . , injection of Dulbecco’s Phosphate Buffered Saline ( PBS; GIBCO , Life Technologies ) ; these parasites were derived from EATRO 1125 stock [88] and grown from cryopreserved parasites in immunocompromised ( 600r from a 127 Cesium source ) C57BL/6 donors prior to injection . Parasitemia was assessed in tail blood by dilution in DPBS and counting using a hemocytometer . Weight was measured using a Ohau brand digital scale . Blood packed cell volume was calculated after spinning blood in heparinized capillary tubes for 3 minutes in a hematocrit centrifuge ( ADAMS MHCTII; BD , San Diego ) . Avidin-APC-Cy7 ( San Diego ) , anti-CD49b-biotin ( clone MD5-1 ) , anti-FasL-PE ( clone MFL3 ) , anti-H2kb-PE ( clone eBM2a ) , anti-NKp46-PE ( clone 29A1 . 4 ) , anti-IgM-PE ( clone II/41 ) , anti-NK1 . 1-APC ( clone pk136 ) , anti-Ly49 C/I/F/H-PE ( clone 14B11 ) , anti-Ly49-FITC ( clone 3D10 ) , anti-TRAIL-biotin ( clone N2B2 ) , anti-CD11b-APC-Cy7 ( clone M1/70 ) , anti-CD23-APC-Cy7 ( clone B3B4 ) , anti-CD23-FITC ( clone B3B4 ) , anti-CD45R ( B220 ) -FITC ( clone RA3-6B2 ) , anti-CD93–APC ( clone AA4 . 1 ) , anti-CD107a ( clone 1D4B ) and anti-CD122-PE ( Clone 5H4 ) were purchased from eBioscience ( San Diego , CA ) . Anti-CD5-APC ( clone 53–7 . 3 ) , ant-CD11c-PE ( clone HL3 ) , anti-CD21-APC ( clone 7G6 ) , and anti-CD49b FITC were purchased from BD Biosciences ( San Diego , CA ) . 7-amino-actinomycin D ( 7AAD ) was purchased from EMD Chemicals ( San Diego , CA ) . IFA—Anti-MOMA-1 ( biotin ) AbCam was purchased from ( Cambridge MA ) , avidin-FITC was purchased from BD Pharmingen ( San Diego , Ca ) and eFluor 615 anti-human/mouse CD45 ( B220 ) was purchased from eBiosciences ( SanDiego , Ca ) . Mice were killed by lethal CO2 inhalation and their spleens were excised and mechanically dissociated in cold FACS Buffer ( 1 . 0% fetal bovine serum , FBS , [Atlanta Biologicals] in DPBS ) . Cell pellets were prepared by centrifugation ( 500 g , 10 min , 4°C ) and suspended in 10ml of cold ammonium chloride red blood cell lysis buffer ( ACK; 0 . 15M NH4Cl , 1 . 0 mM KHCO3 , 0 . 1mM Na2-EDTA ) and incubated for 4 minutes on ice . Remaining leukocytes were pelleted as above , washed twice in DPBS and suspended in FACs buffer . Aliquots ( 100 μl ) containing 106 cells were added to the wells of a 96 well V bottom plate , stained with combinations of specific monoclonal antibodies against B cell , T cell and NK cell differentiation antigens ( listed in Table 1 ) and analyzed by multicolor flow cytometry as described by us [22] . Briefly , cells were incubated with Fc block ( anti-CD16/CD32 Fc III/II , eBioscience , San Diego , CA; 1:1000 dilution ) for 20 minutes at 4°C , pelleted , resuspended in 100 μl aliquots of biotin- or fluorochrome-conjugated primary antibodies ( listed above ) for 30 minutes at 4°C , washed twice in cold FACs buffer , resuspended in 100 μl aliquots of FACs buffer with or without streptavidin ( SA ) conjugated fluorochromes and incubated for an additional 30 minutes at 4°C . Samples were washed twice and resuspended in 300 μl FACs buffer with 1 μg of 7AAD , a fluorescent DNA intercalating agent that binds to DNA in membrane permeable ( dead or dying ) cells , ( EMD Chemicals , San Diego , CA ) . Analyses were performed using a flow cytometer ( LSRII BD Biosciences , San Jose , CA ) and data processed using FLOWJO software ( Tree Star Inc . , Ashland , OR ) to determine the percentages of 7AAD- and where stated also 7AAD+ cells in antibody and fluorochrome-defined subsets . The total number of cells in each population was determined by multiplying the percentages of subsets within a series of marker negative or positive gates by the total cell number of viable leukocytes in the donor spleen . Spleens from uninfected and infected C57BL/6 and Prf1-/- mice were embedded in OCT ( Sakura Finetek , Torrance , CA ) , frozen in dry ice , stored frozen at -80°C and incubated at -20°C for 30 minutes before cryostat sectioning . Frozen sections ( 10 ) μm were cut and adhered to Poly-Prep slides ( Sigma-Aldrich , St . Louis , MO ) , air dried and fixed in ice cold acetone for 10 minutes . Slides were blocked with 5% bovine serum albumin ( BSA ) and Fc blocker CD16/CD32 ( eBioscience ) for 30 minutes and stained with biotin-conjugated antibody MOMA-1 at room temperature for one hour . Sections were washed and treated for one hour at room temperature with Avidin FITC and antibody B220 . Stained sections were washed , air dried at room temperature and mounted with ProLong Gold antifade reagent ( Life Technologies ) and covered with glass coverslips ( Corning ) . All the slides were read on a Zeiss MOT200 inverted microscope with a Zeiss apotome at 20x magnification . Mice were injected intraperitoneally with 500 ug anti-NK1 . 1 IgG2a monoclonal antibody purified from hybridoma ( PK136 , ATCC Manassas , Virginia ) culture supernatant by Protein G chromatography , or with an irrelevant control IgG2a immunoglobulin ( M9144 , Sigma-Aldrich ) . Treatments were repeated on days 4 and 7 after the first injection of anti-NK1 . 1 and splenic B cell , T cell and NK cell populations assessed on days 3 , 7 and 10 using antibody staining and multicolor flow cytometry as described above . Erythrocyte depleted spleen cell suspensions were prepared as discussed for flow cytometry . NK cells and B cells were isolated by negative selection using respectively Miltenyi kits ( #130-096-892 and #130-095-831 ) . The yield of NK cells was 1 . 5% to 2 . 0% of input spleen cells and >98% of these were CD3-NK1 . 1+ by staining and flow cytometry analysis ( Becton Dickenson , Fortessa ) . The yield of splenic B cells was 15% to 18% of input spleen cells from both naïve mice and day 8 infected mice , and >98% of these cells were B220+IgM+ by staining and flow cytometry . For tracking studies these cells were labeled with eFluor*670 ( eBiosciences Affimetrix #65-0840-85 ) which did not affect viability . NK cell cytotoxicity assays were performed in 96 well V bottom plates at 37°C in a humid atmosphere of 5% CO2 in air . Each well contained 200 μl medium ( RPMI 1640 supplemented with 10% Hyclone fetal bovine serum , Penicillin Streptomycin , L-glutamine and 0 . 2 μg rat anti-mouse anti-CD40 mAb 3/23/ml medium Becton and Dickenson ) and 105 eFluor 670-labelled B cells with or without NK cells to give ratios of between 50 NK cells/ 1B cell and 1 NK cell/4 B cells . Incubations were for 3 hours after which each well was inoculated with the same number of fluorescent beads ( Accudrop , Becton and Dickenson ) and 7AAD prior to flow cytometric analysis to allow determination of viable B cells ( eFluor 670+ 7AAD- ) relative to bead number and thus the number of viable B cell remaining in each culture . Results are presented as: relative viable B cell recovery ( where 1 = number of viable B cells after incubation in the absence of NK cells ) . A pellet containing 108 T . brucei AnTat 1 . 1 purified from the blood of infected irradiated mice by DEAE52 chromatography [89] and washed twice in PBS was suspended in 5 ml lysis buffer ( PBS , containing 0 . 5%NP40 and protease inhibitor cocktail [Complete mini tablets , Roche , Indianapolis , IN] ) and incubated at room temperature for 30 minutes . Insoluble material present in the lysate was removed by centrifugation at 1000g for 5 mins . Protein content was determined by Bradford assay and an aliquot ( 600ug protein content ) of trypanosome lysate was fractionated by reducing SDS-PAGE on a 10% polyacrylamide gel in a single gel-spanning slot and separated polypeptides were transferred to a polyvinylidene difluoride membrane using a mini trans-blot transfer apparatus ( Bio-Rad ) according to the manufacturer’s protocols . The membrane was blocked with 5% nonfat milk in 10 mM Tris , pH 8 . 0 , 150 mM NaCl , 0 . 5% Tween 20 ( TBST ) for 60 min , washed once with TBST , inserted in a slot blot apparatus ( Mini Protean Multi Screen II , Biorad ) and serum , prepared from blood of control and trypanosome-infected mice ( 1:200 dilution , 600 μl/slot ) , was added to individual slots and incubated at 4°C for 2 h . Membranes were washed three times ( 10ml TBST , room temperature , 10 min ) and incubated with horseradish peroxidase-conjugated anti-mouse IgM , or IgG1 antibodies ( ABCAM 0 . 6 μg antibody/ml TBST ) for 1 hour , then washed 3 times with TBST before developing with chemiluminescence substrate ( ThermoPierce ) . Wells in a 96 well plate ( NUNC ) were coated with 0 . 1 μg T . brucei Antat1 . 1E lysate ( prepared as above ) in 100 μl PBS containing 0 . 05% NP40 , washed with ELISA wash buffer ( PBS + 1% bovine serum albumin [BSA] + 0 . 05% TWEEN20 ) and non-specific protein binding sites were blocked by incubation with 200 μl aliquots of PBS + 1% BSA . Aliquots ( 100 μl ) of control and post-infection mouse serum diluted in PBS were added to each well , incubated for 2 hours at room temperature and wells were washed 3 times with ELISA wash buffer to remove unbound serum immunoglobulins . Bound antibodies were revealed by addition of biotinylated anti-mouse IgM or anti-mouse IgG ( BD biosciences ) to each well and bound second step antibody was quantified after washing by incubation with avidin-conjugated horse radish peroxidase ( BD biosciences ) . Captured antibody was visualized with Ultra TMB ELISA ( Thermo scientific ) and absorbance readings were made at 450nm , using a 96 well plate spectrophotometer . NK cells ( >99 . 8% CD3- NK1 . 1+ ) were isolated using a fluorescence activated cell separator from cell suspensions of pooled spleens of 5 naïve C57BL/6 mice and 5 C57BL/6 mice that had been infected 10 days earlier with 5 x103 T . brucei Antat 1 . 1 . Total DNAse treated RNA was extracted from purified NK cells using a RNeasyPlus Mini Kit ( Qiagen ) , amount of isolated RNA estimated by on-chip electrophoresis using an Agilent RNA 6000 nano kit on an Agilent 2100 Bioanalyzer ( Agilent Technologies ) , and its integrity confirmed by a RIN number of 9 or greater . mRNA libraries were prepared from isolated RNA using a TruSeq Stranded mRNA Library LT kit ( Illumina ) as recommended by manufacturer . Briefly , polyA containing mRNA was isolated from total RNA , fragmented , primed for cDNA synthesis , reverse transcribed to cDNA using Superscript II reverse transcriptase and second strand synthesis performed using dUTP instead of dTTP to quench 2nd strand amplification during subsequent PCR . Double stranded ( ds ) cDNA was separated from the 2nd strand mix using AMPure XP beads ( Beckman Coulter ) , a single “A” nucleotide added at 3’ ends , the preparation end ligated with proprietary bar coded paired end adapters each bearing a single T nucleotide at 3’ ends and unique to each library , and tagged ds cDNA isolated using AMPure XP beads . cDNA fragments with adapter molecules at both ends were amplified using a primer cocktail that anneals to the ends of the adapters , the quality of the resulting library was estimated by on-chip electrophoresis ( Agilent DNA 1000 chip ) on Qubit 3 . 0 Fluorometer ( ThermoFisher Scientific ) and single run sequencing ( 2 x 150 ) was performed using a NextSeq 500 mid output kit on a NextSeq 500 sequencer ( Illumina ) . The sequences were trimmed at the 3’ end to 125 bp using the FASTAQ Toolkit and analyzed using RNA express in BaseSpace ( Illumina ) ; sequence alignment analysis was performed against Mus musculus UCSC reference genes within the BaseSpace program . Cell population and other data obtained from infected animals and from non-infected controls were subjected to two-tailed T tests with significant differences reported as follows: p≤0 . 05 , ( ** ) p≤0 . 01 , ( *** ) p≤0 . 001 . Differences among multiple groups were analyzed using ANOVA and means were compared using Tukey’s honest significant difference ( HSD ) test when p≤0 . 05 ( GraphPad Prism v . 4 . 0 , GraphPad Software Inc . San Diego , CA ) . | Trypanosomiasis-susceptible mammals that are infected with African trypanosomes develop anemia , lose weight , lose immune competence and have an increased susceptibility to secondary infections . Our earlier studies in the T . b . brucei AnTat 1 . 1 mouse model of African trypanosomiasis show that infection-induced loss of humoral immune competence correlates with depletion of splenic transitional , marginal zone and follicular B cells . Here , we show that splenic B2 B cells in the infected mice acquire an NK cell activating phenotype after trypanosome wave remission and are deleted by antibody-independent , splenic NK cell-mediated cytotoxicity , which is a perforin dependent process . In the absence of this killing process , i . e . , in perforin gene knock out mice , T . brucei infection did not cause depletion of splenic B cells , or disruption of splenic architecture , or anemia , or weight loss , or early mortality and did elicit high-titer trypanosome polypeptide-specific antibody responses . The studies show that T . brucei co-opts host NK cells to degrade the antibody limb of the adaptive immune system and elicit life-threatening trypanosomiasis pathology . | [
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| 2016 | Trypanosoma brucei Co-opts NK Cells to Kill Splenic B2 B Cells |
FoxP3+ regulatory CD4 T cells ( Tregs ) help to maintain the delicate balance between pathogen-specific immunity and immune-mediated pathology . Prior studies suggest that Tregs are induced by P . falciparum both in vivo and in vitro; however , the factors influencing Treg homeostasis during acute and chronic infections , and their role in malaria immunopathogenesis , remain unclear . We assessed the frequency and phenotype of Tregs in well-characterized cohorts of children residing in a region of high malaria endemicity in Uganda . We found that both the frequency and absolute numbers of FoxP3+ Tregs in peripheral blood declined markedly with increasing prior malaria incidence . Longitudinal measurements confirmed that this decline occurred only among highly malaria-exposed children . The decline of Tregs from peripheral blood was accompanied by reduced in vitro induction of Tregs by parasite antigen and decreased expression of TNFR2 on Tregs among children who had intense prior exposure to malaria . While Treg frequencies were not associated with protection from malaria , there was a trend toward reduced risk of symptomatic malaria once infected with P . falciparum among children with lower Treg frequencies . These data demonstrate that chronic malaria exposure results in altered Treg homeostasis , which may impact the development of antimalarial immunity in naturally exposed populations .
FoxP3+ regulatory CD4 T cells ( Tregs ) play a central role in preventing autoimmunity and maintaining self-tolerance . In the setting of infection , Tregs help to maintain the delicate balance between pathogen-specific immunity and immune-mediated pathology . Preserving this equilibrium requires a complicated balance between regulatory and effector T cell activity . For instance , in the murine leishmania model , Treg-mediated suppression of effector immune responses interferes with complete parasite clearance—but paradoxically , the resulting pathogen persistence fosters the long-term maintenance of effector immune responses that are required for protection from reinfection [1 , 2] . Given their central role in immunoregulation , the timing , magnitude , and duration of Treg activity must be fine-tuned for promote resolution of the effector immune response only after control of the pathogen has been achieved . Malaria , like many other parasite infections , has been reported to induce an expansion of the Treg population [3] . However , the factors governing Treg homeostasis in the setting of P . falciparum infection , which in high transmission regions is characterized by both recurrent symptomatic episodes in young children and persistent asymptomatic infection in older individuals , remain unclear , as does the role of Tregs in the immunopathogenesis of malaria . P . falciparum infection in humans induces multiple immunoregulatory pathways that likely evolved to protect the host from severe malaria by down-modulating the acute inflammatory response , perhaps at the cost of interfering with clearance of parasitemia and development of immunologic memory . Several lines of evidence suggest that Tregs are induced during human P . falciparum infection and play a role in modulating the host response . Following experimental P . falciparum sporozoite infection of naïve human subjects , FOXP3 mRNA is upregulated and peripheral blood CD25+CD4+ T cells expand [4] . In rural Gambia , the percentage and absolute count of FoxP3+CD127low CD4 T cells were shown to increase following the malaria transmission season , and are significantly higher among malaria-exposed rural Gambians than among ethnically matched urban Gambians with no malaria exposure [5] . Moreover , a number of studies have shown that peripheral Treg frequencies correlate with parasite burden in infected individuals [6–8] . Together these data suggest that Tregs are induced by P . falciparum infection in vivo . This conclusion is further supported by in vitro studies demonstrating that FoxP3+ Tregs are induced by co-culture of PBMC with P . falciparum-infected red blood cells or parasite schizont extracts [9–13] . Induction of Tregs by parasite antigens may have implications for the development of a host-protective immune response . FOXP3 mRNA levels in children with acute malaria have been shown to correlate inversely with the magnitude of the subsequent Th1 memory response to P . falciparum measured 28 days after infection [6] . Similarly , FOXP3 expression among malaria-naive adults following experimental sporozoite vaccination correlates inversely with the subsequent Th1 memory response [14] . It is possible that P . falciparum induction of Tregs may contribute to the failure of the adaptive immune response to mediate parasite clearance , as has been demonstrated in other parasitic infections such as leishmania and filariasis [1 , 2 , 15] . However , the role of Tregs in protection or risk from symptomatic malaria remains unclear . High frequencies of CD25high T cells ( putatively regulatory T cells ) were associated with increased risk of malaria in one prospective cohort study [16] . Consistent with this , among previously naïve adults experimentally infected with malaria , Treg induction was associated with increased parasite replication rates [4] . Further , a recent study in children and adults in Indonesian Papua found a trend towards lower proportions of activated Tregs in individuals who had asymptomatic infection compared to symptomatic malaria or healthy controls , suggesting dampened activation of Tregs may be associated with decreased risk of disease [17] . However , it has also been suggested that Tregs may serve a protective role in preventing immunopathology during infection [18 , 19] . Murine studies have failed to provide clear resolution of this issue , as different models have yielded conflicting data . Early reports described enhanced control of parasitemia and improved survival in mice experimentally depleted of Tregs [20] , but subsequent studies that used more precise definitions of Tregs , different depletion regimens , or different parasite strains have failed to demonstrate a consistent host-protective role ( summarized in [19] ) . To better understand the role of Tregs in the immunopathogenesis of malaria in the setting of chronic exposure , we assessed the frequencies and phenotypic features of Tregs in Ugandan children of varying ages and malaria exposure histories . Our results indicate that while Treg frequencies are expanded in a high compared to low transmission settings , in high transmission settings children with repeated malaria infection experience a marked and progressive decline in peripheral blood Tregs , accompanied by reduced in vitro induction of Tregs by parasite antigen and decreased expression of TNFR2 . This loss of circulating Tregs may have implications for the development of protective immunity to malaria , and suggests that chronic antigen stimulation , such as that observed in areas of chronic Plasmodium infection , may result in pathogen-driven alteration of Treg homeostasis .
To investigate the relationship between Treg frequencies and prior malaria exposure , we measured peripheral blood Treg frequencies in 2 separate cohorts of children in the high malaria transmission region of Tororo District , Uganda ( annual entomological inoculation rate ( aEIR ) 310 bites ppy [21] ) . In both cohorts , participants were followed prospectively from enrollment at approximately 6 months of age , with comprehensive documentation of all malaria episodes at a dedicated study clinic , and at the time of analysis were either 2 years old ( PROMOTE cohort , no chemoprevention control arm , n = 82 ) or 4 years old ( TCC cohort , n = 75 ) ( S1 Table ) . Treg frequencies were measured as the percentage of CD4 T cells that were FoxP3+CD25+ ( for gating strategy see S1 Fig ) . Within both the 2-year-old and 4-year-old cohorts , there was a strong inverse relationship between Treg frequencies and prior malaria incidence ( Spearman’s r = -0 . 27 , p = 0 . 01 , and r = -0 . 28 , p = 0 . 01 respectively; Fig 1A and 1B ) . This inverse relationship was strengthened by further gating on the CD127dim subset , which more stringently defines suppressive Tregs ( Spearman’s r = -0 . 36 , p = 0 . 001; assessed in 4-year-old cohort only , for gating strategy see S1B Fig ) . The frequency of Tregs among children who had asymptomatic P . falciparum infection at the time of assessment ( determined by blood smear ) did not differ from that of uninfected children ( Wilcoxon ranksum p = 0 . 951 ) . Furthermore , there was no relationship between the frequency of Tregs and the duration of time since the last malaria episode , which might be expected if Tregs transiently increase in response to acute malaria ( Spearman’s r = 0 . 094 , p = 0 . 422 ) , similar to what has been shown in malaria-naïve adults [4] . We measured CD4 T cell responses to P . falciparum-infected red blood cells from blood samples obtained concurrently ( TCC cohort , n = 56 ) , but we observed no statistical relationship between the frequency of Tregs and other effector or regulatory T cell populations , including cells producing IFNγ ( p = 0 . 65 ) , TNFα ( p = 0 . 17 ) , or the recently described IL10-producing “self-regulatory” CD4 T cells ( p = 0 . 99 ) [22–25] . The cross-sectional data above are consistent with either a malaria-driven decline in peripheral Treg frequencies or an increased susceptibility to symptomatic malaria among children whose Treg frequencies are inherently low . To distinguish between these possibilities , we measured Treg frequencies in a third cohort of children residing in the same high transmission Nagongera , Tororo District ( PRISM cohort , age 1 to 11 years , n = 91 [21] ) , in whom mosquito exposure was directly measured using CDC light traps within the homes of individual cohort participants [26] . In this cohort , we observed an inverse relationship between Treg frequencies and mean daily household mosquito exposure , consistent with a parasite-driven decline in Tregs ( Spearman’s rho = -0 . 265 , p = 0 . 014 , Fig 1C ) . In contrast to the younger cohorts of children described above , we did not observe an inverse correlation between Tregs and the incidence of prior clinical malaria in this cohort ( Spearman’s rho = 0 . 043 , p = 0 . 685 ) , likely because older children do not develop symptomatic clinical malaria with each P . falciparum infection , and thus malaria incidence is not a good measure of total P . falciparum exposure beyond early childhood . There was , however , a strong inverse relationship between Tregs and age ( Spearman’s rho = -0 . 385 , p = 0 . 0002; Fig 1D ) , suggesting that Tregs progressively decline with age in this high endemnicity setting . This decline was not attributable to age-related changes in total lymphocyte counts , as a similar relationship was observed when absolute numbers of Tregs ( per μl of blood ) were calculated by normalization to absolute CD4 cell counts in a subset of children ( r = -0 . 424 , p = 0 . 025; n = 28 , S2 Fig ) . To investigate whether the age-related decline in Treg frequencies was unique to this high malaria transmission setting , we compared Treg frequencies among children age 1 . 5 to 11 years who were enrolled in the observational malaria cohort ( PRISM ) , but at the low transmission Jinja District ( aEIR 2 . 8 bites ppy [21] ) . Among children at the lower transmission site , Treg frequencies did not decline with age ( r = -0 . 05 , p = 0 . 83; n = 34; Fig 1E ) . Together these data suggest that exposure to malaria parasites may contribute to a loss of peripheral blood Tregs in this high transmission setting . To investigate whether changes in Treg frequencies within individual subjects over time correlates with their malaria incidence , we measured Treg frequencies longitudinally in 41 subjects at 16 , 24 and 36 months of age ( PROMOTE cohort , SP chemoprevention arm ) . Subjects were stratified into tertiles based on their number of malaria infections between 16 and 36 months . Among children in the highest tertile of malaria incidence ( n = 14 ) , there was a consistent decline in Treg frequencies between 16 and 36 months of age ( Wilcoxon signed rank test , p = 0 . 009 , Fig 2A ) . In contrast , among children in the lowest tertile of incidence ( n = 13 ) , there was no change in Treg frequencies between 16 and 36 months ( Wilcoxon signed rank test p = 0 . 54 ) . Repeated-measures analysis using generalized estimating equations confirmed that changes in Treg frequencies over time differed between the exposure groups ( p = 0 . 0011 , Fig 2B ) . Together , these data suggest that very high malaria exposure during childhood results in the loss of peripherally circulating Tregs within individuals over time . Prior studies have shown that experimental and natural P . falciparum infection induces the expansion of regulatory T cells in vivo [4 , 5] . To investigate whether and how Treg dynamics differ between settings of high and low exposure , we compared Treg frequencies between children over a range of ages in the high transmission district of Tororo to children from the low transmission district of Jinja ( PRISM cohort age 1 to 11 years ) . Children in the high transmission Tororo District experienced a much higher malaria incidence ( median 3 . 6 vs . 0 ppy ) and a much shorter duration since last infection ( median 62 vs . 230 days ) than children in the low transmission Jinja District ( full details in S1 Table ) . Overall , Treg frequencies ( FoxP3+CD25+CD127dim ) were higher in children from the high transmission district compared to the lower transmission district across all age groups ( Wilcoxon ranksum p<0 . 0001 , Fig 3A ) , and this difference was most marked in the youngest age group ( Fig 3B ) , possibly reflecting an earlier expansion of Tregs in response to initial infections during early childhood or even in utero [27–30] . The difference in Tregs frequencies between the low and high transmission study sites decreased with increasing age , and this trend extended to adulthood ( Fig 3B ) . Thus , our data suggest that in areas of high malaria transmission malaria infections early in life induce Tregs , as has been previously described among naïve or comparatively low-exposure individuals [4 , 5 , 7 , 8] . However , in areas of intense and continual malaria exposure , parasite-driven induction of Tregs is diminished , and instead there is a progressive decline of Tregs with repeated malaria episodes . This decline does not appear to be transient , as Treg frequencies do not correlate with the duration of time since last malaria episode or asymptomatic parasite infection . Instead , there appears to be sustained and progressive loss of Tregs with age among children heavily exposed to malaria . We next assessed expression of TNFR2 on Treg cells , as this receptor has been shown to be critical for both proliferative expansion of Tregs and maintenance of FOXP3 expression in inflammatory environments [31 , 32] . Furthermore , Tregs expressing TNFR2 have been shown to have enhanced suppressive capacity [8 , 33 , 34] , and are increased during malaria infection [8] . We found that the percentage of Tregs expressing TNFR2 was significantly lower among PRISM cohort children from the high transmission Tororo District than among children of similar age from the low transmission Jinja district ( p<0 . 0001 , Fig 4A , see S3 Fig for gating strategy ) . Among Tororo children , expression of TNFR2 was inversely correlated with number of recent malaria episodes ( Coef = -0 . 31 , p = 0 . 032 , Fig 4B ) , although expression was slightly higher on Tregs from children currently PCR-positive for P . falciparum infection ( p = 0 . 043 ) . These data suggest that TNFR2 expression is transiently up-regulated during parasitemia but declines over time following repeated malaria episodes . This decrease in TNFR2 expression could contribute to the loss of FoxP3+ Tregs from peripheral blood by decreasing the stability of FOXP3 expression [31 , 32] . The progressive decline in circulating Tregs in children heavily exposed to malaria could be explained by changes in Treg homeostasis such as decreased induction , increased loss ( due to apoptosis or downregulation of FOXP3 ) , or both . Therefore , we next examined whether heavy prior malaria infection altered the propensity for Treg induction or apoptosis . It has previously been shown that in vitro stimulation of adult PBMCs with P . falciparum antigen induces regulatory T cells [9 , 10 , 12 , 35] . To investigate whether the propensity of CD4 T cells to differentiate into Tregs in response to parasite antigen is influenced by age and/or prior malaria exposure , we measured induction of Tregs following in vitro stimulation with P . falciparum schizont extracts ( PfSE ) in malaria-naïve adults , malaria-exposed children ( 28 months of age ) , and malaria-exposed adults from the high incidence district of Tororo ( gating strategy and ex vivo Treg frequencies shown in S4 Fig ) . As previously reported , co-culture of PBMCs from naïve adults with PfSE resulted in consistent induction of Tregs ( Fig 5A and S4C Fig ) . However , using PBMC from malaria-exposed children and adults , induction of Tregs was reduced compared to naïve adults ( Fig 5A ) . Further , whereas all children with low prior malaria exposure ( <2 episodes ppy ) exhibited Treg induction ( fold change >1 ) , only 55% of children with high prior malaria exposure ( >6 episodes ppy ) induced Tregs following PfSE stimulation ( p = 0 . 03; Fig 5B ) . This suggests that heavy prior exposure to malaria may limit the propensity of CD4 cells to differentiate into Tregs upon re-encounter with parasite antigens . We next investigated whether heavy prior malaria exposure increased the susceptibility of Tregs to apoptosis , as has been shown in chronic HIV-1 infection [36] . The percentage of Tregs expressing the pro-apoptotic marker CD95 increased with age among Tororo children ( Rho = 0 . 175 , p = 0 . 079 ) , as did the level of CD95 expression ( as calculated by MFI of CD95 on CD95+ Tregs , Spearman’s Rho = 0 . 44 , p = 0 . 0001 ) ( Fig 5C ) . Conversely , expression of the anti-apoptotic marker Bcl2 on Tregs declined with age ( Rho = -0 . 266 p = 0 . 035 ) ( Fig 5D ) . However there was no independent relationship between expression of these markers and prior malaria incidence , current parasite infection , nor time since last malaria episode , suggesting that age may independently affect the sensitivity of Tregs to apoptosis . To further investigate this , three distinct measures of apoptosis ( YoPro , Annexin V and activated Caspase 3 ) were measured on Tregs both ex vivo and following stimulation with camptothecin ( an activator of apoptosis ) or PfSE in 28-month infants with low or high prior malaria incidence ( PROMOTE no-chemoprevention control arm ) . There was no difference in sensitivity to apoptosis as measured by any of the markers either ex vivo or following stimulation with camptothecin or parasite antigen; the frequencies of positively stained cells ex vivo , and the fold change of apoptosis staining , were the same regardless of prior malaria exposure ( Fig 5E and S5 Fig ) . Together these data suggest that Treg homeostasis may be altered in the setting of heavy malaria exposure , in part due to reduced induction of peripheral Treg cells , with little evidence for increased susceptibility to antigen-driven apoptosis . We finally asked whether the frequency of circulating Tregs influences susceptibility to malaria . We assessed the influence of Treg frequencies on protection from malaria in both the 2-year-old PROMOTE no chemoprevention control arm and 4-year-old TCC cohorts using two methods; a time-to-event analysis ( time to next malaria episode ) , and negative binomial regression of the relationship of Treg frequencies to malaria incidence in the year following assessment . Among 2-year-olds , we found that higher Treg frequencies were associated with an increased time to next malaria episode and a lower future malaria incidence in univariate analysis ( Table 1 ) . However , after adjusting for prior malaria incidence in a multivariate model to account for heterogeneity in environmental exposure to infected mosquitoes [22 , 37 , 38] , this relationship was no longer significant , suggesting that differences in environmental exposure intensity may underlie this association [37] . Among 4-year-olds , Treg frequencies were not associated with time to next malaria infection or malaria incidence during follow-up . Similarly , no relationships between Treg frequencies and malaria incidence in follow-up or time to next malaria episode were observed in the PRISM 1–11 year old cohorts , in either the low or the high transmission study sites , even after adjustment for household mosquito exposure . Thus we did not find clear evidence that Tregs are associated with the risk of clinical malaria . Given their immunoregulatory role , is also possible that Tregs play a role in protecting the host from the symptomatic manifestations of malaria once P . falciparum infection is established [1 , 2 , 15 , 19] . To assess whether Tregs may influence the risk of clinical disease once infected , we analyzed the relationship between Treg frequencies and the probability of symptoms once parasitemic using generalized estimate equations with robust standard errors , accounting for repeated measures [16 , 39] . Comparing children with the lowest compared to highest tertiles of Tregs , lower Treg frequencies were associated with an increased monthly probability of infection , consistent with the exposure induced decline in Tregs described . However , lower Treg frequencies were also associated with an overall decreased probability of becoming symptomatic once infected ( 2 year old PROMOTE cohort; OR = 0 . 4 , p = 0 . 039 , 4 year old TCC cohort; OR = OR = 0 . 37 , p = 0 . 06 ) , suggesting that the decline in circulating Tregs may be associated with the acquisition of clinical immunity .
Here , we have shown through both cross-sectional and longitudinal studies that the percentage and absolute number of FoxP3+ Tregs in peripheral blood are influenced by repeated exposure to malaria . While children in settings of intense exposure have higher Treg frequencies during early childhood , frequencies decline throughout childhood in settings of high ( but not low ) exposure , and the extent of Treg loss correlates with the intensity of P . falciparum exposure . We provide both in vivo and in vitro evidence that among children in high exposure settings , there is a reduction of parasite induced Treg expansion during infection . Further , we show a down-regulation of TNFR2 , which is required for stabilization of the FoxP3+ regulatory phenotype in inflammatory environments [31] . These data demonstrate that chronic exposure to malaria results in altered Treg homeostasis in vivo , which may have a downstream impact on the acquisition of immunity and control of infection . Our data indicate that the dynamics of Treg induction and homeostasis differ markedly between high and low malaria transmission settings . Although children residing in high transmission areas had higher Treg frequencies overall , perhaps in response to a parasite-driven Treg expansion in early childhood or in utero [27–30] , we observed a marked decline in Treg frequencies beginning at 1–2 years of age , which appeared to be driven by persistent parasite exposure . Further , among highly exposed children , we saw no association between Treg frequencies and current or recent infection , suggesting that Tregs have reduced in vivo induction during infection of these chronically exposed children . Consistent with this , we demonstrated that induction of Tregs following parasite stimulation of PBMC was diminished in heavily exposed adults and children , providing in vitro evidence that chronic antigen exposure may blunt the proliferative expansion of Tregs in response to malaria . This is in contrast to published studies suggesting that Tregs expand in response to malaria in vivo and in vitro [4 , 7–13 , 18 , 19] . The most likely explanation for the difference in our findings is that these earlier studies were performed largely on malaria-naïve volunteers or relatively low-exposed populations . Overall our data suggest that while Tregs may be induced in initial encounters with parasites , induction capacity is diminished after repeated parasite exposure and instead Tregs undergo a steady decline in the periphery . The induction of Tregs by Plasmodium is believe to occur through activation of latent membrane-bound TGFβ [11 , 40] , which can be blocked by antibodies to the P . falciparum thrombospondin-related adhesive protein ( PfTRAP ) [11] . This Treg induction mechanism is shared by related protozoal pathogens Toxoplasma and Leishmania [35] and may represent an immune evasion strategy . The reduced capacity of parasite antigen to induce Tregs in heavily malaria exposed children suggests that the host may be able to circumvent parasite induction of Tregs , potentially enabling enhanced control of infection . Another potential mechanism for the observed decline in Tregs among children chronically exposed to malaria is via loss of FOXP3 expression by “unstable” Tregs , which has been reported to occur in highly inflammatory immune environments [41–45] . Sustained expression of the canonical transcription factor FOXP3 by Tregs is critical for maintenance of regulatory function [46] . Several recent studies suggest that Tregs can become “unstable” and lose FOXP3 expression in response to cues in the microenvironment , although the significance , extent , and triggers of this phenomenon remain subject to considerable debate [44 , 45 , 47–52] . Lineage tracking experiments have elegantly shown that antigen-driven activation and inflammation can drive a subset of FoxP3hi Tregs to lose both FOXP3 expression and suppressor function [44] , and even acquire an effector phenotype [53] . Further , repeated TCR stimulation leads to the loss of FOXP3 expression and the conversion to pro-inflammatory cytokine producing cells in natural Tregs in vitro [54] . Our data suggest a potential mechanism for such Treg destabilization in malaria infection , as recurrent exposure resulted in down-regulated Treg expression of TNFR2 , which has been shown to be critical for both the proliferative expansion of Tregs and stabilization of their FOXP3 expression in inflammatory environments [31 , 32 , 55] . In the setting of malaria , TNFR2+ Tregs have previously been shown to have higher FOXP3 expression and enhanced suppressive function [8] . Thus our data are consistent with mounting evidence suggesting that peripherally induced Tregs have significant plasticity in response to inflammatory environments such as that observed in malaria infection , which may culminate in loss of FOXP3 expression and suppressive function . Several additional processes might contribute to the observed loss of Tregs in peripheral blood . Because Tregs track to sites of inflammation , it is possible that Tregs induced by P . falciparum traffic to the liver , spleen , or secondary lymphoid organs during infection . Invasive sampling was not possible in our study cohorts; therefore we were unable to exclude a preferential sequestration of Tregs in tissues or lymphoid organs . However , we did not observe any statistical relationship of Treg frequencies with the presence of parasitemia , nor with the amount of time elapsed since the last P . falciparum infection , as might be expected if Tregs migrate to sites of local inflammation during active infection . Alternatively , loss of Tregs through apoptosis might contribute to their decline following repeated malaria infections . However , we observed no relationship between prior malaria incidence and the expression of the pro-apoptotic molecule CD95 or the anti-apoptotic marker Bcl2 on Tregs ( after controlling for age ) , nor did we observe a differential susceptibility towards apoptosis ex vivo , or following in vitro re-stimulation with parasite antigens . The observed decline in Treg frequencies with increasing prior malaria incidence contrasts with that of another regulatory T lymphocyte population , IL10-producing Th1 cells , which we have recently shown to dominate the P . falciparum-specific CD4 T cell response among heavily malaria-exposed children , including among children from both the TCC and the PRISM Nagongera , Tororo cohorts tested here [22 , 56] . This “autoregulatory” population consists predominantly of IL10/IFNγ co-producing cells that express the canonical Th1 transcription factor Tbet and appear to be short-lived in the periphery , exhibiting a strong association with recent infection [22] . We observed no statistical relationship between frequencies of IL10-producing Th1 cells and conventional FoxP3+ Tregs , in contrast to an earlier small cohort study that reported a positive correlation between these two regulatory cell populations [57] . This suggests that in highly exposed children , the loss of peripherally circulating Tregs is not directly compensated by increased frequencies of IL10 producing CD4 responses . In addition , we did not observe any statistical relationships between Treg frequencies and P . falciparum-specific CD4 effector responses . In prior studies , FOXP3 mRNA levels measured during acute malaria were shown to correlate inversely with the magnitude of the subsequent Th1 memory response to P . falciparum measured 28 days after infection [6] . Similarly , FOXP3 expression among malaria-naive adults following experimental sporozoite vaccination was shown to correlate inversely with the Th1 memory response measured >100 days later [14] . Thus , while Tregs are likely to influence the development of parasite-specific T cell memory responses , no relationship between these populations could be demonstrated through our concurrent measurements in peripheral blood , which maybe due in part to the chronicity of malaria exposure in these children and/or the substantial heterogeneity within the cohort with regard to time elapsed since the last infection . Furthermore , additional parameters of Treg function that cannot readily be measured in peripheral blood in large cohorts , such as suppression of T cell proliferation and modulation of APC function , are likely to influence the cellular immune response to malaria , but could not be assessed in the present study . While our results clearly suggest that repeated malaria impacts peripherally circulating Tregs in children , the role of these cells in protection from malaria and the development of immunity remains unclear . We observed no association between Treg frequencies and future malaria incidence or time to next malaria episodes in any of our cohorts . However , our data suggest that although children with the lowest Treg frequencies had a higher monthly probability of infection , they were less likely to become symptomatic once infected compared to children with the highest Treg frequencies over the entire study period . While these data suggest that clinical immunity is acquired as Tregs decline , the role of Tregs in mediating clinical immunity remains unclear , and may not be causal—rather , declining Treg frequencies may coincide with other immune changes that mediate protection . Because all children in our study cohorts have easy access to dedicated study clinics and prompt antimalarial drug treatment , the incidence of severe malaria was extremely low , preventing assessment of the potential role of Tregs in protection from severe disease . We were similarly unable to assess the impact of Treg activity on pathogen persistence following infection , because all cases of symptomatic malaria were promptly treated with potent artemisinin-based drugs , thus altering the natural course of infection . In other protozoal infections , such as leishmaniasis and toxoplasmosis , pathogen-induced Tregs have been reported to curb the inflammatory response , allowing long-term pathogen persistence [1 , 2 , 15] . Indeed , in murine models of leishmania , pathogen persistence resulting from Treg-mediated immune suppression has been shown to be a requirement for immunity to re-infection [1] . The long-term asymptomatic maintenance of low-burden P . falciparum infection that is commonly observed among adults in high-transmission areas [58] may represent a similar phenomenon , but the role of Tregs in mediating this process is not known . Although we did not observe higher frequencies of peripheral blood Tregs among children with asymptomatic P . falciparum infection , which is not routinely treated in Uganda , this does not exclude a role for Tregs in maintaining this state of host-parasite equilibrium . In conclusion , we observed a progressive loss of Tregs from the peripheral blood of children following chronic repeated malaria infections , accompanied by downregulation of TNFR2 and diminished in vitro induction of Tregs by parasite antigen . Together these data demonstrate that the impact of chronic malaria antigen exposure on the FoxP3+ regulatory T cell population is quite different from that of acute infection of malaria-naïve individuals . Our findings also add to mounting data suggesting that the stability and homeostasis of FoxP3+ Tregs are perturbed under highly inflammatory conditions . The implications of this pathogen-driven Treg loss for pathogen clearance , host-parasite equilibrium , and the development of clinical immunity in regions of intense malaria transmission require further investigation .
Written informed consent was obtained from the adult individual or parent/guardian of all study participants . Study protocols were approved by the Uganda National Council of Science and Technology and the institutional review boards of the University of California , San Francisco , Makerere University and the Centers of Disease Control and Prevention . Samples for this study were obtained from children enrolled in 3 longitudinal childhood malaria cohort studies conducted in Tororo District and Jinja District of eastern Uganda . Cohort characteristics are described in S1 Table . For all cohorts , samples were selected on the bases of availability of PBMCs . For the PROMOTE-Chemoprevention , TCC and PRISM Nagongera high transmission area , the estimated entomological inoculation rate ( aEIR ) is approximately 310 bites ppy . In contrast , at the PRISM Walukuba low transmissions site the aEIR is estimated at 2 . 8 [21] . On enrollment all study participants were given an insecticide treated bed net and followed for all medical care at dedicated study clinics . Children who presented with a fever ( tympanic temperature ≥38 . 0°C ) or history of fever in the previous 24 hours had blood obtained by finger prick for a thick smear . If the thick smear was positive for malaria parasites , the patient was diagnosed with malaria regardless of parasite density and treatment with artemether-lumefantrine or dihydroartemisinin-piperaquine for all episodes of malaria . Incident episodes of malaria were defined as all febrile episodes accompanied by any parasitemia requiring treatment , but not preceded by another treatment in the prior 14 days . The incidence of malaria was calculated as the number of episodes per person years ( ppy ) from the time of enrolment into the cohort . In a subset of PRISM cohort children used to assess TNFR2 expression on Tregs parasite infection was assessed via PCR from dried blood spots as previously described [61] . Treg frequencies were enumerated from whole blood and fresh and cryopreserved PBMCs as indicated below . For enumeration of Tregs from whole blood ( PROMOTE-Chemoprevention , control arm 2-year-old samples ) , 100 μl of fresh whole blood was stained with BD Pharmingen anti-CD3-FITC ( UCHT1 ) , anti-CD4-PE-CY7 ( SK3 ) , and CD25-APC ( M-A251 ) for 20 minutes and then lysed and permeabilized with eBioscience RBC lysis buffer . Cells were washed and then incubated with eBioscience anti-FoxP3-PE ( PCH101 ) . Samples were acquired on Accuri C6 Cytometer . For analysis of Tregs from fresh PBMCs ( PRISM Nagongera cohort ) , PBMCs were isolated by Ficoll density gradient centrifugation and rested over night in 10% fetal bovine serum . PBMCs were stained with BD Pharmingen anti-CD3 PerCP ( SK7 ) , anti-TNFR2-Alexa646 ( hTNFR-M1 ) , anti-CD95-PECy7 ( DX2 ) and Biolegend anti-CD4-APC-Cy7 ( OKT4 ) , anti-CD25-BrillantViolet510 ( M-A251 ) , anti-CD127-PacificBlue ( A019D5 ) . Following surface staining , cells were fixed and permeabilized with eBioscience FoxP3 staining set and intracellular stained with FoxP3-PE ( PCH101 ) and BD Pharmingen anti-Bcl2-FITC ( Bcl-2/100 ) as per manufacturers protocol . Samples were acquired on three laser BD FACsCantoII with FACSDiva software . For analysis of Tregs from frozen PBMCs ( Tororo Child Cohort 4-year-olds , PROMOTE-Chemoprevention SP arm longitudinal samples at 16 , 24 , and 28 months of age , PRISM Walukuba cohort ) , cryopreserved PBMCs were thawed using standard methods , and immediately stained with the following panels of antibodies; BD Pharmingen anti-CD3-FITC ( UCHT1 ) , anti-CD4-PE-CY7 ( SK3 ) , CD25-APC ( M-A251 ) and Biolegend anti-CD127 Pacific Blue ( A019D5 ) ; or Biolegend anti-CD3-BrilliantViolet650 ( OKT3 ) , anti-CD4-PerCP ( OKT4 ) , anti-CD127-FITC ( A019D5 ) ; or Biolegend anti-CD3-PerCP ( OKT3 ) , anti-CD4-APC-Cy7 ( OKT4 ) , anti-CD25-BrillantViolet510 ( M-A251 ) , anti-CD127-PacificBlue ( A019D5 ) , anti-TNFR2-APC ( 3G7A02 ) . Live/dead aqua amine ( Invitrogen ) was included in all panels . Following surface staining , cells were fixed and permeabilised with eBioscience FoxP3 staining set and intracellular stained with FoxP3-PE ( PCH101 ) as per manufacturers protocol . Samples were acquired on LSR2 three laser flow cytometer ( Becton Dickinson ) with FACSDiva software . For calculation of absolute Tregs counts ( i . e . cells per μl , PRISM Nagongera cohort ) , peripheral blood CD4 T cell concentrations were measured from whole blood using counting beads , and Treg frequencies were calculated by normalization to total CD4 T cell numbers . Analysis of CD4+ T cell responses to P . falciparum infected RBCs via intracellular cytokine staining was performed as previously described [22 , 56] . PBMCs were stimulated with P . falciparum infected RBCs or uninfected RBCs and CD4 T cell production of IFNγ , IL10 , and TNFα were measured via intracellular staining . PBMCs from PROMOTE subjects ( 28 months of age; no chemoprevention control arm ) and adults from the high malaria transmission region of Tororo were thawed and washed in 10% Human serum ( AB ) media ( Gemini ) , and 3–6X106 PBMC were labeled with 1 ml of 1 . 25 mM 5 , 6-carboxyfluorescein diacetate succinimidyl ester ( CFSE; Molecular Probes ) for seven minutes . CFSE-labeled PBMC were incubated in 96-well , deep-well culture plates ( Nunc , Roskilde , Denmark ) at 106 PBMC/ well in 1 ml for 7 days with P . falciparum schizont extract ( PfSE ) ( W2 strain ) or protein extract from uninfected RBCs ( uE ) at a effector to target ratio equivalent to 1:1 PBMC:infected RBC . PHA ( 1μg/ml ) was used as a positive control . PfSE extracts were made from the W2 stain grown in standard culture conditions and confirmed to be free of mycoplasma contamination using MycoAlert ( Lonza ) . Mature stage parasites were magnet purified from culture using MACs purification columns . Purified parasites or uninfected RBCs were freeze thawed 3+ times ( via snap freezing on liquid nitrogen and then transfer to 37°C water bath ) to produce PfSE and uE and stored at -20°C . Following culture of PBMCs with protein extracts , cells were treated with 100 units of DNase I ( Invitrogen ) in culture media for 5 minutes and then surface stained with Biolegend anti-CD3-BrilliantViolet650 ( OKT3 ) , anti-CD4-PerCP ( OKT4 ) , anti-CD25-PE-Cy7 ( BC96 ) , anti-CD127 Pacific Blue ( A019D5 ) , BD Pharmingen anti-CD8-ABC-H7 ( SK1 ) and Live/dead aqua amine ( Invitrogen cells ) . Following surface staining , cells were fixed and permeabilized with eBioscience FoxP3 staining set and stained with FoxP3-PE ( PCH101 ) . Proliferation with PHA was used to ensure cell viability , and cells incubated with uE were used as a background control . Of the infants tested , the prior median malaria incidence was 1 . 2 episodes ppy in the low exposed group and 8 . 5 episodes ppy in the high exposed group . PBMCs from PROMOTE subjects ( 28 months of age; no chemoprevention control arm ) were thawed and rested overnight either in standard media ( untreated ) , 5uM camptothecin ( Sigma ) or P . falciparum schizont extract ( PfSE ) or protein extract from uninfected RBCs ( uE ) at an effector:target ratio of 3:1 . To test for induction of apoptosis , stains for AnnexinV ( Biolegend ) or YoPro ( Invitrogen ) , or activated Caspase 3 FITC ( BD ) were used according to the manufacturer’s instructions in combination with the following antibodies: AnnexinV and YoPro staining—CD3 ( OKT3 ) Brilliant Violet 650 , CD4 ( RPA-T4 ) APC-Cy7 , CD127 ( A019D5 ) APC , CD25 ( BC96 ) PE-Cy7 from Biolegend; for Caspase3—CD3 ( OKT3 ) Brilliant Violet 650 , CD4 ( RPA-T4 ) PerCP , CD127 ( A019D5 ) Pacific Blue , CD25 ( BC96 ) PE-Cy7 . Tregs were gated as CD3+CD4+CD25+CD127dim . Sensitivity to apoptosis was measured ex vivo ( untreated control ) , after induction with camptothecin ( fold change compared to untreated ) , and after stimulation with P . falciparum schizont extract ( fold change comparing PfSE to uE ) . Unless otherwise indicated , samples were acquired on an LSR2 flow cytometer ( Becton Dickinson ) with FACSDiva software . Flow cytometry data were analysed using FlowJo software ( Tree Star , San Carlos , CA ) . Color compensation was performed using single color cell controls or beads stained for each fluorochrome . Gating strategies are outlined in Supplementary Figures . Fluorescence minus one controls were used for gating of CD25 , CD95 , HLA-DR and Bcl2 . For FoxP3 staining , an anti-Rabbit-Isotype control was used . Data analysis was performed using Stata version 12 ( Stata Corp , College Station , Tx ) and PRISM version 6 ( Graph Pad ) . Associations between Treg frequencies and other continuous variables ( prior malaria incidence , age , time since last malaria episode ) were assessed using Spearman’s correlation . Changes in Treg frequencies within an individual over time were assessed using the Wilcoxon signed rank test . All other two-group comparisons of continuous variables were performed using the Wilcoxon rank sum test . Repeated measures analysis of longitudinal changes in Treg frequencies was performed using generalized estimating equations , with adjustment for concurrent parasitemia , age and duration since last malaria episode . Categorical variables were compared using Chi sq test . Associations between Treg frequencies and time to next malaria episode were evaluated using the Kaplan-Meier product limit formula , and a multivariate cox proportional hazards model was used to adjust for surrogates of malaria exposure ( cumulative episodes since enrollment in study for TCC and PROMOTE cohorts , or age for PRISM cohorts ) . Negative binomial regression was used to estimate associations between Treg frequencies and the prospective incidence of malaria in the following year ( incidence rate ratios , IRR ) and prevalence of asymptomatic parasitemia in the following year ( prevalence rate ratios , PRR ) , adjusting for malaria exposure as above . Two-sided p-values were calculated for all test statistics and p<0 . 05 was considered significant . In the PRMOTE and TCC cohorts , associations between the highest and lowest tertiles of Treg frequencies and the monthly risk of parasitemia , probability of symptoms if parasitemic , and incidence of malaria , stratified by year of age , were evaluated using generalized estimating equations with robust SEs accounting for repeated measures in the same patient , for the period of the inter study ( 6 months to 3 or 5 years of age ) [62] . | In malaria endemic regions , immunity is slow to develop and does not provide substantial protection against reinfection . Rather , following repeated exposure , older children and adults eventually develop protection from most symptomatic manifestations of the infection . This may be due in part to the induction of immunoregulatory mechanisms by the P . falciparum parasite , such as FoxP3+ regulatory T cells ( Tregs ) . Prior human studies have shown that Tregs are induced by malaria parasites both in vivo and in vitro , but the role of these cells in immunity in children who are chronically exposed to malaria remains unclear . In this study , we assessed the frequency and features of Tregs among children from areas of high malaria transmission in Uganda . We found that this regulatory T cell population declined markedly with increasing malaria episodes . This loss was associated with decreased expression of TNFR2 , which is a protein implicated in stability of Tregs . Additionally , T cells from highly malaria exposed children demonstrated a reduced propensity to differentiate into Tregs following parasite stimulation . Together our data suggest that repeated episodes of malaria alter Treg homeostasis , which may influence the development of immunity to malaria in children . | [
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| 2015 | Decline of FoxP3+ Regulatory CD4 T Cells in Peripheral Blood of Children Heavily Exposed to Malaria |
Trachoma is the most common cause of infectious blindness . Hot , dry climates , dust and water scarcity are thought to be associated with the distribution of trachoma but the evidence is unclear . The aim of this study was to evaluate the epidemiological evidence regarding the extent to which climatic factors explain the current prevalence , distribution , and severity of acute and chronic trachoma . Understanding the present relationship between climate and trachoma could help inform current and future disease elimination . A systematic review of peer-reviewed literature was conducted to identify observational studies which quantified an association between climate factors and acute or chronic trachoma and which met the inclusion and exclusion criteria . Studies that assessed the association between climate types and trachoma prevalence were also reviewed . Only eight of the 1751 papers retrieved met the inclusion criteria , all undertaken in Africa . Several papers reported an association between trachoma prevalence and altitude in highly endemic areas , providing some evidence of a role for temperature in the transmission of acute disease . A robust mapping study found strong evidence of an association between low rainfall and active trachoma . There is also consistent but weak evidence that the prevalence of trachoma is higher in savannah-type ecological zones . There were no studies on the effect of climate in low endemic areas , nor on the effect of dust on trachoma . Current evidence on the potential role of climate on trachoma distribution is limited , despite a wealth of anecdotal evidence . Temperature and rainfall appear to play a role in the transmission of acute trachoma , possibly mediated through reduced activity of flies at lower temperatures . Further research is needed on climate and other environmental and behavioural factors , particularly in arid and savannah areas . Many studies did not adequately control for socioeconomic or environmental confounders .
The neglected tropical disease ( NTD ) trachoma , caused by Chlamydia trachomatis , is the world's leading cause of infectious blindness [1] and an important cause of chronic discomfort in 57 endemic countries , mainly in Africa [2] . Over 40 million people are infected with C . trachomatis , 8 million of whom have painful , in-turned eyelashes ( trichiasis ) [3] . It is estimated that 1 . 2 billion people live in trachoma-endemic areas [4] . The current distribution of trachoma aligns with low and middle-income countries , within which poorer individuals and communities are at highest risk [2] , [5] . The World Health Organization ( WHO ) classifies trachoma into active and chronic stages . The signs of active trachoma , according to the simplified grading system [6] , are trachomatous follicles ( TF ) and trachomatous inflammation ( TI ) . The chronic , potentially blinding stages of trachoma are characterised by visible scarring of the under surface of the upper eyelid ( TS ) , in-turned eyelashes , trachomatous trichiasis ( TT ) , and corneal opacity ( CO ) . The incidence of chronic trachoma increases with age . Active infection principally occurs in children , where it is self limiting , whereas the blinding stages of trachoma are not seen until later in life . Although repeated episodes of infection during childhood are thought to lead to the scarring stages of trachoma , the natural history and pathophysiology are not fully understood . Both clinical presentation and transmission of trachoma are complex and multi-faceted . The investigation and monitoring of active trachoma are complicated as evidence of infection with Chlamydia does not correlate well with clinical signs of the disease ( i . e . TF and TI ) [7] . Trachoma is transmitted via contact with infected eye and nasal secretions by hands , fomites , and eye-seeking flies [8] . Individual factors such as rural residence , overcrowded living conditions , and keeping cattle close to the home are also associated with trachoma [5] , [9]–[11] . The strongest environmental risk relates to poor hygiene , often reflecting poor access to water and lack of sanitation , which promotes transmission by providing more effective breeding sites for eye-seeking flies [12]–[17] . The strategy for trachoma control is the SAFE strategy: S = surgery to correct the upper eyelid deformity [18]; A = antibiotics for active infection via mass drug administration ( MDA ) [19]; F = facial cleanliness [20] , and E = environmental improvement [21] . The ‘F’ and ‘E’ elements aim to reduce C . trachomatis transmission by improving hygiene behaviour and reducing environmental factors which promote eye-seeking flies . There has been growing interest in the direct effects of climate on disease , reflecting concerns about climate change as well as improvements in the use of climate information for disease control [22] . Epidemiological studies of weather ( daily temperature or rainfall ) use time series methods to detect acute ( short-term ) effects on health outcomes . Long term exposures ( i . e . climate ) have also been studied in cross-sectional studies [23] . Climate factors vary in time and space and it is important that the study design includes appropriate adjustment for social or environmental factors ( confounders ) . Associations between meteorological variables and health outcomes are likely to depend on local contexts , and have also been shown to change over time [22] . Climate is the average weather conditions observed over a long time period ( decades ) . Globally , the world can be divided into five main climate types ( with multiple sub-types ) based on annual average and monthly temperature and precipitation values . The best known classification scheme is the Koppen-Geigen system which describes the following climate types: tropical; dry ( arid and semiarid ) ; mild temperate; continental and polar [24] . Climatic factors may influence the distribution and prevalence of trachoma indirectly through poor access to water , which limits hygiene behaviour , or low rainfall , which may influence the distribution , abundance or seasonal activity of Musca sorbens , the principal eye-seeking fly implicated in trachoma transmission . At broad geographic levels , climate also influences agricultural productivity and livelihoods in resource-poor settings; communities at risk of trachoma often depend on livestock and subsistence farming . Direct influences on the eye may include humidity , dust and aridity . The purpose of this study was to examine the association between climatic factors and the distribution , frequency and severity of trachoma . A systematic literature review was conducted for evidence of climatic effects on active trachoma , to identify factors associated with transmission , as well as cicatricial/blinding trachoma , to identify factors associated with scarring .
Searches were undertaken separately for active and chronic trachoma ( hereafter termed trachoma outcomes ) ( See search terms in Supplementary Material Table S1 ) . The following electronic databases were searched: CAB Abstracts; Embase; Global Health; Medline; Web of Science . Websites of international agencies were also searched: the World Health Organization ( WHO ) , WHO Special Programme for Research and Training in Tropical Diseases ( TDR ) ; Intergovernmental Panel on Climate Change ( IPCC ) ; United Nations Children's Fund ( UNICEF ) ; UN-Habitat; The Carter Center; the International Trachoma Initiative ( ITI ) ; Sightsavers; Helen Keller International; Fred Hollows Foundation; Christian Blind Mission . Studies were only included if they quantified an association between a climate factor ( temperature , rainfall , altitude , etc ) and a trachoma outcome . Papers were not excluded based on geographic location of study , age of participants , or language of journal publication . Peer reviewed journal articles and reports from leading international agencies ( e . g . , WHO ) published between 1 January 1950 and 1 April 2012 were included . Data extraction and analysis were conducted by two readers . Results were screened and reviewed in three stages: i ) title; ii ) title and abstract; and iii ) title , abstract , and manuscript . References for which title and abstract were available and which seemed to meet the inclusion criteria were reviewed by two reviewers for eligibility , quality and data extraction . The quality of each study included was assessed using the parameters described in the STROBE checklist for cross-sectional studies [25] . The quality of observational studies was assessed using the following criteria: sampling of study population to estimate trachoma prevalence , study design and control of confounding , reporting of negative results , and the measurement of climate exposures . It was expected that heterogeneity in the study designs and exposure measurements would preclude meta-analyses . We also reviewed the evidence for climate type and seasonality on trachoma outcomes .
Temperature was assessed in all four studies of moderate quality , giving different results . In one of the studies from Mali , the prevalence of active trachoma was significantly lower in areas with higher annual average temperature and higher sunshine fraction [28] . Monthly average temperatures however , gave different results , with hotter areas having a higher prevalence , but this did not reach statistical significance . However , in this study the range of monthly average temperatures was relatively low ( tertiles: <34 . 6 , 34 . 6–38 . 7 , >38 . 7°C ) . In the study from Burkina Faso , multivariate analysis also showed the prevalence of active trachoma to be lower in areas with higher minimum temperature , with a 43% lower risk with every 1°C higher minimum temperature [29] . The other paper from Mali however , gave different results in multivariate analysis , reporting a significantly higher prevalence of active trachoma with higher mean daily temperature [26] . In Sudan , temperature did not predict the distribution of active trachoma once rainfall was included in the explanatory model [27] . Altitude was used as a proxy for temperature in all eight studies ( Table 1 ) . Three studies , all of lower quality , reported a lower prevalence of trachoma at higher altitude [17] , [30] , [32] , but two did not take account of other climatic or environmental factors [30] , [32] . A further study reported the converse , with a higher odds of trachoma at higher altitude which persisted after adjusting for some confounders [31] . In the four other studies , all of which were of moderate quality and which included a range of other climate and/or environmental risk factors , altitude was dropped in final statistical models [26]–[29] . In one of these papers all study sites were at low altitude ( 27–669 m ) [26] . In all the papers , there is likely to have been residual confounding given that altitude is a very broad indicator of temperature . This means that there is low confidence in the results , especially as the effects of altitude were not consistent . Rainfall was investigated in all four studies of moderate quality but rainfall exposures were parameterised differently . Two of the four studies gave significant findings . In the study in Sudan , long-term average rainfall was the strongest climatic predictor of active trachoma , with every 100 mm increase in rainfall being associated with a 79% lower prevalence [27] . In one of the studies in Mali , rainfall was measured as the number of rainy days , and this study also showed rainfall to lower the risk of active trachoma in children ( odds ratio 0 . 63 , 95% CI 0 . 41–0 . 97 ) [28] . The two other studies had weaker evidence of an association between rainfall and trachoma , as rainfall was not retained in their final statistical models as the meteorological variables were highly correlated . In univariate analyses one study reported lower odds of active trachoma with greater annual rainfall [26] while the other reported greater odds of active trachoma in children [29] . Relative humidity and sunshine fraction were considered by some authors but with no clear hypotheses . In one study , relative humidity was dropped from the final multivariate model as it was highly correlated with temperature and rainfall . One study found an association between sunshine fraction and active trachoma , with a lower risk of trachoma in areas with higher sunshine fractions [28] . Latitude was measured in only two studies: one of lower quality from Tanzania which did not report findings [17] , and one of moderate quality from Mali . In the latter , a multivariate analysis , the latitudes 10–15°N ( with higher average temperatures ) were associated with a higher risk of chronic trachoma but a lower risk of active trachoma when compared with the latitude of 15–21°N as baseline [26] . One study ( mapping study in southern Sudan ) that met our review criteria investigated associations between trachoma and climate type [27] . Two further studies from Nigeria and Australia [11] , [33] ( Table 2 ) were also identified: the Nigerian study reported the proportion of blindness due to trachoma in a national survey of blindness and visual impairment . Overall , the prevalence of trachoma appears to be higher in semi-arid Savannah areas where the climate is characterised by a winter dry season , a relatively short but heavy rainy summer season , and high year-round temperatures . This finding is consistent with anecdotal evidence but the association was only formally tested in the Nigerian study [33] . The Sudanese study also suggested that savannah and grassland had a higher prevalence of active trachoma than wooded savannah [27] . The prevalence of active trachoma was also reported to higher in the drier and dustier areas ( zones 1 and 2 ) in Australia ( Table 2 ) . There is very limited evidence that active trachoma is seasonal as none of the eight studies investigated the intra-annual distribution of trachoma . Three further papers were identified which described monthly rates of active trachoma: one from Australia [34] and two from India [35] , [36] . In north-western Australia , higher rates of trachoma were observed during the wet season months ( 14–59% in dry season compared with 46–69% in wet season ) in two Aboriginal communities [34] . The Indian papers ( based on surveys undertaken in 1956–63 ) reported monthly cases of active and chronic trachoma which showed no seasonal pattern . However , two seasonal peaks in conjunctivitis were observed in the population ( in the pre- and post-monsoon periods ) , which were associated with observed seasonal fly abundance [37] .
Studies designed to explore associations between climate and disease should ideally use data on temperature and precipitation that is valid for the population under study , for example , from weather station observations . For large populations and areas , gridded data from weather stations or satellite data might have to be used [38] . The latter are particularly relevant in sub-Saharan Africa where coverage by weather stations is low , although care should be taken in highland areas , where use of wrong climate data can give spurious results [39] . Altitude is often used as a crude proxy for temperature as temperatures decrease with increasing altitude . However , altitude also influences a number of other factors , including rainfall; highland areas may be wetter or drier than surrounding low land areas depending on the local climate and topography . Furthermore , vegetation , land use , and population characteristics can also vary by altitude . It is therefore important that studies assessing the influence of altitude on disease take account of these potential confounders . Temperature is also a more robust indicator than rainfall over a geographic area , due to the higher spatial and temporal variability in precipitation . Meteorological variables are often correlated in space and time and therefore it is important that the most appropriate parameterizations ( including meteorological indices ) are decided a priori . This systematic literature review found evidence that low rainfall is associated with a higher prevalence of active trachoma , which is consistent with the finding that trachoma prevalence is greater in savannah areas . In one study , however , rainfall was removed from the final multivariate model due to collinearity with latitude and humidity . Low rainfall warrants further investigation as a risk factor for the distribution and prevalence of trachoma . Despite a wealth of anecdotal information , there is very little high quality observational evidence on the role of temperature on the distribution or prevalence of active trachoma . There is even less evidence regarding chronic trachoma ( and related blindness ) . One study reported a significant effect of lower latitude on trachomatous scarring and trichiasis in adult women in Mali [26] . Using altitude as a proxy for temperature , there is some evidence that in highly endemic areas of East Africa the prevalence of trachoma is lower at high altitudes but two papers , both risk mapping papers which also measured several co-linear variables ( e . g . , rainfall , humidity ) did not find statistically significant associations . Although it is plausible that low temperatures have a limiting effect on the distribution of trachoma , the association could be due to confounding by social or other environmental factors . For example , in Kenya it has been observed that households of higher socio-economic status and less over-crowding tend to reside at higher altitudes than poorer households [17] . The activity and distribution of eye seeking flies may be a mechanism by which low temperatures limit trachoma prevalence , as climatic factors directly affect the seasonal activity and distribution of Muscid flies ( e . g . , M . sorbens ) . Laboratory studies have also shown that the lifespan of M . sorbens ranges from less than 12 days at 32°C to 35 days at 24°C [29] , [40] . Field studies show that the distribution of M . sorbens is strongly correlated with altitude [41] . Climate may also effect fly activity indirectly , for example , through access to faeces for breeding , as high temperature and sunshine may induce rapid drying of faecal matter , rendering it a less effective breeding site [29] . Historically , trachoma studies have focused on changes in distribution ( e . g . , amongst migrant populations ) and few doubted the association between dust , heat , and trachoma [42]–[44] . The systematic review found no studies that quantified an association between dust and trachoma , probably because it is difficult to measure dust exposures , especially across large spatial scales . Only one paper reported a seasonal pattern in active trachoma [34] . Assessing seasonality in relation to trachoma is problematic for several reasons . Not only is there poor correlation between PCR-confirmed Chlamydial infection and the clinical signs of active trachoma , but the interval between infection and the development and resolution of clinical signs is not yet known . It is therefore important to evaluate the method of assessment of active trachoma when interpreting temporal patterns of the disease . This review has several limitations . The quality of the climate exposure assessment in many studies was poor due to the use of broad categories ( e . g . altitude ) or lack of local measures for temperature and rainfall , due to limited coverage by weather stations . However , the Sudanese study used satellite data to obtain comprehensive exposures of average rainfall and temperature at a reasonable resolution , although the data would have been interpolated leading to some parameter uncertainty [27] . All the studies were undertaken in Africa , which limits generalisability of the findings . It is possible that climate-trachoma associations are location specific . For example , the relationship between trachoma and altitude may be different in Asia where overcrowding during the winter among populations living at high altitude is thought to promote transmission ( B . Qureshi , personal communication ) . Other limitations are that some studies used relatively small sample sizes and prevalence estimates of trachoma had wide 95% confidence intervals , which reduce the power to detect statistically significant differences in outcomes . Most trachoma research is conducted either within foci where trachoma is endemic , or is undertaken to identify trachoma endemic areas where control programmes are required . This is a major limitation when exploring climatic factors where the edges of the distribution are of interest . It is also possible that some publication bias has occurred , with studies not finding an association not being published . The current understanding of trachoma transmission is that multiple factors combine to propagate this preventable disease . Many of these factors are difficult to quantify , but are very important when considering the entire transmission cycle and interactions between socio-economic and environmental factors . The findings of this study suggest that climatic factors may also play a role in the distribution and prevalence of trachoma . Socioeconomic factors ( e . g . , poverty ) and certain behaviours ( e . g . , migration ) warrant further attention as they impact on those at risk of trachoma and are , in turn , affected by environmental factors [5] , [45] , [46] . The findings of this review support the call for greater investment in the “E” element of the SAFE strategy . The WHO-led Alliance for the Global Elimination of Blinding Trachoma ( GET2020 ) aims to eliminate blinding trachoma by the year 2020 , and there are international partnerships , including a drug donation programme for control [4] , [47] , [48] . There are ongoing international efforts to map several NTDs , including trachoma , providing up to date information on the distribution of trachoma . The findings of this review are of value for those mapping the distribution of trachoma , as altitude , temperature and rainfall may be additional parameters for consideration at the planning stages . Better delineation of trachoma endemicity , which will identify the edge of endemic foci , will allow more informative studies of the impact of climate over a large scale on the distribution of trachoma . The findings of this review add impetus to trachoma control because if the climate in sub-Saharan Africa was to become hotter and drier ( due to either natural variability or anthropogenic forcing ) , this may potentially influence the distribution and severity of trachoma . Finally , this review highlights the relative paucity of studies exploring these potential associations and the poor quality of much of the climate data both in terms of coverage and frequency at which the data were collected . | Trachoma – the leading cause of infectious blindness – is spread through contact with infected persons by hands and towels , and by ‘eye-seeking flies . ’ Trachoma prevalence is high in areas characterised by poverty , inadequate water supply , and poor sanitation . Trachoma is controlled by the SAFE strategy: S = surgery to the upper eyelids; A = antibiotics for active infection; F = facial cleanliness; and E = environmental improvement . In this study we reviewed the scientific literature to assess the extent to which climatic factors ( e . g . , rainfall , heat , dust , altitude ) influence trachoma distribution . A systematic review of the literature found eight papers that measured an association between a climatic factor and trachoma in children or adults . Several studies reported that trachoma is less common at higher altitudes , indicating that temperature may play a role in trachoma transmission . Some studies also reported that trachoma is higher in areas with low rainfall , which is consistent with anecdotal evidence that trachoma is associated with dry environments . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
]
| []
| 2013 | The Impact of Climatic Risk Factors on the Prevalence, Distribution, and Severity of Acute and Chronic Trachoma |
Genome-wide association studies ( GWASs ) have recently revealed many genetic associations that are shared between different diseases . We propose a method , disPCA , for genome-wide characterization of shared and distinct risk factors between and within disease classes . It flips the conventional GWAS paradigm by analyzing the diseases themselves , across GWAS datasets , to explore their “shared pathogenetics” . The method applies principal component analysis ( PCA ) to gene-level significance scores across all genes and across GWASs , thereby revealing shared pathogenetics between diseases in an unsupervised fashion . Importantly , it adjusts for potential sources of heterogeneity present between GWAS which can confound investigation of shared disease etiology . We applied disPCA to 31 GWASs , including autoimmune diseases , cancers , psychiatric disorders , and neurological disorders . The leading principal components separate these disease classes , as well as inflammatory bowel diseases from other autoimmune diseases . Generally , distinct diseases from the same class tend to be less separated , which is in line with their increased shared etiology . Enrichment analysis of genes contributing to leading principal components revealed pathways that are implicated in the immune system , while also pointing to pathways that have yet to be explored before in this context . Our results point to the potential of disPCA in going beyond epidemiological findings of the co-occurrence of distinct diseases , to highlighting novel genes and pathways that unsupervised learning suggest to be key players in the variability across diseases .
Comorbidity studies show that some distinct diseases tend to co-occur [1]–[6] , pointing to a shared genetic and/or environmental component . In the era of genome-wide association studies ( GWASs ) , direct evidence of shared genetic risk factors of diseases comes to light [7] . For example , while it has been previously shown that rheumatoid arthritis and type-1 diabetes co-occur [1] , GWASs have identified 12 genes associated with both diseases [8]–[16] . More broadly , disease genes obtained from the Online Mendelian Inheritance in Man [17] were used to assemble the Human Disease Network ( HDN ) [18] , [19] , a visual representation of genetic similarity between diseases . Pleiotropy of complex diseases and traits has also been explored by searching genome-wide for variants implicated in more than one disease [16] , [20] , [21] . Such studies promise to reveal shared genes and offer an expanded understanding from a genetic standpoint of why some diseases tend to co-occur . Methods for exploring shared genetic risk factors between diseases belong to two main categories ( see also recent review [7] ) . The first category of methods focuses on finding individual variants that are associated with a pair or more of diseases being investigated . In one set of such methods , a GWAS is carried out on a pooled set of individuals with different diseases [10] , [16] , [20] , [21] , or by analyzing information for multiple diseases available for the same individuals [22] , [23] . Alternatively , and based only on summary statistics of the association test for each single nucleotide polymorphism ( SNP ) , one can simply combine p-values from several GWASs using Fisher's method [24] . The CPMA ( cross-phenotype meta-analysis ) statistic [25] is another statistic that tests whether a SNP is associated to more than one phenotype . In addition , methods such as the conditional false discovery rate or mixed-models for multiple traits have used known pleiotropy between diseases or traits to increase power [26] , [27] . Studies employing these methods have found shared associations between pairs of diseases such as Crohn's disease and celiac disease [16] , other autoimmune disease pairs [20] , [21] , bipolar disorder and schizophrenia [26] and multiple sclerosis and schizophrenia [28] . They have additionally shown that SNPs associated with one autoimmune disease are likely to be associated to other ( though not all ) autoimmune phenotypes [25] . The second category of methods focuses on using shared variants to learn about the genetic similarity between diseases . One method employed by Sirota et al . utilizes the correlation between association signals across many SNPs to assess the similarity between pairs of diseases and showed that there are likely two distinct autoimmune classes where a risk allele for one class may be protective in another [29] . Similar methods based on classifier [30] and linear mixed model approaches [27] , [31] have also been proposed for assessing the shared genetic variation between two diseases . These exciting new methods are powerful for studying shared genetic risk variants between diseases . At the same time , overcoming some of their limitations can improve the study of shared pathogenesis using data from multiple GWASs . First , some methods have focused on analysis of individual SNPs . Though well suited for scenarios of a single causal SNP in a locus , such methods would suffer a reduction in power when several causal SNPs exist or if different SNPs tag the same underlying causal variant , which is especially relevant for diseases with rare causal variants [32] , [33] and when the different GWASs are across different populations [34] or have used different genotyping arrays . Second , when considering the correlation between association statistics of different studies , it might be beneficial to not consider all variants equally ( as is the case in [29] ) , whether or not they play a role in disease susceptibility . Third , most methods assume as known which diseases share pathogenesis , and while the shared pathogenesis of autoimmune disease has been well established [25] , [29] , it is worthwhile to study shared pathogenesis of other disease classes [6] , [35] , [36] . And fourth , while some approaches perform well for two correlated traits or diseases , extending the analysis to more than two traits can become difficult [27] . In this study , we present a novel method , disPCA , which uses principal component analysis ( PCA ) to learn about the shared genetic risk of distinct diseases . PCA maps data from the original axes into new axes in principal component ( PC ) space via a stretch and rotation of the original axes . Each new axis or PC captures the maximal level of variation in the data not captured by previous PCs . Thus , each PC can potentially capture a different , orthogonal story told by the data . Our method is based on summary level statistics from GWASs of different diseases . We combine data from individual SNPs into gene-based statistics via several p-value combination methods . PCA is applied to a matrix across genes and GWAS datasets , with entries representing the strength of association between a gene and the disease studied in a dataset . Thus , disPCA reveals principal components that are linear combinations of all genes , weighed in accordance with their role in differentiating between the different GWASs . It can be applied to study multiple diseases without prior knowledge of their shared pathogenesis , thereby overcoming all the limitations of existing methods outlined above . disPCA also accounts for potential confounders due to methodological differences between studies , such as in genotyping array , which can otherwise lead to these differences being captured by the PCA . Equipped with this novel method and with data from 31 GWAS datasets , we considered the level of shared pathogenesis between diseases and classes of diseases from all genes , which we term shared pathogenetics . Diseases with more similar underlying genetics are more likely to be located closer together in PC space . As PCA is a non-parametric method , it makes no assumptions regarding which diseases are more similar and does not aim to model it , thereby allowing discovery of new relationships between diseases by examining the top PCs . Each PC captures a different combination of genes that distinguish well between some diseases , or the remaining variation between diseases . No separation between diseases along a PC indicates that they tend to share the pathogenetics underlying that PC . By studying the set of genes underlying each PC for enrichment in specific pathways , we further assessed the function and relationship of genes that separate different disease clusters in PC space .
We developed a method , disPCA , for studying the relationship between diseases based on their level of disease risk genes shared . The method works on the gene-level by first combining information from all SNPs in and around each gene . Considering gene-level statistics compensates for different tag SNPs being associated in different datasets even in cases where they capture the same causal variant . It also aggregates information across multiple tag SNPs in each dataset , as well as allows for different underlying causal variants in the same gene being associated with the risk of different diseases . To be widely applicable , disPCA is based solely on the p-values of association of each SNP with the disease under study . Importantly , all SNPs and consequently all genes are considered , rather than focusing on genes that meet a genome-wide significance level of association with a disease . We apply PCA to many different GWASs to axiomatically find and assign importance to genes based on their contribution to distinguishing between diseases and disease classes . The ensuing distance between different disease datasets in PC space inversely corresponds to their level of shared pathogenetics . For each protein-coding gene from the HGNC database [37] , we mapped all SNPs that are in the gene or within 0 . 01 cM from it ( genetic distances were determined via the Oxford genetic map based on HapMap2 data [38] , [39] ) . We discarded all SNPs that were not mapped to within 0 . 01 cM of any gene . If a SNP lay between two genes , it was assigned to the closer gene . For each GWAS dataset , we determined the significance of association of each gene with the assayed disease using the following simulation procedure . Let the observed p-value of a gene be the minimum p-value of the n SNPs mapped to the gene . We compared the observed p-value to that of 100 , 000 groups of n consecutive SNPs chosen in random . Based on these groups , we assign a new p-value to each gene as the proportion of groups for which the observed minimum p-value for that gene is less significant than that of the group . This random sampling procedure may be biased in regions of high linkage disequilibrium ( LD ) when mapping SNPs to genes using genetic distance ( e . g . consecutive SNPs in regions of high LD will be more correlated than those in regions of lower LD ) . However , for any given gene , these will equally affect each of the datasets . To validate this , we also applied disPCA to p-values obtained from mapping SNPs to genes using physical distance: a SNP was mapped to a gene if it was in the gene or within 10 kb of it . Comparing these results to results based on mapping via genetic coordinates revealed the same clustering of diseases ( Figure S1 ) . Furthermore , in studying the loading of each gene , namely their contribution to each PC , we found that the genes with the top 50 average loadings on the first two PCs were significantly correlated ( r>0 . 67 , p-value<8 . 4×10−8 , Table S1 ) . Thus , in the main text we present results based on mapping by genetic distance as described above . To consider information from beyond only the most significant SNP in a gene , we also implemented the truncated tail strength [40] and the truncated product methods [41] to combine p-values in each gene in replacement of the minimum p-value , and followed a similar procedure for assigning new gene-level p-values . For the analyses presented in the following , results from all methods were similar though results with the minimum p-value approach clusters similar diseases better ( Figure S2 , S3 ) . We thus only report in the main text results from the minimum p-value approach . Code to carry out this procedure is publicly available at http://keinanlab . cb . bscb . cornell . edu/content/tools-data . Assume a matrix Z , a d×g matrix of the −log10 gene-level p-values , where d is the number of GWAS datasets , and g is the number of genes present in all datasets . We center the matrix by subtracting the column means from each column . Thus the centered matrix B has entries: ( 1 ) To obtain the PCs of matrix B , we must find the eigenvectors and eigenvalues of its covariance matrix BBT . Let vi be a vector of length d and let be a scalar . vi is the eigenvector and λ the eigenvalue of BBT if the following is satisfied: ( 2 ) The principal components of B are the normalized eigenvectors of its covariance matrix , BBT , where the eigenvectors are ordered such that the largest eigenvalue corresponds to the first principal component . Each eigenvector is additionally orthogonal to all other eigenvectors . Thus , from ( 2 ) , we can decompose BBT as follows: ( 3 ) Where the columns of U contain the principal components and ∑ is a diagonal matrix with entries equal to the eigenvalues of B's covariance matrix . One can similarly construct the singular value decomposition ( SVD ) of B . The SVD of B can be written as: ( 4 ) where V is a d×d matrix , D is a d×g diagonal matrix , and W is a g×g matrix . V and W contain the left and right singular vectors of B , respectively , and D contains the singular values of B in its diagonal . Substituting equation ( 4 ) for B in equation ( 3 ) , we find that ( 5 ) Thus , the principal components of B , the eigenvectors of its covariance matrix , are equivalent to the left singular vectors of B . In addition , the eigenvalues of B are equivalent to the square of its singular values . We applied SVD to the matrix B using the R [42] implementation of PCA/SVD ( prcomp ) , with no scaling of the data . Due to the heterogeneity of the GWAS datasets ( Table S2 ) , variation uncovered by PCA can also reflect differences in features such as genotyping array , association method , and sample size , rather than underlying disease risk genes . To ensure that these features did not influence our results , we first tested each gene for association with each of these features . Let zi = Zi , · be the vector corresponding to the association statistic for gene i across the d datasets . We considered a linear regression of zi as a function of the covariates: , where C1 , C2 , C3 are vectors of length d that represent the genotyping array , association method and the log10 of the sample size respectively , in each of the studies ( Table S2 ) . Testing the significance of regression coefficients can reveal genes that are associated with any of these potential confounders . In our following analysis , 19 genes were significantly associated with association method . However , genes not significantly associated to the above confounders may similarly have an effect . Hence , we also applied SVD ( as described above ) to the residualized matrix , namely matrix R with rows . We found that applying SVD to R results in the top PCs capturing a higher fraction of the variance of the data than when applied to the original matrix Z , though results are qualitatively similar between the two . We thus present results derived from the residualized matrix R . Resulting distances between datasets were assessed visually by plotting datasets in PC space . To quantify the clustering of datasets , we additionally applied hierarchical clustering in R [42] ( hclust ) to the Euclidean distance between pairs of datasets across the first two PCs . We simulated a matrix Z for two disease classes , each with 5 diseases ( A1 , A2 , A3 , A4 , A5 , B1 , B2 , B3 , B4 , B5 ) and 10 , 000 genes . In general , under the null hypothesis of a region containing no risk variant and assuming no confounding factors ( e . g . population stratification ) , p-values should be uniformly distributed between 0 and 1 . On the other hand , associated risk variants should be enriched for smaller p-values . We thus considered three sets of genes . The p-values for the first set of genes was drawn from the U ( 0 , 1 ) distribution for all diseases , thus no pleiotropy was captured in this set of genes . The second set of genes was distributed U ( 0 , 0 . 05 ) for the first disease class ( A1 , … , A5 ) and distributed U ( 0 , 1 ) for the second disease class ( B1 , … , B5 ) . Finally the third set of genes was distributed U ( 0 , 0 . 05 ) for the following diseases: A1 , A2 , B1 , B2 and distributed U ( 0 , 1 ) for all other diseases . Thus the second set of genes simulates pleiotropy between diseases in disease class A , while the last set of genes simulates pleiotropy between diseases in both disease classes . Disease enrichment analysis was completed using the online tool WebGestalt [43] , [44] to query the PharmGKB [45] database . WebGestalt tests for enrichment of a category of genes in the observed set of genes using the hypergeometric test [43] . Bonferroni correction for multiple tests was applied and all reported p-values are following this correction . We restricted analysis to categories that contained a minimum of 5 genes in our analysis with the largest 50 weightings in the top two PCs . For gene categories with overlapping or the same set of genes , we list the most significant category . To reduce biases introduced by the clustering of genes with similar function , we filtered our list of genes with the top 50 loadings on the top two PCs by removing the latter gene out of a pair of genes within 0 . 1 cM of each other . We then applied WebGestalt to this filtered subset of genes . Pathway enrichment analysis was completed using the Gene Set Enrichment Analysis ( GSEA ) tool [46] . GSEA sorts genes according to a score , which here is the weighting of a gene in the PC under study . It then assesses whether genes belonging to a certain category ( e . g . pathway ) are non-randomly distributed in the sorted list . As input to GSEA , we utilized the weights of genes in the top two PCs . GSEA carried out 10 , 000 gene-set permutations to determine FDR ( false discovery rate ) q-values . We queried the BioCarta and KEGG pathway databases . We restricted analysis to categories that contained a minimum of 5 genes in our analysis . Throughout we present enrichment analysis only for the top two PCs , though other PCs are available and can be assayed for further insight into the diseases studied . We considered an FDR of 0 . 25 , suggested by GSEA [46] ( GSEA manual online ) , though this entails that 1 in 4 of our results are false positives on average . As above , to reduce biases introduced by the clustering of genes with similar function , we filtered our full list of genes by removing the latter gene out of a pair of genes within 0 . 1 cM of each other and reanalyzed this subset of genes ( n = 5 , 298 ) with GSEA . We followed a similar approach to that implemented in Zhernakova et al . 2011 [21] while applying it to genes instead of individual SNPs to test for non-random distribution of association values . For each disease pair we retained all k genes that were nominally significant in one disease ( p-value<0 . 01 ) . We then tested the null hypothesis of a uniform distribution of p-values in the second disease using Fisher's method for combining p-values: , where pi is the p-value for association of gene i in the second disease . Nearby genes in linkage disequilibrium may violate the independency assumption in Fisher's method . We thus performed a separate analysis after removing the latter of the two genes that were within 0 . 1 cM of each other and nominally significant in one disease . We analyzed a total of 31 GWAS datasets [10] , [47]–[76] that spanned different types of cancers , autoimmune diseases , neurological disorders , psychiatric disorders , type-2 diabetes ( T2D ) , ischemic stroke and body mass index ( BMI ) ( Table S2 ) . Datasets were publicly available , obtained from dbGaP or obtained via collaborations . These datasets had non-overlapping samples and were of European ancestry only . For Wellcome Trust Case Control ( WT ) related datasets , we distributed controls between the five datasets such that none had overlapping samples . For WT type-1 diabetes , rheumatoid arthritis and Crohn's disease , we obtained further controls from the WT hypertension , cardiovascular disease and bipolar disorder case data [10] . After obtaining gene-level association statistics for 14 , 018–17 , 438 autosomal genes for each dataset , we limited our analysis to the 11 , 927 genes that overlapped all studies . Nineteen of these genes were significantly associated with association method after multiple-testing correction ( see above ) . We tested the replicability of disPCA when applied to real GWASs using six datasets for which we had access to the original data [10] , [57] , [60] , [61] , [74] , [75] . Each dataset was split into independent subsets of equal size ( +/− two samples ) . We then used PLINK's logistic regression [77] to evaluate association of each SNP to disease risk . We additionally incorporated covariates derived from EIGENSOFT into the regression analysis [78] to control for population structure . We randomly chose one subset of each of the six datasets for one disPCA analysis , and the rest for another . Hence , these two analyses consist of independent samples .
We first applied disPCA to a simulated dataset ( Materials and Methods ) . We varied the number of genes that have correlated association results across simulated datasets , thereby varying the level of pleiotropy between the simulated diseases . disPCA clearly clustered pleiotropic diseases when diseases shared at least 40 shared genes with p-values randomly distributed below 0 . 05 in each disease ( Figure 1a–b , S4 , S5 , S6 ) . This can be seen both visually via PCA plots , and via hierarchical clustering based on the Euclidean distance between datasets in the presented space of the first two principal components ( PCs ) ( Figure 1 , S4 , S5 , S6 ) . When diseases are indeed clustered by their simulated pleiotropy according to disPCA ( Figure 1b ) , the first two PCs explain a similar fraction of the variance ( Figure 1c ) , which may increase or decrease depending on the number of genes contributing to pleiotropy ( Figure S7 ) . We next examined the contribution of each gene to each PC as captured by its absolute “loading” . Considering the first two PCs in this disPCA analysis , genes with p-values<0 . 05 ( Materials and Methods ) are also enriched for larger absolute loadings , stressing their role in differentiating between the simulated disease classes ( Figure 1d–e ) . We next applied disPCA to empirical data from GWAS datasets . First , we considered only diseases for which we had two datasets: autoimmune diseases ( for which we had the most pairs of datasets ) and a pair of schizophrenia datasets ( as schizophrenia has a high heritability [79] ) . We observed that datasets of the same diseases were generally clustered together ( Figure 2–3 ) . We additionally observed that Crohn's disease is separated from other autoimmune diseases . This result is consistent with previous reports that inflammatory bowel disorders ( IBDs ) are distinct from other autoimmune disorders [29] . As in the simulated scenarios , the variance explained by each PC was similar ( Figure 2b ) , and the results suggest that less than a hundred genes contribute to the similarity between each pair of datasets ( Figure 3c–d ) . To test the replicability of the results , we further divided each of the six datasets , for which we had the raw data , into two subsets consisting of the same or similar number of cases and controls ( Materials and Methods ) . We then performed two disPCA analyses , one based on a randomly chosen subset of each of the six datasets , and another based on the remaining subset of each dataset . We found that both independent sets produced the same clustering of diseases ( Figure S8 , S9 ) . Loadings for 50 genes with the largest average loading across the two disPCA analyses of PC1 and PC2 were also significantly correlated across the two ( r>0 . 44 , p-value<1 . 2×10−3 , Table S3 ) . These results point to disPCA capturing some of the same pleiotropy in both cases , and further support the replicability of its results . We applied disPCA to a final set of 31 datasets ( Table S2 ) , including autoimmune diseases , cancers , obesity-related diseases and traits , psychiatric disorders and neurological disorders . The first two PCs capture visually-interpretable separation of diseases . PC1 for the most part splits the two systemic lupus erythematosus ( SLE ) and the one dataset of celiac disease from all other datasets ( Figure 4 ) . Independent of that separation , PC2 splits autoimmune diseases ( in purple ) from other diseases , and within autoimmune diseases , inflammatory bowel disorders ( Crohn's disease and ulcerative colitis ) are clustered together ( Figures 4–5 ) . Schizophrenia , major depressive disorder , cancers , T2D and neurological disorders lie on the negative end of PC2 , while attention deficit hyperactivity disorder ( ADHD ) , and some autoimmune diseases that are not well separated on this PC from other diseases , lie near the origin . PCs beyond the first two explain almost the same fraction of the variance ( Figure 4b ) and hence merit further investigation ( see Discussion ) . As disPCA teases out the important genes of shared and distinct pathogenetics across disease datasets , we next investigated which genes strongly contribute to each PC based on their absolute loadings . Specifically , we retrieved the genes with the top 50 absolute loadings for each of the top two PCs underlying Figure 4 and tested their disease enrichment ( Materials and Methods ) . The top genes underlying the first PC were significantly enriched for genes associated with lupus and autoimmune related diseases , while genes underlying the second PC were mostly enriched for association to IBD ( Table 1 ) . These enrichment results are consistent with the separation of studies across each of these 2 PCs with PC1 mostly separating studies of SLE and celiac diseases , and PC2 mostly separating studies of IBD from other diseases . The results were largely unchanged following filtering genes that were within 0 . 1 cM of each other to account for linkage disequilibrium and for similar genes being co-located to each other , such as gene families ( Table 1 ) ( Materials and Methods ) . Though the results of the disease enrichment analysis support that disPCA extracts biologically relevant signals , the arbitrary cutoff of the 50 top genes goes against the potential of PCs being linear combinations of all genes . We thus used GSEA [46] , which supports analyzing a pre-ranked list of all genes , to perform pathway enrichment of each PC . GSEA assesses whether genes belonging to a certain pathway are non-randomly distributed in the list of pre-ranked genes . We ranked all genes by the absolute loading in the PC under study . Results of this pathway analysis revealed enrichment for immune related pathways on the first 2 PCs ( Table 2 ) at an FDR of 0 . 25 . The top two pathways enriched on PC1 were the antigen processing and presentation and the intestinal immune network IgA production pathways , which are crucial immune-related pathways . In particular , intestinal IgA antibodies may have a role in celiac disease [80] and inflammatory bowel disease [81] , [82] . On PC2 , the most significant pathway was the NOD-like receptor signaling pathway . NOD-like receptors have been associated to Crohn's disease , while other immune-related genes likely interacting with NOD2 have been associated to ulcerative colitis [83] . Other immune system pathways were enriched , including the Fc epsilon RI signaling pathway that is related to the antibody IgE , which induces inflammatory response [84] . Two enriched pathways are related to neurons ( i . e . the neurotrophin signaling pathway and the Trk-A pathway ) . In particular , the neurotrophic factor BDNF ( brain-derived neurotrophic factor ) , which is a part of the neurotrophin pathway , has been previously associated to Alzheimer's , Parkinson's disease and depression [85]–[87] . More recently , an intronic variant in this gene has also been associated to BMI [88] . The contribution of genes in these pathways to PC2 may explain the separation of neurological , psychiatric and BMI studies along that PC . As above , we reran GSEA after filtering genes that were within 0 . 1 cM of each other ( Materials and Methods ) . The top two pathways on the first PC remained significant , while only the top pathway in PC2 remained significant ( Table S4 ) . This is likely due to the contribution to enrichment of several genes that are co-located , which should hence not necessarily be discounted . Many autoimmune diseases share associations from the HLA region . We thus reran disPCA after removing all genes in and around the HLA region , and found a slightly different visual PCA map ( Figure 6 ) . SLE and celiac disease were no longer distinguished from other autoimmune diseases and instead lay near the origin . PC1 now differentiated IBD from other diseases , and PC2 separated some autoimmune diseases from the rest on one extreme , and schizophrenia from the rest on the other . This was further supported by clustering results on the first two PCs ( Figure S10 ) . A GSEA analysis of the PC loadings retained the NOD-like receptor signaling pathway on PC1 instead of PC2 ( Table 3 ) . Analysis of PC2 loadings revealed additional immune related pathways that were not enriched in our previous analysis that included the HLA region . Results such as PC1 in the main analysis clustering schizophrenia close to some autoimmune diseases ( Figure 4 ) prompted us to further explore the shared pathogenetics between diseases by testing for the non-random distribution of gene-based p-values in one disease based on their nominal significance in another disease ( Materials and Methods ) . Generally , the results show that association statistics are non-randomly distributed when considering most pairs of autoimmune diseases , i . e . testing for non-random distribution in one autoimmune disease dataset based on significance in another autoimmune disease dataset ( Figure 7 ) . As a control , we tested for non-random distribution for a random set of genes and found that no disease pair was significant for non-random distribution ( Figure S11 ) . Our results reported a similar story as observed via disPCA . Genes nominally significant in rheumatoid arthritis , type-1 diabetes and ankyolosing spondylitis were non-randomly distributed in SLE and vice versa . We also found that genes nominally significant for one schizophrenia study were non-randomly distributed in a number of autoimmune diseases ( Figure 7 ) . These signals remained even after genes within 0 . 1 cM of another gene were removed ( Figure S12 ) ( Materials and Methods ) .
In this study we introduced a new method , disPCA , to explore the shared pathogenetics of various diseases and disease classes based on GWAS data . PCA has been widely used in population and medical genetics . Applied to genome-wide genotyping data , it can recapitulate population structure such as revealing European geography [89] , has been used as a tool to assess and correct for population stratification in GWAS [78] , [90] and has also been proposed as a tool for reducing the dimensionality of multiple phenotypes for association analysis [91] . Our disPCA method considers PCA on a different type of matrix , whereby different GWASs are studied in the space of all genes . It can group GWASs of different diseases together based on gene-level association statistics , while accounting for biases due to heterogeneity in sample size , association method , genotyping array and other confounders between studies . This implementation of PCA assigns weights to each gene and each PC in a manner that maximizes the variation between diseases . Hence , the higher the level of shared pathogenetics between diseases , the closer they will be in PC space . This is in contrast to methods that considered the correlation between diseases across all SNPs [29] . In fact , when we consider such correlations in our data , it is generally very low , even when considering it on the gene rather than on the SNP-level and even when the same disease is studied . For example , the correlation coefficient between the −log10 p-values of the two Crohn's disease studies is 0 . 048 , and it is 0 . 063 and 0 . 031 between ulcerative colitis and each of the two Crohn's disease studies . More generally , the highest correlation between pairs of datasets of the same disease was obtained for schizophrenia ( 0 . 13 , p-value = 2 . 2×10−16 ) while the lowest was obtained for type-2 diabetes ( 0 . 0031 , p-value = 0 . 73 ) . These results show that there is less power when aggregating information across all genes and that disPCA is able to tease out and weigh the suitable set of genes underlying shared pathogenetics . Though disPCA is designed to uncover shared disease etiology between diseases , other sources of correlation between datasets can also contribute to its results . Potential confounders include shared samples between datasets , technical artifacts , and population structure ( if risk factors vary across ancestry ) . We accounted for technical artifacts introduced by the genotyping array , association method and sample size by regressing out variation in the data attributed to these sources ( Materials and Methods ) . To minimize the impact of population structure and shared samples , we only applied disPCA to studies of individuals of European ancestry and datasets that had no overlapping case or control data . Though we cannot account for other potential confounders that are unknown , our results strongly suggest that the remaining correlation between studies represent shared disease etiology . We applied disPCA to data from 31 GWASs that cover a range of diseases in four main classes: autoimmune diseases , cancers , neurological disorders and psychiatric disorders . We additionally analyzed GWASs on T2D , BMI and ischemic stroke . We first observed that different studies of the same diseases tend to lie closer together on the lead PCs ( Figure 2 ) . This is in support of studies of the same disease replicating many of the same signals of associations when samples are of similar ancestry . We additionally find that disPCA positions diseases within the same class closer together ( Figure 4 ) . This was especially the case for the major types of IBDs ( i . e . Crohn's disease and ulcerative colitis ) , which clustered close together ( Figure 5 ) . This points to distinct etiology shared between IBDs , that is not shared between IBDs and most other autoimmune diseases . Indeed , it has recently been suggested that IBD is at least in part a primary immunodeficiency disorder [92] , [93] . Between the different disease classes , the main 2 PCs in disPCA found overlap between non-autoimmune diseases and traits , as well as pointed to a potential connection between schizophrenia and some autoimmune diseases . Using the weightings of genes on each of the leading PCs , we performed disease and pathway enrichment analysis . We found that PC1 , which mainly splits some autoimmune disorders from other autoimmune disorders , is significantly enriched for genes associated to immune and autoimmune disorders . PC2 , which splits IBD studies from studies of other diseases , is significantly enriched for genes in some inflammatory related pathways and genes associated with IBD . Further results in PC2 highlighted neuron-related pathways that can be in line with evidence that abnormal neurotrophins levels in the brain have been associated to schizophrenia [94] , [95] . Excluding the HLA region revealed significant enrichment for genes in other immune-related pathways . Though the specific analysis presented in this paper focused on the top two PCs , further PCs estimated by disPCA can be examined . For example , PC4 of disPCA on all GWASs distinguishes rheumatoid arthritis from other diseases ( Figure S13 ) . Pathway enrichment analysis highlighted the calcineurin pathway ( FDR = 0 . 182 ) , which is involved in T-cell activation . Additionally , though schizophrenia and vitiligo datasets are further apart on the first two PCs , each pair of datasets is clustered closer together on PC3 and PC4 . Altogether , these results support the validity of the enrichment analysis based on disPCA . The analysis in turn also raises new hypotheses of disease etiology by pointing to additional pathways and enrichment for other diseases that were not previously observed . Prompted by the results of disPCA , we further explored shared pathogenetics by testing for the non-random distribution of association statistics between pairs of disease studies ( Figure 7 ) . Autoimmune diseases show non-random distribution of association statistics with one another . Interestingly , genes nominally associated with one of the schizophrenia studies were non-randomly distributed in studies of several autoimmune diseases ( i . e . ankyolosing spondylitis , systemic lupus erythematosus , and T1D ) , in support of the above disPCA results . Interestingly , this relationship was only observed for one of the two schizophrenia studies we analyzed , which may be due to a number of factors , including high number of risk factors for schizophrenia , with different ones being associated in different studies . If indeed autoimmune diseases and schizophrenia share disease etiology , then just as one would not include individuals with ulcerative colitis as controls for a Crohn's disease GWAS since they both are IBDs , one should also be wary of including individuals with autoimmune disorders in a schizophrenia GWAS ( and vice versa ) as doing so may decrease power in loci implicated in both diseases . Lack of power due to such or other reasons might also underlie our lack of observation of significant shared etiology between the second schizophrenia dataset and autoimmune diseases . Finally , we make a few recommendations for future applications of disPCA to additional studies: ( 1 ) Biases can be introduced when studies share sample data; ( 2 ) As disPCA maximizes variance across diseases , genes that are implicated in all analyzed diseases will not contribute to the lead PC as they do not distinguish diseases from each other; ( 3 ) While here we only focused on using the strength of association and on gene-level signals , the method itself is highly flexible . One can further utilize the direction of association ( protective versus deleterious ) , the heritability at each locus [96] , an analysis at the pathway-level or in linkage disequilibrium blocks , include other non-genic functional elements , and/or environmental risk factors; ( 4 ) disPCA can be used to generate new hypotheses , which can then be tested by conducting more focused association studies in independent data or by using its output to better combine different diseases in an independent meta-analysis . New hypotheses can also be generated with regard to the genes that contribute to comorbidity between diseases . In conclusion , disPCA offers users a unique general overview of the disease landscape by studying their distinct and shared pathogenetics and flagging pathways and genes for further investigation . disPCA's flexibility and computational efficiency proves itself as an excellent tool to be applied to additional diseases and disease classes to further our knowledge of shared pathogenetics . | Epidemiological studies have revealed distinct diseases that tend to co-occur in individuals . As genome-wide association studies ( GWASs ) have increased in numbers , more evidence regarding the genetic nature of this shared disease etiology is revealed . Here , we present a novel method that utilizes principal component analysis ( PCA ) to explore the relationships and shared pathogenesis between distinct diseases and disease classes . PCA groups and distinguishes between data points by uncovering hidden axes of variation . Applying PCA to 31 GWASs of autoimmune diseases , cancers , psychiatric disorders , neurological disorders , other diseases and body mass index , we report several findings . Diseases of similar classes are located near each other , supporting the genetic component of shared disease etiology . Genes that contributed to distinguishing between diseases are enriched for various pathways including those related to the immune system . These results further our knowledge of the genetic component of shared pathogenesis , highlight possible pathways involved and provide new guidelines for future genetic association studies . | [
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| 2014 | Principal Component Analysis Characterizes Shared Pathogenetics from Genome-Wide Association Studies |
A common complementary strategy in Genome-Wide Association Studies ( GWAS ) is to perform Gene Set Analysis ( GSA ) , which tests for the association between one phenotype of interest and an entire set of Single Nucleotide Polymorphisms ( SNPs ) residing in selected genes . While there exist many tools for performing GSA , popular methods often include a number of ad-hoc steps that are difficult to justify statistically , provide complicated interpretations based on permutation inference , and demonstrate poor operating characteristics . Additionally , the lack of gold standard gene set lists can produce misleading results and create difficulties in comparing analyses even across the same phenotype . We introduce the Generalized Berk-Jones ( GBJ ) statistic for GSA , a permutation-free parametric framework that offers asymptotic power guarantees in certain set-based testing settings . To adjust for confounding introduced by different gene set lists , we further develop a GBJ step-down inference technique that can discriminate between gene sets driven to significance by single genes and those demonstrating group-level effects . We compare GBJ to popular alternatives through simulation and re-analysis of summary statistics from a large breast cancer GWAS , and we show how GBJ can increase power by incorporating information from multiple signals in the same gene . In addition , we illustrate how breast cancer pathway analysis can be confounded by the frequency of FGFR2 in pathway lists . Our approach is further validated on two other datasets of summary statistics generated from GWAS of height and schizophrenia .
A common objective in genetic association studies is to search for associations between phenotypes and genomic constructs that are larger than a Single Nucleotide Polymorphism ( SNP ) . One popular unit of analysis is the set of all SNPs that are located near a list of related genes; inference on these sets is generally referred to as gene set analysis ( GSA ) or pathway analysis [1] . In recent years , GSA has successfully identified novel gene sets associated with a wide range of outcomes [2–4] . GSA does not yet possess the popularity of individual SNP approaches [5] such as the Genome-Wide Association Study ( GWAS ) , but there are advantages to testing for associations at a higher level [6] . Many biological processes are driven by mechanisms involving more than one variant , and thus set-based inference may offer more useful interpretations [7] . In addition , set-based inference can increase power over individual-SNP methods by pooling many weaker pieces of evidence into a larger , more detectable signal [8] . GSA can also improve power by alleviating the multiple testing burden of GWAS [9] . However , realizing the aforementioned benefits is hampered by a lack of consensus surrounding the most suitable methods for GSA . The literature contains dozens of tools [10–18] for performing gene set analysis , but there is little agreement on how to choose between so many competing ideas [19–21] . Existing GSA methods are frequently cited for flaws including insufficient power [22] , an inability to provide statistically valid tests under certain parameter settings [23] , and a reliance on permutation-based inference [24] . More specifically , many existing methods fail to control the Type I error rate for genes with unconventional characteristics—for example , genes with a small number of SNPs or a large amount of correlation [25 , 26] . Permutation offers a valid solution , but complicated resampling schemes often muddle the null hypotheses being tested and result in confusing interpretations [18 , 27] . Permutation can also be extremely computationally expensive when attempting to control for multiple testing , e . g . a Bonferroni correction to control the family wise error rate over 10 , 000 tested pathways requires approximately ten million iterations . Another key challenge , which few methods have attempted to address , is the lack of standardized gene set lists in the public domain [28 , 29] . While there exist multiple sources for such information , gene set definitions can be highly differentiated across databases , and small differences can lead to large inconsistencies in results . As a representative example , we consider the case of the FGFR2 gene in breast cancer . In the largest breast cancer GWAS cohort to date , SNPs near FGFR2 demonstrate association at p < 10−300; these are the smallest p-values across the entire genome . Subsequent pathway analysis of this GWAS [30] tests approximately 4 , 000 pathways—182 containing FGFR2—and concludes that 86 of the top 100 pathways all contain FGFR2 . Clearly a pathway is extremely likely to be found significant if it contains FGFR2 , and thus pathways unrelated to breast cancer may be artificially driven to the top of the results based on the decision to include or exclude a single gene . For instance , the Gene Ontology [31] pathway Ear Morphogenesis includes FGFR2 and is ranked among the top 100 most significant gene sets , but as we will show later , the same pathway defined without FGFR2 possesses minimal association with breast cancer . Pathway lists that do not include FGFR2 in their version of an ear morphogenesis pathway may have difficulty replicating a seemingly strong association . In this paper we make three key contributions toward overcoming the above challenges . First , we introduce a class of supremum-based goodness-of-fit tests for gene set analysis , demonstrating how they can be adapted for use in the GSA framework and focusing in particular on use of the Generalized Berk-Jones ( GBJ ) statistic . Originally developed for optimal detection of sparse signals in independent data [32] , the aforementioned class includes the Higher Criticism ( HC ) and Berk-Jones ( BJ ) statistics , which have been adapted for correlated data through the Generalized Higher Criticism ( GHC ) and GBJ . These statistics possess , in a certain sense , optimal power for detection of a set-based effect when many elements of the set may individually demonstrate no association , as in testing for the effect of a gene set that may contain an appreciable subset of neutral variants . Among tests derived from this class of statistics , GBJ demonstrates more robust and powerful performance than the Generalized Higher Criticism [33] when testing gene sets in a range of practical settings [34] , while other tests in the class have not yet been adapted to account for correlation . Unlike other GSA methods that rely on permutation to adjust for correlation and frequently summarize information at the gene level with a single value [28] , GHC and GBJ admit analytic p-value calculations that avoid permutation and automatically account for features such as the size of a gene set and LD patterns between SNPs . Both tests have also been shown to protect Type I error rates at very stringent levels [34] . Secondly , we propose a step-down GSA inference procedure that can identify gene sets driven to significance solely by a few genes , as opposed to gene sets containing signals spread throughout the entire group . This procedure relies on the self-contained [6] , one-step nature of GBJ , which allows for a unified approach to testing the association between single genes and the outcome . Re-analyzing a set after removal of its most significant genes can uncover the gene sets and themes that will still demonstrate replicable associations over different pathway databases . Such sets are also arguably more important from a biological standpoint . Thirdly , we illustrate the utility of both GBJ and the step-down procedure through simulation and re-analysis of summary statistics from a large breast cancer GWAS dataset . Simulation demonstrates the additional power of GBJ over alternatives including GHC , the popular Gene Set Enrichment Analysis ( GSEA ) [11] , and MAGMA [18] . Simulations also show that the step-down procedure is adept at distinguishing between pathways with single-gene signals and those containing multi-gene signals . Review of the breast cancer step-down results illustrates that many seemingly significant sets are completely dependent upon FGFR2 and a few other genes for their strong association signals . These sets deserve further screening before their significant association with breast cancer is reported . We conclude with an application of GBJ to cross-phenotype analysis of breast cancer , height , and schizophrenia , and we investigate certain gene set properties that have received less attention in the literature . This work finds that the pathways significantly associated with human height are much more likely to contain signals spread throughout an entire gene set , while pathways associated with breast cancer are more likely to see their signals localized to a few genes . We additionally observe that immune system pathways are highly associated with schizophrenia , while growth pathways are often linked with height and breast cancer .
The Generalized Berk-Jones statistic provides a powerful parametric approach for testing the association between a set of SNPs and a phenotype using marginal SNP summary statistics . Consider a gene set that contains d SNPs . The d summary statistics for these SNPs , Z = ( Z1 , … , Zd ) T , follow a joint multivariate normal distribution , Z ∼ M V N ( μ Z , Σ ) . GBJ aims to robustly test H0: μZ = 0d×1 against H1: μZ ≠ 0d×1 when μZ contains a subset of zeros and while accounting for the correlation between test statistics . Thus common GSA features such as LD between SNPs and multiple neutral variants are automatically incorporated into the statistical framework . GHC and other tests in the goodness-of-fit class operate similarly , but for reasons of space , we will limit comparisons to simulation results . The null hypothesis corresponds to the situation where no SNPs in the entire set are associated with the outcome after correction for confounders . When performing GSA with genotype-level data for each subject it is necessary to both calculate Z and estimate Σ , and when summary statistics are available , it is only necessary to estimate Σ . Suppose we have genotype-level data for i = 1 , 2 , … , n subjects at a set of j = 1 , 2 , … , d SNPs , so that the genotype vector for subject i is Gi = ( Gi1 , … , Gid ) T . Let G = [G1 , … , Gn]T be the n × d genotype matrix . Suppose also that we have a set of q additional covariates contained in X = [X1 , … , Xn]T , which is an n × q matrix with Xi = ( Xi1 , … , Xiq ) T for i = 1 , … , n . Denote the outcome by Yi and let μi be the mean of Yi conditional on Gi and Xi . Consider the generalized linear model [35] ( GLM ) for μi given by g ( μ i ) = α 0 + α T X i + β T G i , where g ( ⋅ ) is a canonical link function , for instance , g ( μi ) = μi for normally distributed phenotypes and g ( μi ) = logit ( μi ) for binary phenotypes . We are interested in testing the null hypothesis of no gene set effect H0: β = 0d×1 against the alternative H1: β ≠ 0d×1 . As some SNPs in a gene set are likely neutral variants , we allow elements of β to equal 0 under H1 . The marginal score test statistic under the null is Z j = G . j T ( Y - μ ^ 0 ) G . j T P G . j ( 1 ) for any SNP j = 1 , … , d in the gene set . Here Y = ( Y1 , ‥ , Yn ) T , μ ^ 0 = { μ ^ 01 , … , μ ^ 0 n } T = { g ( α ^ 0 T X 1 ) , … , g ( α ^ 0 T X n ) } T , and α ^ 0 is the MLE of α under H0: β = 0d×1 . Also we define the single variant vector G·j = ( G1j , … , Gnj ) T , the projection matrix P = W − WX ( XT WX ) −1 XT W , and the GLM weight matrix W = diag { a 1 ( ϕ ^ ) v ( μ ^ 01 ) , … , a n ( ϕ ^ ) v ( μ ^ 0 n ) } . The standard GLM dispersion parameter is given by a i ( ϕ ^ ) , and the standard GLM variance function is v ( μ ^ 0 i ) , where v ( μ ^ 0 i ) = 1 for a normally distributed phenotype and v ( μ ^ 0 i ) = μ ^ 0 i ( 1 - μ ^ 0 i ) for a binary phenotype . The Zj are asymptotically equivalent to the individual SNP test statistics calculated by many popular tools , such as the Wald statistics produced by PLINK [36] . A consistent estimate of the correlation between Zj and Zk is then given by Σ ^ j k = G . j T P G . k G . j T P G . j G . k T P G . k . ( 2 ) Note that H0: β = 0d×1 in the regression model corresponds to the null hypothesis H0: μZ = 0d×1 from above . This relationship can be derived directly by calculating the expectation of the score statistic in Eq ( 1 ) as a function of β . The multivariate normality and covariance matrix follow from a Taylor expansion [37] of Eq ( 1 ) . Thus testing for an effect in the gene set corresponds directly to testing for a non-zero mean among the multivariate normal test statistics . Precalculated marginal summary statistics are much more available than subject-level data . When using these summary statistics , we need to estimate their correlation structure from a reference LD panel containing individuals of a similar ethnicity , e . g . data from the 1000 Genomes Project [38] . Specifically , let G . j ( r ) and G . k ( r ) denote the genotype of each subject in the reference panel at SNPs j and k . Also let X ( r ) = ( 1 , PC1 , … , PCm ) denote a modified design matrix , where m is the same number of principal components used in the original analysis , and PC1 , ‥ , PCm are principal components calculated from the reference data . Using X ( r ) instead of X , ( G . j ( r ) , G . k ( r ) ) instead of ( G . j , G . k ) , and any constant in place of g ( α ^ 0 T X i ) in Eq ( 2 ) provides a good approximation to Σ ^ j k . The motivation for this approximation arises from the observation that Eq ( 2 ) is exactly the correlation structure of the genotypes in the set if X includes only an intercept term . The correlation structure of the genotypes can be well-approximated by a reference panel . When X does include PCs or non-genetic terms , the PCs can still be well-approximated from a reference panel , and non-genetic terms often demonstrate negligible correlation with the genotypes , so they will possess minimal impact on the estimation of Eq ( 2 ) . Using a constant for g ( α ^ 0 T X i ) is appropriate because the fitted mean generally does not vary much with the PCs , and since our approximation utilizes a design matrix with only the PCs , g ( α ^ 0 T X i ) will be close to the same value for each subject i . Next assume we have calculated or been supplied a vector of test statistics Z ∼ H 0 N ( 0 d × 1 , Σ ) . Denote by Φ ¯ ( t ) = 1 - Φ ( t ) the survival function of a standard normal random variable , with Φ−1 ( t ) denoting its inverse . Further designate |Z| ( j ) as the order statistics of the vector that arises from applying the absolute value operator to each element of Z , so that |Z| ( 1 ) is the smallest element of Z in absolute value . Finally define the significance thresholding function S ( t ) = ∑ j = 1 d 1 ( | Z j | ≥ t ) , where t > 0 is the threshold . It is helpful to think of S ( t ) as the “number of significant SNPs at threshold t . ” For example , in GWAS , some researchers set t = 5 . 45131 so that S ( t ) counts the number of SNPs with p-values less than 5 × 10−8 , the commonly-used cutoff for declaring genome-wide significance [39] . Other set-based methods implicitly set t equal to |Z| ( d ) and carry forward |Z| ( d ) as the representative test statistic for the entire set [23] . However in both of these examples , the choice of t is rather arbitrary and relies on a one-size-fits-all-sets approach . In particular , neither choice of t makes full use of the data , ignoring factors such as the size of the set and the LD pattern among SNPs . More importantly , moderately significant SNPs that do not reach a GWAS threshold or demonstrate the lowest p-value in a gene can cumulatively produce a major contribution to the phenotype . A key concept behind GBJ is that it can adaptively find the threshold t that best maximizes power for any given set while adjusting for the size of the set and the correlation structure of the SNPs . Consider first the case of no LD , Σ = I , where I is the identity matrix . Then for a fixed t under the null hypothesis , S ( t ) has the binomial distribution S ( t ) ∼Bin ( d , π ) with π = 2 Φ ¯ ( t ) . This observation motivates the Berk-Jones ( BJ ) statistic [40] , which can be written as [41] , B J d = max 1 ≤ j ≤ d / 2 log [ Pr { S ( | Z | ( d - j + 1 ) ) = j | E ( Z ) = μ ^ j , d · J d } Pr { S ( | Z | ( d - j + 1 ) ) = j | E ( Z ) = 0 · J d } ] × 1 { 2 Φ ¯ ( | Z | ( d - j + 1 ) ) < j d } where J d T = ( 1 , 1 , … , 1 ) 1 × d , and μ ^ j , d > 0 solves the equation j / d = 1 - { Φ ( | Z | ( d - j + 1 ) - μ ^ j , d ) - Φ ( - | Z | ( d - j + 1 ) - μ ^ j , d ) } . In other words , Berk-Jones is the maximum of a set of likelihood ratio tests performed on S ( t ) at all observed test statistic magnitudes greater than or equal to the median observed magnitude . This interpretation relies on the characterization of S ( t ) as a binomial random variable , which we further emphasize by defining μ ^ j , d so that the indicator functions 1 ( |Zj| ≥ t ) may be viewed as the identically distributed binary variables that constitute a binomial variable . While the BJ statistic can be written without μ ^ j , d , we find that this notation helps simplify the interpretation and also provides a natural transition to the GBJ . By taking the maximum of the likelihood ratio test over many possible thresholds , Berk-Jones allows the data to set the threshold that provides the most power in the presence of an appreciable subset of neutral variants . When SNPs in a gene set are in LD and Σ ≠ I , then S ( t ) no longer has a binomial distribution , and the Berk-Jones statistic can lose much of its power in finite samples . However the Generalized Berk-Jones incorporates the additional correlation information by explicitly conditioning on Σ , G B J d = max 1 ≤ j ≤ d / 2 log [ Pr { S ( | Z | ( d - j + 1 ) ) = j | E ( Z ) = μ ^ j , d · J d , cov ( Z ) = Σ } Pr { S ( | Z | ( d - j + 1 ) ) = j | E ( Z ) = 0 · J d , cov ( Z ) = Σ } ] × 1 { 2 Φ ¯ ( | Z | ( d - j + 1 ) ) < j d } . GBJ is still the maximum of a set of likelihood ratio type tests , but it gains notable power over BJ by accounting for the correlation between test statistics . When Σ = I , GBJ reduces to the standard Berk-Jones . The p-value of the GBJ statistic can be calculated analytically . In integrating the test statistic for each SNP in the gene set , GBJ incorporates much more information than methods that only keep the most significant p-value in each gene [11 , 14 , 16] and discard the rest in an ad-hoc fashion . Other tests may not discard individual test statistics but instead summarize the values in mean [12] or count-based procedures [14 , 15] so that the individual magnitudes are lost . For example , test statistics of Z1 = −2 . 5 and Z2 = 5 make equal contributions to a procedure that counts the number of p-values less than 0 . 05 . However , the variant with test statistic Z2 = 5 clearly conveys more information about genotype-phenotype association than the variant with Z1 = −2 . 5 . In contrast , GBJ does not discard or summarize information and utilizes each marginal test statistic , as well as the joint correlation structure . When Σ = I , GBJ enjoys certain asymptotic power properties that other tests may not demonstrate [41] . As an extension of GBJ , we propose a step-down inference procedure to filter out gene sets that are driven to significance based on the signal from only a very small proportion of genes , such as the earlier example involving Ear Morphogenesis and FGFR2 . The procedure begins by performing gene-level association analysis . First , create a list of the unique genes over all gene sets under consideration , then define each gene as its own set and apply GBJ over each single gene to find single gene p-values . Sort the single genes in increasing order of p-value , which can be interpreted as ranking the genes by their level of association with the outcome . For any given gene set , obtain a measure of how much its association signal is dependent on a few highly associated genes by applying GBJ to the set after removing all SNPs that belong to the top k genes . Setting k = 1 will identify gene sets where the signal is driven by a single gene . If a gene set remains significant even after removing k > 1 genes , then the set possesses signals dispersed through many different genes . In this work we will use k = 1 and k = 3 . As we show below , FGFR2 drives the significance of many gene sets in a breast cancer analysis , but the step-down procedure allows us to uncover pathways that show no association outside of FGFR2 and may be less suitable for follow-up . Three large , publicly available summary statistic datasets are analyzed in this study . We first obtained summary statistics from the largest breast cancer GWAS cohort to date [30] , with 122 , 977 cases and 105 , 974 controls of European ancestry . Most subjects were genotyped on the OncoArray , a custom-designed array for cancer studies that also has genome-wide coverage of over 570 , 000 SNPs . Data for these subjects were imputed using the full 1000 Genomes Project Phase 3 reference panel , resulting in estimated genotypes for approximately 21 million variants . Other subjects were included from various smaller studies , including the iCOGS project [42] and 11 smaller GWAS . Results across studies were then meta-analyzed , and after quality control , approximately 12 million SNPs produced a final test statistic for association with breast cancer . For height , we downloaded summary statistics from the Genetic Investigation of Anthropometric Traits ( GIANT ) GWAS [43] . In this study , 253 , 288 individuals of European ancestry were genotyped on multiple Affymetrix , Illumina , and Perlegen arrays . All individuals were then imputed to the Phase II CEU HapMap release . After meta-analysis and quality control , there were over 2 . 5 million SNPs with a final summary statistic for association with height . The last dataset used was downloaded from the Psychiatric Genomics Consortium schizophrenia mega-analysis [44] . In the primary GWAS of this study , 34 , 241 cases and 45 , 604 controls were genotyped across 49 cohorts . The vast majority of samples were obtained from subjects of European descent , but three cohorts did contain individuals of East Asian ancestry . All subjects were imputed using 1000 Genomes Project data as a reference panel , and test statistics were meta-analyzed across cohorts . For this study , summary statistics were made available for approximately 9 . 5 million variants . To provide a fair comparison between GBJ and GSEA in re-analysis of the breast cancer data , we conduct all pathway analysis using the same gene set database ( Gary Bader Lab , Human GOBP all pathways , no GO IEA; April 1 , 2017 ) used in the original breast cancer pathway analysis [30] . This file compiles gene sets from a number of databases including Gene Ontology ( GO ) Biological Process [31] , Reactome [45] , Panther [46] , and others . In all , the file contains 16 , 528 gene sets . While we are unaware of a comprehensive method to assess the quality of gene set databases , advantages of this list include the incorporation of multiple different sources , public availability , and monthly updates . As the selected pathway database is a direct aggregation of multiple sources possessing varying levels of curation , some preprocessing of the list is necessary before beginning analysis . We first truncate the database to remove all pathways with more than 200 genes or less than three genes . Removing pathways with a large number of genes is common practice [47 , 48] , as extremely large pathways are difficult to interpret , and this step was performed in the original GSEA analysis as well . The original GSEA analysis further removed all pathways with fewer than ten genes , which is also a relatively common strategy to reduce false positives and lower the multiple testing burden [49] , however it has been noted that this threshold may exclude certain specific and informative functional sets such as protein complexes [26] . We choose to set the lower limit for pathways at three genes because there may be interesting insights to be gleaned from smaller gene sets and because we believe GBJ is powerful enough to overcome the increased multiplicity burden . In total , there are 10 , 742 pathways with between three and 200 genes . For each set of summary statistics , we map individual SNP test statistics to gene sets if they lie within 5 kb of a gene in the set , with coordinates provided by Ensembl 90 gene annotations [50] . Estimates of the correlation between summary statistics are calculated using unrelated subjects from European cohorts ( TSI , FIN , GBR , IBS , CEU ) in the 1000 Genomes Phase 3 data release . Summary statistics belonging to SNPs that have minor allele frequency less than 3% in the reference panel are removed as their data would be unstable for estimation . The original GSEA analysis maps SNPs to genes in a slightly different manner and also performs some additional manual curation . These additional steps are unique to the breast cancer dataset , and we do not include them here to preserve generality and facilitate comparison of results across traits . To further reduce the computational burden of a GBJ analysis , we additionally trim SNPs that are in the same gene and are correlated at r2 > 0 . 5 . This pruning is performed in PLINK and occurs at successive multiples of 0 . 5 for very large sets , so that all tested sets are less than 1 , 500 SNPs in size . Even after the pruning procedure , GBJ still incorporates a very large amount of information , as the median number of SNPs in our breast cancer analysis is 470 . In contrast , the median number of genes in the GSEA analysis is 26; a test on 26 genes with GSEA incorporates only the 26 minimum p-values from those genes . The data-intensive nature of GBJ does pose problems for a small number of extremely large pathways . Some gene sets are larger than 1 , 500 SNPs even after pruning all pairs of SNPs in the same gene with r2 > 0 . 0625 . These sets are not tested to remain consistent in our analysis protocol across all three phenotypes . However for typical GSA focusing on a single outcome , it would be straightforward to perform additional pruning or manual curation to accommodate testing the largest pathways . We use simulation to compare the performance of GBJ against the self-contained version of GSEA ( as described by Wang et al . [11] ) , self-contained MAGMA , GHC , and SKAT [51] . GSEA and MAGMA are two of the best-performing methods in the comprehensive simulation study of a recent GSA review [28] . SKAT is not often mentioned in the GSA literature , possibly because it was developed as a test requiring individual-level genotype data [51 , 52] , but it is known to demonstrate excellent set-based testing power and can be implemented with summary statistics if the same correlation matrix approximation we have introduced for GBJ is applied . To match the settings of our real data analysis , we use genotype data from the reference panel of n = 350 unrelated Europeans in the 1000 Genomes Project . These genotypes have been pruned as described above , and we use all SNPs located in 10 , 000 genes chosen at random . Each of the 10 , 000 genes contains between 7 and 25 SNPs , which corresponds to the middle 50 percentile of pruned gene size over the entire gene database . For each iteration of the power simulation , we choose 10 genes at random to be the tested pathway , and a of the 10 genes are given b causal SNPs each . The true disease model is then Y i = ∑ j = 1 a * b β G i j + ϵ i , ϵ i ∼ N ( 0 , 1 ) , where Gij is the genotype of subject i at causal SNP j . We study various sparsity levels , causal SNP configurations , and causal effect sizes , demonstrating how the relative performance of each test can vary widely depending on the number and location of signals in a set . We also consider random placement of causal SNPs throughout the pathway . Testing is performed at at α = 0 . 01 with 100 runs performed at each parameter setting except for a Type I error simulation with 4 , 000 runs performed at β = 0 . Marginal summary statistics for each SNP in the pathway are calculated according to Eq ( 1 ) . As the SNPs used in simulation are correlated due to linkage disequilibrium , the test statistics calculated in these simulations will also be correlated . The joint covariance matrix of the test statistics is estimated according to Eq ( 2 ) , and this estimate is used for the GBJ and GHC statistics . A simulation demonstrating the accuracy of our proposed approximation to Eq ( 2 ) for precalculated summary statistics uses a separate breast cancer dataset [53 , 54] and is described in S1 Appendix . MAGMA is applied with default self-contained settings , and 500 permutations are used for GSEA inference . SKAT is implemented with default settings as well . We also perform a simulation to assess the validity of the step-down inference procedure . Data is generated as in the power simulation , but in each iteration we remove the most significant gene—chosen separately for each test—from the pathway before generating a pathway p-value . For GBJ , GHC , MAGMA , and SKAT , we use the default gene-level analysis to determine the significance of each gene , and for GSEA we remove the gene with the most significant SNP . In the simulations with only one causal gene ( a = 1 ) we are benchmarking the discriminatory ability of this procedure , as the step-down procedure should remove the only causal SNPs , resulting in power equal to the Type I error rate regardless of effect size . In settings with a > 1 , we can assess power to identify gene sets with dispersed signals . To summarize our results from applying GBJ across multiple phenotypes , we search for the biological systems that demonstrate the largest degrees of significance across each phenotype . Pathways are categorized into different systems by exploiting the directed acyclic graph structure of Gene Ontology ( GO ) Biological Process pathways . Starting with the top-level Biological Process category , GO defines successively smaller groups of pathways so that each child term is more specialized than its parent term . Using this natural structure , it is possible to group categories of pathways at different levels of granularity . We create categories from the first level immediately following the Biological Process root . Specifically , we use the 11 top-level sets Biological Adhesion , Cellular Component Organization or Biogenesis , Developmental Process , Growth , Immune System Process , Localization , Locomotion , Metabolic Process , Reproduction , Response to Stimulus , and Signaling . For a given phenotype and category , we first calculate the expected number of significant pathways in the category conditional on the total number of significant pathways for the outcome . If pathways in each category truly have the same chance of reaching significance , then the expectation is simply equivalent to the percentage of all tested pathways that belong to the category multiplied by the total number of pathways significantly associated with the outcome . For each phenotype , we calculate the difference between the observed and expected number of significant pathways arising from each category , taken as a percentage of the expected number , to determine which categories harbor more significant pathways than expected . Pathways are deemed significant using the Bonferroni-corrected significance level of α = 0 . 05/10 , 742 = 4 . 65 ⋅ 10−6 .
We first compared the power of GBJ to other gene set methods through simulations carried out with genotypes from the 1000 Genomes Project . One clear trend from these results was the impact of signal configuration on the relative power of each test . Because non-GSEA tests utilized data from multiple SNPs in each gene , we expected such tests to perform better at detecting signal configurations where multiple signals were placed into a single gene . However , even when each gene held only one signal ( Fig 1B and 1D ) , a situation that would appear very favorable for GSEA , we found that GSEA rarely achieved power close to the best test . GBJ , GHC , and SKAT generally performed well in these settings , and MAGMA often lagged behind GSEA . When signals were more densely packed into a smaller number of genes ( Fig 1A and 1C ) , GBJ , GHC , and SKAT increased their advantage over GSEA and MAGMA significantly , demonstrating the ability of these tests to account for grouped signal configurations . GBJ appeared to show the most robust performance across different signal configurations , never falling too far behind the best-performing test , unlike GHC ( Fig 1D ) or SKAT ( Fig 1A ) . Another clear trend was the difference in relative power as signal sparsity was modified . GHC , GBJ , and SKAT were previously observed [33 , 34] to demonstrate their best performance in very sparse ( number of signals less than d 1 4 ) , moderately sparse ( number of signals between d 1 4 and d 1 2 ) , and dense ( number of signals greater than d 1 2 ) settings , respectively , and we noted similar trends in our simulations . The number of SNPs in each simulated gene set was determined randomly but generally fell in the low hundreds , and so the very sparse regime included scenarios with four or less causal SNPs . The moderately sparse regime spanned from approximately five to 14 causal SNPs , and about 15 or more signals constituted a dense setting . GHC frequently demonstrated the most power in the very sparse settings ( S1 Fig ) , although GBJ followed closely and even overtook GHC in certain scenarios . SKAT , GSEA , and MAGMA performed much worse , an expected development given that these three tests were not specifically developed to detect sparse signals . Under the moderately sparse settings of Fig 1 , GBJ possessed the most power more often that any other test . With more abundant signals ( S2 Fig ) , SKAT and GBJ generally showed the most power , and GHC notably lagged behind in certain configurations ( S2C and S2D Fig ) . The effect of grouped signals was still observed as MAGMA and GSEA again appeared to possess relatively less power than the other three tests in dense settings when more signals were placed in the same gene ( S2A and S2B Fig ) . Simulations with random placements of causal SNPs further emphasized the large impact of sparsity on relative performance . With only two or five causal SNPs ( Fig 2A and 2B ) , GHC and GBJ generally produced the most power . As the number of SNPs increased , GBJ continued to show either the best or second best power , with SKAT overtaking GHC in the settings with many causal SNPs ( Fig 2E and 2F ) . The differences between tests diminished as both the number of causal SNPs and the effect size rose , with all methods performing reasonably well at the largest effect sizes in dense settings . This behavior illustrated that many different tests excel at detecting strong and abundant signals , but the rare and weak settings offer more challenges . As researchers never know the signal sparsity prior to testing , utilizing GBJ or GHC provides some protection against signals that are difficult to detect and can offer more advantages than selecting a test for the dense setting , where most methods will perform well . GBJ in particular shows exceptional robustness across different settings and offers comparable power to SKAT even when there are many signals . Further , we would usually expect GBJ to outperform GHC in GSA because gene set analysis often involves many genes and because the very sparse regime becomes much smaller in size relative to the other settings as the number of SNPs in the set grows . For example , when there are only 100 SNPs in a set , the very sparse setting encompasses 1-3 signals , while the moderately sparse setting encompasses 4-10 signals . When there are 1 , 000 SNPs , the very sparse setting only stretches from 1-6 signals while the moderately sparse setting stretches from 7-32 signals . GBJ also generally runs at a faster speed than GHC ( S1 Table ) . GHC would be a better choice under extreme sparsity , as in only one or a few signals in the entire set . All methods appeared to control the Type I error rate fairly well at α = 0 . 01 ( S3 Fig ) , as their power remained approximately equal to the nominal size of the test when there was no true effect . This observation suggested that our inference was valid for the situations considered in simulation . Assessments of the correlation matrix approximation for summary statistics demonstrated the accuracy of the approach for GSA settings ( S4 Fig and S2 Table ) . Simulations for the step-down procedure ( Fig 3 ) showed that the proposed method was able to recognize pathways containing signal in only one gene , regardless of the choice of GSA test statistic . When only one gene contained causal SNPs ( Fig 3A ) , the power for all tests remained close to zero regardless of effect size , as the causal SNPs were removed before pathway level inference . GBJ again showed good power when multiple causal SNPs were located in each gene , and GSEA performed well with only one causal SNP per gene ( Fig 3D ) . As the method demonstrated excellent segregation of single-gene effects , we suggest the step-down procedure as an important complementary tool in GSA . We next investigated whether the same trends seen in our simulation could be found in re-analysis of the breast cancer summary statistic dataset . Self-contained GSEA was originally used to analyze a total of 4 , 507 pathways containing more than ten genes , finding 448 to be significant when using permutation to control the false discovery rate ( FDR ) at q = 0 . 05 . GBJ was applied to 10 , 742 pathways ( with more than three genes ) from the same master list and found 2 , 703 to be significant at the Bonferroni-corrected family wise error rate of 4 . 65 ⋅ 10−6 . When we restricted comparisons to the 3 , 952 pathways tested by both approaches , GSEA found 352 significant while GBJ found 2 , 095 significant at their respective error rates . From the raw significance numbers alone , GBJ appeared to offer far more power in a real GWAS summary statistic dataset . GBJ found more than twice as many significant pathways as GSEA on a percentage basis , even when controlling a more stringent error rate and using a conservative Bonferroni correction . The increased power could not be attributed to smaller pathways alone , as GBJ declared a higher percentage of pathways significant across various pathway sizes ( S5 Fig ) . To further investigate , we compared the GSEA and GBJ significance ranking ( Fig 4 ) of the 3 , 952 pathways tested by both methods ( p-value or q-value rank out of 3 , 952 , lower is more significant ) . To emphasize the role of moderately strong associations , points were colored according to their density of suggestive signals , which we defined as a pathway’s proportion of SNPs with p < 10−5 . SNPs demonstrating such a level of association would generally not stand out as the strongest signal in their region , but a large proportion of suggestive signals in any single gene or pathway could still indicate biologically relevant gene sets . For the sake of presentation , we only plotted pathways ranked in the top ten percentile by GSEA , in the top ten percentile by GBJ , in the 30th-40th percentile by GBJ , and in the 60th-70th percentile by GBJ ( see S6 Fig for full data ) . The frequency of blue pathways—indicating higher density of suggestive signals—clearly increased for pathways that were ranked as very significant according to GBJ . Such a pattern was desirable , as pathways with a higher density of small p-values should be more significant . However , scanning horizontally across the plot , there did not appear to be a strong relationship between GSEA rank and frequency of blue pathways . Approximately the same number of blue pathways could be found near the GSEA 25th , 50th , and 75th percentiles . This pattern suggested that GSEA was not very sensitive to the density of medium-strength signals . The proportion of SNPs with p < 1 ⋅ 10−5 is not a perfect measure for evaluating the significance of pathways , as other factors including linkage disequilibrium and strength of the largest signal do also play an important role . However , while GBJ takes into account all of the above factors , GSEA ignores signal density and disregards all p-values that are not the smallest in a gene . This difference was likely a major contributing factor to the large discrepancy between GSEA and GBJ rankings for points in the top left and bottom right hand corners of Fig 4 . Other methods employing the same strategy of choosing a minimum p-value to represent each gene , for example , non-default versions of MAGMA , may possibly experience similar drawbacks . Application of Generalized Berk-Jones to height and schizophrenia resulted in even more significant pathway findings than the breast cancer analysis ( S3 Table ) . To uncover the gene sets that were driven to significance by only one or a few genes , we applied the GBJ step-down inference procedure ( see Materials and Methods ) for all pathways in each phenotype possessing an initial p-value of p < 1 ⋅ 10−12 ( Fig 5 ) . In a typical GSA , these pathways would likely receive the most attention for their extremely high levels of association . Many pathways from both breast cancer and schizophrenia dropped below the Bonferroni-corrected significance level after their most significant gene was removed , but breast cancer pathways appeared to show more drastic changes in p-value ( Fig 5A ) , although some remained highly significant ( S4 and S5 Tables and S7 Fig ) . Thus a single gene boosted evidence of association by many orders of magnitude for a large number of breast cancer pathways . In contrast , pathways associated with height generally remained significant even after removal of their most highly associated gene ( Fig 5B ) . This trend persisted when removing the top three most highly associated genes from each pathway ( Fig 5C ) . Only about 12% of breast cancer pathways survived the Bonferroni-corrected significance level after three genes were removed , while approximately 38% of schizophrenia pathways and 69% of height pathways still passed this threshold . A possible interpretation of these results could be that height was much more driven by pathway-level effects of many genes working together , while breast cancer risk factors were more localized to a few key genes . It is also possible that breast cancer signal was attenuated because the summary statistics included patients with multiple different subtypes , so signal may have been diluted compared to an ER-positive only or ER-negative only analysis . Ear Morphogenesis was one example of a breast cancer pathway where the signal was almost entirely confined to a single gene , with an initial GBJ ranking of 100th most significant gene set ( p < 1 ⋅ 10−12 ) before the step-down procedure . When FGFR2 was removed from this pathway , the p-value of the modified gene set increased drastically to p = 0 . 041 , far from the corrected significance level . As expected , the other genes were not very relevant to breast cancer; it is not recommended to further pursue replication of the set’s association , despite initially promising results from GBJ and GSEA . On the other hand , a gene set such as the Nature Pathway Interaction Database TRAIL Signaling pathway demonstrated more robustness to removal of its top gene , MAP3K1 . TRAIL Signaling retained some set-level signal even as more and more top genes were removed from the gene set ( Fig 5D ) . Along with MAP3K1 , the pathway contained the significant genes CASP8 , CFLAR , DAP3 , and TNFSF10 . All of these genes possessed single gene p-values less than 1 ⋅ 10−5 for association with breast cancer , while Ear Morphogenesis contained no such genes other than FGFR2 . TRAIL Signaling as a mechanism has been studied extensively for its role in breast cancer [55] , supporting our finding of a pathway-wide effect that extends past the most significant genes . Over the entire pathway database , 172 pathways containing FGFR2 were tested for association with breast cancer , and 172 were significant at the Bonferroni-corrected threshold according to GBJ . Additionally , FGFR2 was the most significant gene in 169 of these pathways . After removal of FGFR2 from the pathway , only 71 of the 169 were still significant at the same threshold ( S6 Table ) . Clearly , the composition of significant gene sets in any breast cancer pathway analysis will depend on the number of times FGFR2 and select other genes ( S7 and S8 Tables ) appear in the pathway definition database . To summarize the results of our GBJ-based pathway analysis across three different phenotypes , we identified the biological processes where significant pathways in breast cancer , schizophrenia , and height were most likely to congregate ( see Materials and methods ) . In two reassuring results , we found that the percentages of significant pathways from the Growth category were higher than expected in breast cancer and height ( Fig 6 ) . Growth mechanisms have previously been found to play important roles in studies of breast cancer [30] and height [3] . Another theme that has often been corroborated in the literature is the importance of the immune system in schizophrenia [44] . Immune-related pathways have been studied in connection with many psychiatric diseases , and our analysis underscored the reasons for such an approach , as we found a high density of significant schizophrenia gene sets arising from immune processes . Seeing that GBJ could identify outcome-category pairs known to be associated with each other offered further validation that our approach was selecting relevant pathways . On the other hand , GBJ also illuminated some outcome-category relationships that were not as widely familiar . For instance , we saw that there was a dearth of significant pathways related to schizophrenia in the Reproduction category . Thus there may be less benefit to searching for common drivers of risk between schizophrenia and breast cancer , which showed many more significant Reproduction pathways than expected . Similarly , all three phenotypes showed fewer than expected significant pathways in Locomotion , indicating that it may be more useful to prioritize other types of pathways when studying these outcomes . While negative findings are reported less often than their positive counterparts , these results still have the potential to inform researchers of the mechanisms that may not generate as many fruitful results .
Interest in GSA will likely continue to grow as more and more genotyping data is collected [28] , especially since individual SNPs are still unable to explain much of the heritability in various phenotypes [56] . However , without appropriate statistical models to test for set-based effects , it will be difficult to correctly identify the gene sets that are truly associated with various outcomes . Many current GSA methods possess unknown operating characteristics and are difficult to interpret [18 , 29] . Our work demonstrates that GBJ can provide significantly more power than popular alternatives such as GSEA or MAGMA while still protecting the Type I error rate across various different pathway structures and also eliminating the need for computationally intensive genome-wide resampling . Intuitively , GBJ and the goodness-of-fit methods owe their high power to two major factors . First , the structures of the test statistics allow for full incorporation of available GSA data when performing inference , in particular using the magnitude of each marginal summary statistic in the set as well as the joint SNP correlation structure . Secondly , these statistics are backed by strong theoretical results in simplified set-based settings , where they possesses asymptotic power guarantees . In finite samples , GBJ has been shown to provide better performance than GHC . In addition , we have provided a step-down inference procedure to mitigate the bias introduced through choice of a gene set definition file . Pathways that demonstrate strong associations based on a single gene are regularly identified as a serious problem [25 , 57 , 58] and hinder important replication efforts . We show step-down inference can lessen these issues by highlighting the pathways that demonstrate effects over many genes , as opposed to pathways that rely on one or a few genes to drive their significance . Reporting only those findings that are still significant after the step-down procedure may help ensure that associations are replicable in studies with different pathway definitions . One issue we have not discussed much is the philosophical difference between a self-contained test , such as the tests we have considered in this report , and a competitive test , such as certain variants of GSEA . In general , we recognize that both approaches possess unique strengths and weaknesses , and we believe both have their uses in GSA . Previous literature [6] and the preceding work have demonstrated many of the advantages of self-contained tests , but there are certainly areas where a competitive analysis could provide additional benefits . In particular , a competitive test may have been able to provide more succinct lists of significant pathways by accounting for strong background signal present in the datasets we studied . However , we note that most studies will contain far less background signal , as the cohorts used in this paper are some of the largest ever assembled , and we have shown how GBJ is still able to provide useful inference even in highly polygenic settings . GBJ could also be recast as a competitive test using the gene permutation methods of other competitive strategies . Another limiting factor for GBJ arises as a consequence of the data-intensive approach that affords it additional power . Very large gene sets containing over 1 , 500 SNPs can greatly slow down calculation of the test statistic and p-value , which can create difficulties analyzing the largest gene sets . While other tests may sacrifice large amounts of information by discarding more of the data , they can also produce results much more quickly as a consequence of utilizing fewer inputs . This issue can be alleviated by pruning or otherwise reducing the number of SNPs in a gene set so that GBJ still uses a large amount of information while running at an acceptable speed . Also , large amounts of data can cause issues with the default level of numerical precision in R , so that the current implementation of our software may not provide very precise p-values between 0 < p < 1 ⋅ 10−12 . Still , 1 ⋅ 10−12 is generally a low enough significance level to account for multiple testing adjustments in GSA . GSEA , for example , can only provide a family-wise error rate as precise as 0 . 001 when testing a single pathway with its default of 1 , 000 permutations . Generalized Berk-Jones represents a substantial departure from standard gene set analysis methods and offers distinct advantages over competing ideas , but there is still much room for future work . One possible extension would be a correction for background signal so that GBJ could provide an analytic p-value for the competitive null hypothesis . It would also be useful to develop some type of sequential multiple testing process for the step-down procedure , instead of relying solely on a significance threshold designed for the original , full pathway . A principled , adaptive method to relax the significance threshold depending on the number of genes removed may offer more power for identifying gene sets with dispersed signals . Computationally , it would be useful to develop algorithms that can calculate the GBJ statistic faster and with more precision so that larger gene sets can be tested quickly . Finally , it would be of interest to see how other set-based tests with similar asymptotic guarantees to Berk-Jones and Higher Criticism perform in the GSA paradigm . A number of such tests exist for independent summary statistics [32 , 59] and could be modified to consider correlated data . These other methods may prove to provide even more finite sample power in the gene set analysis setting . | Researchers are frequently interested in the association between a biologically related set of genes—for example , a particular immune response pathway—and a complex phenotype . Such associations are often explored by applying various gene set analysis methods to genotype data from genome-wide association studies . However , many common methods are ad-hoc in nature and possess unknown statistical operating characteristics; reviews of existing procedures often show poor Type I error and power . We propose conducting gene set analysis with a class of tests that possesses both rigorous statistical motivation and excellent performance in application . Comparisons with popular alternatives including GSEA and MAGMA show a substantial increase in power . In addition , we introduce a novel step-down inference procedure that mitigates the confounding introduced by different gene set databases . For example , this procedure identifies that a seemingly strong association between breast cancer and Ear Morphogenesis is actually an association between breast cancer and just one single gene in the Ear Morphogenesis pathway . Use of the step-down procedure can improve reproducibility and result in much more interpretable findings when performing gene set analysis . | [
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]
| 2019 | Powerful gene set analysis in GWAS with the Generalized Berk-Jones statistic |
Inter-individual variation in gene regulatory elements is hypothesized to play a causative role in adverse drug reactions and reduced drug activity . However , relatively little is known about the location and function of drug-dependent elements . To uncover drug-associated elements in a genome-wide manner , we performed RNA-seq and ChIP-seq using antibodies against the pregnane X receptor ( PXR ) and three active regulatory marks ( p300 , H3K4me1 , H3K27ac ) on primary human hepatocytes treated with rifampin or vehicle control . Rifampin and PXR were chosen since they are part of the CYP3A4 pathway , which is known to account for the metabolism of more than 50% of all prescribed drugs . We selected 227 proximal promoters for genes with rifampin-dependent expression or nearby PXR/p300 occupancy sites and assayed their ability to induce luciferase in rifampin-treated HepG2 cells , finding only 10 ( 4 . 4% ) that exhibited drug-dependent activity . As this result suggested a role for distal enhancer modules , we searched more broadly to identify 1 , 297 genomic regions bearing a conditional PXR occupancy as well as all three active regulatory marks . These regions are enriched near genes that function in the metabolism of xenobiotics , specifically members of the cytochrome P450 family . We performed enhancer assays in rifampin-treated HepG2 cells for 42 of these sequences as well as 7 sequences that overlap linkage-disequilibrium blocks defined by lead SNPs from pharmacogenomic GWAS studies , revealing 15/42 and 4/7 to be functional enhancers , respectively . A common African haplotype in one of these enhancers in the GSTA locus was found to exhibit potential rifampin hypersensitivity . Combined , our results further suggest that enhancers are the predominant targets of rifampin-induced PXR activation , provide a genome-wide catalog of PXR targets and serve as a model for the identification of drug-responsive regulatory elements .
Adverse reactions to drug treatment constitute a substantial health problem that is a leading cause of morbidity and mortality in hospitalized patients [1] . Differential expression of drug metabolizing enzymes and drug transporters is a major determinant of inter-individual drug response variability [2]–[5] . By sequestering and metabolizing drug compounds in the liver and intestine , these enzymes and transporters effectively determine whether target organs and tissues are exposed to optimal drug dosages . Several coding mutations in these proteins have been detected which lead to adverse outcomes [6]–[10] and reduced drug activity [11] , [12] . Regulatory elements , including promoters and enhancers , also likely play an important role that has so far been largely uncharacterized [13] , [14] . The systematic identification of drug-responsive regulatory elements would thus provide a unique resource to discover novel genetic variants that lead to differences in drug response . The vast majority of pharmaceutical compounds are metabolized by the cytochrome P450 family ( CYP ) of enzymes . Of these , CYP3A4 is the most abundantly expressed in sites of drug disposition in the liver [15] and is also thought to be responsible for the metabolism of at least 50% of prescribed pharmaceuticals [16] . CYP3A4 activity can vary 5–20 fold between individuals [17] and its mRNA expression can vary as much as 120 fold [18] . Only a few single nucleotide polymorphisms ( SNPs ) in the immediate CYP3A4 locus have been found to be associated with CYP3A4 hepatic expression [19]–[21] , suggesting that its variable expression could be caused by other genes and distant regulatory elements . CYP3A4 is one of many targets of the nuclear receptor PXR ( coded by NR1I2 ) , which is expressed predominantly in the liver and intestine [22] and is essential for activating Phase I and II enzymes in response to xenobiotics . PXR's broad substrate specificity allows it to be activated by a wide variety of drugs including the antibiotic rifampin , the malaria resistance drug artemisinin , the hypolipidemic agent mevastatin , and the chemotherapeutic agent paclitaxel [2] . Relatively little is known about the mechanism by which PXR drives CYP3A4 transcription in vivo , although PXR response elements have been identified in the putative CYP3A4 promoter [23] and upstream cis-regulatory elements [24] , [25] that drive its expression in vitro . Additional PXR responsive enhancers have been found for other CYPs [26] , [27] . Chromatin immunoprecipitation followed by sequencing ( ChIP-seq ) of PXR-bound DNA elements in livers from mice treated with PCN ( a mouse PXR agonist ) identified >3 , 000 drug-induced binding sites [28] . ChIP-seq for other drug-associated transcription factors such as LXR , RXR and PPARA has also been carried out in mouse liver [29] . However , the inherent drug metabolism differences between mouse and humans , in particular for PXR and the mouse homolog of CYP3A4 [22] , [30] , [31] , hinder the ability to directly translate these results to humans . To identify PXR-associated regulatory elements in a genome-wide manner , we carried out RNA sequencing ( RNA-seq ) and ChIP-seq with antibodies for PXR and three different enhancer marks on primary human hepatocytes treated with rifampin or vehicle control . These included the E1A binding protein p300 ( EP300/p300 ) which has been used to identify functional enhancers in vivo with high success rates [32] , and two histone marks , H3K4me1 and H3K27ac . H3K4me1 marks both poised and active regulatory regions [33] while H3K27ac was shown to selectively mark active regions [34] , [35] . We identified thousands of sequences that had rifampin induced ChIP-seq peaks . A reporter validation screen of proximal promoters associated with these peaks yielded only a few functional rifampin-dependent sequences . A similar assay for distal enhancers resulted in the identification of several novel drug-dependent enhancers . Analyses of nucleotide variants in selected sequences found a common African haplotype in the GSTA locus to possibly affect rifampin sensitivity .
Our RNA-seq analyses found several differentially expressed genes , the majority of which are known to be involved in drug response . The number of differentially expressed genes using a p-value cutoff , after adjustment for multiple testing less than or equal to 0 . 05 , was 157 ( Table S1 ) . Amongst them , 11 were CYPs , with the top differentially expressed gene being CYP3A4 , similar to our qPCR results . Of the eleven differentially expressed genes identified by qPCR , seven ( 64% ) were also found to be differentially expressed by RNA-seq . It is worth noting that two ( CYP2C9 , CYP2C19 ) of the four genes that didn't replicate in the RNA-seq data , showed a non-statistically significant induction by rifampin in our RNA-seq . We observed a massive recruitment of PXR binding across the genome following rifampin treatment . PXR-bound DNA fragments clustered into 1 , 158 discrete peaks with DMSO treated cells versus 6 , 302 after treatment with rifampin ( Figure 1A , Table S2 ) , with only 239 overlapping in both datasets ( Figure 1B ) . Rifampin treatment led to a small increase in the percentage of promoters ( 25 . 6% versus 24 . 1% ) bound by PXR and a larger increase for intronic ( 35 . 18% versus 29 . 9% ) and exonic ( 5 . 3% versus 1 . 8% ) regions ( Table S2 ) . In contrast , there was a reduction in the percentage of rifampin-induced PXR binding sites in intergenic regions ( 33 . 9% versus 44 . 2% ) . An analysis of the location of rifampin-induced PXR peaks found them to be enriched at transcription start sites ( TSSs ) , but not at a particular location upstream or downstream to the TSS ( Figure S1 ) . The binding of p300 was also more extensive after rifampin treatment , with 13 , 811 peaks compared to 10 , 253 in DMSO-treated cells ( Figure 1A , Table S2 ) . There was a larger overlap between rifampin and DMSO treated ChIP-seq peaks compared to PXR , with 4 , 374 ( 31 . 7% ) in common between the two sets ( Figure 1B , Table S2 ) . We also observed a change in the functional distribution of binding sites , with rifampin increasing the percentage of intronic ( 42 . 6% versus 36 . 3% ) and intergenic ( 38 . 0% versus 31 . 7% ) p300 binding versus a small change in exons ( 3 . 8% versus 2 . 6% ) and a reduction in promoter binding ( 15 . 5% versus 29 . 4% ) ( Table S2 ) . This result was consistent with our observation that only 1 , 076 rifampin-induced p300 peaks overlapped rifampin-induced PXR peaks ( Table S2 ) . In contrast to PXR and p300 , the distribution of histone marks was relatively stable , with about 49 , 000 enriched islands of H3K4me1 activity and about 40 , 000 H3K27ac islands in both treatments ( Figure 1A ) . We also observed a large overlap between rifampin and DMSO treated H3K4me1 ( 82 . 07% ) and H3K27ac ( 87 . 89% ) enriched islands ( Figure 1B , Table S2 ) . Combined , these results suggest that histone marks are more stable in response to rifampin treatment compared to PXR and p300 . We next looked at overlaps between the different ChIP-seq peaks . Amongst the 6 , 302 PXR rifampin treated peaks 1 , 037 ( 16% ) overlapped p300 and around half overlapped histone marks ( 3 , 553 for H3K27ac and 2 , 942 for H3K4me1 ) . This was similar for PXR peaks in the DMSO treated cells ( Table S2 ) . For p300 we observed a greater overlap with histone marks , with ∼70% of the peaks overlapping either H3K27ac ( 9 , 487/13 , 811 ) and H3K4me1 ( 9 , 840/13 , 811 ) . In the DMSO treated cells , we observed a much higher overlap for p300 peaks with the active H3K27ac mark ( 9 , 487/10 , 253; 92% ) versus H3K4me1 ( 7 , 789/10 , 253; 76% ) , suggesting that the p300 peaks in this condition tend to be in active regions . There are multiple examples of promoter nucleotide variants that are associated with inter-individual drug response [4] , [5] , [13] , [38] , [39] . We thus sought to identify drug-dependent promoters in our dataset which may harbor common variants with novel effects on drug response . We selected 227 promoters for 200 genes ( some genes had more than one promoter; Table S3 ) from the LightSwitch Promoter Collection ( SwitchGear Genomics ) for genes whose expression was induced by rifampin or reside near rifampin-induced ChIP-seq peaks ( Table S3 ) . Of the 227 promoters , 154 overlap a PXR peak , 45 overlap p300 , 164 overlap H3K27ac and 84 overlap H3K4me1 ( Table S3 ) . This library consists of ∼1 , 000 bp proximal promoter fragments cloned into pLightSwich_Prom vector ( see Methods , Table S3 ) . We also included two positive controls: 1 ) The beta-actin promoter ( ACTB ) , a strong constitutive promoter that should not be induced by rifampin and 2 ) The CYP3A4 proximal promoter , which is known to be induced by rifampin . We tested the 227 promoters in HepG2 cell lines co-transfected with human PXR and treated with rifampin or vehicle control ( DMSO ) . Out of the 227 tested promoters , 179 were found to be functional promoters ( >2 fold luciferase activity above empty vector ) in the DMSO treated cells ( Figure 2A , Table S3 ) . Among those promoters , only 10 exhibited >2 fold increase in promoter activity upon rifampin treatment including our CYP3A4 proximal promoter control ( Figure 2A ) . To confirm that the effects of rifampin were mediated through PXR , we also tested the 10 rifampin-induced promoters ( including CYP3A4 ) in a similar assay , only this time without co-transfecting human PXR , and found only 2 of them to be induced by rifampin ( >2 fold increase in promoter activity upon rifampin treatment ) and at much lower levels ( Figure 2B , Table S3 ) . In addition , the CYP3A4 promoter was also not induced by rifampin in this assay . In both experiments , our ACTB control was a strong promoter , but not induced by rifampin . The overall lack of rifampin-sensitive promoters and previous results finding a role for enhancers in driving this drug response [24]–[27] suggests that other regulatory sequences , such as enhancers , may be involved in driving the effects of rifampin treatment on gene expression . Since most of the promoters tested in our assay did not demonstrate increased activity in the presence of rifampin , we broadened our search for inducible regulatory elements to include enhancers . To be more stringent in our analyses , we selected regions across the genome which showed PXR rifampin-induced binding in addition to all three enhancer marks . For both the DMSO and rifampin treatments , we generated a merged track of all four marks , with each region in the track overlapping one to four peaks/island . If , for example , a p300 peak is near a PXR peak , but they don't overlap , while both overlap a H3K4me1 and/or a H3K27ac island , they were considered all as one region . Only 225 such regions were present in the DMSO treatment , while 1 , 387 were identified in the cells treated with rifampin ( Figure 3A ) . Of the latter group , 1 , 297 regions were exclusive to the rifampin treatment and termed Rifampin-Induced Regions ( RIRs ) for downstream analyses . CYP3A4 is by far the most well studied target of PXR , with well characterized regulatory sequences: the proximal promoter , a −7 . 5 kb upstream xenobiotic responsive enhancer module ( XREM ) [24] , and a −11 kb constitutive liver enhancer module of CYP3A4 ( CLEM4 ) [25] . A second potential XREM , putatively regulating CYP3A7 , was additionally identified intergenically between CYP3A7 and CYP3A4 [26] . Our ChIP-seq data completely recapitulates this picture of regulation in primary human hepatocytes , with two large RIRs encompassing multiple rifampin-induced peaks ( Figure 3B ) . It is also worth noting that the CYP3A4 locus is one of the few in which we observed a substantial difference in rifampin-induced enrichment of the H3K4me1 and H3K27ac marks . To identify enriched biological pathways and functions within the set of 1 , 297 RIRs , we carried out a genomic analysis using the Genomic Regions Enrichment of Annotations Tool ( GREAT [40] ) . Our top enriched term ( p-value 1 . 85×10−9; binominal fold enrichment ) , originating from Pathway Commons ( http://www . pathwaycommons . org ) , was ‘xenobiotics’ ( Figure 3C ) . This was attributed to RIRs residing near the following genes: ABCB4 , ACSL1 , ADH1A , ADH6 , AKR1C2 , AKR1C3 , ALDH1A1 , CNDP2 , CYP26A1 , CYP2A6 , CYP2B6 , CYP2C19 , CYP2C8 , CYP2C9 , CYP2W1 , CYP3A4 , CYP3A7 , CYP4F12 , CYP4F3 , CYP7A1 , GCLC , GCLM , GSTA2 , GSTO1 , GSTO2 , HNF4A , MAT1A , MAT2A , MGST2 , MGST3 , NCEH1 , NNMT , PAPSS2 , PTGIS , SLC35D1 , SULT1B1 , SULT2A1 , UGDH , UGT1A1 . In addition , we observed significant ( FDR adjusted p-value≤0 . 05 ) gene ontology enrichment terms for drug catabolic processes and other terms fitting with drug response ( Figure 3C , Table S4 ) . We performed a similar analysis for RIR neighboring genes using QIAGEN's Ingenuity Pathway Analysis ( IPA , QIAGEN Redwood City , www . qiagen . com/ingenuity ) and found enrichment for the PXR/RXR Activation Canonical pathway ( Figure S2 , Table S5 ) . Combined , these results suggest that our RIRs are enriched near drug-associated genes . Although ChIP-seq is a valuable tool for the identification of putative regulatory elements , functional studies are essential for the validation of such sequences . We selected forty-nine putative enhancer sequences for validation using two different selection criteria: 1 ) Forty-two RIRs residing near drug-associated genes as manually determined by the literature . 2 ) Seven rifampin-induced PXR ChIP-seq peaks harboring SNPs that are in linkage disequilibrium ( LD ) with pharmacogenomics or drug related genome-wide association study ( GWAS ) lead SNPs , termed GWAS linked peaks ( GLPs ) ( Table S6 ) . Previous studies have shown that several GWAS SNPs reside near potential regulatory elements that encompass SNPs that are in LD with the lead GWAS SNP [41]–[43] . To increase our chances to identify these regulatory elements , we used our rifampin treated PXR-ChIP-seq dataset , instead of the RIRs , since it had a larger number of peaks . SNPs in LD with pharmacogenomics GWAS hits were significantly enriched near rifampin-dependent PXR peaks compared to SNPs in LD with non-pharmacogenomic GWAS hits ( p<0 . 0001 , Chi-squared test ) . None of the sequences chosen overlapped promoter regions ( i . e . were within −2500/+500 bp of a TSS ) . Candidate enhancer sequences were cloned into the pGL4 . 23 ( Promega ) enhancer assay vector , which contains a minimal promoter followed by the luciferase reporter gene . Since the peaks ( or islands ) enriched for the two histone marks are relatively long ( average length ∼5 kb , Table S2 ) , we selected shorter sequences within each RIR that encompass only the PXR/p300 peaks along with additional flanking sequence , up to 500 bp on each side of the peak . We also used different positive controls: 1 ) The ApoE liver enhancer [44] whose activity should not be enhanced by rifampin . 2 ) A CYP3A4 promoter-enhancer combination ( p3A4-362 ( 7836/7208ins ) [24] ( Table S7 ) whose activity should be increased by rifampin treatment . All constructs were tested for their enhancer activity in HepG2 cells transfected with human PXR and treated with either rifampin or DMSO , as previously done for the promoter screen . Out of the 49 sequences tested , 19 ( 38 . 7% ) showed significant reporter expression levels versus the empty vector ( ≥2 two fold ) in either condition: 15 RIRs and 4 GLPs ( Figure 4A , Table S7 ) . Among these 19 positive enhancers , we observed three types of enhancers: 1 ) Seven enhancers that were active at similar levels with DMSO and rifampin , termed ‘rifampin independent’ . 2 ) Five enhancers that were active without rifampin , but whose expression levels significantly increased upon rifampin treatment , termed ‘rifampin increased’ . 3 ) Seven enhancers that were active only when treated with rifampin and were called ‘rifampin dependent’ . Two of these ( GLP1 and GLP2 ) are located in the CYP2C locus ( Figure S3 ) and contain SNPs in LD with pharmacogenomics GWAS SNPs for warfarin maintenance dose [45] , [46] , acenocoumarol maintenance dose [47] and response to clopidogrel therapy [48] . Combined , these results show that enhancer activity can be modulated by rifampin . We next determined whether common nucleotide variation within functional , drug-dependent enhancers could alter their activity . For these experiments we selected five enhancers that were either rifampin increased or dependent and near important drug-response genes . These included RIR7 , which overlaps the putative CYP3A7 XREM ( Figure 3B; chr7: 99339411–99341549; hg19 ) [26] and was rifampin dependent in our assays . We also selected RIR46 , which is located in the glutathione S-transferase alpha ( GSTA ) locus near GSTA2 ( chr6: 52609942–52611507; hg19 ) ( Figure S4 ) and was rifampin-increased in our assays . The GSTA family of enzymes are known to be involved in the metabolism of various xenobiotics [49] . We also selected three different GLP sequences: GLP1 , 2 , and 5 . Both GLP1 ( chr10:96507473–96508107; hg19 ) and GLP2 ( chr10:96696182–96696970; hg19 ) are located in the CYP2C locus ( Figure S3 ) , which harbors several CYP metabolizing enzymes and has been analyzed extensively in various pharmacogenomic studies . GLP5 ( chr2:234672744–234673398; hg19 ) harbors a single SNP , rs3771341 , that is in LD with several GWAS lead SNPs correlated with altered bilirubin levels [50]–[53] and was rifampin-increased in our study . This element is located in the UDP glucuronosyltransferase 1 family , polypeptide A cluster ( UGT1A ) , ∼4 kb upstream of the UGT1A1 transcription start site ( Figure S5 ) . UGT1A enzymes have important roles in the metabolism of xenobiotics and both coding and promoter variants within them have been associated with adverse drug reactions [54] . We determined common haplotypes in all five sequences using the phased 1000 Genomes data ( Table S8 ) . Common haplotypes for all five sequences ( Table S8 ) were then cloned into our enhancer assay vector ( pGL4 . 23 ) either by amplifying DNA from individuals from various ethnic backgrounds from the studies of pharmacogenomics in ethnically diverse populations ( SOPHIE ) cohort [55] or by site-directed mutagenesis and sequence verified . The sequences were then tested for enhancer activity in HepG2 cells transfected with human PXR and treated with either rifampin or DMSO , and compared to the ancestral haplotype . Out of the five tested enhancers , one haplotype in RIR46 showed a substantial difference in enhancer activity . For RIR46 , we observed 1 . 85-fold ( 4 . 01/2 . 16 ) increase in response to rifampin for haplotype 3 , however after adjusting for multiple testing the variance in the response was too high to be significant ( adjusted p-value = 0 . 095 by FDR; ANOVA; Figure 4B , Table S8 ) . This haplotype is present at a frequency of 6 . 7% in the 1000 Genomes AFR population . It is worth noting that our success rate in finding haplotypes that significantly alter enhancer activity was low . Nonetheless , the observation that a haplotype in RIR46 could possibly affect enhancer activity suggests that nucleotide variants in these enhancers could lead to differential enhancer activity .
By carrying out RNA-seq and ChIP-seq on rifampin and DMSO treated human hepatocytes , we have uncovered numerous drug-dependent regulatory elements . We observed that promoters bearing a rifampin-dependent signature were largely unable to independently induce the expression of a reporter upon rifampin treatment . This result suggested that other gene regulatory elements , such as enhancers , could constitute the predominant group of target sequences that are activated by this drug . An analysis of nucleotide variation in these enhancers showed that specific variants could affect enhancer activity , raising the possibility that nucleotide variants in enhancers could contribute to pharmacogenomic phenotypes more broadly . Our RNA-seq analyses that combined eight different individuals identified 157 differentially expressed genes , using a p-value cutoff that adjusted for multiple testing less than or equal to 0 . 05 ( Table S1 ) . Many of these genes are known to be involved in drug response and the top differentially expressed gene was CYP3A4 , fitting with its role as the most abundantly expressed gene in sites of drug disposition in the liver [15] . The number of differentially expressed genes ( 157 ) , using our 0 . 05 cutoff , was much lower than the number of RIRs ( 1 , 297 ) and PXR and p300 rifampin treated ChIP-seq peaks . This difference could be attributed to having multiple regulatory elements regulating the same gene . It could also be due to our conservative RNA-seq differential expression cutoff , which if relaxed would increase our gene list . Another cause for this can be that several of our identified peaks are not functional regulatory elements . Our functional characterization for example , found only 19 of the 49 tested sequences ( 38 . 7% ) to have significant reporter expression levels versus the empty vector ( ≥2 two fold ) . Of note though , that this could also be due to the different cell types and conditions that were used in this assay , as later described . Similar studies have been conducted to detect environmentally induced regulatory elements [28] , [56]–[59] . For example , testing for changes in estrogen receptor α and RNA polymerase II occupancy due to estradiol , tamoxifen or fulvestrant treatment in MCF-7 cells , found differences in ligand regulation with tamoxifen leading to downregulation while fulvestrant increased RNAPII occupancy [58] . In our study , we observed a large rifampin-induced recruitment of PXR and p300 binding across the genome . This was particularly notable in the CYP3A locus , which was radically altered by rifampin treatment . For the most part , the regulatory hotspots that we identified in this region are consistent with established literature . It is worth noting , however , that we saw almost equal recruitment of PXR and p300 to the XREMs that putatively regulate CYP3A4 and CYP3A7 , despite the fact that we observed substantially less CYP3A7 mRNA induction by qPCR and RNA-seq . We did not observe large changes in the assayed histone marks , H3K4me1 and H3K27ac . This could be attributed to these sites being poised to be activated by various xenobiotic responses , though H3K27ac has been previously shown to mark active enhancers [34] , [35] . To get at these differences more systematically , we performed a second analysis in which H3K4me1 and H3K27ac peaks were called for the rifampin treatment using the DMSO treatment as the control ( Table S9 ) . We observed 110 differential islands for H3K27ac , and only 2 for H3K4me1 ( one of which was in the CYP3A4 locus ) . The fact that we observed more rifampin-induced peaks for H3K27ac is consistent with its hypothesized role in marking active enhancers . Combined , these results suggest that while these two marks are incredibly stable in the face of massive changes in PXR/p300 binding , there are still measurable drug-induced changes in chromatin structure . Previous reports have found enhancers to be responders of drug treatment [24]–[27] . Our functional validation results broadly suggest that promoters on their own are largely incapable of driving the effects of rifampin induction . Instead , our results imply that enhancers appear to be induced by rifampin , and through their interaction with promoters , drug response genes are activated . We tested over 200 promoters of genes based on relatively loose selection criteria: that their respective genes either showed increased expression following rifampin treatment , were tagged by our rifampin ChIP-seq peaks , or were near them . On the other hand , for enhancers we required that candidates had all four marks . While it is possible that our promoter selection criteria missed out on important drug response promoters , we would still expect to achieve a higher rifampin induction success rate than what we observed in our assays ( 10/227; 4 . 4% ) . While our assays may have not been ideal for these purposes , i . e . testing promoters in vitro in PXR-transfected HepG2 cells , both our negative and positive controls showed the expected results ( Figure 2A ) . Furthermore , rifampin-induced promoters tested in non-PXR transfected conditions showed much lower induction by the drug ( Figure 2B ) . Further systematic assays including ones carried out in vivo will be needed to better address this hypothesis . While 12 out of the 19 functional sequences identified by our screen exhibited basal enhancer activity , 7 sequences exhibited enhancer activity only upon rifampin treatment ( Figure 4A ) . These sequences would not have been identified by conventional ChIP studies conducted in physiological conditions , nor would they be validated in functional assays without drug treatment . Together our results suggest that ChIP-seq datasets are dependent on the environmental conditions in which they were performed , and that there are likely many hidden enhancers which only become active following a specific stimulus . We identified several functional enhancers that were rifampin-increased or rifampin-induced whose location was near pharmacogenomic-associated variants . One of these elements is RIR46 , which resides near GSTA2 , a Phase II enzyme involved in the detoxification of numerous drugs [49] , and is thus a likely target . Coding variants in GSTA2 have been shown to affect its detoxification efficiency [60] , [61] and promoter variants in GSTA2 have been suggested to affect its expression levels [62] , [63] . We identified a haplotype present in the 1000 Genomes AFR population that confers increased rifampin sensitivity ( Figure 4B ) . Both GSTA2 and GSTA1 ( which is 28 kb downstream to GSTA2 ) , have been shown to have important roles in catalyzing carcinogenic substrates and nucleotide variation in them has been shown to be associated with cancer [60] , [64] . Previous work has shown a significant difference in the distribution of coding SNPs in both these genes between African Americans and Caucasians [60] . However , these coding SNPs are not associated with prostate cancer disease status [60] , suggesting that other factors could be playing a role . Future studies could examine whether variants in RIR46 or other RIRs are associated with these phenotypes . It is worth noting that there are several caveats to our study . Our ChIP-seq experiments only analyzed hepatocytes from a single donor at a single time point ( 24 hours post treatment ) , selected for being commonplace in rifampin induction studies . It is possible that there are enhancers that play a role much earlier than 24 hours post-treatment . To test regulatory sequences in reporter assays , it was also necessary to clone smaller fragments from each RIR bearing the PXR and p300 peaks . It is possible that we missed flanking sequences that were essential for enhancer function . Furthermore , because we had a limited amount of primary hepatocytes from the same donor , our reporter assays were carried out in PXR-transfected HepG2 cells , which could result in false negatives . Finally , we employed the pGL4 . 23 vector , which is a commonly used enhancer assay vector , but possesses a very short TATA-box containing minimal promoter that may not be compatible with all of the enhancer sequences tested . Promoter regions upstream of drug metabolizing enzymes and transporters are presumed to be the major targets of xenobiotic activators of PXR , such as rifampin . Our work challenges this conception and strongly supports the idea that the direct targets could be enhancer elements , which may subsequently interact with promoters to enhance gene transcription . Combined , our results show that ChIP-seq in combination with drug treatment has a large potential in identifying novel regions in the genome associated with drug response . These regions can provide exceptional candidates for the detection of nucleotide variants associated with inter-individual differences in drug response . Our methodology could easily be adapted to other drugs/target/tissue combinations . With whole genome datasets becoming more widely used as a clinical toolbox , the ability to highlight these important drug response regions in the genome is of extreme importance .
Cryopreserved human hepatocytes from a 19 year old Caucasian male donor with no history of medications ( Lot Hu8080 , Life Technologies ) were thawed in CHRM recovery media ( Cat# CM7000 , Life Technologies ) and cultured in CHPM plating media ( Cat#CM9000 , Life Technologies ) on 6-well collagen-coated plates ( Life Technologies ) . After 6 hours , the media was swapped with maintenance media , consisting of phenol-free Williams E media containing culture incubation supplements ( Cat#CM4000 , Life Technologies ) and 0 . 01% DMSO or 10 µM rifampin ( Sigma ) for 24 hours . The rifampin dose ( 10 µM for 24 hours ) was used based on previously reported assays achieving high induction rates in human hepatocytes [65] , [66] . Dexamethasone , which is included separately with the culture incubation supplements , was not added to the media as it can activate PXR . Cultured hepatocytes were treated with DMSO or rifampin for 24 hours in triplicate . The cells were then washed with PBS , and lysed directly with Buffer RLT from the RNAeasy mini kit ( Qiagen ) with the on-column DNase digestion step . One µg of total RNA was used to generate cDNA using the RT2 First Strand Kit ( Qiagen ) . Gene expression levels for 84 genes of interest was determined using the “Drug Metabolism: Phase I Enzymes” RT2 Profiler PCR Array ( Qiagen ) . Five housekeeping genes ( B2M , HPRT1 , RPL13A , GAPDH , ACTB ) were used to control for loading . Fold induction was calculated using the ΔΔCt method [67] . Total RNA was acquired as described for qPCR for two replicates each of DMSO and rifampin treated hepatocytes . Libraries were made with ScriptSeq v2 RNA-Seq Library Preparation Kit ( Epicenter ) . Briefly , 3–5 ug of total RNA were subjected to rRNA removal using Ribo-Zero Magnetic Kit ( Epicenter ) prior to library construction . 5 ng of the rRNA-depleted sample was fragmented enzymatically and annealed with random hexamer to create the first strand of cDNA . Upon removal of the RNA template transcript by RNase , Terminal-Tagging Oligo ( TTO ) , a known 5′-sequence tag , a 3′-random sequence , and a terminally blocked 3′ end to prevent priming of DNA synthesis , was added to create cDNA with known sequence tags at their 5′ and 3′ ends for directionality . Upon purification , adaptors with barcodes were added to cDNA fragments and enriched by 15 cycles of PCR . Sequencing was carried out on an Illumina HiSeq . The resulting reads were demultiplexed and aligned to the human genome ( hg19 ) using TopHat v2 . 0 . 10 [68] . Read counts for each gene in the RefSeq annotation were obtained using NGSUtils [69] so as to allow comparison to the RNA-seq data from the other 7 primary hepatocytes treated with and without rifampin and sequenced with SOLiD as described in [37] . Analysis for differential expression across the nine replicates was performed using DESeq2 [70] . DESeq2 was chosen due to its capability in handling multifactorial experimental designs , in this case treated with rifampin versus control and SOLiD sequences versus Illumina . DESeq2 was then used to perform a likelihood ratio test between the model of treatment plus sequencing type versus the simplified model of just sequencing type in order to identify genes differentially expressed upon treatment with rifampin . Twelve million cells ( an entire 6 well plate ) per immunoprecipitation were fixed with 1% formaldehyde for 15 min and quenched with 0 . 125 M glycine . The remainder of the ChIP-seq protocol was carried out by Active Motif Inc . , as follows . Chromatin was isolated by adding lysis buffer , followed by disruption with a Dounce homogenizer . Lysates were sonicated and the DNA sheared to an average length of 300–500 bp . Genomic DNA ( Input ) was prepared by treating aliquots of chromatin with RNase , proteinase K and heat for de-crosslinking , followed by ethanol precipitation . Pellets were resuspended and the resulting DNA was quantified on a NanoDrop spectrophotometer . Extrapolation to the original chromatin volume allowed quantitation of the total chromatin yield . An aliquot of chromatin ( 30 ug ) was pre-cleared with protein G agarose beads ( Invitrogen ) . Genomic DNA regions of interest were isolated using 4 ug of antibody against PXR ( sc-25381 , Santa Cruz ) , 4 ug of antibody against p300 ( Cat#sc-595 , Santa Cruz ) , 20 ug of antibody against H3K27ac ( Cat#ab4729 , Abcam ) and 5 ug antibody against H3K4me1 ( Cat#ab8895 , Abcam ) . Due to the limited amount of cryopreserved human hepatocytes from this donor , we elected to do various enhancer-associated ChIP-seq marks instead of additional replicates . Complexes were washed , eluted from the beads with SDS buffer , and subjected to RNase and proteinase K treatment . Crosslinks were reversed by incubation overnight at 65 C , and ChIP DNA was purified by phenol-chloroform extraction and ethanol precipitation . ChIP and input DNAs were prepared for amplification by converting overhangs into phosphorylated blunt ends and adding an adenine to the 3′ ends . Illumina adaptors were added and the library was size-selected ( 175–225 bp ) on an agarose gel . The adaptor-ligated libraries were amplified for 18 cycles . The resulting amplified DNAs were purified , quantified , and tested by qPCR at the same specific genomic regions as the original ChIP DNA to assess quality of the amplification reactions . Amplified DNA libraries were sequenced on the Illumina Genome Analyzer II . The 36-nt sequence reads identified by the Sequencing Service were mapped to the genome using the ELAND ( Illumina ) . Default settings were used: only tags that map uniquely , have no more than 2 mismatches , and that pass quality control filtering were considered . PXR and p300 peaks were called against input using MACS version 1 . 4 [71] and for H3K27ac and H3K4me1 using SICER version 1 . 1 [72] . A p-value cutoff of 1×10−7 was used for MACS and an FDR of 0 . 01% was used as a cutoff for SICER , and the remaining parameters set to the default . For the alternate analysis presented in Table S9 , we used a more stringent threshold in SICER of 1×10−10 . For the analysis of the distribution of PXR ChIP-seq peaks following rifampin treatment , we used knownGene from the UCSC Genome Browser to define the transcription start site ( TSS ) as the center and pybedtools [73] for the calculations followed by matplotlib ( http://matplotlib . org ) to plot . Selected promoters were obtained from the LightSwitch Promoter Collection ( SwitchGear Genomics ) and sequence verified in the pLightSwitch_Prom vector . Candidate enhancers were amplified from human genomic DNA ( Roche ) using oligonucleotides designed in Primer3 with 16 bp overhangs ( 5′-GCTCGCTAGCCTCGAG-3′ and 5′CGCCGAGGCCAGATCT-3′ ) complementary to the sequence flanking the BglII and XhoI sites in the pGL4 . 23 vector ( Promega ) . Primers were designed to encompass the PXR/p300 ChIP-seq peak plus up to 500 bp of sequence on either side of the peak . PCR products were cleaned using the QIAquick PCR Purification Kit ( Qiagen ) and cloned into BglII and XhoI digested pGL4 . 23 using the Infusion HD cloning system ( Clontech ) . Haplotypes were generated either by PCR amplification of DNA from various ethnic individuals or by site-directed mutagenesis using mutant primers amplified by PfuUltra High Fidelity DNA polymerase ( Agilent ) followed by DpnI digestion of the parental DNA ( New England Biolabs ) and transformation into competent cells . Sequences were verified by Sanger sequencing using RVPrimer3 ( 5′-CTAGCAAAATAGfGCTGTCCC-3′ ) and a custom reverse primer ( 5′-TCTTCCATGGTGGCTTTACC-3′ ) . We manually curated the NHGRI GWAS catalog [74] , dated December 15th , 2011 , to identify 397 SNPs from 92 pharmacogenomic GWAS studies . Using data from the 1000 Genomes March 2012 Interim release ( http://1000genomes . org ) , we identified a larger set of SNPs in linkage disequilibrium with those SNPs ( r2>0 . 8 ) in the race/ethnicity corresponding to the pharmacogenetics study . Ten rifampin-induced PXR peaks overlapped at least one of these GWAS-LD SNPs ( Table S6 ) and seven were selected for functional enhancer assays . To determine enrichment , we compared overlap of the LD blocks defined by the 397 lead SNPs from pharmacogenomic GWAS to 5 , 117 lead SNPs from non-pharmacogenomic GWAS . These assignments were curated manually . Each list of SNPs was intersected with the 6 , 302 rifampin-induced PXR peaks , resulting in 27 and 162 overlapping SNPs respectively . Using a two-tailed Chi-squared test with Yates' correction , we found the enrichment to be highly significant ( p<0 . 0001 ) . HepG2 cells ( ATCC ) were maintained in D-MEM ( Life Technologies ) supplemented with FBS ( JRS Scientific ) , Penicillin-Streptomycin and Glutamine ( UCSF Cell Culture Facility ) . On the day of transfection , the cells were trypsinized , washed , and diluted to a density of 1 million cells/ml in Opti-MEM I Reduced Serum Media ( Life Technologies ) . Ninety thousand cells and 125 , 000 cells were added to each well of a 96-well clear bottom black tissue culture plate ( Greiner Bio-One ) containing the transfection mixture for the promoter and enhancer assay respectively . The transfection mixture consisted of 50 ng of human PXR ( PXR cDNA cloned into the pcDNA3 . 1 ( + ) vector ) , 10 ng of pGL4 . 74 , 21 . 5 ul Opti-MEM , 100 ng of reporter construct , and 0 . 48 ul of Lipofectamine LTX reagent ( Life Technologies ) . After 18 hours of incubation at 37 degrees Celsius , the cells were washed with 150 ul Opti-MEM and the media was replaced with 100 ul of Opti-MEM supplemented with 10 uM Rifampin or 0 . 1% DMSO . After 24 hours of incubation , the cells were washed with PBS and the promoter assay cells were placed at −80 degrees Celsius for 30 minutes to lyse and the enhancer assayed cells were lysed with 25 ul of Passive Lysis Buffer ( Promega ) . All LightSwitch promoter vectors were measured with the LightSwitch Luciferase Assay reagents ( SwitchGear Genomics ) following the manufacturers protocol . For enhancers , reporter activity was measured using the Dual-Luciferase Reporter Assay System ( Promega ) . Both assays were measured on a microplate luminometer ( Promega ) . We used the 1000 Genomes March 2012 Interim release ( ) , which contained genotypes for 379 White , 286 Asian , 181 Latino , and 246 African race/ethnicity individuals . For each gene RIR/GLP region , we identified haplotypes determined by computationally phasing the SNPs in the program PLINK v1 . 07 [75] , again using the corresponding race/ethnicity . ChIP-seq and RNA-seq data has been made publically available through NCBI ( ChIP-seq BioProject ID: PRJNA239635; RNA-seq BioProject ID: PRJNA239637 ) . | Drug response varies between individuals and can be caused by genetic factors . Nucleotide variation in gene regulatory elements can have a significant effect on drug response , but due to the difficulty in identifying these elements , they remain understudied . Here , we used various genomic assays to analyze human liver cells treated with or without the antibiotic rifampin and identified drug-induced regulatory elements genome-wide . The testing of numerous active promoters in human liver cells showed only a few to be induced by rifampin treatment . A similar analysis of enhancers found several of them to be induced by the drug . Nucleotide variants in one of these enhancers were found to alter its activity . Combined , this work identifies numerous novel gene regulatory elements that can be activated due to drug response and thus provides candidate sequences in the human genome where nucleotide variation can lead to differences in drug response . It also provides a universally applicable method to detect these elements for other drugs . | [
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| 2014 | Genome-Wide Discovery of Drug-Dependent Human Liver Regulatory Elements |
Cutaneous mechanoreceptors transduce different tactile stimuli into neural signals that produce distinct sensations of touch . The Pacinian corpuscle ( PC ) , a cutaneous mechanoreceptor located deep within the dermis of the skin , detects high frequency vibrations that occur within its large receptive field . The PC is comprised of lamellae that surround the nerve fiber at its core . We hypothesized that a layered , anisotropic structure , embedded deep within the skin , would produce the nonlinear strain transmission and low spatial sensitivity characteristic of the PC . A multiscale finite-element model was used to model the equilibrium response of the PC to indentation . The first simulation considered an isolated PC with fiber networks aligned with the PC’s surface . The PC was subjected to a 10 μm indentation by a 250 μm diameter indenter . The multiscale model captured the nonlinear strain transmission through the PC , predicting decreased compressive strain with proximity to the receptor’s core , as seen experimentally by others . The second set of simulations considered a single PC embedded epidermally ( shallow ) or dermally ( deep ) to model the PC’s location within the skin . The embedded models were subjected to 10 μm indentations at a series of locations on the surface of the skin . Strain along the long axis of the PC was calculated after indentation to simulate stretch along the nerve fiber at the center of the PC . Receptive fields for the epidermis and dermis models were constructed by mapping the long-axis strain after indentation at each point on the surface of the skin mesh . The dermis model resulted in a larger receptive field , as the calculated strain showed less indenter location dependence than in the epidermis model .
Mechanoreceptors , a major component of the somatosensory system , detect specific physical stimuli and produce neural signals that give rise to sensations such as touch and pain [1] . Cutaneous mechanoreceptors respond to mechanical stimuli and consist of afferent nerve fibers surrounded by specialized end organs that collectively encode a wide range of different touch sensations [2 , 3] . The Pacinian corpuscle ( PC ) is a cutaneous mechanoreceptor that responds primarily to vibratory stimuli in the frequency range of 20–1000 Hz [4 , 5] . The PC has low spatial sensitivity across the surface of the skin , and the receptive field of a single PC may span an entire hand [2] . The PC is located in the dermis of glabrous skin [6 , 7] . The PC has been widely studied because of its relatively large size ( Fig 1 ) . The PC is about 1 mm in length by 0 . 67 mm in width and has an ovoid shape with a single myelinated nerve fiber located along the long axis of the receptor [8 , 9] . The nerve fiber is surrounded by three main zones: an inner core , which contains bilaterally arranged cytoplasmic lamellae; an intermediate growth zone; and an outer core , which consists of 30 or more concentrically aligned collagenous lamellae [9] . The lamellae are believed to act collectively as a high-pass filter that shields the nerve fiber at the receptor center from low frequency , high amplitude stimuli [2 , 10 , 11] . The PC is a difficult organ to understand because its function involves a complex , interrelated set of biological , chemical , mechanical , and electrical phenomena . Much recent work has focused on the development of the PC and identification of relevant transcription factors , such as c-Maf and ER-81 [12 , 13] . Release of GABA from the inner core has also been suggested to play a role in the rapid adaptivity of the PC [14] . In the electrical and neural engineering arena , Lesniak and Gerling [15] have recently put forward computational models of the tactile mechanosensory system . None of these studies , however , address the fundamental mechanics of the PC and the role of its structure in determining how skin displacement is transmitted to the PC neurite . The work of Güçlü et al . [7] was an important exploration of PC mechanics . They used finite-element modeling to investigate the role of the PC’s geometry in its mechanical response to static indentation . Experimental data in which PCs were indented by cylindrical contactors with step waveforms of various amplitudes were compared to the computational models . Semi-infinite plane and ovoid models produced similar displacements within the PC in response to static indentation , and neither model matched the localization of strain near the contractor seen in the experiment . The purpose of this study was to test two specific hypotheses about the biomechanics of the PC . First , Güçlü et al . rejected the hypothesis that receptor shape leads to the observed mechanical behavior of the PC , leaving open the question of how the strain concentrates near the indentation site; herein , we tested the hypothesis that mechanical anisotropy contributes to the strain localization . Second , having concluded that a structural model of the PC is mechanically acceptable , we used that model to test the hypothesis that deep embedding within the skin contributes to the low spatial sensitivity and large receptive field of the PC .
A multiscale scheme [16–18] was used to model the response of the PC to indentation . The method is summarized here and described in detail elsewhere [16–18] . A finite-element model at the macroscopic level was coupled with representative volume elements ( RVE ) , each comprised of a fiber network , at the microscopic scale ( Fig 2 ) . Each finite element contained eight Gauss points , each with an associated RVE . Each RVE contained a network of 500–700 fibers in a constrained mixture ( cf . [19 , 20] ) with a nearly incompressible neo-Hookean matrix . In this study , each element received a unique set of fiber networks depending upon its location within the mesh . Macroscopic-level deformations were passed down to the microscopic level , the networks within each RVE were thus stretched , and the force exerted by each fiber , F , was calculated using the fiber constitutive equation F=AB ( exp ( BEf ) −1 ) ( 1 ) where A is a measure of fiber stiffness , B is a measure of fiber nonlinearity , and Ef is the fiber Green strain computed from the fiber stretch , λf , Ef=0 . 5 ( λf2−1 ) ( 2 ) From the fiber forces on the RVE boundaries , the volume-averaged Cauchy stress at each Gauss point within the macroscopic element was calculated as Sijmacro=1V∫VSijmicro dV=∑bcxiFj ( 3 ) where V is the RVE volume , Sijmicro is the microscale stress , bc refers to summation over all network nodes on the RVE boundary , xi is the boundary fiber cross-link i-coordinate , and Fj is the force acting on the boundary fiber cross-link by the fiber in the j-direction . The averaged stress balance was given as [17] ( 4 ) where uk is the RVE boundary displacement and nk is the normal vector to the RVE boundary . The displacements were updated until the stress balance Eq ( 4 ) had equilibrated . Simulations were run on 64 cores at the Minnesota Supercomputing Institute . All finite-element meshes were generated in ABAQUS . A mesh convergence study on the isolated PC problem gave average errors of 5% in nodal displacement between the coarsest mesh and the finest mesh ( which was used in the study ) . Based on this result and our previous studies [16] , we expect our numerical results to have errors of at most 5% . Delaunay networks were created to populate the RVEs within the multiscale model . To capture the anisotropy of the collagenous lamellae within the PC , the networks for each finite element were aligned with the PC’s surface ( Fig 3A ) . In the embedded models , the networks populating skin elements were made transverse orthotropic and aligned with the surface of the skin to reflect dermal collagen organization [21] . Material constants A and B for eq ( 2 ) were set at 114 μN and 10 respectively , obtained from fitting the multiscale model to data from a published study on dermal mechanics [22] . The Poisson’s ratio for the neo-Hookean matrix was set at ν = 0 . 47 for the simulations to model a nearly-incompressible matrix . The matrix shear modulus was set at G = 4 . 2 kPa [16] . The properties of the Pacinian corpuscle lamellae are not known , and estimates of the modulus of the lamellar layers have ranged from 1 KPa [23] to 0 . 5 MPa [11] . Neither of those bounds was based on a published mechanical measurement of the properties of collagen fibers within the PC; the small value was estimated based on assumed high compliance of the basement membrane , and is consistent with an anecdotal reference from the literature ( [24] cited by [10] , but no published data in [24] ) , and the latter was based on arterial wall . Recent work [23] has found that using a low modulus for the lamellar stiffness gives more accurate predictions of PC response in the high-frequency range , but collagen fibers have been found to have moduli in the MPa range [25] , as have basement membranes from the renal tubule [26] and ocular lens [27–29] . It is thus clear that a better theoretical and structural description is needed , and it is likely that the choice of modulus will depend on the structure of that model . Since the emphasis of this work was on the effect of anisotropy , not on differences in stiffness between the corpuscle and the surrounding skin ( which could be a very important factor and which should be explored in future studies ) , we chose to give the fibers in our PC model the same properties in as the fibers in the skin . The isolated Pacinian corpuscle was meshed as a half-ellipsoid , with a major axis of length 1 mm and a minor axis of length 0 . 5 mm . The PC model contained 2984 hexagonal elements . The isolated PC model was subjected to 10 μm indentation by a 250 μm diameter indenter to simulate the experiments performed by Güçlü et al . and the associated finite-element model [7] . The indenter displaced nodes vertically , as shown in Fig 3A . For consistency with the Güçlü experiments , the nodal displacements in the isolated PC model were analyzed . The displacement of nodes located along the top 100 μm of the z-axis was calculated after 10 μm indentation . Strain ( εyy ) along the long axis of the PC was calculated for comparison to the embedded model .
The isolated PC was subjected to a 10 μm indentation with an indenter of diameter 250 μm to mimic the experiment and simulation of Güçlü et al . The multiscale model captured the nonlinear trend in displacement seen in the experimental data and not predicted by an isotropic linear elastic model ( Fig 4A ) . The multiscale model populated with isotropic Delaunay networks rather than circumferentially-aligned networks also produced results similar to Güçlü et al . ’s isotropic linear elastic model . Fig 4B shows the displacement of nodes along a cross section through the x-z plane of the PC at y = 0 . As seen in Fig 4B , the multiscale model predicted a nonlinear spacing between nodes , with a greater nodal gap occurring with increasing depth . The Von Mises stress was calculated at each element in the PC after 10 μm indentation for the cases of isotropic networks and circumferentially-aligned networks . In Fig 5 , the isotropic case shows stress of approximately 2x higher magnitude around the indenter than those shown for the circumferentially-aligned case . The strain along the long axis of the PC was calculated over 25 steps of 1 μm indentation . As seen in Fig 6 , the strain along the long axis of the axon increased with indentation into the PC . The strain calculated for the isotropic network case was approximately 10x higher than that calculated for the circumferentially-aligned network case . The long-axis strain along the PC resulting from indentation at various nodes along the surface was also compared for the epidermis and dermis models ( Fig 7 ) . The epidermal PC model showed large strain in response to loading directly above the PC that dropped off quickly as the indenter moved away from the PC . This drop-off implies more spatial sensitivity and thus a smaller receptive field . The dermal PC model shows less indenter position dependence and thus a larger receptive field . The Von Mises strain was calculated within every element in the dermal- and epidermal-embedded cases after 10 μm indentation for indentations above the center of the PC and 750 μm down its long and short axes . As seen in Fig 8 , the Von Mises strain in the PC in the dermis case shows little variation when the structure is indented at different locations . In both cases , PC strain is less than that of the immediately surrounding tissue because of its greater degree of anisotropy . The strain in the PC in the epidermis case shows greater variations with indenter location . The long-axis strain along the PC resulting from 10 μm indentation at various nodes along the surface of the skin was compared for horizontally-aligned and vertically-aligned PCs embedded within a dermal mesh ( Fig 9 ) . The horizontal PC model showed positive strains or no strain resulting from indentation . The vertical PC model always showed negative strains in response to indentation . This result shows that indentation within the receptive field of a horizontally-aligned PC always results in positive axial stretch of the neurite . Indentation of the vertical model does not result in neurite stretch .
This study used a multiscale finite-element model to determine that the structure of the PC is an important contributor to the nonlinear behavior of the receptor . In addition , it showed that the deep dermal location of the PC provides it with lower spatial sensitivity . Several factors must be considered when interpreting our results . First , the mechanical stimulus was a fixed indentation into the PC with no transient effects . As such , this study addresses the location and magnitude of the stimulus but it does not take stimulus frequency into account when determining the PC mechanical response , as others have [11] . A static model was chosen as suitable for comparison with experiment [7] , but it would not be appropriate for simulating the vibrotactile response . While Hubbard [31] also investigated PC mechanics , the results from the current paper cannot be directly compared to that study , which placed the PC within a hinged apparatus rather than stimulating with a vertical indenter . Therefore , the experiments performed by Güçlü et al . were used to validate the current model . Second , the PC was treated as incompressible , and no fluid movement was allowed within the PC even though such flow is known to be important [11]; thus , our model must be interpreted as the instantaneous response of the PC . The time-dependent response of the PC is necessary to address , as it is crucial to the PC’s role as a high-pass filter to vibration [5 , 11] , so insights from the current study should center on instantaneous response . Also , the experimental PC literature [4 , 5 , 32] focuses appropriately on the use of directly applied sinusoidal displacements to elicit the response of the PC to vibratory stimuli , providing a rich data set on the dynamical response of the PC . A model of PC mechanics should include a dynamical component ( fluid flow , viscoelasticity , or both ) to account for the phase difference that can occur between skin and PC stimulation , and also between PC stimulation and receptor response . The mechanical model used in this study also simplified the structure of the PC to account only for anisotropy within the receptor and not for its specific components and detailed structure . The receptor capsule is composed of concentrically-arranged collagenous lamellae through which mechanical forces are transduced . The lamellae consist of condensed cell layers separated by layers of pressurized fluid [33] . The structure of the capsule is believed to play an important role in determining which mechanical forces are transmitted to the axon [34] . Each lamella contains a single layer of flat squamous epithelial type cells , with interlamellar spacing increasing with distance from the inner core [10] . Tight junctions between cells within each lamella prevent fluid flow across lamellae , but flow within the fluid layer between adjacent lamellae is possible and is significant at low frequencies . Loewenstein and Skalak first proposed that the role of the PC capsule’s lamellar structure is that of a series of mechanical high-pass filters to shield the nerve fiber at the center of the receptor from low frequency , high amplitude stimuli [11] . To place this model in the broader context , it is more advanced than that of Güçlü , which is isotropic and linear elastic , but does not provide the single-lamellar-level description of Lowenstein and Skalak or of subsequent variations thereon [23 , 35] . The multi-scale approach of the current model , in which RVE’s are introduced with position-dependent fiber orientations , could be extended to more complex microstructures as greater structural information becomes available . It is also notable that the lamella-based models [11 , 23 , 35] can account for the apparent viscoelasticity of the tissue by incorporating interlamellar flow; our model would not be able to do so but could incorporate a continuous viscous contribution similar to that derived previously [33] in a homogenized model of the PC . Clearly , there is need for a more detailed microstructural model that can address other aspects of PC behavior and perhaps can resolve the disconnect between the high stiffness typical of collagenous tissues and the low stiffness reported experimentally [24] and used to describe vibrotactile mechanics of the PC [23] . The mechanical model presented in this study simplified the neurophysiology of the PC action potential generation into axial stretch of its central nerve fiber . The solid black line in Fig 7 indicates the division between the positive and negative stretch . Because the exact mechanism of axon excitation is unknown , it is possible that the neurite could also be stimulated during compression along the long axis . Long-axis compression could , for example , be experienced as positive stretch in other directions due to neurite incompressibility . Thus , while it is possible that long-axis compression could lead to axon excitation , only long-axis stretch was considered in this study . It was initially proposed by Gray & Ritchie [30] that sensory receptors respond to mechanical stimulation resulting from nerve stretch . After PC compression studies performed by Hubbard in 1958 were unable to measure a change in axon length within the error of measurement , other possible mechanical mechanisms for transduction were proposed [31] . Specifically , Hubbard proposed that a change in the ratio between the major and minor axes of the cross section of the nerve fiber leads to a change in surface area and thus membrane stretch . Fig 10 shows a comparison between the model of axon stretch along the long axis used in this study and the area change proposed by Hubbard . Both long-axis strain and area strain increase monotonically with indentation into the PC . The area change of the PC is approximately six times the long-axis strain after 25 μm of indentation . The orientation of the PC with respect to the surface of the skin was analyzed in this study . The results presented in Fig 9 show that indentation within the receptive field of a horizontally-aligned PC always results in either axial stretch of the neurite or no neurite stretch . Indentation within the receptive field of a vertically-aligned PC always results in neurite compression . The assumption used in this study that PC action potential generation is the result of axial stretch of the neurite and not axial compression means that the vertically-aligned PC was never activated by static compression . This study only shows the results of two PC orientations to static compression . A vibratory stimulus would involve both application and removal of an indenter due to oscillations , creating a more complex field that would likely include long-axis stretch in both cases . Thus , it is expected that different results on PC orientation would be obtained from models of vibration . It has previously been shown [36] that the electrophysiological response of an intact PC or its decapsulated nerve terminal changes polarity as it is rotated 90 degrees along its long axis . The same study also showed that a decapsulated terminal reversed the polarity of its neural response when compressed horizontally to the nerve or vertically to the nerve and proposed that the bilateral arrangement of cells around the terminal is responsible for changing how compression is transmitted to the neurite in these different orientations . It has also been shown [37] that compression of an intact PC along its long axis requires a much stronger stimulus to cause depolarization than that required in compression along the short axis . The current study models the transmission of mechanical stimuli through the lamellae to the neurite , and showed differences with PC orientation , but does not address the electrophysiological effects reported by others [36 , 37] . A combined model ( cf . [33 , 38] ) could lend greater insight into the interaction between mechanical and electrophysiological events . Incorporation of isotropic Delaunay networks into the PC multiscale model rather than circumferentially-aligned networks resulted in a lack of shape dependence similar to that observed in Güçlü’s finite-element study [7] . The current study thus confirms the finding that the ellipsoidal shape of the PC is not per se responsible for the observed mechanical behavior . In addition , the use of isotropic Delaunay networks further confirmed Güçlü‘s result that a homogeneous isotropic model of the PC cannot predict the experimentally observed displacement pattern in response to indentation . This study showed that the internal anisotropic structure is an important factor leading to the nonlinear displacements through the PC . The nonlinear reduction in displacement of lamellae located closer to the central core is in agreement with the hypothesis that the lamellar structure can help protect the nerve from large deformations under large skin surface loads . The current model bases the mechanical properties of the networks representing the skin on data from uniaxial mechanical tests on dermis [22] . There are many factors that can influence the mechanics of skin , which can vary with anatomical location , proximity to blood vessels , thickness , body weight , hair-cycle stage , skin disease , and experimental conditions such as humidity [39–42] . Specifically , mechanical and structural properties such as viscoelasticity and anisotropy of skin can vary with age and anatomical location [42] . The mechanical properties of a skin sample are influenced by structural components such as the collageneous fiber network and the presence of different layers , which exhibit different mechanical properties , within the skin [35 , 40 , 41 , 43] . Selecting different data for fitting of the skin element mechanical properties used in this study would likely change the quantitative results of the current study due to differences in the aforementioned factors . The overall qualitative results of this study , however , are not expected to change . The mechanics of skin can also vary depending on the type of load applied , as skin behaves differently under compression and tension [39 , 40] . In vivo skin can also be in different amounts of tension depending on anatomical location , the body position , and the individual [42] . All of the aforementioned factors could be considered in future models , with the current model functioning as a basis for subsequent studies and as a low-order model of skin behavior . Past experiments have shown that the isolated PC is a highly sensitive mechanoreceptor with nanometer sensitivity [2] . The simulations performed in this study model the sensitivity of an isolated and embedded PC to micrometer-scale indentations . Sensitivity thresholds of the PC have been reported previously as 3 nm applied directly to the PC and 10 nm applied to the surface of the skin [2] . In the current study , the smallest indentation tested on the isolated PC was 1 μm , which corresponds to an amplitude of approximately 7 μm applied to the skin after comparison of the strain along the long axis of the PC in the isolated and dermis-embedded models . This ratio could change for a more anatomically detailed model , in which multiple receptors rather than just one are located within the dermis . There are currently no published experimental data on the mechanics on a PC embedded in skin . While this work focused on the PC , its results can also provide insight into other cutaneous mechanoreceptors . The embedded PC simulations showed that a PC located in the dermis of the skin was able to replicate the low spatial sensitivity of a PC in vivo . The PC embedded in the epidermis had a higher spatial sensitivity within the receptive field tested in the simulations . Receptors located closer to the surface of the skin , such as the Meissner corpuscle and Merkel cell-neurite complex , show decreased receptive fields and thus increased spatial sensitivity to mechanical events on the skin surface [44] . The current study could also be expanded to include the different geometries and cutaneous locations of other mechanoreceptors . | We performed computer simulations of the mechanical behavior of the Pacinian corpuscle ( PC ) , a sensory receptor in the skin that helps detect short-term contact and high-frequency vibration . The PC is composed of a series of tissue layers , and we found that this characteristic structure may explain the response of the PC in indentation experiments . We also found that the deep placement of the PC within the skin allows it to detect stimuli over a wide area of the skin but not to distinguish the specific location of the stimulus . These findings are a step but still an early step towards the broad goal of understanding how mechanical stimuli to the skin are translated into neural signals to the spinal cord and brain , and many open questions still remain about how the different sensors of the nervous system work together to create our sense of touch . | [
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| 2015 | Multiscale Mechanical Model of the Pacinian Corpuscle Shows Depth and Anisotropy Contribute to the Receptor’s Characteristic Response to Indentation |
Uganda is the only country where the chronic and acute forms of human African Trypanosomiasis ( HAT ) or sleeping sickness both occur and are separated by < 100 km in areas north of Lake Kyoga . In Uganda , Glossina fuscipes fuscipes is the main vector of the Trypanosoma parasites responsible for these diseases as well for the animal African Trypanosomiasis ( AAT ) , or Nagana . We used highly polymorphic microsatellite loci and a mitochondrial DNA ( mtDNA ) marker to provide fine scale spatial resolution of genetic structure of G . f . fuscipes from 42 sampling sites from the northern region of Uganda where a merger of the two disease belts is feared . Based on microsatellite analyses , we found that G . f . fuscipes in northern Uganda are structured into three distinct genetic clusters with varying degrees of interconnectivity among them . Based on genetic assignment and spatial location , we grouped the sampling sites into four genetic units corresponding to northwestern Uganda in the Albert Nile drainage , northeastern Uganda in the Lake Kyoga drainage , western Uganda in the Victoria Nile drainage , and a transition zone between the two northern genetic clusters characterized by high level of genetic admixture . An analysis using HYBRIDLAB supported a hybrid swarm model as most consistent with tsetse genotypes in these admixed samples . Results of mtDNA analyses revealed the presence of 30 haplotypes representing three main haplogroups , whose location broadly overlaps with the microsatellite defined clusters . Migration analyses based on microsatellites point to moderate migration among the northern units located in the Albert Nile , Achwa River , Okole River , and Lake Kyoga drainages , but not between the northern units and the Victoria Nile drainage in the west . Effective population size estimates were variable with low to moderate sizes in most populations and with evidence of recent population bottlenecks , especially in the northeast unit of the Lake Kyoga drainage . Our microsatellite and mtDNA based analyses indicate that G . f . fuscipes movement along the Achwa and Okole rivers may facilitate northwest expansion of the Rhodesiense disease belt in Uganda . We identified tsetse migration corridors and recommend a rolling carpet approach from south of Lake Kyoga northward to minimize disease dispersal and prevent vector re-colonization . Additionally , our findings highlight the need for continuing tsetse monitoring efforts during and after control .
The tsetse fly ( genus Glossina ) is the major vector of human African trypanosomiasis ( HAT ) and animal African trypanosomiasis ( AAT ) . The diseases occur throughout sub-Saharan Africa , causing extensive morbidity and mortality in humans and livestock [1][2] . Human disease is caused by two different subspecies of the flagellated protozoa Trypanosoma brucei; T . b . rhodesiense in eastern and southern Africa , and T . b . gambiense in west and central Africa . The two HAT diseases are separated geographically more or less along the line of the Great Rift Valley [3] . Although the animal disease ( or Nagana ) is caused by different trypanosome subspecies; T . b . brucei , T . congolense and T . vivax , animals are also known to be reservoirs of the human infective T . b . rhodesiense . Thus , while AAT is a problem in its own right because of economic losses and reduced availability of nutrients [4][5][6] , the animals also act as important reservoirs for human infective T . b . rhodesiense . Although the human diseases have been on a decline[7] , they still put 60 million people at risk in 37 countries covering about 40% of Africa [8][9] . The human disease T . b . gambiense is near elimination while control of T . b . rhodesiense remains more complicated because of animal reservoirs . For both T . b . gambiense and T . b . rhodesiense , there are no prophylactic drugs or vaccines available . Furthermore , the drugs for treatment are expensive , can cause severe side effects , and are difficult to administer in remote villages [10][11] . Although AAT can be prevented with prophylactic drugs and effectively treated with trypanocidal drugs , progress towards elimination of the animal disease has been slow because of the high cost of drug administration and repeated emergence of drug resistance [12] . Thus , AAT instances remain high and continue to burden livestock farmers [13] and provide animal reservoirs of T . b . rhodesiense . As a consequence , the most effective way to control both AAT and HAT is control of the tsetse vector [14] . Uganda is in the precarious position of being the only country that harbors both forms of HAT , with T . b . gambiense present in the northwestern corner of the country and T . b . rhodesiense found in the center and southeast [15] . There is a significant risk that the two sleeping sickness subspecies will merge in the north-central districts of Uganda , a region already burdened by political and social instability [16] . Merging of the two disease belts would complicate treatment and diagnosis [17] , and may lead to the emergence of unforeseen pathologies if there is recombination between the T . b . gambiense and T . b . rhodesiense trypanosomes [18][19][20] . Glossina fuscipes fuscipes is a member of the palpalis group of tsetse and is the main vector implicated in the transmission of both AAT and HAT in Uganda . The vector is distributed over vast regions of sub-Saharan Africa ( Fig 1 ) , where it occupies discrete patches of riverine and lacustrine habitats distributed among pasture and agricultural land . Assessing the population structure and the extent to which these apparently discrete populations are connected by dispersal and migration patterns is central to defining the most effective scale for vector control [21][22] . For example , the major challenge that faces most control efforts is tsetse rebound following short-term control efforts . The source of rebounding populations could be residual pockets of surviving individuals or migrant flies coming from neighboring untreated regions , or both [23] . Increased knowledge of vector population dynamics through application of population genetics can help in assessing the suitability of the operational units selected for vector control and result in more effective tsetse control . Although previous studies have made great strides towards understanding the population biology of G . f . fuscipes in Uganda [24][25][26][27][28] , regions north of Lake Kyoga remain a high priority for additional study . Northern Uganda harbors both forms of HAT in close geographic proximity [7][20] . Identifying the precise extent of the two disease belts and possible risk of merger has been difficult until recently because of social and political upheaval experienced in these regions [29][30] . Our previous population genetic studies have identified three major genetic units present in north and south of Lake Kyoga and in western Uganda [24][25][31] . Each of these units consists of genetically distinct populations with high differentiation between sampling sites and evidence of further sub-structuring [22][25][31][24] . A more detailed understanding of the genetic structure and population dynamics of G . f . fuscipes in northern Uganda will help estimate the likelihood of the merger of the two HAT disease forms , and identify the best tsetse control strategies for the region . In this study , we comprehensively sampled G . f . fuscipes from 42 sites in areas north of Lake Kyoga and assessed variation in 16 nuclear microsatellites and over a 570 bp region of mitochondrial DNA ( mtDNA ) to understand both short time scale resolution of demographic events [32][33] and inference of phylogeographic events dating further back in time [34][35] . We compared this new knowledge of fine scale population structure , migration patterns and population dynamics in northern Uganda with previous studies that concentrated on the southern and central regions of the country . This comparative approach allowed us deeper insights into the evolutionary forces at play and enriched our ability to make recommendations for G . f . fuscipes control strategies in northern Uganda .
The map in Fig 1 shows the sampling sites . We used biconical traps [36] to collect from 30 sites between January 2014 and April 2015 , and also included 12 collections from a previous study between January 2008 and January 2012 [31][25] . Sampling sites were chosen to detect fine spatial scale population structure . To do this , we collected from multiple sites separated by just over 5 km , which is the smallest unit area for which genetic differentiation has been observed in G . f . fuscipes in Uganda [25] . At each site , we placed an average of 6 traps at least 100 m from each other and collected an average of 18 flies per trap over a period of 3–4 days . Flies were stored individually in 95% ethanol and information on sex , collection date , trap number , and geographic coordinates of each trap was recorded . The genotypic data included from previous studies [25][29] were separated from our samples by a time span of 3–7 years ( approx . 22–52 generations ) , which opened up the possibility of genetic change . However , a previous study showed no evidence of large demographic changes between the temporal collections [37] , justifying the combined analyses of 42 sampling sites spanning these time points ( details in S1 Table ) . We extensively sampled in areas north of Lake Kyoga , which includes tsetse flies that in a previous study were grouped into two genetic clusters [25] , with an effort to sample major water drainage systems . [25] described one genetic cluster north of the Lake Kyoga and the Victoria Nile , and one in western Uganda . In the northern genetic cluster , we sampled the Albert Nile , the Achwa River , the Okole River , and Lake Kyoga drainages ( see Table 1 ) . The Albert Nile basin is in the far northwest corner of Uganda , a region known to be an active focus of T . b . gambiense sleeping sickness [20][38] . The Albert Nile is bordered to the east and eventually joined by the Achwa River , and both drainage systems generally consist of patchy habitat suitable for G . f . fuscipes , characterized by lowland woodland near semi-permanent water bodies [2] . Habitat patches are surrounded by unsuitable savannah , agricultural and pastoral lands . Although the district of Arua was recently included in a pilot vector control program in 2011–2013 [[39]] , our samples from 2014 ( DUK , AIN and GAN ) that may have been impacted did not overlap spatially with the program . Further south , we sampled the Okole River and Lake Kyoga basins ( Fig 1 , Table 1 ) . These regions form vast areas of marsh and swampland , and are the most northerly geographical extent of the T . b . rhodesiense HAT disease belt [40] . Some districts in the Lake Kyoga drainage , such as Dokolo and K’maido , were targets of the Stamp Out Sleeping Sickness ( SOS ) campaign of 2006–2009 [41] , which may have impacted our samples from this region from 2009 ( OC ) and 2014 ( OCU , AMI and KAG ) . Finally , in the western genetic cluster described by [25] , we collected samples along and south of the Victoria Nile ( Fig 1 , Table 1 ) , which flows northwest from Lake Kyoga into Lake Albert on the edge of the western rift valley . Here we sampled sites along and on minor tributaries of the Victoria Nile in the districts of Masindi and Kiryandongo ( Table 1 ) . This region is characterized by lowland woodland and the Uganda Wildlife Authority protects much of the region as part of the Murchison Falls National Park . DNA was extracted from two to three legs per sample , using PrepGEM Insect DNA Extraction kit ( ZyGEM New Zealand , 2013 ) , following the manufacturer’s protocols and stored at -20°C . We collected genotypic data from 16 microsatellite loci ( details in S2 Table ) . Amplifications were performed with fluorescently labeled forward primers ( 6-FAM , HEX and NED ) using a touchdown PCR ( 10 cycles of annealing at progressively lower temperatures from 60°C to 51°C , followed by 35 cycles at 50°C ) in 13 . 0μl reaction volumes containing 2 . 6 μl of 5X PCR buffer , 1 . 1 μl of 10 mM dNTPs , 1 . 1 μl of 25mM MgCl2 and 0 . 1 μl of 5 units/μl GoTaq ( Promega , USA ) , 0 . 1 μl of 100X BSA ( New England Biolabs , USA ) , 0 . 5 μl of 10 μM fluorescently-labeled M13 primer , 0 . 5 of μl 10 μM reverse primer , and 0 . 3 μl of 2 μM M13-tailed forward primer . For loci C7b and GmL11 , 0 . 5 units of Taq Gold polymerase ( Life Technologies , USA ) were used instead of Promega GoTaq . PCR products were multiplexed in groups of two or three and genotyped on an ABI 3730xL Automated Sequencer ( Life Technologies , USA ) at the DNA Analysis Facility on Science Hill at Yale University ( http://dna-analysis . yale . edu/ ) . Alleles were scored using the program GENEMARKER v2 . 4 . 0 ( Soft Genetics , State College , PA , USA ) with manual editing of the automatically scored peaks . We followed the protocol designed by [25] to sequence a 570 bp fragment of mtDNA that spans the COI and COII genes . Briefly , we used primers COIF1 ( 5’–CCT CAA CAC TTT TTA GGT TTA G– 3’ ) and COIIR1 ( 5’–GGT TCT CTA ATT TCA TCA AGT A– 3’ ) to amplify 570 bp with an initial denaturation step at 95°C for 5 min , followed by 40 cycles of annealing at 50°C , and a final extension step at 72°C for 20 min . We used a reaction volume of 13 . 0 μl containing 1 μl of template genomic DNA , 2 . 6 μl of 5X PCR buffer , 1 . 1 μl of 10 mM dNTPs , 0 . 5 μl of 10mM primers , 1 . 1 μl of 25 mM MgCl2 , and 0 . 1 μl ( U/μL ) of GoTaq polymerase ( Promega , USA ) . The PCR products were purified using ExoSAP-IT ( Affymetrix Inc . , USA ) as per the manufacturer’s protocol . Sequencing was carried out for both forward and reverse strands on the ABI 3730xL automated sequencer at the DNA Analysis Facility on Science Hill at Yale University ( http://dna-analysis . research . yale . edu/ ) . Sequence chromatograms were inspected by eye and sequences trimmed to remove poor quality data using GENEIOUS v6 . 1 . 8 ( Biomatters , New Zealand ) . The forward and reverse strands were used to create a consensus sequence for each sample , and the sequences trimmed to a length of 490 bp . Only a subset of the samples screened for microsatellite variation was also sequenced at the mtDNA locus ( Table 1 ) . For nuclear microsatellite marker validation , we tested for neutrality and independence with GENEPOP v4 . 2 [42] . We tested for departures from Hardy-Weinberg ( HW ) proportions in each sample and microsatellite locus using an approximation of an exact test based on a Markov chain iteration ( 10 , 000 dememorization steps , 1000 batches , 10 , 000 iterations per batch in the Markov chain ) ; significance values were obtained following the Fisher’s method that combines probabilities of exact tests [43] . We tested for genotypic linkage disequilibrium ( LD ) among each pair of loci using the Guo and Thompson method [44] . To correct for false assignments of significance by chance alone for all simultaneous statistical tests and comparisons , we used the Benjamini-Hochberg False Discovery Rate ( FDR ) method [45]as opposed to the Bonferroni correction , because of lower incidence of false negatives[45][46] . For nuclear microsatellite data , we assigned individuals to genetic units without prior information on sampling locality with STRUCTURE v2 . 3 . 4 [47][48] . STRUCTURE simultaneously identifies unique genetic units and provides a probability of assignment ( q-value , ranging from 0 to 1 ) for each individual . Twenty independent replicate runs for each K = 1–10 were carried out with an admixture model , independent allele frequencies , and a burn-in value of 50 , 000 steps followed by 250 , 000 iterations . The optimal value of K was determined using STRUCTURE HARVESTER v0 . 6 [49] to calculate the ad hoc statistic “ΔK” [50] , and independent replicates were aligned with CLUMPP v1 . 1 . 2 [51] . In addition to STRUCTURE , we performed Discriminant Analysis of Principal Components ( DAPC ) with the "adegenet" package v1 . 4–2 [52] in the R version 3 . 0 . 2 environment [53] . The DAPC is a multivariate , model-free method that makes no assumptions about deviations from Hardy Weinberg and linkage disequilibrium , designed to describe patterns of genetic clustering among groups of individuals [54] . In this analysis , we grouped samples by their site of origin and used the cross-validation formula available to choose number of principal components ( PCs ) to retain . To understand the partitioning of microsatellite variance within and between genetic units , we performed an analysis of molecular variance ( AMOVA ) in ARLEQUIN v3 . 5 [55] . Genetic diversity indices including observed heterozygosity ( HO ) , expected heterozygosity ( HE ) , number of alleles , allelic richness ( AR ) and [56] estimator of inbreeding coefficient ( FIS ) were calculated in GENALEX v6 . 501 [57] . Pairwise differentiations at different hierarchical levels were estimated with two F-statistics . For comparison with previous G . f . fuscipes studies , we calculated Wright’s F-statistics [58] , following the variance method developed by [56] using 10 , 000 permutations in ARLEQUIN . For accuracy with highly polymorphic markers [59] , we estimated Jost’s DEST with the R package DEMEtics [60][53] , where p-values and confidence intervals were calculated based on 1000 bootstrap resamplings . With the resulting F-statistics , we tested for isolation by distance ( IBD ) using Rousset’s procedure [61] implemented in the “isolation by distance” v3 . 23 web service [62] . Geographic distances were generated using the web-based “geographic matrix generator” v1 . 2 . 3 [63] . The significance of the regression was tested by a Mantel test with 10 , 000 randomizations [64] . Using microsatellite data , we estimated effective population size ( Ne ) for each sampling site independently . We did not group sites based on assignment to genetic clusters because strong evidence of substructure within clusters would violate assumptions . We estimated Ne using two methods implemented in NEESTIMATOR v2 . 01 [65]: the modified two-sample temporal method [66] based on [67] for sites with multiple temporal samples , and the one-sample linkage disequilibrium ( LD ) method [68] for all 42 sites . We used two methods to estimate Ne because they each have different strengths and weaknesses [66 , 69–71] . The two-sample temporal method [64] is useful because it is robust when there are overlapping generations [71] , but only provides an average estimate across two time points assuming a closed population , so cannot be used to assess the impact of control efforts . On the other hand , the LD method [66] is useful because it can provide an estimate for each sampling point and employs the bias corrections by [72] , but is influenced by bias associated with non-overlapping generations and is not powerful enough to distinguish from infinite population sizes when there are insufficient polymorphisms and numbers of markers to detect patterns of LD [67 , 71] . We tested for population bottlenecks using two methods implemented in the program BOTTLENECK v1 . 2 . 02 [73] . The first method tested for an excess of heterozygosity relative to observed allelic diversity [74] . We used the two-phase mutation model ( TPM ) , the most appropriate for microsatellites [75] , with 70% single-step mutations and 30% of multiple-step mutation . Significance was assessed using Wilcoxon’s signed rank test , as is recommended when fewer than 20 loci are used [73] . The second method tested for a bottleneck-induced mode shift in allele frequency distributions that is usually evident in recently bottlenecked populations [76] . We investigated the mixed ancestry suggested by STRUCTURE analysis in the Achwa and Okole River regions . These sampling sites displayed an average probability of assignment ( q-values ) of less than 0 . 8 ( See Table 1 ) , which could either be caused by methodological shortcomings ( i . e . low genetic distance and inability of the markers to distinguish clear genetic clusters ) , or by accurate detection of interbreeding of two distinct lineages . Following [77] , we tested for accurate detection of interbreeding by comparing observed admixture data against two alternative admixture models; a pure mechanical mixing model representing a scenario of strong reproductive barriers and free migration , and a hybrid swarm model representing a scenario of free hybridization and admixture using HYBRIDLAB v1 . 0 [78] . For the mechanical mixing model , we simulated individual admixture proportions by randomly drawing alleles from the observed allele frequency distribution of 'pure' samples where the average probability of assignment ( q-values ) were greater than 0 . 8 to a single STRUCTURE cluster ( Table 1 ) . For the hybrid swarm model , we simulated individual admixture proportions from the observed allele frequency distribution of 'admixed' samples where the average probability of assignment ( q-values ) were less than 0 . 8 to any single STRUCTURE cluster ( Table 1 ) . We chose localities from the geographic extremes of the northwest and northeast units to represent 'pure' samples , and regions with generally uncertain assignment from the Achwa and Okole Rivers to represent 'admixed' samples . Then , we used STRUCTURE to estimate individual probability of assignment ( q-value ) with all three datasets; the true observed genotypes , the simulated genotypes under a mechanical mixing model , and the simulated genotypes under a hybrid swarm model . Finally , we used a Wilcoxon signed rank test to assess differences between observed and simulated distributions . We interpret significant differences between simulated mechanical mixing and hybrid swarm datasets as evidence that uncertain STRUCTURE assignments do not represent a methodological shortcoming . Likewise , we interpret non-significant differences between the observed data and the simulated hybrid swarm data as evidence for interbreeding . Using microsatellite data , we determined if patterns of observed genetic structure could be attributable to sampling related individuals , by testing for relatedness between individuals using the program ML-Relate [79] . We assigned pairwise relationships within each genetic unit into one of four relationship categories: unrelated ( U ) , half siblings ( HS ) , full siblings ( FS ) or Parents/offspring ( PO ) . Detection of first generation migrants and progeny of successful mating of very recent migrants between genetic regions was done using GENECLASS v2 . 0 [80] , and using FLOCK v3 . 1 [81] , a program that provides accurate assignment of individuals to genetic units of origin even in the absence of pure genotypes . In GENECLASS , we used the "detect migrant function" , which calculates the likelihood of finding an individual in the locality in which it was sampled ( Lh ) , the greatest likelihood among all sampled localities ( Lmax ) , and their ratio ( Lh/max ) to identify migrants . To distinguish true from statistical migrants ( type I error ) , we selected the Rannala and Mountain criterion [82] , and the Monte Carlo resampling algorithm of [83] ( n = 1000 ) to determine the critical value of the test statistics , Lh/Lmax . Individuals were considered immigrants when the probability of being assigned to the reference population was lower than 0 . 05 . In FLOCK , we used a K value of 4 , starting partitions chosen by location of origin , ran 500 iterations and used a log-likelihood difference threshold ( LLOD ) value of 1 . For the mtDNA sequence data , all statistical parameters and tests were calculated using the program ARLEQUIN [55] . Genetic diversity within populations was estimated by computing haplotype diversity ( Hd ) and nucleotide diversity ( Nd ) [84] in DnaSP v5 . 0 [85] . Relationships among haplotype lineages were inferred by constructing a parsimony network using TCS [86] implemented in the free , open source population genetics software PopART ( http://otago . ac . nz ) . We used nucleotide diversity to estimate genetic differentiation ( ΦST ) and performed an analysis of molecular variance ( AMOVA ) in ARLEQUIN . We tested for IBD with the same methods described above for the microsatellites . Finally , we compared mtDNA haplogroup assignment with the Microsatellite STRUCTURE assignment , and tallied the percent mismatch . Individuals were considered a mismatch if they displayed a high q-value ( probability of assignment ) score to one microsatellite based cluster but low frequencies of the haplogroup generally found in the same geographic region as the microsatellite based cluster .
Of the 17 microsatellite markers considered , most were under HW equilibrium in the majority of populations with the exception of pg17 , which was dropped from the analyses , as it showed significant departures from HWE at P<0 . 05 . All remaining loci were polymorphic in all populations analyzed except D101 , which was monomorphic in one population ( OC ) . The most polymorphic locus was GpB20b ( 24 alleles ) , while the least polymorphic was B05 ( 5 alleles; details in S3 Table ) . None of the LD tests on pairs of microsatellite loci gave a significant result after the Benjamini-Hochberg correction , confirming neutrality and independence of markers . Fig 2A shows the results from STRUCTURE analyses . In this analysis , individuals fell into three genetic clusters with clear geographic variation in probability of assignment ( q-value ) ( Table 1 , S4 Table ) . The DAPC multivariate analysis ( S1 Fig ) corroborated the results of STRUCTURE . Based on the results of the STRUCTURE and DAPC analyses and their geographic locations , we grouped sampling sites into four units: West , Northwest , Transition Zone , and Northeast . Sampling sites west of lake Kyoga along the Victoria Nile ( i . e . UWA , KR , KF , MS ) had average q-values > 0 . 8 to a single cluster ( blue in Fig 2 ) . The samples north of Lake Kyoga ( Fig 1 ) belong to two genetic clusters ( gray and orange , Fig 2 ) and were grouped into three units . The “Northwest” unit comprises samples from the Albert River drainage ( e . g . DUK , GAN , and AIN ) and the most northerly Achwa River sites ( i . e . NGO , LAG and PAW ) with average q-values > 0 . 8 to a single cluster ( gray in Fig 2 ) . The “Northeast unit” comprises samples from north of Lake Kyoga ( e . g . KAG , AM , OCU and AMI , Table 1 ) with average q-values > 0 . 8 to one cluster ( orange in Fig 2 ) . Sampling sites between the Northwest and Northeast units in the Achwa and Okole River basins ( Fig 1 ) had a much lower average q-value ( 0 . 54 ) than the other three units , and moving west to east , probability of assignment to one cluster ( gray in Fig 2 ) progressively decreased while it increased for the other cluster ( orange in Fig 2 ) . We refer to this region between the Northwest and Northeast units as the "Transition Zone" . Microsatellite based FST between sampling sites either within or between the Structure-defined clusters ranged from 0 to 0 . 229 with most comparisons being statistically significant ( S5A Table ) . Table 2 reports average FST between the four units ( Northwest , Transition Zone , Northeast , and West ) . The West unit is the most genetically distinct from the other three ( FST = 0 . 162 , 0 , 163 , and 0 . 218 for Northwest , Transition Zone , and Northeast , respectively ) . Lower but still statistically significant FST values were estimated between the Northwest and Northeast units ( FST = 0 . 064 ) and even lower values between these units and the Transition Zone ( FST = 0 . 021 and 0 . 035 , respectively ) . DEST values showed the same trend as FST , except with overall higher estimates ( S6 Table ) . Isolation by distance ( IBD ) analyses ( S7 Table ) showed a significant correlation between genetic distance and geographic distance for all sampling sites combined ( R2 = 0 . 438 , p = 0 . 0001 ) and for sampling sites within the Northwest ( R2 = 0 . 259 , p = 0 . 00 ) and Transition Zone ( R2 = 0 . 216; p = 0 . 001 ) . No significant IBD was detected among sampling sites in the Northeast and West units . AMOVA results using microsatellites showed that most of the variation was between individuals within sampling sites ( 89 . 63% ) but differences were statistically significant at all levels of comparison , including between sampling sites and among the four units ( Table 3 ) . Overall , all sites showed moderate to high levels of genetic variability ( S1 Table ) . HO ranged from 0 . 461 in LAG to 0 . 690 in OM and HE ranged from 0 . 537 in KAG to 0 . 678 . For most of the sites , HO and HE microsatellite diversities were similar , indicating random mating within sites . Averaged over all samples and loci , the inbreeding coefficient ( FIS ) were generally low with an overall grand mean of 0 . 048±0 . 008 , and with significant heterozygote excess in 7 out of 42 populations ( S1 Table ) . Allelic richness ranged from a high of 7 . 785 in KR to a low of 4 . 188 in AMI , with an overall mean of 5 . 186 ( S1 Table ) . Generally , microsatellite diversity was highest in flies sampled in the Northwest and the Transition Zone sites ( Table 1; S1 Table ) and lowest in flies from the Northeast ( e . g . in sites KAG , AM , OCU and AMI ) . The trend of decline in diversity from the Northwest to the Northeast is apparent and significant when allelic richness values were linear-regressed over longitude ( R2 = 0 . 121; p = 0 . 032; S2 Fig ) . Flies in the West unit had microsatellite diversity values similar to or on par with the Northwest unit . S8 Table shows the results of the Ne estimates based on microsatellite data using the LD and the temporal methods . Estimates using the heterozygote excess method were infinite for all sites tested . Using the one-sample LD method , Ne estimates ranged widely from 101 . 6 ( 36 . 4-infinite 95% confidence interval [CI] ) in OC to 1685 . 7 ( 234 . 2-infinite CI ) in UGT and were all bound by a CI that included infinity ( S8 Table ) . Ne estimates using the two-sample temporal method ranged from 103 ( 73–138 CI ) in KTC to 962 ( 669–1309 CI ) in OCU ( S8 Table ) . Where a comparison between the two methods was possible , estimates were largely congruent except for one site ( OCU ) , where Ne using the temporal method was 962 ( 669–1309 CI ) , and using the LD method was 112 ( 47 . 7-infinite CI; S8 Table ) . Results based on the TPM model indicate a genetic bottleneck in 5 sampling sites ( NGO from the Northwest , OCA from the Transition zone , AMI and OC from the Northeast , and MS from the West; S8 Table ) . Results based on allele frequency distributions showed a genetic bottleneck in only one sample ( AMI from the Northeast; S8 Table ) . Fig 3 shows the results of the HYBRIDLAB analyses . The distribution of STRUCTURE assignments from the simulated hybrid swarm and mechanical mixing datasets are clearly distinct ( Fig 3 ) with a Wilcoxon two-tailed p-value of 0 . 002 ( S9 Table ) . This indicates power to detect interbreeding in the transition zone and thus evidence of hybridization . Comparisons of these models with the observed data ( S9 Table ) indicate that the observed data ( Fig 3A ) matches most closely with the hybrid swarm model ( Fig 3B ) than the mechanical mixing ( Fig 3C ) from which it is statistically distinct . Relatedness analyses showed that the majority ( >86% ) of the individuals in all units are unrelated ( Table 4 ) . The percentage of individuals that were full siblings was very low , ranging between 0 . 33% and 0 . 91% for all units . An even lower number of individuals had parent-offspring relationships ranging from 0% in the Transition Zone to 1 . 04% in the Northeast . Microsatellite-based migrant detection using GENECLASS and FLOCK showed a higher number of migrants between the Northwest , the Transition Zone , and the Northeast than between these areas and the West ( Fig 4 ) . GENECLASS indicated slight asymmetry in migration into the Northwest . There are 20 migrants from the Transition Zone into the Northwest and 10 migrants in the reverse direction , with both sexes almost equally represented ( 10 and 2 male migrants vs . 10 and 6 female migrants; S10 Table ) . We also detected two first generation female migrants from the Northeast to the Northwest . In contrast , migration between the Transition Zone and the Northeast is symmetrical with 8 migrants from the Northeast into the Transition Zone and 9 migrants in the opposite direction . Both sexes are moving in both directions , although the low sample sizes ( 2 and 3 male migrants vs . 5 and 1 female migrants; S10 Table ) precludes any strong conclusion . We also detect two migrants between the Northwest and West , one in each direction . FLOCK analysis provided similar migration rates between regions ( Fig 4 ) , but showed less asymmetry from the Transition Zone into the Northwest ( 23 from the Transition Zone into the Northwest , and 17 in the opposite direction ) , and more asymmetry from the Northeast into the Transition Zone ( 13 from Northeast into the Transition Zone , two in the opposite direction; S10 Table ) . FLOCK showed no migration between the West and any other region ( S10 Table ) . The mtDNA dataset consisted of 481 sequences ( 490 bp long ) , which included 289 sequences from individuals sampled for this study ( a subset of the ones screened for microsatellite loci variation , Table 1 ) plus 192 sequences from individuals from previous ones [25][31] , Table 1 ) . Sequences could be grouped into 30 haplotypes ( S11 Table ) , displayed as a TCS network ( Fig 2B ) . There are three major haplogroups ( groups of related haplotypes ) ; Haplogroup A , Haplogroup B , and Haplogroup C ( Fig 2B ) . Table 1 reports haplogroup frequencies for each site and for the four units . Haplogroup A occurs in all studied regions , but is most frequent in the Northwest and West units ( 75 . 1% and 74 . 4% , respectively ) . It occurs less commonly in the Transition Zone ( 24 . 0% ) and only rarely in the Northeast ( 4 . 8% ) . Haplogroup B occurs most commonly in the Northeast unit ( 95 . 2% ) and it is less common going from Northeast unit to the Transition Zone ( 76 . 0% ) and to the Northwest unit ( 24 . 9% ) . Haplogroup B does not occur in the West unit and Haplogroup C occurs only in this unit ( 25 . 6% , Table 1 , Fig 2C ) . The number of haplotypes at each sampling site ranged from 1 to 6 ( S1 Table ) . Haplotype 1 is the most frequent ( 186 individuals ) and occurs in all units except the West , and falls into Haplogroup B ( S11 Table ) . Haplotype 2 from Haplogroup A is the second most common ( 140 individuals ) and it is found in all units ( S11 Table ) . The third and fourth most common haplotypes fall in Haplogroup A and C , and only occur in the West ( 41 individuals and 19 individuals , respectively ) . Thirteen haplotypes were singletons ( observed once in the sample ) and fall into a mix of haplogroups , eight of which were from the Northwest , four from the Transition Zone and one from the West ( S11 Table ) . Nucleotide diversity averaged 0 . 002 and ranged from 0 ( OSG and KF ) to 0 . 008 ( UWA; S1 Table ) . Likewise , average haplotype diversity was 0 . 757 , and ranged from 0 ( KF and OSG ) to 0 . 836 ( UWA; S1 Table ) . There was no apparent difference in haplotype diversity from Northwest to Northeast units . S6B Table shows estimates of genetic differentiation ( ΦST ) between sampling sites . ΦST ranged from 0 to 1; with some sampling sites showing no evidence of differentiation ( e . g . PD in the Transition Zone vs AMI in the Northeast ) , while reached 1 for pairs that did not share haplotypes at all ( e . g . OSG in the Northwest vs KF in the West ) . S7 Table shows the results of the IBD analyses using mtDNA-based ΦST . Like in the microsatellite-based test , the correlation between genetic distance and geographic distance was significant for all sampling sites combined ( R2 = 0 . 490 , p = 0 . 001 ) and for samples within the Northwest unit ( R2 = 0 . 425 , p = 0 . 001 ) , but non-significant for the Northeast and West units . Unlike in the microsatellite-based IBD tests , the correlation between geographic and genetic distance in the Transition Zone was non-significant ( R2 = 0 . 002; p = 0 . 374 ) . AMOVA results based on mtDNA agree with the Microsatellite ( Table 3 ) , with most of the variation between individuals within sites ( 59 . 84% ) and significant values at all levels of comparison , including between the four units ( Northwest , Transition Zone , Northeast , and West; Table 3 ) . To evaluate the possible role of differential introgression of nuclear vs mitochondrial markers we assessed levels of mismatches by comparing individual assignments for each marker type ( S4 Table ) , and calculated percent individuals with discordant nuclear vs mitochondrial assignment ( Table 1 ) . This analysis could only be done for the three northern units because the common microsatellite based cluster ( blue in Fig 2 ) in the West was not clearly associated with a single mitochondrial haplogroup , as both Haplogroup A and Haplogroup C occur there . On the contrary , the Northeast and Northwest were clearly associated each with a single haplogroup , so we scored any individual from the north with a microsatellite-based q-value greater than 0 . 9 as a “match” if both nuclear and mitochondrial assignments were associated with the same region ( grey/grey or orange/orange in Fig 2 ) , or a “mismatch” if assignments were associated with different regions ( grey/orange or orange/grey in Fig 2 ) . The highest percentage of mismatches were found in the Northwest unit ( 22 . 8% ) , followed by the Transition Zone ( 20 . 03% ) , and then the Northeast unit ( 4 . 0% ) ( Table 1 ) .
Genetic diversity at both microsatellite and mtDNA markers ( Table 1 ) were generally low compared to many Diptera and Coleoptera species , which is consistent with reproductive limits imposed by the tsetse's viviparous life history [92] . The mtDNA haplotype network ( Fig 2B ) was congruent with the network published by [24] with more haplotypes because of the higher spatial resolution of this study . Levels of diversity in both markers ( Table 1 ) were similar to previous estimates for sampling sites north of Lake Kyoga [25][31] , but higher than estimates of southern Uganda populations [27] . We found a subtle decline in genetic diversity from west to east ( S1 Fig ) in northern Uganda similar to the pattern previously observed in central and southern Uganda [25] . [25] suggested that this gradient reflected sequential founder events originating from the main tsetse belt in the Northwest and moving eastward . Conversely though , our results for northern Uganda are not consistent with a single genetic origin from the main tsetse belt because we found two distinct genetic backgrounds ( Fig 2A ) and two distinct mtDNA haplogroups ( Fig 2B ) . This apparent inconsistence between past and current results could be due to the inability of previous studies to pick up the spatial differentiation and admixture patterns that we detected because of their much sparser sampling than in this study . Rather than sequential founder events pushing for a northwest to northeast range expansion , our results suggest that sustained connectivity to the greater G . f . fuscipes distribution and recent human induced population processes , such as vector control and habitat destruction , may account for the higher genetic diversity in the Northwest vs the Northeast . The G . f . fuscipes range extends continuously westward as far as Cameroon and Gabon ( Fig 1; [93][94] ) and has been sustained since the last glacial maximum ~15–20 ka [95][2][96] , with the Uganda sites being at the extreme northeast of G . f . fuscipes’ contiguous distribution . The size of this range and its temporal stability suggest that populations from the main part of its distribution are likely to be interconnected and old enough to harbor the high levels of genetic diversity characteristic of large and stable populations . This may have facilitated intermittent gene flow and can be a factor in explaining the higher genetic diversity in the Northwest than in Northeast of Uganda . In contrast , populations to the east and south of Lake Kyoga are bordered by unsuitable habitat to the east [93] , and have experienced recent arid periods during the last glacial maximum ~15–20 ka , and again during the latest Pleistocene ~14 ka , when the lakes in Uganda completely desiccated multiple times [97][95] . These climate events could have led to contractions and expansions of populations , accentuating the effects of genetic drift and creating isolated populations with low genetic diversity such as that found in UGT , AMI , AM , OC , KAG , and OCU ( Table 1; S2 Fig ) . Despite high genetic diversity in some localities in the West such as UWA ( Table 1; S2 Fig ) , which conforms with the general pattern of high to low diversity from west to east ( S2 Fig ) , connectivity with the rest of the G . f . fuscipes distribution in the West is limited by Lake Albert and the less suitable habitat in the bordering Blue Mountains . Thus , we suggest that the high allelic richness and haplotype diversity in the West was created by contact between the distinct genetic lineages at the Victoria Nile with a small amount of asymmetrical introgression ( see below ) rather than through connectivity with the central part of the species range . As expected , the gradient from higher to lower effective population size estimates ( Ne ) from the northwest to the southeast parallels the results on genetic diversity , and is likely caused by similar evolutionary forces , as Ne calculations are based on diversity estimates . Our interpretation of Ne was somewhat limited because we were only able to draw inference from the two-sample temporal method . As expected , the one-sample LD based Ne estimates yielded high confidence intervals that overlapped with infinity ( S8 Table ) . Improved Ne estimates based on a larger number of nuclear markers will be an important focus of future research using Single Nucleotide Polymorphisms ( SNPs ) . Despite uncertainty in Ne estimates from the LD method [67] , the temporal method [64] provided estimates that ranged from 100 to 1000 , had low confidence intervals ( S8 Table ) , and showed higher estimates in the Northwest . This result is in line with the distinct life-history traits of tsetse flies such as lower population sizes , reproductive outputs , and longer generation times than other insects . Ne and genetic diversity results that we report for the Northwest were similar to what has been reported in G . f . fuscipes sampled from northern Uganda [31][25] and in G . palpalis , another riverine species [1] . However , estimates were higher than reported in populations from southern Uganda [27] . This suggests that Northwest populations are influenced by either high connectivity with the rest of the G . f . fuscipes range , or by lower levels of vector control in the Northwest as compared to regions impacted by the SOS campaign in the Northeast . Detection of recent bottlenecks ( S8 Table ) provides further evidence that Ne has been influenced by vector control campaigns . The bottleneck analysis we used can detect extreme reductions in population sizes that occurred more recently than 2–4 Ne generations [67][98] , which corresponds to 25–500 years in G . f . fuscipes , depending on the exact Ne and generation time of the population in question [99][25][31][27] . Signals of bottlenecks from these short time scales can be due to natural or human induced changes in population size . Both of these causes may be at play given G . f . fuscipes’ patchy distribution , unique life history traits , and history as the target of intense , even if somewhat irregular , vector control campaigns . Bottlenecks in OC and AMI can be attributed to the SOS campaign of 2009 [100][41] . Similar tsetse control projects , like the Farming in Tsetse Controlled Areas ( FITCA ) in southeastern Uganda in places not included in this study like Okame , Otuboi , and Bunghazi , resulted in detection of bottlenecks in these areas in previous studies [26][25] . On the other hand , we found no evidence of bottlenecks in the 2014 samples most proximal to the location of the pilot vector control program conducted by [39] in the district of Arua ( DUK , AIN and GAN ) . This may have been because the location of sampling was too spatially distant ( minimum of ~20 km ) to influence the population sampled , or because the time of sampling was too temporally near ( ~6 months ) for a genetic signal to propagate . Survey data indicates that some control efforts resulted in long-term reduction in tsetse census [26 , 31] , while other control efforts such as the SOS campaign in the Northeast resulted in only short-term population size reductions . Population rebound is evidenced by the similar number of flies caught per trap at sites in the SOS region and at sites where no control activities have been carried out . For example , during sampling in 2014 , traps set at two sites in the SOS region ( OCU and AMI ) caught an average of 67 and 14 flies per trap , which are numbers that are similar to or higher than the average catch of 18 flies per trap . This underscores the importance of long-term control and monitoring campaigns following tsetse control . A population bottleneck in GOR ( S8 Table ) remains unexplained by known vector control campaigns , which may indicate that some bottlenecks are induced by natural evolutionary processes such as weather events or changes in ecological interactions . Thus , there is evidence that the joint influence of natural processes such as the greater connectivity to the rest of the G . f . fuscipes distribution in the Northwest , the dramatic climate change including arid periods in central and southern Uganda in the last ~35 ka years , and recent vector control programs have determined the west to east gradient in G . f . fuscipes genetic diversity and population dynamics in northern Uganda . Clustering ( Fig 2A ) and multivariate ( S1 Fig ) analyses detected three distinct genetic clusters each composed of multiple sampling sites and broadly corresponding to the Northwest , Northeast , and West units ( Fig 2C ) . mtDNA haplotypes also clustered into three haplogroups ( Fig 2B ) , which approximately correspond to these same three regions ( Fig 2C ) . Two of these genetic lineages have been described by previous research as Northern and Western clusters [24][25][31] , and we find evidence of a previously undescribed divergence in the Northern cluster , which is now partitioned into Northeast , Transition Zone , and Northwest units . Our data confirm deep genetic divergence between the G . f . fuscipes nuclear lineages found at the Victoria Nile , which harbor a mix of mtDNA haplotypes of both northern and southern associated lineages ( Fig 2 ) . STRUCTURE clustering showed close geographic proximity of distinct clusters at the confluence of the Okole River and the Victoria Nile ( Fig 2 ) . This stark genetic break in the nuclear genetic make-up may be due in part to insufficient sampling between UWA in the West and AKA and OLE in the North for accurate description of the shape and geographic span of the genetic divergence between these regions . Future sampling efforts should encompass detailed sampling in this region to determine if there is indeed another transition zone between the West and units identified in this study ( i . e . Northwest , Transition Zone , and Northeast ) , and between the West and previously described units [24][25][31] . Divergence between the North and West is thought to have originated during past allopatry more than 100 , 000 years ago [25] . Subsequent changes in the river systems associated with the opening of the great rift valley 13 , 000–35 , 000 years ago [95] created the modern outflow from Lake Victoria into Lake Kyoga and the reversal of the Kafu river to meet the Victoria Nile before flowing into Lake Albert [97] . These changes may have shifted the range of the Western G . f . fuscipes populations into contact with the Northern units at the Victoria Nile . We find mixed mtDNA ancestry in the West , which [24] described and suggested indicates recent rare female dispersal from the north and chance amplification of northern haplotypes by drift . Another possible explanation is the preferential introgression of organelle DNA from the resident population into the colonizing genetic background when two divergent lineages come into secondary contact during range expansion [101] . This scenario is supported by changes in the river systems and multiple drying cycles of the lakebeds [85] that would have promoted repeated retraction and expansion of G . f . fuscipes in central Uganda . If northern lineages had recolonized central Uganda before a northward shift of the southwestern lineage , the result would be a large number of northern mtDNA haplotypes in a Western nuclear genetic background . There are also possible ecological interactions at play because this region is unique in the co-distribution of other tsetse species , G . morsitans submorsitans and G . pallidipes [2] especially in the large protected area of the Murchison Falls National Park ( Fig 1 ) . Thus , evidence supports that strong evolutionary and ecological forces maintain genetic distinctiveness between the West and the other genetic units , but the details remain unclear and an important focus for future work . More fine scale sampling of all tsetse species across the North/West genetic break as well as experiments that test mating compatibility would help shed light on the mechanism ( s ) that maintain genetic discontinuity . Both microsatellite based FST and mtDNA based ɸST showed significant differentiation despite our fine scale sampling effort , which aligns with previous studies that have found significant differentiation across small geographic scales of as little as 1–5 km2 in Uganda [102][27][25] . Tsetse flies are known to be sensitive to environmental conditions and exist in discrete patches [2] . We suggest that low connectivity between adjacent habitat patches coupled with small Ne has allowed genetic drift to create significant differentiation at small spatial scales in G . f . fuscipes in northern Uganda . High signals of isolation by distance we detected in both microsatellites and mtDNA ( S7 Table ) further support the idea that population structure is maintained by the dual action of migration and genetic drift . The genetic break between the Northwest and Northeast forms a broad region of mixed microsatellite and mtDNA assignment along the Achwa and Okole rivers , in what we call the Transition Zone . The genetic break between the Northwest and Northeast and the one between the broad northern and southern clusters described by [25] are both characterized by what we think are secondary contact with admixed individuals and introgression of mtDNA haplotypes . However , the width of the transition zone , the level of differentiation , and the patterns and levels of admixture , is different across these two contact zones , with a broader , less differentiated , and more gradual pattern of admixture in the Transition Zone than in the North/South contact . The Transition Zone extends more than 200 km ( Fig 2 ) , while the secondary contact zone between the North and South clusters extends less than 75 km [25][31] . This difference in width may have been facilitated by colonization patterns and the distinct geographical break imposed by the swampy upper reaches ( southern extent ) of Lake Kyoga at the contact zone between the North and South , while less conspicuous physical breaks only partially limit movement of flies to and from the Transition Zone ( Fig 1 ) . The Transition Zone is characterized by uninterrupted suitable habitat along the entire length of the Achwa River , with only short distances of less than 15 km between the Achwa and Okole Rivers and neighboring drainage basins of Lake Kyoga and the Albert Nile ( Fig 1 ) . The levels of differentiation are also different between these two contact zones . In the Transition Zone , microsatellite-based FST estimates are lower ( average FST = 0 . 064 , Table 2 ) than the comparable values for the North and South clusters ( average FST = 0 . 236; [25] ) . This pattern was even more extreme in mtDNA ΦST estimates , with an average ΦST of 0 . 080 between the Northwest and Northeast ( Table 2 ) and 0 . 535 between the North and South [25][103][31] . Similarly , the patterns of admixture are distinct between the two contact zones . In the North/South contact zone , there is a dramatic increase in mismatched individuals that assign with high frequency ( >90% ) to one nuclear based genetic cluster but with mtDNA haplotypes found in another [31] at the zone of contact , with 16 . 98% in the contact zone vs 0–2% on either side . The pattern of mismatch in the North/South contact zone suggest differential introgression of mtDNA and nuclear loci , which could be due to Wolbachia infections [25][104][103][31] , given its maternal inheritance and ability to induce cytoplasmic incompatibility in G . morsitans [105] and other insects [106] . In contrast , in northern Uganda , the Transition Zone does not display an increase in mismatches , with 19 . 9% in the contact zone vs 26 . 2% to the north , which leaves no evidence of differential levels of introgressions of the two markers ( Table 1 ) or asymmetrical introgression . The match of observed data with the hybrid swarm model in the HYBRIDLAB analysis ( Fig 3 ) provides further evidence of relatively free and symmetrical interbreeding in the Transition Zone over multiple generations . Taken together , our results suggest that for the northern secondary contact area , isolation by distance and genetic drift are the two most likely processes that have shaped the distribution of the nuclear and mtDNA polymorphisms , rather than Wolbachia infections . Nonetheless , symmetrical interbreeding in the Transition Zone of this study remains tentative without the ability to classify hybrid classes because of wide and overlapping 95% confidence intervals around expected q-values ( Fig 3A ) . Further genetic characterization of the northern hybrid zone as well as characterization of the circulating Wolbachia strain ( s ) in the North would improve understanding of the forces shaping the genetic cline that lies between the disease belts of the two forms of HAT in northern Uganda . The methods we used to detect migrants reflects both first generation migrants and progeny of successful mating of very recent migrants rather than dispersal , and thus allowed us to assess recent gene flow across the full geographic range of our study [82][81] . We detect comparatively high migration rates among the northern clusters and low migration between these units and the West . High gene flow between the three northern units supports the assertion by [91] and others that waterways , in this case the Achwa River , maintain connectivity in tsetse populations . The vast majority of the migrants were a result of short-range dispersal from geographically proximate sampling sites connected by rivers . GENECLASS detected only two long-range migrants from the Northwest into the Northeast , which would not be expected with available ecological and physiological data that indicate tsetse cannot disperse over long distances [107] . Thus , it is likely these long-range migrants are offspring of assortative mating between first generation migrants found in geographically intermediate locations rather than first generation migrants . The overall direction of migration we detected was slightly asymmetrical towards the Northwest from the Northeast . However , we found no evidence of sex-bias ( S10 Table ) . These findings agree with previous studies which detected similar movement rates for the two sexes for G . f . fuscipes from the southeast into the northeast [25][31] . [25] suggested that movement along riverine habitats might be linked to passive dispersal of pupae via seasonally flooded river systems . Transportation of adults and pupae downstream may also be aided by large floating islands with dry substrate that form in backwaters and eddies and move northwards for sometimes hundreds of km along the major rivers in the region such as the Nile and its tributaries , and potentially , the Achwa river [108] [109] . Nonetheless , this hypothesis remains to be tested , and alternatives include the movement of flies with livestock [3][110] , shifting distribution of suitable habitat with human activities , and ongoing migration along corridors of suitable habitat that connect the north and south of Uganda . The observations from this study have important implications on the epidemiology of the two HAT diseases , as well as on future vector control and monitoring efforts in this region . A dense sampling scheme across a relatively small geographic area allowed an unprecedented spatial resolution of genetic structure in this region . Our results point to the presence of four genetic units , three of which have high levels of gene flow among them . The genetic distinctiveness of the West from the other three units suggests that this unit could be treated as a separate entity from the Northern ones . However , when planning control and monitoring strategies , it is opportune to look at the patterns and levels of genetic discontinuities between West vs . South and West vs . North genetic backgrounds in more detail to more precisely define the boundaries of each genetic unit at a country-wide scale . Given the results of this work , for sampling sites North of Lake Kyoga , control efforts undertaken at the unit level are unlikely to produce long-lasting results due to re-invasion from adjacent units , unless physical barriers are incorporated to avoid re-invasion from adjacent units . The best strategy would be a “rolling-carpet” initiative where control is initiated from the Northeast through the Transition Zone into the Northwest followed by intense monitoring and additional control to manage fly migration from previously cleared sites due to population recrudescence after control . Our results suggest that ecological and geographic features , especially the river systems in northern Uganda , play a major role in keeping G . f . fuscipes populations connected–a fact that should be taken advantage of when designing control . The genetic connectivity we found along waterways provides further support for a vector control strategy that incorporates targets along waterways and barriers to recolonization from adjacent stretches of riverbanks . This idea is also supported by a recent study that comprehensively evaluated a “tiny targets” vector control strategy along riverine savannah and found that a target density of 20 per linear km can achieve >90% tsetse control [39] . Our data also suggest that there is current movement of flies from the Northeast and Northwest into the Transition Zone but with a slight asymmetry towards the Northwest . Given that previous studies also demonstrated northward migration from the east [25][31] , it is possible that tsetse , besides livestock movement , is contributing to the northwards expansion of the T . b . rhodesiense sleeping sickness . Of major relevance for disease control is the finding of high levels of genetic intermixing and gene flow in the Transition Zone , which implies that a fusion of the two diseases ( T . b . gambiense and T . b . rhodesiense ) is unlikely to be prevented by an incompatibility between vector populations in the region of contact . Given the extent of connectivity in the three northern genetic units and the apparent genetic stability of G . f . fuscipes populations in the region [37] , ongoing monitoring following control would be paramount if interventions are to be sustainable . Monitoring programs should involve a combination of both ecological and genetic surveys to check on changes in population density and re-emergence either from residual pockets of tsetse or dispersal from proximal locations . For example , our results from the Northeast highlight the risk of population rebound following control . In this region , we found strong evidence of genetic bottlenecks indicating initial success of the SOS campaign in reducing tsetse density . However , our 2014–2015 surveys in the same sampling sites returned some of the highest tsetse trap densities . It appears , therefore , that when control activities were relaxed at the end of the SOS campaign , tsetse populations recovered to high densities . Focused monitoring could provide early detection of such population rebound and allow for identification of the source and proper mitigation of the recolonizing tsetse . | Northern Uganda is an epidemiologically important region affected by human African trypanosomiasis ( HAT ) because it harbors both forms of the HAT disease ( T . b . gambiense and T . b . rhodesiense ) . The geographic location of this region creates the risk that these distinct foci could merge , which would complicate diagnosis and treatment , and may result in recombination between the two parasite strains with as yet unknown consequences . Both strains require a tsetse fly vector for transmission , and in Uganda , G . f . fuscipes is the major vector of HAT . Controlling the vector remains one of the most effective strategies for controlling trypanosome parasites . However , vector control efforts may not be sustainable in terms of long term reduction in G . f . fuscipes populations due to population rebounds . Population genetics data can allow us to determine the likely source of population rebounds and to establish a more robust control strategy . In this study , we build on a previous broad spatial survey of G . f . fuscipes genetic structure in Uganda by adding more than 30 novel sampling sites that are strategically spaced across a region of northern Uganda that , for historical and political reasons , was severely understudied and faces particularly high disease risk . We identify natural population breaks , migration corridors and a hybrid zone with evidence of free interbreeding of G . f . fuscipes across the geographic region that spans the two HAT disease foci . We also find evidence of low effective population sizes and population bottlenecks in some areas that have been subjects of past control but remain regions of high tsetse density , which stresses the risk of population rebounds if monitoring is not explicitly incorporated into the control strategy . We use these results to make suggestions that will enhance the design and implementation of control activities in northern Uganda . | [
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| 2017 | Genetic diversity and population structure of the tsetse fly Glossina fuscipes fuscipes (Diptera: Glossinidae) in Northern Uganda: Implications for vector control |
There is heterogeneity in invariant natural killer T ( iNKT ) cells based on the expression of CD4 and the IL-17 receptor B ( IL-17RB ) , a receptor for IL-25 which is a key factor in TH2 immunity . However , the development pathway and precise function of these iNKT cell subtypes remain unknown . IL-17RB+ iNKT cells are present in the thymic CD44+/− NK1 . 1− population and develop normally even in the absence of IL-15 , which is required for maturation and homeostasis of IL-17RB− iNKT cells producing IFN-γ . These results suggest that iNKT cells contain at least two subtypes , IL-17RB+ and IL-17RB− subsets . The IL-17RB+ iNKT subtypes can be further divided into two subtypes on the basis of CD4 expression both in the thymus and in the periphery . CD4+ IL-17RB+ iNKT cells produce TH2 ( IL-13 ) , TH9 ( IL-9 and IL-10 ) , and TH17 ( IL-17A and IL-22 ) cytokines in response to IL-25 in an E4BP4-dependent fashion , whereas CD4− IL-17RB+ iNKT cells are a retinoic acid receptor-related orphan receptor ( ROR ) γt+ subset producing TH17 cytokines upon stimulation with IL-23 in an E4BP4-independent fashion . These IL-17RB+ iNKT cell subtypes are abundantly present in the lung in the steady state and mediate the pathogenesis in virus-induced airway hyperreactivity ( AHR ) . In this study we demonstrated that the IL-17RB+ iNKT cell subsets develop distinct from classical iNKT cell developmental stages in the thymus and play important roles in the pathogenesis of airway diseases .
Natural killer T ( NKT ) cells , unlike conventional T cells bearing diverse antigen receptors , are characterized by the expression of an invariant T cell receptor ( TCR ) , Vα14Jα18 paired with Vβ8 , Vβ7 , or Vβ2 in mice [1] and the Vα24Jα18/Vβ11 pair in humans [2] , [3] , that recognizes glycolipid antigens in conjunction with the monomorphic MHC class I-like CD1d molecule [4] , [5] . Therefore , these cells are termed invariant NKT ( iNKT ) cells . Another characteristic feature of iNKT cells is their rapid and massive production of a range of cytokines , such as those typically produced by T helper cell ( TH ) 1 , TH2 , and TH17 cells [6]–[8] , upon stimulation with their ligand , α-Galactosylceramide ( α-GalCer ) [9] , . It is speculated that the ability of iNKT cells to produce these various cytokines is due either to the microenvironment in which they undergo priming or to the existence of functionally distinct subtypes of iNKT cells producing different cytokines; however , there is no clear-cut evidence to support the latter notion . It has been reported that iNKT cells include both CD4+ and CD4− subtypes [6] , [11] , each of which produces different cytokines . Human CD4+ iNKT cells produce both TH1 and TH2 cytokines , whereas CD4− iNKT cells produce mainly TH1 cytokines [12] , [13] . Although such functional differences were originally less apparent in mouse CD4+ and CD4− iNKT cells , two functionally distinct subtypes of iNKT cells in the mouse thymus have since been identified based on NK1 . 1 expression; NK1 . 1− iNKT cells produce a large amount of IL-4 and little IFN-γ , whereas NK1 . 1+ iNKT cells produce less IL-4 and more IFN-γ [14] , [15] . Furthermore , CD4− iNKT cells in the liver have been found to be more effective in mediating tumor rejection than CD4+ iNKT cells in the liver or any other tissues [16] . There is also further heterogeneity of CD4+ iNKT cells in terms of expression of the IL-17 receptor B ( IL-17RB ) , a receptor for IL-25 [17] . IL-25 is a key factor in TH2 immunity , including allergic reactions and airway hyperreactivity ( AHR ) . The CD4+ IL-17RB+ iNKT cells produce large amounts of IL-13 and IL-4 but little IFN-γ in response to IL-25 , mediating a key role in IL-25-driven AHR [17] , [18] . Another subset of newly identified iNKT cells within the NK1 . 1− CD4− subset is the retinoic acid receptor-related orphan receptor ( ROR ) γt+ iNKT cells . These cells can induce autoimmune disorders by their production of IL-17A and IL-22 [8] , [19] , even though IL-17A-producing iNKT cells are not restricted to a particular iNKT cell subset [20] . The emergence of functionally distinct subpopulations of iNKT cells is reminiscent of how iNKT subtypes develop in the thymus and expand in the periphery . Here , we demonstrate IL-17RB+ iNKT cells are a subtype distinct from the CD122+ iNKT cells producing IFN-γ . Moreover , there are two subtypes of IL-17RB+ iNKT cells; CD4− produces TH17 cytokines in an IL-23-dependent fashion , whereas the other CD4+ produces TH2 and TH17 cytokines in an IL-25 dependent manner . In addition , these IL-17RB+ iNKT cells contribute to the induction of virus and viral antigen-induced chronic AHR .
We previously identified a fraction of splenic CD4+ iNKT cells that expresses IL-17RB and produces TH2 cytokines after treatment with IL-25 [17] . In order to directly analyze the function of IL-17RB on iNKT cells , we generated IL-17RB-deficient mice by the disruption of exon 1 and exon 2 of the Il17rb gene ( Figure S1 ) . We then compared the number and function of iNKT cells in the spleen and the liver from Il17rb−/− mice on a C57BL/6 ( B6 ) background to those of wild type ( WT ) B6 mice . We also included in our comparison Il15L117P mice , in which leucine ( CTT ) at amino acid position 117 of IL-15 was substituted with proline ( CCT ) , because IL-15 is reported to be critical for the development and homeostatic maintenance of iNKT cells [21] , [22] , as well as other cell types such as NK and CD8+ memory T cells [23] , [24] . As shown in Figure 1A , the number of iNKT cells in Il17rb−/− mice was only slightly decreased in the spleen , and was almost comparable in the liver , compared with B6 mice , findings consistent with our previous analysis on the distribution of IL-17RB+ iNKT cells ( 3%–5% in the spleen and almost none in the liver ) as detected by a specific monoclonal antibody [17] . Similarly , Il15L117P mice appear to recapitulate the previously reported phenotype of Il15−/− mice [21] , [22] because iNKT and NK cells were decreased by 50% in the spleen and by 90% in the liver , indicating that the L117P mutation resulted in the loss of IL-15 function . We then analyzed the frequency of IL-17RB+ subtypes among α-GalCer/CD1d dimer+ iNKT cells in the spleen and liver of WT , Il17rb−/− , and Il15L117P mice ( Figure 1B ) . The percentage of IL-17RB+ iNKT cells was increased more than 4 times in the spleen and 10 times in liver of the Il15L117P mice . By using Il17rb−/− and Il15L117P mice , we further analyzed the iNKT cell subtypes in terms of their ability to produce cytokines ( Figure 1C and 1D ) . α-GalCer/CD1d dimer+ TCRβ+ iNKT cells from the spleen of WT , Il17rb−/− , and Il15L117P mice ( Figure 1C ) and those from the liver of B6 and Il17rb−/− mice ( Figure 1D ) were sorted and co-cultured with GM-CSF-induced bone marrow derived dendritic cells ( BM-DCs ) in the presence of α-GalCer . The Il17rb−/− iNKT cells produced normal levels of IFN-γ , but this was significantly decreased in Il15L117P iNKT cells . Intriguingly , there was impaired production of not only TH2 cytokines such as IL-9 , IL-10 , and IL-13 , but also of TH17 cytokines IL-17A and IL-22 in Il17rb−/− iNKT cells , but not in Il15L117P iNKT cells in the spleen ( Figure 1C and 1D ) , even though the number of iNKT cells were only slightly decreased in Il17rb−/− ( see Figure 1A ) . The iNKT cells derived from WT , Il17rb−/− , or Il15L117P failed to produce any indicated cytokines when co-cultured with BM-DCs from Cd1d1−/− mice ( unpublished data ) , indicating the cytokine production from iNKT cells are absolutely CD1d/α-GalCer dependent . To examine the functional activity of Il17rb−/− iNKT cells in vivo , we administered α-GalCer ( 2 µg ) intravenously ( i . v . ) and monitored serum cytokine levels ( Figure 1E ) . The production of IFN-γ peaked normally at 12 to 24 h after stimulation in the Il17rb−/− mice . On the other hand , the production ( around 1–6 h ) of other cytokines , such as IL-9 , IL-10 , IL-13 , IL-17A , and IL-22 , was severely impaired in the Il17rb−/− mice . The results suggest that IL-17RB+ iNKT cells are distinct from IL-17RB− iNKT cells , which mainly produce IFN-γ , and also that IL-17RB+ iNKT cells produce IL-9 , IL-10 , and IL-13 among TH2 cytokines and IL-17A and IL-22 TH17-type cytokines . iNKT cells in the spleen and liver from il17rb−/− mice are defective in the production of IL-9 , IL-10 , IL-13 , IL-17A , and IL-22 , while IFN-γ production is diminished in Il15L117P iNKT cells ( Figure 1 ) . We therefore attempted to identify the origin of IL-17RB+ iNKT cells in the thymus by comparing α-GalCer/CD1d dimer+ iNKT cells in B6 with those in Il17rb−/− and in Il15L117P mice on a B6 background ( Figure 2A ) . The percentage and number of iNKT cells in the thymus were severely decreased in Il15L117P mice to a similar extent as previously reported in Il15−/− mice [21] . By contrast , the percentage and number of iNKT cells in Il17rb−/− mice was only slightly decreased , to a similar extent to that seen in the spleen and liver ( Figure 1C and 1D ) . In order to analyze their phenotype precisely , enriched α-GalCer/CD1d dimer+ iNKT cells were further divided based on the expression of CD44 and NK1 . 1 ( Figure 2B ) , because iNKT cells can be classified into developmental stages based on the cell surface expression of these molecules , i . e . , CD44lo NK1 . 1− ( Stage 1 ) , CD44hi NK1 . 1− ( Stage 2 ) , and CD44hi NK1 . 1+ ( Stage 3 ) [14] , [25] . In agreement with earlier results [21] , there was a decrease in the CD44hi NK1 . 1+ ( Stage 3 ) population of α-GalCer/CD1d dimer+ iNKT cells in the thymus of Il15L117P mice . By contrast , the percentage and number of iNKT cells in Il17rb−/− mice were reduced , especially in the CD44lo NK1 . 1− ( Stage 1 ) and CD44hi NK1 . 1− ( Stage 2 ) populations , although the CD44hi NK1 . 1+ ( Stage 3 ) population was unchanged ( Figure 2B ) . To determine whether the reduction in absolute numbers of developmental Stages 1 and 2 iNKT cell populations in Il17rb−/− mice is due to a developmental defect or to bypassing of these developmental stages , we analyzed surface expression of IL-17RB and CD122 , a receptor for IL-15 ( Figure 2C ) . Consistent with the observation shown in Figure 2B , IL-17RB expression was detected mainly in the Stage 1 and Stage 2 populations in both CD4− and CD4+ fractions ( Figure 2C ) , whereas CD122 expression was mainly in the Stage 3 population as previously reported [21] , and is inversely correlated with the expression of IL-17RB ( Figure 2C ) . In order to investigate whether IL-17RB+ iNKT cells are distinct from IL-15-dependent iNKT cells , thymic iNKT cells from B6 and Il15L117P mice were divided based on the expression of IL-17RB and CD4 , and were further analyzed in the expression of CD44 and NK1 . 1 ( Figure 2D and 2E ) . The percentage of CD4− and CD4+ , IL-17RB+ iNKT cells was higher in Il15L117P mice ( Figure 2D ) , due to the reduction in the numbers of IL-17RB− iNKT cells . Concerning the distribution of the expression of CD44 and NK1 . 1 in iNKT cell subtypes , even though IL-17RB+ iNKT cells comprised only ∼10% of the thymic iNKT cells , more than half of them were Stage 2 , while almost all ( >97% ) of the CD4− and CD4+ , IL-17RB− iNKT cells were Stage 3 ( Figure 2E ) . Furthermore , more than 80% of Stage 1/2 iNKT cells were IL-17RB+ iNKT cells , while only ∼2% of the Stage 3 iNKT cells were IL-17RB+ ( Figure 2D ) . The percentage ( Figure 2F ) and absolute number ( Figure 2G ) of IL-17RB+ iNKT cells among the total iNKT cells and in developmental Stages 1 and 2 were similar to those of Il15L117P mice , while those of IL-17RB− iNKT cells ( i . e . , CD122+ iNKT cells ) among the total and in developmental Stage 3 were also comparable to those in Il17rb−/− mice , indicating that two distinct iNKT cell subsets are present in the different stages of iNKT cell development , i . e . , the IL-17RB+ subtype in Stages 1 and 2 and the CD122+ subtype in Stage 3 . In order to determine if iNKT cell subtypes arise as a distinct population in the thymus of each other , each subtype in Stage 1 or Stage 2 was sorted and co-cultured with a fetal thymus ( FT ) lobe from Jα18−/− mice ( Figure 2H and 2I ) . IL-17RB− subtype in Stage 1 gave rise to cells in Stage 2 and Stage 3 with IL-17RB− phenotype ( Figure 2H and 2I , lower left ) , whereas IL-17RB+ subtype in Stage 1 gave rise to cells in Stage 2 but not to Stage 3 with IL-17RB+ phenotype ( Figure 2H and 2I , upper left ) . Furthermore , IL-17RB− subtype in Stage 2 gave rise to cells in Stage 3 with IL-17RB− phenotype ( Figure 2H and 2I , lower left ) , whereas IL-17RB+ subtype in Stage 2 kept in Stage 2 with IL-17RB+ phenotype ( Figure 2H and 2I , upper left ) , indicating that IL-17RB+ iNKT cells arise in the thymus as distinct phenotypic subtypes from IL-17RB− iNKT cells , which undergo a series of developmental stages ( i . e . Stages 1–3 ) previously characterized [14] , [15] . To confirm the differences among subtypes of iNKT cells , we compared global gene expression profiles in WT B6 CD4− or CD4+ , IL-17RB+ or IL-17RB− , iNKT cells to each other ( Figure S2A ) , and also WT B6 CD4− or CD4+ , IL-17RB+ iNKT cells to the same cell types from Il15L117P mice ( Figure S2B ) . The genome-wide expression profile of the CD4− and CD4+ , IL-17RB+ iNKT cells were similar to each other but different from those of CD4− and CD4+ , IL-17RB− iNKT cells ( Figure S2A ) . Moreover , the gene expression profiles of CD4− or CD4+ , IL-17RB+ WT iNKT cells were similar to those in Il15L117P mice ( Figure S2B ) . Therefore , it is likely that IL-17RB+ iNKT cell development in the thymus is distinct from the IL-17RB− ( i . e . CD122+ ) iNKT cells . The gene expression profiles of the CD4+ IL-17RB+ iNKT cells were quite similar to those of the CD4− IL-17RB+ cells rather than the CD4− or CD4+ , IL-17RB− iNKT cells ( Figure S2A ) , suggesting that these two subtypes , CD4− and CD4+ , IL-17RB+ iNKT cells , develop from the same precursors , whereas the precursors for IL-17RB− iNKT cells are distinct . In order to investigate functional differences in the IL-17RB+ and IL-17RB− subsets of iNKT cells , we analyzed the ability of thymic iNKT cells in B6 , Il17rb−/− and Il15L117P mice to produce cytokines in response to α-GalCer ( Figure S3 ) . IFN-γ was produced at similar levels by Il17rb−/− and WT iNKT cells , but was greatly reduced in the Il15L117P iNKT cells , while the production of IL-9 , IL-10 , IL-13 , IL-17A , and IL-22 was impaired in the Il17rb−/− but not in the Il15L117P iNKT cells , similar to what we had observed in the spleen and liver ( Figure S3 ) . In a previous study , IL-17RB+ iNKT cells were fairly abundant in the spleen of TH2-prone mice , but were barely detectable in TH1-prone mice [17] . Thus , we examined whether the frequency of IL-17RB+ iNKT cells in the thymus of BALB/c mice is different from that of B6 mice . Intriguingly , more than one-third of thymic iNKT cells were IL-17RB+ in TH2-prone BALB/c mice , four times higher than in TH1-prone B6 mice ( Figure S4A ) . The genome-wide expression profiles of CD4− or CD4+ , IL-17RB+ iNKT cells in BALB/c were similar to each other , but different from those of CD4− or CD4+ , IL-17RB− iNKT cells ( Figure S4B ) . Cluster analysis also showed that CD4− or CD4+ , IL-17RB+ or IL-17RB− iNKT cells in B6 and BALB/c mice were essentially equivalent ( Figure S4C ) . iNKT cells in the thymus can be divided into four populations based on their expression of CD4 and IL-17RB ( Figures 2D and S4A ) , and thymic Il17rb−/− iNKT cells had a decreased ability to produce TH2 and TH17 cytokines ( Figure S3 ) . Therefore , we analyzed the function of iNKT cell subtypes in the thymus of B6 ( Figure 3 ) and BALB/c mice ( Figure S5 ) . We first used quantitative real-time PCR to investigate TH1/TH2/TH17-related gene expression patterns in FACS sorted thymic iNKT subtypes . The levels of Cd4 and Il17rb transcripts were correlated with the surface expression of these molecules ( Figures 3A and S5A ) . Il2rb ( = Cd122 ) expression was restricted to CD4− and CD4+ , IL-17RB− iNKT cell subtypes ( Figures 3A and S5A ) in correlation with their surface protein expression ( Figure 2C ) . The expression levels of TH1-related transcripts , such as Ifng , Tbx21 , and Stat4 , were more than 10 times higher in those of CD4− and CD4+ IL-17RB− iNKT cells . Higher levels of TH2-related transcripts , such as Il4 , were detected in CD4+ IL-17RB+ iNKT cells , even though Gata3 , a transcription factor essential for TH2 cytokine production , was expressed at a similar level in all subtypes ( Figures 3B and S5B ) . On the other hand , the expression of TH17-related transcripts , such as Il17a , Il22 , and Rorc , were restricted to the CD4− IL-17RB+ iNKT cells ( Figures 3B and S5B ) . We then investigated the gene expression level in cells derived from Stages 1 and 2 by FT organ culture ( Figure 2H and 2I ) . Consistent with the findings above , Ifng expression was restricted to the cells derived from CD4− and CD4+ , IL-17RB− iNKT precursors ( Figure S6A and S6B ) . Higher levels of Il4 were detected in CD4+ IL-17RB+ derived cells and restricted expression of Il17a in cells derived from CD4− IL-17RB+ precursors ( Figure S6A and S6B ) , supporting each subtype arise from Stage 1 as a functionally distinct subtype . Based on these findings , we analyzed potential production of cytokines from these thymic iNKT cell subtypes . Sorted iNKT cell subtypes were stimulated with PMA plus ionomycin ( Figure S7A ) . Similar to the cytokine expression , IFN-γ was exclusively produced by the CD4− and CD4+ , IL-17RB− subtypes , while IL-10 and IL-13 were mainly produced by the CD4+ IL-17RB+ iNKT cells and IL-17A was produced predominantly by CD4− IL-17RB+ iNKT cells . It should be noted that all four subtypes have a potential to produce IL-4 , in correlation with their mRNA expression of Il4 and Gata3 ( Figures 3B and S5B ) . We then further analyzed cytokine production after α-GalCer activation . Sorted iNKT cell subtypes were co-cultured with BM-DCs in the presence of α-GalCer ( Figures 3C and S5C ) . IFN-γ was exclusively produced by the CD4− and CD4+ , IL-17RB− subtypes , while IL-4 , IL-9 , IL-10 , and IL-13 were mainly produced by the CD4+ IL-17RB+ iNKT cells . Similarly , IL-17A and IL-22 were produced predominantly by CD4− IL-17RB+ iNKT cells . These cytokine production patterns correlated with their differential expression of TH1/TH2/TH17-related genes in the different iNKT subtypes . We also analyzed the expression profiles of cytokine receptor genes . Il12rb2 transcript was expressed in CD4− and CD4+ , IL-17RB− iNKT cells , and Il23r expression was restricted to CD4− IL-17RB+ iNKT cells ( Figures 3D and S5D ) , suggesting that CD4− and CD4+ , IL-17RB− iNKT cells respond to IL-12 through IL-12Rβ2/IL-12Rβ1 , while CD4− IL-17RB+ iNKT cells respond to IL-23 through IL-23R/IL-12Rβ1 . In fact , CD4− and CD4+ , IL-17RB− iNKT cells produced large amounts of IFN-γ but not TH2 and TH17 cytokines in response to IL-12 ( Figures 3E and S5E ) , while CD4− IL17RB+ iNKT cells produced large amounts of TH17 cytokines , IL-17A and IL-22 , but not IFN-γ and TH2 cytokines in response to IL-23 ( Figures 3F and S5F ) . IL-25-mediated activity requires not only IL-17RB but also IL-17RA expression [26] , which is expressed on all iNKT cell subtypes ( Figures 3D and S5D ) . IL-25 acts on thymic CD4+ IL-17RB+ iNKT cells to induce a large amount of TH2 cytokines , along with moderate amounts of TH17 cytokines ( Figures 3G and S5G ) similar to previous observations in the CD4+ IL-17RB+ iNKT cell subtype in the spleen [17] . Interestingly , however , IL-25 does not stimulate CD4− IL-17RB+ iNKT cells , despite their expression of IL-17RB ( Figures 3G and S5G ) . We also found that cytokine production from iNKT cells in these experimental settings was hardly observed when BM-DCs derived from Cd1d1−/− mice ( unpublished data ) , indicating signals from TCR are also required for cytokine production from iNKT cells . These results suggest that three types of iNKT cells , i . e . CD4− IL-17RB+ ( iNKT-TH17 , IL-23 reactive ) , CD4+ IL-17RB+ ( iNKT-TH2/17 , IL-25 reactive ) , and CD4− and CD4+ , IL-17RB− ( iNKT-TH1 , IL-12 reactive ) , exist as distinct subpopulations in the thymus . The chemokine receptor expression patterns are also distinct among thymic iNKT cell subtypes . Ccr4 and Ccr7 expression was restricted to both CD4− and CD4+ , IL-17RB+ iNKT cells , and Ccr6 expression was only observed on CD4− IL-17RB+ iNKT cells ( Figures 3H and S5H ) . Cxcr3 expression was several times higher on IL-17RB− iNKT cells than on the other subtypes . Surprisingly , the expression of Cxcr6 , which has been reported to be abundantly expressed by all iNKT cells [27] , [28] , was also restricted to the IL-17RB− iNKT cells ( Figures 3H and S5H ) . Note that the expression patterns and levels of all of the genes tested were almost equivalent between B6 and BALB/c mice , consistent with our finding that all iNKT subtypes are present in these strains . Distinct expression of chemokine receptors among thymic iNKT cell subtypes ( Figures 3H and S5H ) may reflect the differential distribution of iNKT cell subtypes in the periphery . We thus investigated the frequency of total iNKT cells and subtypes in the spleen , liver , BM , lung , inguinal lymph node ( LN ) , and mesenteric LN in WT B6 , BALB/c , and Il17rb−/− mice ( Figures 4 and S8 ) . The absolute number and percentage of iNKT cells were slightly decreased in the spleen , lung , inguinal LN , and mesenteric LN of Il17rb−/− mice , but were unchanged compared to WT mice in liver and BMs ( Figures 4A and S8A ) . We then gated on α-GalCer/CD1d dimer+ TCRβ+ iNKT cells and further analyzed them for the expression of CD44 and NK1 . 1 in B6 background mice ( Figure 4B ) . The percentage of NK1 . 1− subtype cells was higher in the spleen , lung , inguinal LN , and mesenteric LN , but lower in the liver and BM , and was decreased in Il17rb−/− mice , suggesting that the majority of iNKT cell subtypes maintain surface expression of NK1 . 1− after emigration from the thymus ( Figure 2B ) . Similarly , we examined the expression of CD4 and IL-17RB on the iNKT subtypes ( Figures 4C and S8B ) . Interestingly , IL-17RB+ iNKT cells were abundant in the lung , inguinal LN , and mesenteric LN , but barely detectable in the liver and BM of both B6 and BALB/c mice . More than 40% of iNKT cells were IL-17RB+ in the lung , inguinal LN , and mesenteric LN , whereas more than 90% were IL-17RB− in the liver and BM ( Figures 4C and S8B ) . Therefore , the distribution patterns of the iNKT cell subtypes are distinct in the tissues . In agreement with a previous study [21] , we found that the number of iNKT cells was decreased in the spleen ( ∼1/3 ) and liver ( ∼1/30 ) in Il15L117P mice ( Figure S9A ) . Reduction of iNKT cell number was also observed in BM ( ∼1/8 ) in these mice ( Figure S9A ) , probably due to the selective reduction of the IL-17RB− subtypes ( Figure S9B ) . We finally compared iNKT cell subtypes in the thymus and periphery of B6 and BALB/c mice ( Figure 4D ) . The total iNKT cell number was almost equivalent between these two strains , but BALB/c had ∼4 times more CD4+ IL-17RB+ subtype cells , but lower ( ∼1/3 ) numbers of CD4− IL-17RB− cells , resulting in a higher number of CD4+ IL-17RB+ cells in the spleen ( ∼5 times ) , lung ( ∼2 times ) , inguinal LN ( ∼1 . 5 times ) , mesenteric LN ( ∼4 times ) , and lower numbers of CD4− IL-17RB− cells , especially in liver ( ∼1/6 ) and BM ( ∼2/5 ) of BALB/c mice ( Figure 4D ) . To confirm the distribution profiles of each subtype in the periphery , we performed intracellular cytokine staining after PMA plus ionomycin stimulation ( Figure S7B ) and quantitative real-time PCR analysis ( Figure S10A–D ) on these iNKT cells that were tested in the thymic iNKT cell subtypes ( Figures 3A , 3B , 3D , 3H , S7A ) . The gene expression profiles and potential cytokine production in the iNKT cell subtypes were almost equivalent among those in the different peripheral tissues , but higher than those in the thymus , strongly suggesting that each iNKT subtype in the periphery is derived from the same iNKT subtypes in the thymus . We next compared global gene expression profiles of CD4− or CD4+ , IL-17RB+ or IL-17RB− iNKT subtypes in the thymus and spleen in order to test whether each subtype is functionally and phenotypically stable or plastic . Each of the four subtypes in spleen was highly correlated with the corresponding subtype in the thymus ( Figure 5A ) , suggesting that iNKT subtypes can be divided by CD4 and IL-17RB expression both in the thymus and the periphery . Furthermore , iNKT cell subtypes in the periphery ( Figure S10 ) showed similar quantitative gene expression profiles as in the thymus ( Figure 3 ) . In order to confirm the stability and plasticity of iNKT cell subtypes , we sorted thymic iNKT cell subtypes based on the expression of CD4 and IL-17RB from WT B6 and transferred them into iNKT cell-deficient Jα18−/− mice . Ten days after transfer , we analyzed the IL-17RB expression by iNKT cell subtypes in the spleen . The results clearly showed that the majority of transferred cells maintained their surface IL-17RB expression ( Figure 5B ) , suggesting that IL-17RB expression is stable as the cells migrate from the thymus to the periphery . We further analyzed in cytokine production of splenic iNKT cell subtypes from B6 and BALB/c mice . The cytokine production profiles of splenic iNKT cell subtypes in response to α-GalCer ( Figures 5C and S11A ) , IL-12 ( Figures 5D and S11B ) , IL-23 ( Figures 5E and S11C ) , and IL-25 ( Figures 5F and S11D ) were quite similar to those of the thymic iNKT cell subtypes ( Figures 3C , 3E , 3F , 3G , S5C , S5E , S5F , S5G ) . Taken together , all of the iNKT subtypes detected in the thymus also exist as phenotypically and functionally distinct subtypes in the peripheral tissues . Both thymic and peripheral iNKT cells in the steady state contain Ifng mRNA in the CD4− and CD4+ , IL-17RB− cells ( Tbx21 expressed , iNKT-TH1 , IL-12 reactive ) , and Il17a and Il22 mRNA in the CD4− IL-17RB+ cells ( Rorc expressed , iNKT-TH17 , IL-23 reactive ) . The expression of these cytokine transcripts is thought to result from the fact that peripheral iNKT cells are not truly quiescent , but instead appear to be continuously activated at a low level due to their recognition of endogenous self-glycolipid ligand ( s ) in vivo . However , the CD4+ IL-17RB+ iNKT cells do not contain Il9 , Il10 , Il13 ( unpublished data ) , Il17a , or Il22 mRNA ( Figures 3B , S5B , S10B ) in the steady state , even though these cytokines are immediately produced after activation by α-GalCer , similar to cases of IFN-γ from IL-17RB− iNKT cells . These results suggest differences in the transcriptional regulation of cytokine genes in the different iNKT cell subtypes . One of the candidate genes is E4BP4 , a mammalian basic leucine zipper transcription factor that regulates IL-10 and IL-13 production not only by CD4+ T cells and regulatory T cells but also by iNKT cells [29] . E4BP4 expression was markedly induced in IL-25-treated iNKT cells , and its expression level correlated with Il10 and Il13 expression [29] . Furthermore , iNKT cells lacking E4bp4 had reduced expression of IL-10 and IL-13 in response to either IL-25 or α-GalCer stimulation , but the IFN-γ and IL-4 production were unaffected [29] , indicating that E4bp4 controls the TH2 cytokine production in a particular iNKT cell subtype . Therefore , we analyzed the role of E4bp4 in iNKT cell subtypes . The expression of E4bp4 was selectively and strongly induced by IL-25 treatment in CD4+ IL-17RB+ iNKT cells both from thymus and spleen ( Figure 6B ) . However , CD4− IL-17RB+ iNKT cells failed to induce E4bp4 expression even after treatment with IL-23 ( Figure 6B ) , suggesting the cell type-specific function of E4bp4 and its possible role not only in Il10 and Il13 expression but also in Il9 , Il17a , and Il22 expression by IL-25-treated CD4+ IL-17RB+ iNKT cells . To test this hypothesis , we analyzed cytokine production by CD4+ IL-17RB+ iNKT cells lacking E4bp4 after treatment with IL-25 in the presence of BM-DCs ( Figure 6C ) . The production of IL-9 , IL-10 , IL-13 , IL-17A , and IL-22 cytokines by both thymic or splenic CD4+ IL-17RB+ iNKT cells in response to IL-25 was completely abrogated , indicating E4BP4 turned out to be an intrinsic regulator of IL-25-mediated production , not only of IL-10 and IL-13 but also of IL-9 , IL-17A , and IL-22 . We then investigated the role of IL-17RB+ iNKT cells in the pathogenesis of virus-induced AHR , which is known to be different from allergen-induced AHR [30] . Certain viruses , such as respiratory syncytial virus ( RSV ) , Sendai virus , metapneumovirus , and parainfluenza virus , cause childhood asthma and COPD-like symptoms , which include AHR , airway inflammation , and mucus hypersecretion [31]–[33] . However , it has been very difficult to understand how such symptoms develop , even long after the apparent clearance of viruses . It has been reported that , in mouse models of infection with parainfluenza virus or Sendai virus , virus-induced chronic inflammation leads to asthma that resembles human asthma and COPD [34] . The chronic pulmonary symptoms evolved independently of CD4+ T cells but required CD4− iNKT cells and did not occur in Cd1d−/− and Jα18−/− mice [34] . Therefore , we attempted to determine whether or not the CD4− IL-17RB+ iNKT cells are responsible for chronic inflammatory lung disease induced by RSV infection . We used the secreted form of recombinant G protein of RSV ( rec Gs ) ( Figure S12 ) as an immunogen because priming with a recombinant vaccinia virus ( rVV ) expressing rec Gs induced a more TH2-biased response and enhanced pulmonary eosinophil and macrophage infiltration following RSV challenge than did priming with rVV expressing either wild-type G or membrane anchored G ( Gm ) proteins [35] , [36] . Mice were inoculated i . n . with RSV ( 106 pfu/100 µl ) or PBS as a control four times at 10-d intervals and were intraperitoneally ( i . p . ) immunized with rec Gs/alum ( 50 µg/2 mg ) 4 d after the first RSV infection . Three days after the last RSV administration , mice were exposed i . n . to 50 µg rec Gs and then , 24 h later , measured for AHR ( Figure 7A ) . In this experimental setting , RSV/rec Gs-induced AHR was observed in WT BALB/c but not in Jα18−/− or Il17rb−/− mice , which had a similar response level as PBS/rec Gs-induced WT controls , indicating that IL-17RB+ iNKT cells contribute to the development of RSV plus viral antigen-induced AHR ( Figure 7B ) . Airway macrophage and lymphocyte numbers , which were relatively higher than eosinophils and neutrophils , were recruited into the bronchoalveolar lavage ( BAL ) fluid of RSV/rec Gs-induced WT mice but not the other mice ( Figure 7C ) . These results suggest that IL-17RB+ iNKT cells are required for the development of RSV-induced AHR . Low level of cytokines ( IL-4 , IL-9 , IL-10 , IL-13 , IL-17A , and IL-22 ) in the BAL fluid was detected in this experiment ( Figure 7D ) . The production of IL-13 and IL-22 , which plays a crucial role in the activation of macrophages and neutrophils , respectively , was detected higher in RSV/rec Gs-induced WT mice . Hematoxylin and eosin ( H&E ) staining of the lung tissue revealed that a large number of inflammatory mononuclear cells had infiltrated into the peribronchiolar region , a response that was higher in RSV/rec Gs-induced WT mice compared to RSV/rec Gs-induced Jα18−/− or Il17rb−/− , mice ( Figure 7E , upper panel ) . By periodic acid-Schiff ( PAS ) staining , mucus-producing cells were abundant only in RSV/rec Gs-induced BALB/c mice but not in Jα18−/− or Il17rb−/− mice ( Figure 7E , lower panel ) . To confirm the findings that IL-17RB+ iNKT cells are essential for the development of RSV/rec Gs-induced AHR , we transferred enriched splenic IL-17RB+ iNKT cells into Jα18−/− mice and tested their ability to develop AHR ( Figure 7F ) . The cell transfer of IL-17RB+ iNKT cells , but not IL-17RB− iNKT cells nor PBS alone , restored AHR induced by RSV plus rec Gs , dependent of cell number transferred , demonstrating the important contribution of IL-17RB+ iNKT cells in the pathogenesis of development in virus plus viral antigen-induced AHR .
In the present study , we identified IL-17RB− and IL-17RB+ subtypes of iNKT cells both in the thymus and the periphery . The IL-17RB− iNKT cells express CD122 ( IL-15Rβ chain ) , expand in an IL-15-dependent manner , and produce IFN-γ in response to IL-12 . On the other hand , the IL-17RB+ iNKT cells do not express CD122 or respond to IL-15 . The IL-17RB+ iNKT cells can be further divided into at least two subtypes: ( 1 ) CD4+ IL-17RB+ iNKT cells produce TH2 , TH9 , and TH17 cytokines in an E4BP4-dependent fashion in response to IL-25 , and ( 2 ) CD4− IL-17RB+ iNKT cells are RORγt+ and produce TH17 cytokines in response to IL-23 , but independently of E4BP4 . In the thymus , the IL-17RB+ iNKT cells have a developmental pathway distinct from the IL-17RB− iNKT cells . It has been proposed that iNKT cell differentiation stages can be categorized based on the expression patterns of CD44 and NK1 . 1 , for example CD44lo NK1 . 1− for Stage 1 , CD44hi NK1 . 1− for Stage 2 , and CD44hi NK1 . 1+ for Stage 3 [14] , [25] . However , the majority ( >80% ) of IL-17RB+ iNKT cells was present in both the Stage 1 and Stage 2 subsets , while IL-17RB− iNKT cells were enriched in Il17rb−/− mice and were mainly detected in Stage 3 , suggesting that a certain but not all of the Stage 1 and Stage 2 IL-17RB+ iNKT cells are not precursors for the Stage 3 cells . It is believed that iNKT cells acquire their ability to produce IL-4 and IL-10 , but make little IFN-γ in Stages 1/2 populations , whereas iNKT cells in Stage 3 produce abundant IFN-γ but less if any IL-10 [14] , [15] , [25] , [37] . These finding are in agreement with the present results that the Stage 1/2 populations mainly contain IL-17RB+ iNKT cells that can produce IL-4 and IL-10 , but not IFN-γ , whereas the majority of the Stage 3 iNKT cells are IL-17RB− iNKT cells producing IFN-γ but not TH2 cytokines . The results shown here also indicated that all of the four iNKT subtypes already existed in Stage 1 and developed into phenotypically and functionally distinct iNKT cells as CD4− or CD4+ , IL-17RB+ in Stage 2 and CD4− or CD4+ , IL-17RB− through Stage 2 to Stage 3 . It has reported that IL-15 plays an important role in the expansion of iNKT cells [21] . Our present data showed that IL-15 requires only for the expansion of IL-17RB− iNKT cell subtypes but not for IL-17RB+ iNKT cells , even though it has still been unclear that the cytokine ( s ) are required for the development and expansion of IL-17RB+ subtypes . In fact , IL-17RB− iNKT cell subtypes were greatly reduced in number among iNKT cell subtypes but already had an ability to produce IFN-γ in Il15L117P mice , resulting in the reduced IFN-γ production after iNKT cell activation due to the reduced number of these subtypes . In the previous reports , the IL-17A-producing subtypes were proposed to be contained within the CD44hi NK1 . 1− CD4− RORγt+ subpopulation [8] , [19] . In the present studies , we found that the CD4− IL-17RB+ iNKT cell subtype is CD44hi NK1 . 1− CD4− ( about 50%–70% of the cells are IL-17RB+ ) and has a restricted expression of Il17a , Rorc , Ccr6 , and Il23r genes , for a phenotype similar to the previously reported CD44+ NK1 . 1− CD4− RORγt+ population that produces IL-17A [19] , [38] . These results indicate that IL-17RB ( and CD4− ) is a reliable and specific phenotypic marker for RORγt+ IL-17A-producing iNKT cells in the thymus . In the periphery , the tissue distribution of the iNKT cell subtypes seems to largely depend on the expression of chemokine receptors: CCR6+ CCR4+ CCR7+ expression by CD4− IL-17RB+ iNKT cells , CCR4+ CCR7+ expression by CD4+ IL-17RB+ iNKT cells , and CXCR3+ CXCR6+ by CD4− and CD4+ , IL-17RB− iNKT cells . Indeed , the number of liver iNKT cells , the majority of which are the CD4− and CD4+ , IL-17RB− iNKT cells identified here , depends on the chemokine receptor CXCR6 , whereas iNKT cells in other tissues are less dependent as reported [27] , [28] . In Ccr4−/− mice , the lung has fewer iNKT cells and a corresponding reduction in iNKT cell-mediated AHR [39] , implicating the reduction of pulmonary localization of IL-17RB+ iNKT cells . IL-17A-producing iNKT cells have been described in other studies in the thymus , liver , spleen , lung , LNs , and skin [8] , [19] , [20] , [38] , [40] . In these studies , it was suggested that all NK1 . 1− iNKT cells have the potential to secrete IL-17A . However , in the present study , we show heterogeneity among NK1 . 1− iNKT cells . Accordingly , CD4− but not CD4+ , IL-17RB+ iNKT cells correspond to the IL-17A-producing iNKT cells previously reported , as does the exclusive expression of Ccr6 along with Itgae ( = Cd103 ) and Il1r1 ( = Cd121a ) in CD4− IL-17RB+ iNKT cells ( unpublished data ) [40] . CD4+ IL-17RB+ iNKT cells produce not only the previously described IL-13 and IL-4 [17] , [18] but also IL-9 and IL-10 along with IL-17A and IL-22 in response to IL-25 in an E4BP4-dependent fashion . Even though it is still unclear whether IL-25-reactive CD4+ IL-17RB+ iNKT cells can be further divided into differentially functional subsets ( i . e . iNKT-TH2 , iNKT-TH9 , iNKT-TH17 ) , it is noteworthy that a recently described subset of differentiated T cells [41] , termed TH9 , which can be induced by IL-4 plus TGF-β , produces IL-9 and IL-10 in response to IL-25 . This IL-9 production is IL-4 independent , highlighting the role of IL-25 in the regulation of both TH2 and TH9 cells [42] . We demonstrated here that IL-25 induces not only IL-13 and IL-4 but also IL-9 and IL-10 from CD4+ IL-17RB+ iNKT cells , which can thus be characterized as iNKT-TH2 and iNKT-TH9 cells . Concerning the cytokine production by CD4+ IL-17RB+ iNKT cells in response to IL-25 , not only IL-10 and IL-13 but also IL-9 , IL-17A , and IL-22 were attenuated in the absence of E4bp4 , recently defined as a transcription factor that regulates IL-10 and IL-13 production by CD4+ T cells and iNKT cells [29] , suggesting that E4BP4 also controls IL-25-mediated production of IL-9 , IL-17A , and IL-22 . Although the precise mechanisms by which IL-25 mediates cytokine expression still remains unclear , E4BP4 itself directly or indirectly controls IL-9 , IL-10 , IL-13 , IL-17A , and IL-22 expression by genetic/epigenetic regulation in CD4+ IL-17RB+ iNKT subtypes . It will be of interest to determine if E4BP4 regulates IL-9 , IL-17A , and IL-22 production by CD4+ TH cells . Taken collectively , our studies indicate that CD4− or CD4+ , IL-17RB+ iNKT cells become functionally stable iNKT-TH17 or iNKT-TH2/9/17 , respectively , during their development . The study described here indicates that iNKT cell-mediated AHR was not induced by viral infections in Jα18−/− or Il17rb−/− mice , suggesting that IL-17RB+ iNKT cells are responsible for the pathogenesis of many different forms of airway inflammation . Although distinct subsets of iNKT cells have been reported to be involved in different forms of asthma [17] , [18] , [34] , [43] , they are now consolidated into CD4− and/or CD4+ IL-17RB+ iNKT cell subsets . iNKT cells are also known to mediate regulatory functions controlling various pathological conditions , such as infectious diseases caused by microbes [44] , autoimmune diseases ( colitis , lupus , diabetes ) [45] , [46] , atherosclerosis [47] , and malignancy [48] . It will be interesting to elucidate whether subsets of iNKT cells play differential roles in mediating and controlling these diverse pathological conditions .
B6 and BALB/c mice were purchased from Charles River Laboratories or Clea Japan , Inc . Il17rb-deficient mice were generated as shown in Figure S1 and were backcrossed >8 times to B6 or BALB/c mice . Il15L117P mutant mice were produced by N-Ethyl-N-nitrosourea ( ENU ) mutagenesis by ENU administration to male C57BL/6J mice , and their sperm was mated to wild-type eggs and preserved as founder embryos [49] , [50] . Jα18-deficient mice were generated as previously described [51] and were backcrossed >10 times to B6 or BALB/c mice . Cd1d1-deficient mice [52] were provided by Dr . Luc van Kaer ( Nashville , TN ) . E4bp4-deficient mice were generated as previously described and were backcrossed 8 times to B6 mice [29] . All mice were kept under specific pathogen-free conditions and were used at 8–16 wk of age . All experiments were in accordance with protocols approved by the RIKEN Animal Care and Use Committee . Cytokines except IL-22 in culture supernatants and BAL fluids were analyzed by cytometric bead array ( BD Biosciences ) according to the manufacturer's protocol . IL-22 was quantified by an ELISA reagent set ( eBioscience ) according to the manufacturer's protocol . Cells were analyzed by FACS Calibur ( BD Biosciences ) or FACS Canto II ( BD Biosciences ) and sorted by FACS Aria ( BD Biosciences ) . Antibodies ( BD Biosciences or eBioscience ) used for staining mouse cells were as follows: FITC or APC-Cy7 anti-TCRβ ( H57-597 ) , Pacific blue anti-CD4 ( RM4-5 ) , FITC anti-CD44 ( IM7 ) , PE-Cy7 anti-NK1 . 1 ( PK136 ) , PE anti-CD122 ( TM-β1 ) , FITC anti-CD8α ( 53-6 . 7 ) , PerCP-Cy5 . 5 anti-CD25 ( PC61 ) , PE anti-IFN-γ ( XMG1 . 2 ) , PE anti-IL-4 ( 11B11 ) , PE anti-IL-10 ( JES5-16E3 ) , PE anti-IL-13 ( eBio13A ) , PE anti-IL-17A ( TC11-18H10 ) , and PE rat IgG1 ( A110-1 ) . Biotinylated anti-mouse IL-17RB ( B5F6 ) was generated previously [17] and detected by staining with PE or PE-Cy7 Avidin ( BD Biosciences ) . APC α-GalCer loaded CD1d dimer ( BD Biosciences ) for iNKT cell enrichment and detection was prepared as previously described [53] . The procedures for the coculture with a deoxyguanosine ( dGuo ) -treated FT lobe under high oxygen submersion conditions have been described in detail previously [54] , [55] . Basically , single dGuo-treated FT lobes from Jα18−/− of B6 background were placed into wells of a 96-well V-bottom plate , to which cells from B6 mice to be examined were added . Culture medium was supplemented with IL-7 ( 1 ng/ml ) , IL-15 ( 10 ng/ml ) , and soluble IL-15Rα ( 10 ng/ml ) . The plates were centrifuged at 150× g for 5 min at room temperature , placed into a plastic bag ( Ohmi Odor Air Service ) , the air inside was replaced by a gas mixture ( 70% O2 , 25% N2 , and 5% CO2 ) , and incubated at 37°C . After 10 d of culture , cells were harvested from each well and analyzed by FACS and quantitative real-time PCR . Intracellular cytokine staining was performed as described previously [53] . For cytokine production from sorted iNKT cell subtypes , Brefeldin A ( Sigma-Aldrich ) was added for the last 4 to 5 h of culture to accumulate intracellular cytokines after PMA ( 25 ng/ml , Sigma ) with ionomycin ( 1 µg/ml , Sigma ) treatment . Following fixation with Cytofix/Cytoperm plus ( BD Biosciences ) , cells were stained for indicated intracellular cytokines for 15 min at room temperature . PCR primers and probes were designed with Universal ProbeLibrary Assay ( Roche ) or with TaqMan Gene Expression Assays ( Applied Biosystems ) . Sequence of primers and probes in the latter case are shown in Table S1 . PCR was performed with the TaqMan universal master mix with ROX ( Applied Biosystems ) according to the protocol provided . ABI PRISM7900HT Fast system ( Applied Biosystems ) or Biomark system ( Fludigm ) was used for quantitative real-time PCR according to the manufacturer's instructions . To ensure the specificity of the amplification products , a melting curve analysis was performed . Results were normalized and analyzed by ΔCt or ΔΔCt methods using the internal control gene Hprt1 . Gene expression detected using microarrays was normalized by the quantile normalization method [56] . Pearson's correlation values of logarithms of all signal intensities from 45 , 101 probes were calculated , and we performed hierarchal clustering of correlation matrices to indicate the degree of similarity between cell types . Scatter diagrams were drawn to display how similarly or differently genes were expressed in two samples . These diagrams contain only probes whose signals were present and coefficient values were shown in the figures . Strain A2 of human RSV was used in this study . The general protocol for analyzing airway remodeling during RSV infection in mice is as follows: AHR was induced by sensitizing and challenging with OVA/alum ( 3–4 times ) and/or infection with RSV ( 3–4 times ) , and then challenging with OVA , resulting in the examination of various pathological endpoints as previously described [57]–[59] . In the present study , we modified these protocols in order to analyze the physiological role of iNKT cells in the development of AHR mediated by RSV . In brief , mice were i . n . administered with RSV ( 106 pfu ) or PBS as a control 4 times at 10-d intervals . Mice were i . p . immunized with rec Gs/alum ( 50 µg/2 mg ) 4 d after first RSV infections . Three days after the last RSV administration , mice were exposed i . n . to rec Gs recombinant protein and AHR responses were measured 1 d later . Airway function was measured for changes in lung resistance ( RL ) and dynamic compliance in response to increasing doses of inhaled methacholine ( 1 . 25 , 2 . 5 , 5 , 10 , and 20 mg/ml ) by using an invasive FlexiVent ( SCIREQ Scientific Respiratory Equipment Inc . ) . After measurement of AHR and sacrifice , the mouse trachea was cannulated , the lungs were lavaged twice with 1 ml PBS ( 10-fold PBS dilution ) , and the BAL fluid was pooled as previously described [30] . Lymphocytes from thymus , spleen , liver , lung , BM , inguinal LN , and mesenteric LN were isolated as described previously [53] . The statistical significance of differences was determined by t test , analysis of variance ( ANOVA ) , or the Kruskal-Wallis test . The values were expressed as means ± SEM from independent experiments . Any differences with a p value of <0 . 05 were considered significant ( * p<0 . 05; ** p<0 . 01 ) . | T cells are a diverse group of immune cells involved in cell-mediated acquired immunity . One subset of T cells is the innate-like invariant natural killer T ( iNKT ) cells that recognize glycolipid ligands on target cells instead of peptides . We know that functionally distinct subtypes of iNKT cells are involved in specific pathologies , yet their development , phenotypes , and functions are not well understood . Here , we determine the relationship between various mouse iNKT cell subsets , identify reliable molecular markers for these subsets , and show that these contribute to their functional differences . We identify four iNKT cell subsets that we show arise via different developmental pathways and exhibit different cytokine profiles . Importantly , we show that these subsets can be isolated from the thymus ( the organ of all T cells ) , as well as from peripheral tissues such as spleen , liver , lung , and lymph nodes . Contrary to the general understanding that iNKT cells mature after their exit from the thymus and their migration into peripheral tissues , we conclude that distinct phenotypic and functional iNKT cell subsets can be distinguished in the thymus by virtue of the presence or absence of the cytokine receptor IL-17RB and another cell surface molecule called CD4 , and these subsets then migrate to peripheral tissues where they retain their phenotypic and functional characteristics . Regarding functional significance , we show that those iNKT cell subsets that lead to airway hyper-responsiveness to respiratory viruses are different to those that lead to allergen-induced airway hyperreactivity , which will enable researchers to focus on specific subsets as potential targets for therapeutic intervention . | [
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| 2012 | Development and Function of Invariant Natural Killer T Cells Producing TH2- and TH17-Cytokines |
Computational methods for discovery of sequence elements that are enriched in a target set compared with a background set are fundamental in molecular biology research . One example is the discovery of transcription factor binding motifs that are inferred from ChIP–chip ( chromatin immuno-precipitation on a microarray ) measurements . Several major challenges in sequence motif discovery still require consideration: ( i ) the need for a principled approach to partitioning the data into target and background sets; ( ii ) the lack of rigorous models and of an exact p-value for measuring motif enrichment; ( iii ) the need for an appropriate framework for accounting for motif multiplicity; ( iv ) the tendency , in many of the existing methods , to report presumably significant motifs even when applied to randomly generated data . In this paper we present a statistical framework for discovering enriched sequence elements in ranked lists that resolves these four issues . We demonstrate the implementation of this framework in a software application , termed DRIM ( discovery of rank imbalanced motifs ) , which identifies sequence motifs in lists of ranked DNA sequences . We applied DRIM to ChIP–chip and CpG methylation data and obtained the following results . ( i ) Identification of 50 novel putative transcription factor ( TF ) binding sites in yeast ChIP–chip data . The biological function of some of them was further investigated to gain new insights on transcription regulation networks in yeast . For example , our discoveries enable the elucidation of the network of the TF ARO80 . Another finding concerns a systematic TF binding enhancement to sequences containing CA repeats . ( ii ) Discovery of novel motifs in human cancer CpG methylation data . Remarkably , most of these motifs are similar to DNA sequence elements bound by the Polycomb complex that promotes histone methylation . Our findings thus support a model in which histone methylation and CpG methylation are mechanistically linked . Overall , we demonstrate that the statistical framework embodied in the DRIM software tool is highly effective for identifying regulatory sequence elements in a variety of applications ranging from expression and ChIP–chip to CpG methylation data . DRIM is publicly available at http://bioinfo . cs . technion . ac . il/drim .
This paper examines the problem of discovering “interesting” sequence motifs in biological sequence data . A widely accepted and more formal definition of this task is: given a target set and a background set of sequences ( or a background model ) , identify sequence motifs that are enriched in the target set compared with the background set . The purpose of this paper is to extend this formulation and to make it more flexible so as to enable the determination of the target and background set in a data driven manner . Discovery of sequences or attributes that are enriched in a target set compared with a background set ( or model ) has become increasingly useful in a wide range of applications in molecular biology research . For example , discovery of DNA sequence motifs that are overabundant in a set of promoter regions of co-expressed genes ( determined by clustering of expression data ) can suggest an explanation for this co-expression . Another example is the discovery of DNA sequences that are enriched in a set of promoter regions to which a certain transcription factor ( TF ) binds strongly , inferred from chromatin immuno-precipitation on a microarray ( ChIP–chip ) [1] measurements . The same principle may be extended to many other applications such as discovery of genomic elements enriched in a set of highly methylated CpG island sequences [2] . Due to its importance , this task of discovering enriched DNA subsequences and capturing their corresponding motif profile has gained much attention in the literature . Any approach to motif discovery must address several fundamental issues . The first issue is the way by which motifs are represented . There are several strategies for motif representation: using a k-mer of IUPAC symbols where each symbol represents a fixed set of possible nucleotides at a single position ( examples of methods that use this representation include REDUCE [3] , YMF [4 , 5] , ANN-SPEC [6] , and a hypergeometric-based method [7] ) or using a position weight matrix ( PWM ) , which specifies the probability of observing each nucleotide at each motif position ( for example MEME [8] , BioProspector [9] , MotifBooster [10] , DME-X [11] , and AlignACE [12] ) . Both representations assume base position independence . Alternatively , higher order representations that capture positional dependencies have been proposed ( e . g . , HMM and Bayesian networks motif representations [13] ) . While these representations circumvent the position independence assumption , they are more vulnerable to overfitting and lack of data for determining model parameters . The method described in this paper uses the k-mer model with symbols above IUPAC . The second issue is devising a motif scoring scheme . Many strategies for scoring motifs have been suggested in the literature . One simple yet powerful approach uses the hypergeometric distribution for identifying enriched motif kernels in a set of sequences and then expanding these motifs using an EM algorithm [7] . The framework described in this paper is a natural extension of the approach of [7] . YMF [4 , 5] is an exhaustive search algorithm which associates each motif with a z-score . AlignACE [12] uses a Gibbs sampling algorithm for finding global sequence alignments and produces a MAP score . This score is an internal metric used to determine the significance of an alignment . MEME [8] uses an expectation maximization strategy and outputs the log-likelihood and relative entropy associated with each motif . Once a scoring scheme is devised , a defined motif search space is scanned ( either heuristically or exhaustively ) and motifs with significantly high scores are identified . To determine the statistical significance of the obtained scores , many methods resort to simulations or ad hoc thresholds . Several excellent reviews narrate the different strategies for motif detection and use quantitative benchmarking to compare their performance [14–18] . A related aspect of motif discovery , which is outside the scope of this paper , focuses on properties of clusters and modules of TF binding sites ( TFBS ) . Examples of approaches that search for combinatorial patterns and modules underlying TF binding and gene expression include [19–23] . One issue of motif discovery that is often overlooked concerns the partition of the input set of sequences into target and background sets . Many methods rely on the user to provide these two sets and search for motifs that are overabundant in the former set compared with the latter . The question of how to partition the data into target and background sets is left to the user . However , the boundary between the sets is often unclear and the exact choice of sequences in each set arbitrary . For example , suppose that one wishes to identify motifs within promoter sequences that constitute putative TFBS . An obvious strategy would be to partition the set of promoter sequences into target and background sets according to the TF binding signal ( as measured by ChIP–chip experiments ) . The two sets would contain the sequences to which the TF binds “strongly” and “weakly , ” respectively . A motif detection algorithm could then be applied to find motifs that are overabundant in the target set compared with the background set . In this scenario , the positioning of the cutoff between the strong and weak binding signal is somewhat arbitrary . Obviously , the final outcome of the motif identification process can be highly dependent on this choice of cutoff . A stringent cutoff will result in the exclusion of informative sequences from the target set while a promiscuous cutoff will cause inclusion of nonrelevant sequences—both extremes hinder the accuracy of motif prediction . This example demonstrates a fundamental difficulty in partitioning most types of data . Several methods attempt to circumvent this hurdle . For example , REDUCE [3] uses a regression model on the entire set of sequences . However , it is difficult to justify this model in the context of multiple motif occurrence ( as explained below ) . In other work , a variant of the Kolmogorov-Smirnov test was used for motif discovery [24] . This approach successfully circumvents arbitrary data partition . However , it has other limitations such as the failure to address multiple motif occurrences in a single promoter , and the lack of an exact characterization of the null distribution . Overall , the following four major challenges in motif discovery still require consideration: ( c1 ) the cutoff used to partition data into a target set and background set of sequences is often chosen arbitrarily; ( c2 ) lack of an exact statistical score and p-value for motif enrichment . Current methods typically use arbitrarily set thresholds or simulations , which are inherently limited in precision and costly in terms of running time; ( c3 ) a need for an appropriate framework that accounts for multiple motif occurrences in a single promoter . For example , how should one quantify the significance of a single motif occurrence in a promoter against two motif occurrences in a promoter ? Linear models [3] assume that the weight of the latter is double that of the former . However , it is difficult to justify this approach since biological systems do not necessarily operate in such a linear fashion . Another issue related to motif multiplicity is low complexity or repetitive regions . These regions often contain multiple copies of degenerate motifs ( e . g . , CA repeats ) . Since the nucleotide frequency underlying these regions substantially deviates from the standard background frequency , they often cause false-motif discoveries . Consequently , most methods mask these regions in the preprocessing stage and thereby lose vital information that might reside therein; ( c4 ) criticism has been made over the fact that motif discovery methods tend to report presumably significant motifs even when applied on randomly generated data [25] . These motifs are clear cases of false positives and should be avoided . In this paper we describe a novel method that attempts to solve the above-mentioned four challenges in a principled manner . It exploits the following observation: data often lends itself to ranking in a natural manner , e . g . , ranking sequences according to TF binding signal: ranking according to CpG methylation signal , ranking according to distance in expression space from a set of co-expressed genes , ranking according to differential expression , etc . We exploit this inherent ranking property of biological data in order to circumvent the need for an arbitrary and difficult-to-justify data partition . Consequently , we propose the following formulation of the motif finding task: given a list of ranked sequences , identify motifs that are overabundant at either end of the list . Our solution employs a statistical score termed mHG ( minimal hypergeometric ) [26] . It is related to the concept of rank-imbalanced motifs , which are sequence motifs that tend to appear at either end of a ranked sequence list . In previous work [26] , the authors used mHG to identify sequence motifs in expression data . We use this simple yet powerful approach as the starting point for our study . The rest of this paper is divided into two main parts , each of which is self-contained: in the Results we briefly outline our method and describe new biological findings that were obtained by applying this method to biological data . We address challenge ( c4 ) by testing the algorithm on randomly ranked real genomic sequences . In the Methods , we describe the mHG probabilistic and algorithmic framework and explain how we deal with challenges ( c1 ) – ( c3 ) .
Based on the mHG framework , we developed a software tool termed DRIM ( discovery of rank imbalanced motifs ) for motif identification in DNA sequences . A flow chart of DRIM is provided in Figure 1 . The formal introduction and details of the mHG statistics are given in Methods . However , to facilitate the explanation and interpretation of our biological results , we begin with a brief description of the method . Suppose we are given a set of DNA sequences and some measured signal associated with each sequence . We rank the sequences according to the signal . Now , given a sequence motif , we wish to assess whether that motif tends to appear more often at the “top” of a list compared with the “remainder” of the list . The mHG score captures this type of motif significance . More precisely , the mHG score reflects the surprise of seeing the observed density of motif occurrences at the top of the list compared with the rest of the list under the null assumption that all configurations of motif occurrences in the list are equiprobable . A unique feature of the mHG statistics is that the cutoff between the top and the rest of the list is chosen in a data-driven manner so as to maximize the motif enrichment . This is done by computing the motif enrichment over all possible set partitions and identifying the cutoff at which maximal statistical significance is observed . The search for this optimal cutoff introduces a multiple testing problem . To solve this without resorting to multiple testing corrections , which diminish the score's sensitivity , we provide a novel algorithm for computing the exact p-value of mHG scores ( see Methods , Calculating the p-value of the mHG score ) . This eliminates the need to resort to simulations or exhaustively calculated tables . Our method also includes a new approach to modeling motif multiplicity by incorporating a multidimensional hypergeometric framework ( see Methods , Multidimensional mHG score ) . Unlike some models , which assume linearity ( e . g . , that two binding motifs have twice the binding capacity as one motif ) , our model does not make such pre-assumptions . Instead , the degree of surprise is adjusted for each motif according to its own occurrence multiplicity distribution . DRIM scans through a motif space , computes the mHG p-value of these motifs and reports the significant ones ( see Methods , The DRIM software ) . We begin by testing our method on synthetically generated clear-cut positive and negative control cases . We do this to verify that DRIM accurately identifies motifs in well-characterized and experimentally verified examples and at the same time avoids false identification of motifs in randomly ordered genomic sequences . The latter objective is of particular importance since the issue of false identification has been mentioned as one of the main shortcomings of motif discovery approaches . For example , in a previous study , six different motif discovery applications were used to search for TFBS motifs [25] . Each of the programs attempted to measure the significance of its results using one or more enrichment scores . The authors report that the applications outputted high-scoring motifs even when applied to random selections of intergenic regions . A different paper reports clusters of genes whose expression patterns correlate to the expression of a particular TF [27] . These clusters were then analyzed for enriched motifs . Again , the authors report that random sets , with sizes matching those of the real clusters , contained a large number of motifs with significant scores . To test our method's false-prediction rate , we performed the following negative control experiment: five different random permutations of ChIP–chip data were generated by randomly selecting 400 promoters and randomly permuting their ranks . DRIM was then applied to these ranked lists and scanned more than 100 , 000 different motifs in each one . None of the motifs that were scanned had a significant corrected mHG p-value <10−3 . Note that to get the corrected p-values , two levels of multiple test corrections are performed: correcting for the number motifs that are tested; and correcting for multiple cutoffs that are tested as part of the mHG optimization process . How do the p-values of random motifs compare with those of true biological motifs ? To test this , we chose five TFs ( BAS1 , GAL4 , CBF1 , INO2 , and LEU3 ) whose motif binding sites are well-characterized and experimentally verified . We applied DRIM to the ChIP–chip data of these TFs as reported in [25] . In all instances , the true motifs were identified with corrected p-values of 10−6 , 10−9 , 10−76 , 10−18 , and 10−8 , respectively . A comparison of the p-value distribution of the motifs in the randomly ordered sequences with that of the verified TFBS motifs is given in Figure S3 . In all instances the true TFBS motifs were predicted with p-values that were several orders of magnitude more significant than the best p-value of a motif in the randomly permuted data . This indicates that the enrichment signals of true TFBS , as captured by the mHG p-value , are clearly distinct from the signals we expect to find in random rankings of genomic sequences . To further test the effectiveness of our method , we used it for identification of TFBS in yeast by applying it to the Harbison and Lee–filtered ChIP–chip datasets [25 , 28] , containing measurements of 207 TF binding experiments in several conditions ( for details regarding dataset-filtering see Methods ) . Interestingly , we observed that in many of these datasets longer intergenic regions are biased toward stronger TF binding . We elaborate on this sequence length bias in the Methods section and in Figure S1 . In each of the ChIP–chip experiments , we ranked the intergenic regions according to the TF binding signal ( we use the p-value of enrichment for the sequence represented on the array ) . This was used as input for DRIM , which then searched for motifs that tend to appear densely at the top of the ranked lists . If such a motif does exist , with a p-value less than 10−3 , then we hypothesize that it is biologically significant and that it contributes to the TF's binding , either directly or indirectly . The results on the Harbison filtered dataset are summarized in Table S2 . A TF was assigned a motif if such was found in at least one condition . We compared the DRIM predictions with previously reported TFBS discoveries in ChIP–chip that incorporated predictions of six other motif discovery methods and conservation data [25] . The results of this comparison are summarized in Figure 2 . Overall , DRIM identified 50 motifs that were not picked up by the six other methods as reported in [25] . We further investigated these putative TFBS for additional evidence that they are biologically meaningful . First , we found that seven of them ( ASH1 , GCR1 , HAP2 , MET31 , MIG1 , RIM101 , and RTG3 ) are in agreement with previously published results that are based on experimental techniques other than ChIP–chip . Second , we compared them with a list of conserved regulatory sites in yeast that was recently inferred using conservation-based algorithms [29] . Ten of our putative TFBS match these conserved sites ( ARG81 , ARO80 , ASH1 , CRZ1 , DAL81 , HAP2 , IME1 , MET31 , MIG1 , and RTG3 ) . Taken together , these findings provide a strong indication that at least some of the new motifs identified by DRIM are true biological signals . In the following subsections , we focus on a few of these putative TFBS ( see Figure 3 ) and present additional evidence that supports their biological role . We use these findings to discover new interactions in the yeast genetic regulatory network . To examine our method's ability to predict sequence motifs that stem from data other than TF binding , DRIM was applied to a dataset containing the human cancer cell line–methylated CpG islands ( for dataset details , see Methods ) to seek for motifs that are enriched in hypermethylated regions . The promoters were ranked according to methylation signal , with hypermethylated promoters at the top . Note that different replicates of the same cell line may yield different ranking of the promoters . DRIM identified significantly enriched motifs in each of the four cancer cell lines . Table 1 shows all the motifs that were independently discovered in at least two different replicates of the same experiment or that are in agreement with previous work [2] . Overall , DRIM discovered 13 motifs: ten novel motifs and three that have been previously predicted in hypermethylated CpG island promoters in the same cancer cell lines [2] . Some of these motifs have also been independently identified in methylated CpG regions of other cell lines [39 , 40] . Interestingly , nine of the novel ten motifs were independently identified in DNA regions to which the proteins of the Polycomb complex bind [41–43] . The Polycomb complex is involved in gene repression through epigenetic silencing and chromatin remodeling , a process that involves histone methylation . The fact that these two distinct key epigenetic repression systems , namely histone methylation and CpG methylation , bind to regions that share a similar set of sequence motifs suggests they are linked . To further establish this link we applied DRIM to Polycomb complex bound promoters in human embryonic fibroblasts [44] . We found four motifs that are similar to the CpG methylation motifs ( Table 1 ) . Our findings are consistent with a recent paper that showed that the EZH2 Polycomb protein binds methyltransferases via the Polycomb complex [45] . Most of the motifs we found are similar across more than one type of cancer cell line , e . g . , variants of the GCTGCT motif appear in Caco-2 , PC3 , and Polyp1 cancer cell lines . This suggests that the same DNA binding factors are involved in CpG methylation of different types of cancers . It is also important to note that some of the motifs we discovered are G–C rich . The enrichment of these motifs may be partially attributed to the G–C content bias that is found in CpG methylation data . The DRIM motif identification process can be used not only to identify novel motifs but also to partition the data in a biologically meaningful manner . In [2] the authors used a fixed threshold on the methylation signal ( p-value < 0 . 001 ) to partition the dataset . Consequently , they identified 135 hypermethylated promoters . A data-driven partition would be to use the threshold that yielded the maximal motif enrichment . For example , in the Caco2 cell line , we identified the same motif as in the previous work [2] . However , the motif maximal enrichment was found in the top 209 promoters ( an increase of 54% in target set size ) . Human TFBS tend to be longer and “fuzzier” than TFBS of lower eukaryotes , and it is important to evaluate our method's performance on such motifs . To this end , we applied DRIM to the ChIP–chip experiments of HNF1α , HNF4α , HNF6 in liver and pancreas islets [46] , as well as to that of CREB [47] . For each of the TFs , we generated a list of sequences containing 1 , 000 bases upstream and 300 downstream of the transcription start site ( TSS ) . We ranked the list according to the TF ChIP–chip signal and used it as input to DRIM . DRIM successfully detected the TFBS of these TFs that are reported in TRANSFAC with extremely significant p-values: HNF1α liver—GTTAMWNATT ( p = 10−8 ) , HNF4α Islets—SCGGAAR ( p = 10−53 ) , HNF6 Liver—ATCRAT ( p = 10−57 ) , and HNF6 Islets—ATCRAT ( p = 10−61 ) . In the CREB experiments we identified the palindromic motif TGACGTCA ( p = 10−16 ) , which is known to bind CREB [47] . Three properties of the mHG enrichment score embodied in DRIM offer advantages over other motif discovery methods: the dynamic cutoff , the rigorous control over false positives , and the motif multiplicity model .
In this paper we examine the problem of discovering “interesting” motif sequences in biological sequence data . While this problem has often been regarded as tantamount to discovering enriched motifs in a target set versus a background set , we point out an inherent limitation to this formulation of the problem . Specifically , in most cases , biological measurement data does not lend itself to a single , well-substantiated partition into target and background sets . It does , however , lend itself to ranking in a natural manner . Our approach exploits this natural ranking and attempts to solve challenges ( c1 ) – ( c4 ) ( see Introduction , Open challenges in motif discovery ) . To address challenge ( c1 ) , instead of choosing an arbitrary cutoff for set partition , we search for a cutoff that partitions the data in a way that maximizes the motif enrichment . We present evidence that shows that the flexible mHG cutoff outperforms the rigid cutoff . One example of this is shown in Figure 5 , where the flexible cutoff yields better results for all the tested TFs . Another example of the advantage of a flexible cutoff is the two motifs detected in three TFs involved in the sulfur amino acid pathway ( Met4 , Met31 , and Met32 ) . Figure 7 shows the number of motif occurrences in each of the top 59 promoters that were ranked according to Met32 binding signal ( data from [25] ) . The motifs are highly frequent in the top 18 promoters , after which a strong drop in motif frequency is observed . DRIM identifies this , and partitions the set accordingly . In comparison , relying on the standard cutoff of 10−3 results in a target set of the top 48 promoters , most of which do not contain this motif . The signal-to-noise ratio is thus diminished , which may explain why these motifs were previously overlooked . While the flexible cutoff is advantageous in many instances , it also introduces a multiple testing problem . To circumvent this ( without resorting to strict multiple testing corrections that may mask the biological signal ) , we developed an efficient algorithm for computing the exact p-value of a given mHG score . This addresses challenge ( c2 ) . Another advantage of this exact statistical score is its straightforward biological interpretation: the mHG p-value reflects the probability of seeing the observed density of motif occurrences at the top of the ranked list under the null assumption that all configurations of motif occurrences are equiprobable . Motif multiplicity is often indicative of biological function . It is therefore paramount to incorporate this type of information into the motif prediction model . We do so in a data-driven manner by developing the multi-mHG framework , thus addressing challenge ( c3 ) . The advantages of the multi-mHG model over the binary model are presented in Results , Binary versus multidimensional enrichment . False prediction of motifs in randomly generated data is often mentioned as one of the drawbacks of computational motif discovery [25] . We report the testing of DRIM on random permutations of ranked sequences . When tested on more than 100 , 000 motifs , DRIM did not identify any significant motifs , thus addressing challenge ( c4 ) . The low false-positive prediction of our method is mainly attributed to the fact that it is based on rigorous statistics and relies on an exact p-value . Another important issue that still requires consideration is the characterization of the motif search space . In this study we performed an exhaustive scanning of a restricted motif space ( containing ∼105 motifs ) followed by a heuristic search for larger motifs . However , the motif search space can be further extended to include motifs that are longer , “fuzzier , ” or more complex . Additional considerations such as the distance of the motif from the transcription start site may be taken into account as well as logical relations between different motifs ( e . g . , “OR , ” “AND” operations ) . It is clear that many of these features are required to correctly model complex regulation patterns that are observed in higher eukaryotes . Two inherent limitations need to be considered when extending the search space: first , as the size of the motif search space increases , the problem of efficiently searching the defined space becomes more acute in terms of running time . Second , since the size of the search space is virtually endless , the problem of multiple testing rapidly erodes the signal-to-noise ratio , requiring an appropriate refinement of the statistical models . To test our method , we constructed a dataset containing ChIP–chip experiments of 203 putative TFs in Saccharomyces cerevisiae [25 , 28] . Surprisingly , we discovered a significant length bias in roughly one-third of these experiments . One possible explanation for this phenomenon is nonspecific binding between TFs and DNA , which causes longer sequences to bind more TFs . This explanation is also consistent with the “TF sliding hypothesis” [48] . Why only some TFs exhibit this length bias binding tendency remains an open question . To avoid false positives due to this phenomenon , we opted to filter out all ChIP–chip experiments that had significant length bias . Future work should address this point and focus on developing statistics that are insensitive to this type of bias . We analyzed the filtered dataset using DRIM and report novel putative TFBS motifs . Additional evidence that indicates the newly discovered motifs are biologically functional was also presented . One interesting finding is that the Aro80 motif we identified , which exists only in seven copies throughout the entire yeast genome , resides in Aro80′s own promoter . This finding suggests that Aro80 regulates its own transcription by binding to its own promoter . Additionally , three GATA binding sites that reside in the Aro80 promoter adjacent to the motif occurrence lead us to speculate that Aro80′s putative self binding is inhibited by competing GATA binding factors ( for details see Figure 4B ) . Another interesting observation is the CA repeat motifs , which we identified in seven different yeast TFs as well as in human DNA methylation . This type of low complexity motifs have so far been mostly ignored or filtered out by other computational methods . By contrast there is no need to resort to this type of artificial filtering when using the mHG statistics . Our findings in yeast suggest that for certain TFs there is a significant correlation between a sequence's capacity to bind a TF and the presence of a CA repeat in the sequence . This supports a previous hypothesis that CA repeats alter the structure of DNA and thus contribute to TF binding [34] . Our findings constitute concrete evidence of this phenomenon and suggest it may be more frequent than previously appreciated . We also applied DRIM to high-throughput measurements of methylated CpG islands [2] in human cancer cells , in order to try to identify motifs that are enriched in hypermethylated regions . Interestingly , we identified GA and CA repeat elements as highly enriched in methylated CpG regions of four different cancer cell lines . This is in agreement with previous studies of CpG methylated regions in other cell lines [39 , 40] . It is interesting to ask whether these repeat elements play some active role in CpG methylation . In [40] the authors give statistical argumentation against such a hypothesis . Instead , they hypothesize that CA ( or TG ) repeats are caused by an increased mutation rate of methylated CpGs that are deaminated into TpGs . Even if true , this still does not explain the enrichment of the GA repeats . Further experimental and bioinformatic interrogation of this point is therefore called upon . Overall , DRIM discovered ten novel motifs in methylated CpG regions . Strikingly , nine of them are similar to DNA sequence elements that bind the Polycomb complex in Drosophila and/or human [41 , 42 , 44] . The Polycomb complex is involved in epigenetic silencing via histone methylation . The suggested link between histone methylation and CpG methylation is in agreement with recent work that demonstrated the EZH2 protein interacts with DNA methyltransferases via the Polycomb complex [45] . We also note that the DNA sequence motifs of the two pathways were conserved in Drosophila and human , which is complementary to the observation that the Polycomb proteins are evolutionarily conserved [44 , 49] . Many of the motifs we found in the CpG methylation data are similar across different types of cancer cell lines . This may suggest that the CpG methylation mechanism is orchestrated by DNA binding factors that are similar in different types of cancer cell lines . Perhaps the most important conclusion that can be drawn from this study is that looking at biological sequence data in a ranked manner rather than using an arbitrary fixed cutoff to partition the data enables the detection of biological signals that are otherwise overlooked . This suggests that other motif detection methods that rely on fixed cutoffs may benefit from dynamic partitioning . While the effectiveness of our approach was demonstrated on ChIP–chip and methylation data , it can also be applied to a wide range of other data types such as expression data or GO analysis . The DRIM application is publicly available at http://bioinfo . cs . technion . ac . il/drim .
In this subsection we introduce the basics of the mHG statistics , and demonstrate how it can be applied in a straightforward manner to eliminate the need for an arbitrary choice of threshold . To explain the biological motivation of mHG , consider the following scenario: suppose we have a set of promoter regions each associated with a measurement , e . g . , a TF binding signal as measured by ChIP–chip [1] . We wish to determine whether a particular motif specified in IUPAC notation , say CASGTGW , is likely to be a TFBS motif . We rank the promoters according to their binding signals—strong binding at the top of the list and the weak at the bottom ( Figure 1i ) . Next , we generate a binary occurrence vector with one or zero entries dependent on whether or not the respective promoter contains a copy of the motif ( Figure 1ii ) . For simplicity we ignore cases where a promoter contains multiple copies of the motif ( a refined model , which takes motif multiplicity into account , will be discussed later ) . Motifs that yield binary vectors with a high density of 1′s at the top of the list are good candidates for being TFBS . Let us assume for the moment that we know the correct physical-based cutoff on the TF binding signal . The data could then be separated into “strong binding promoters” ( i . e . , the target set ) and “weak binding promoters” ( i . e . , the background set ) . We are now interested to know whether there is a particular motif for which the target set contains significantly more motif occurrences than the background set . Let N be the total number of promoters B of which contain the motif , and n the size of the target set . Let X be a random variable describing the number of motif occurrences in the target set . Assuming a uniform distribution over all occurrence vectors with these characteristics , the probability of finding exactly b occurrences in the target set has a hypergeometric distribution , namely: The tail probability of finding b or more occurrences in the target set is: As we don't really always have a strict definition of the target set , we employ a strategy that seeks a partition for which the motif enrichment is the most significant , and compute the enrichment under that particular partition . Formally , consider a set of ranked elements and some binary labeling of the set λ = λ1 , … , λN ∈ {0 , 1}N . The binary labels represent the attribute ( e . g . , motif occurrence ) . The mHG score is defined as: where bn ( λ ) = . In words , the mHG score reflects the surprise of seeing the observed density of 1's at the top of the list under the null assumption that all configurations of 1's in the vector are equiprobable . The cutoff between the top of the list and the rest of the list is chosen in a data-driven manner so as to maximize the enrichment ( Figure 1iii ) . We discuss other variants of the mHG score in Texts S2 and S3 . The mHG flexible choice of cutoff introduces a multiple testing complication and therefore gives rise to the need for computing the exact p-value . In Text S1 and Figure S2 we demonstrate several bounds for mHG p-values . These bounds may be used for rapid assessment of the p-value of a given mHG score , which can be instrumental in improving algorithmic efficiency . In this section , we describe a novel dynamic programming algorithm for calculating the exact p-value of a given mHG score . This approach is related to a previously described approach for calculating exact p-values of other combinatorial scores ( [50 , 51] , with details in [52] ) . As noted in the previous section , the mHG score depends solely on the content of the label vector λ . Set N and B , and consider the space of all binary label vectors with B 1′s and N−B 0′s: Λ = {0 , 1} ( N−B , B ) . Assume that we are given a vector λ0∈Λ , for which we calculate the mHG score mHG ( λ0 ) = p . We would like to determine pval ( p ) = Prob ( mHG ( λ ) ≤ p ) under a uniform distribution of vectors in Λ . Given an mHG score p , we do this by means of path counting . The space of all label vectors Λ = {0 , 1} ( N−B , B ) is represented as a two-dimensional grid ranging from ( 0 , 0 ) at the bottom left to ( N , B ) at the top right . Each specific label vector λ∈Λ is represented by a path ( 0 , 0 ) → ( N , B ) composed of N distinct steps . The ith step in the path describing a vector λ is ( 1 , 0 ) if λi = 0 and ( 1 , 1 ) if λi = 1 ( see Figure 8 ) . Each point ( n , b ) on the grid corresponds to a threshold ( on ranks ) n , and the respective value b = bn ( 1 ) . It can therefore be associated with a specific HGT score: HGTn ( λ ) = HGT ( bn ( λ ) ;N , B , n ) . A subset of the points on the grid can be characterized as those points ( n , b ) for which HGT ( b;N , B , n ) ≤ p . We denote this subset R = R ( p ) ( see Figure 8 ) . The ( 0 , 0 ) → ( N , B ) path representing λ visits N distinct grid points ( excluding the point ( 0 , 0 ) ) , representing the N different HGT scores that are considered when calculating its mHG score: mHG ( λ ) = min1≤n<NHGTn ( λ ) . mHG ( λ ) ≤ p if the path representing λ visits R . Denote by Π ( n , b ) the total number of paths ( 0 , 0 ) → ( n , b ) and by ΠR ( n , b ) the number of paths ( 0 , 0 ) → ( n , b ) not visiting R . We then have: We calculate ΠR ( n , b ) by means of dynamic programming . Initially , set ΠR ( 0 , 0 ) = 1 and ΠR ( n , b ) = 0 for b = −1 and along the diagonal b = n + 1 , 0 ≤ n ≤ B . Then , for each 1 ≤ n ≤ N , and max ( 0 , B − N + n ) ≤ b ≤ min ( B , n ) calculate ΠR ( n , b ) using the formula: In total , we perform a O ( N2 ) routine in order to calculate ΠR ( N , B ) for a given score p . Trivially , we have Π ( N , B ) = and pval ( p ) may be directly computed from Equation 4 . So far we have dealt with enrichment of binary attributes , in which a one or zero indicated whether or not the attribute appeared . There are cases where one would like to associate a number with an attribute . We revisit the scenario we described in previous sections in which we tried to determine whether a particular motif is likely to be a TFBS motif . The promoters were ranked according to their binding signals , and the corresponding binary occurrence vector was generated . Notice that some promoters may contain several copies of a particular motif . Clearly , this information is valuable and should be incorporated in the enrichment analysis . How exactly to incorporate this information is not clear . For example , consider two motif occurrence vectors generated for two different motifs , where the top ten entries of the vectors are all 1's and all 2's , respectively . Is the second motif more enriched than the first ? Clearly , this depends on the rarity of double motif occurrences compared with single occurrences in the corresponding vectors . If the frequency of 2's is lower than that of 1's , then the second motif is more significant . However , if they are equally frequent ( this is often the case for degenerate motifs such as poly A's ) then both motifs are equally enriched . To quantitatively capture this notion and address motif multiplicity in a data-driven manner , we propose a multidimensional hypergeometric model , which extends the previously defined framework for enrichment analysis to nonbinary label vectors . Formally , let λ be a uniformly drawn label vector λ = λ1 , … , λN ∈ {0…k}N containing B1 1's , B2 2's … Bk k's and . We would like to test for enrichment of 1's , 2's . . . k's at the top of λ . We define the multidimensional hypergeometric score ( multiHG ) for a set S of size N consisting of k + 1 subsets S0 , S1 , S2 … , Sk of respective sizes N – ( B1 + B2 + …Bk ) , B1 , B2… , Bk . Given a subset S′ ⊂ S of size n , the probability of finding exactly b1 elements of S1 and b2 elements of S2… , bk elements of Sk within S′ is: Let X1 , …Xk be random variables describing the number of 1′s , … , k's , respectively , at the top n positions of λ . The multihypergeometric tail probability ( multiHGT ) of seeing at least b1 1's , at least b2 2's , … , and at least bk k's at the top n positions of the vector is: The definition of the mHG score can now be extended to the minimum of the set of multiHGTs calculated on all prefixes of λ . where bj ( n , λ ) = . Exact p-values for the multidimensional mHG , under a uniform null distribution , can be computed in a k-dimensional space using a path enumeration strategy similar to the one we used in the binary case . The details on how to compute this p-value in a three-dimensional space are explained in Text S4 . The software tool DRIM implements the mHG framework for motif identification in ranked DNA sequences . A flow chart of DRIM is provided in Figure 1 . In the rest of this section we describe the details of this implementation . Exhaustive search of the restricted motif space . Ideally we would like to exhaustively search through the space of all biologically viable motifs and identify those that are significantly enriched at the top of the ranked list . However , this is infeasible in terms of running time ( the space of viable TF binding sites includes motifs of size up to 20 , i . e . , 1520 k-mers ) . We therefore resort to a simple strategy where the motif search is broken into two stages: first an exhaustive search on a restricted motif space is performed . The “motif seeds” that are identified in the preliminary search are used as a starting point for a heuristic search of larger motifs in the entire motif space . The restricted motif space S used in this study is the union of two subspaces S1 and S2: S1 = {A , C , G , T , R , W , Y , S , N}7 , where the IUPAC degenerate symbols ( i . e . , R , Y , W , S , N ) are restricted to a maximum degeneracy of 2 and S2 = {A , C , G , T}3N3−25{A , C , G , T}3 . The rationale behind the usage of the restricted IUPAC alphabet in S1 instead of the complete 15 symbol alphabet stems from DNA–TF physical interaction properties and TFBS database statistics as explained in previous work [53] . S2 captures motifs that contain a fixed gap ( different motifs can have different gap sizes ) , which is characteristic of some TFs such as Zinc fingers ) . mHG enrichment . For each of the motifs in S , we generate a ranked occurrence vector and compute the enrichment in terms of the multidimensional mHG . Due to running time considerations , we restrict the multidimensional mHG to three dimensions . This means that the model assumes each intergenic region contains either 0 , 1 , or ≥2 copies of a motif . To test whether this assumption is reasonable in the case of true TFBS motifs , we examined the occurrence distribution of TFBS motifs that were experimentally verified in S . cerevisiae ( see Figure 9 ) . It can be seen that the assumption holds for the five TFs that were tested since the majority of all intergenic regions contained either zero , one , or two copies of the TFBS . At the end of this stage , only motif seeds with mHG score <10−3 are kept . Similar motifs are filtered ( as explained in Texts S5 and S6 ) , and the remaining motif seeds are fed into the heuristic search module for expansion , Figure 1iii–1iv . Motif expansion by heuristic search . The filtered motif seeds are used as starting points for identifying larger motifs that do not reside in the restricted motif space . This is done through an iterative heuristic process that employs simulated annealing . The objective function is to minimize the motif mHG p-value . We tested two different strategies for determining valid moves in the motif space . In the first , we defined a transition from motif M1 to M2 as valid if M1 and M2 are within a predefined Hamming distance D , with all valid moves being equiprobable . Additional bases can also be added to the motif flanks , thus enabling motif expansion . Note that the mHG adaptive cutoff is recalculated at each step . In the second strategy , all the motif occurrences in the target set that are within Hamming distance D are aligned . A consensus motif above IUPAC is extracted and the algorithm attempts a transition to that motif . While the second strategy converges much faster than the first , it is also more prone to converge to local minima ( in the final application we use the second strategy with D = 1 ) . At the end of the process , the exact p-value of each of the expanded motifs is computed . To correct for multiple motif testing , the p-value is then multiplied by the motif space size . Only motifs with corrected p-value <10−3 are reported . Optimizations and running time . The DRIM application was implemented in C++ . A “blind search” requires ∼100 , 000 motifs to be checked for enrichment in each run . It is therefore paramount to optimize the above-described procedures to enable a feasible running time . There are two bottlenecks in terms of running time: the motif occurrence vector generation and the mHG computation . We developed several optimization schemes to improve both . In the final configuration , the running time on a list of 6 , 000 sequences with an average size of 480 bases took ∼3 minutes on a Pentium IV , 2 GHz . ChIP–chip dataset . A number of assays have been recently developed that use immunopercipitation-based enrichment of cellular DNA for the purpose of identifying binding or other chemical events and the genomic locations at which they occur . Location analysis , also known as ChIP–chip , is a technique that enables the mapping of transcription binding events to genomic locations at which they occur [1 , 54] . The output of the assay is a fluorescence dye ratio at each spot of the array . If spots are taken to represent genomic regions , then we can regard the ratio and p-value associated with each spot as an indication of TF binding in the corresponding genomic region . We applied DRIM to S . cerevisiae genome-wide location data reported in Harbison et al . [25] and Lee et al . [28] . The first consists of the genomic occupancy of 203 putative TFs in rich media conditions ( YPD ) . In addition , the genomic occupancy of 84 of these TFs was measured in at least one other condition ( OC ) . In each of the experiments , the genomic sequences were ranked according to the TF binding p-value . Surprisingly , we observed that 69 of the 203 ranked sequence lists of YPD had significantly longer sequences at the top of the list ( first 300 sequences ) compared with the rest of the list with t-test p-value ≤ 10−3 . We observed a similar phenomenon in 76 of the 148 ranked sequence lists of OC experiments ( see Figure S1 ) . In other words , for some TFs , longer sequences are biased toward stronger binding signals . This observation is unexpected since , although longer probes hybridize more labeled material than shorter probes , the increase should be proportional in both channels . This type of length bias may cause spurious results under our model assumptions and hence the final dataset , termed “Harbison filtered dataset , ” refers to the remaining 207 experiments ( 135 YPD , and 72 OC ) of 162 unique TFs that did not have length bias ( Table S1 ) . An additional ChIP–chip dataset was constructed using the data reported in Lee et al . [28] containing 113 experiments in rich media . The data is partially exclusive to the data of Harbison et al . [25] . The same filtering procedure was performed , resulting in a set of 65 experiments , termed “Lee filtered dataset . ” Methylated CpG dataset . Using a technique similar to ChIP–chip , termed methyl-DNA immunoprecipitation ( mDIP ) , enables the measurement of methylated CpG island patterns [2 , 55] . The third dataset contains the CpG island methylation patterns of four different human cancer cell lines ( Caco-2 , Polyp , Carcinoma , PC3 ) where several replicate experiments were done for each of the cell lines . In each of these experiments , the CpG methylation signal was measured in ∼13 , 000 gene promoters as reported in [2] .
Accession numbers for the genes discussed in the paper are given in Table S5 . | A computational problem with many applications in molecular biology is to identify short DNA sequence patterns ( motifs ) that are significantly overrepresented in a target set of genomic sequences relative to a background set of genomic sequences . One example is a target set that contains DNA sequences to which a specific transcription factor protein was experimentally measured as bound while the background set contains sequences to which the same transcription factor was not bound . Overrepresented sequence motifs in the target set may represent a subsequence that is molecularly recognized by the transcription factor . An inherent limitation of the above formulation of the problem lies in the fact that in many cases data cannot be clearly partitioned into distinct target and background sets in a biologically justified manner . We describe a statistical framework for discovering motifs in a list of genomic sequences that are ranked according to a biological parameter or measurement ( e . g . , transcription factor to sequence binding measurements ) . Our approach circumvents the need to partition the data into target and background sets using arbitrarily set parameters . The framework is implemented in a software tool called DRIM . The application of DRIM led to the identification of novel putative transcription factor binding sites in yeast and to the discovery of previously unknown motifs in CpG methylation regions in human cancer cell lines . | [
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| 2007 | Discovering Motifs in Ranked Lists of DNA Sequences |
Seasonal-long larvicide treatments and/or outdoor space-spray applications of insecticides are frequently applied to reduce Aedes albopictus nuisance in urban areas of temperate regions , where the species has become a permanent pest affecting people’s quality of life and health . However , assessments of the effectiveness of sequential interventions is a difficult task , as it requires to take into account the cumulative and combined effect of multiple treatments , as well as the mosquito seasonal dynamics ( rather than mosquito abundance before and after single treatments ) . We here present the results of the effectiveness assessment of a seasonal-long calendar-based control intervention integrating larvicide treatments of street catch basins and night-time adulticide ground spraying in the main University hospital in Rome ( Italy ) . Cage-experiments and an intensive monitoring of wild mosquito abundance in treated and untreated sites were carried out along an entire season . Sticky traps were used to monitor adult abundance and site-specific eco-climatic variations ( by recording water left over in each trap ) , in order to disentangle the effect of insecticide treatments from eco-climatic drivers on mosquito seasonal dynamics . Despite the apparent limited impact of single adulticide sprayings assessed based on mortality in caged and wild mosquitoes , the results of the temporal analysis showed that mosquito seasonal patterns were initially comparable in the two sites , diverged in the absence of diverging eco-climatic conditions and remained stable afterwards . This allowed to attribute the lack of the expected Ae . albopictus population expansion in the treated site to the combined effect of multiple adulticide sprayings and larvicide treatments carried out during the whole season . The approach proposed was proved to be successful to assess effects of seasonal-long control treatments on adult mosquito population dynamics and could represent a valuable instrument to assess the effectiveness of other control interventions , to evaluate their actual cost-benefits and to possibly minimize space-spraying applications to reduce mosquito nuisance .
In the case of major malaria and Dengue vector species , which are the most frequent targets of insecticide-based interventions , the most important parameter to define the effectiveness of a treatment is its impact on disease transmission and morbidity/mortality . In the absence of disease transmission , standardized methodological and statistical approaches and guidelines to assess the effectiveness of insecticides against mosquitoes mostly focus on the assessment of the effectiveness of single treatments [1 , 2] . In the case of adulticide treatments , this is carried out by measuring either mortality in caged mosquitoes spread in the target area , or percentages of reduction in wild mosquito abundance between pre- and post-treatment ( e . g . by Abbot and Henderson’s formula ) , taking into account technical aspects ( e . g . insecticide product , droplet size , time and length of spraying ) and meteorological conditions ( e . g . wind , temperature ) . Assessments of the effectiveness of sequential insecticide-based interventions is a more difficult task , as it requires to take into account the cumulative and combined effects of multiple treatments , as well as the mosquito seasonal dynamics , rather than mosquito abundance only . Moreover , in order to compare mosquito populations over time it is recommended that similar paired sites ( treated and untreated ) are selected according to mosquito population parameters ( e . g . density , population dynamics , isolation ) , as well as socio-economic , climatic and ecological ( e . g . landscape , availability of breeding sites , presence of competing species ) factors[3 , 4] . Ideally , in order to provide significant preliminary data , the two sites should be selected and monitored along the mosquito reproductive season before the treatments or at least a few weeks beforehand . This exercise is laborious and costly , and even if results show similar vector densities and dynamics , eco-climatic changes occurring in one of the two sites may interfere with the subsequent assessment of the effectiveness of seasonal long control interventions . Seasonal-long outdoor space-spray applications of insecticides , integrated or not with other mosquito control activities , are frequently applied to reduce Aedes albopictus nuisance in urban areas in temperate regions . In fact , this originally Asiatic tropical species has become a permanent pest and is affecting citizen’s quality of life and health [5] in US and Europe since its introduction in the ‘80 [6 , 7] and ‘90[8 , 9] , respectively . Due to above mentioned constraints , only limited field assessments of seasonal-long area-wide strategies to reduce Ae . albopictus densities ( and nuisance ) have been carried out so far . Source reduction campaigns were shown to achieve temporary suppression of immature Ae . albopictus in Spain [10] and in North Carolina [11] , but they were not sufficient to maintain adult counts below a nuisance threshold in New Jersey[12] . In the latter work , Fonseca et al . also showed that integrated area-wide control strategies ( i . e . active source reduction , larviciding , adulticiding and public education ) resulted in a substantial reduction in Ae . albopictus populations in urban sites but not in suburban ones [12] . In Italy—where Ae . albopictus represents a major pest in urban and periurban areas and has already been responsible of a chikungunya virus outbreak [13]—seasonal-long outdoor interventions are frequently carried out to control its nuisance either in public or private urban areas . These interventions include multiple sequential larvicide treatments of street catch basins ( considered the major not-removable urban larval sites [14 , 15] ) and/or outdoor cold fog adulticide applications using vehicle-mounted sprayers . Data by Caputo et al . [14] suggest that the major phase of Ae . albopictus population expansion in Rome may be prevented by seasonal-long larvicide treatments of street catch basins in association with adulticide sprayings carried out during sunset . We here present the results of the assessment of the effectiveness of a seasonal-long calendar-based control intervention integrating larvicide treatments of street catch basins and night-time adulticide ground spraying against Ae . albopictus in the main University hospital in Rome . Cage-experiments and a fine-scale monitoring of wild mosquito abundance in the study site were carried out along an entire season . At the same time , an ad hoc developed easy-to-use approach was implemented to measure micro eco-climatic changes in treated and control sites . Results were exploited to assess the effectiveness of single adulticide treatments on mosquito abundance before and after single sprayings , as well as the overall effectiveness of the integrated intervention on the mosquito population dynamics .
Experiments were carried out in two sites in central Rome at a 1 . 4 km distance from each other ( Fig 1 ) , where presence of Aedes albopictus was previously detected ( BC , personal observation ) . The first was a ~ 40 h-area of the Sapienza University hospital "Policlinico Umberto I" ( 41°54'21'' N 12°30'41'' E ) , characterized by 14 m high XIX century buildings and large boulevards lined by Platanus trees and pedestrian walkways occasionally lined with bushes . The second site was ~2 . 5 h-area of the Department of Philosophy of Sapienza University ( 41°55'07'' N 12°31'01'' E ) including a central 14 m high XIX century building and a neighbouring area characterized by tall trees , bushes , pedestrian walkways . While insecticide treatments were planned in the "Policlinico Umberto I" ( hereafter treated site ) during summer 2013 ( see below ) , no treatments were performed in Department of Philosophy ( hereafter untreated site ) . Eight adulticide treatments ( T1-T8 ) were performed in the treated area by qualified technicians from a private company ( SOGEA s . r . l . ) from June to October 2013 , by spraying 1% water diluted PERMEX 22E ( BlueLine; 92% permethrin + 1 . 64% tetramethrin + 6 . 4% piperonyl butoxide ) with a cannon sprayer ( series "ELITE 345–400" Spray Team snc ) mounted on the back of a flatbed truck . The vehicle was driven at an average speed <20 km/h . Droplet size was set up at 50/60 μM . Spraying started around midnight and lasted for approximately 2 hours . Moreover , all the 227 rain catch basins ( i . e . drain holes in paved streets sealed by grids ) within the treated area ( including empty basins to avoid risk of refilling in case of rain ) were treated every two weeks from June to October by releasing tablets of an Insect-Growth-Regulators ( IGR ) which interferes with larval development and inhibits adult emergence ( i . e . 0 . 5 gr pure Pyriproxyfen , PROXILAR , INDIA Industrie Chimiche ) . Cylindrical cages ( 26 cm in diameter and 31 cm in height ) lined with nylon tulle were manually built following Cooperband et al . ( 2007 ) [16] . Cages—containing Petri dishes with filter paper ( Pall Corporation , 90 mm diameter ) and Ae . albopictus adults ( either 10 or 20 males and 10 or 20 females reared in the lab from wild collected eggs ) —were positioned in the treated site at 1 . 5 m-height . During T2-T8 treatments , cages were located as follows: i ) 12 cages along 3 roads ( hereafter lines ) at a 10 , 30 , 50 and 70 m distance from the crossroad to the itinerary of the cannon sprayer ( hereafter exposed cages ) ; ii ) 2 validation cages within the treated site at 13 m ( VC-1 ) and 41 m ( VC-2 ) from the closest road where the cannon sprayer passed; and iii ) 3 cages in the untreated site ( hereafter control cages ) . Cages were located 1 hour before adulticide spraying and removed approximately 30 minutes after . The filter papers were immediately extracted from cages and introduced in a sealed glass vial for subsequent Gas-Chromatography Mass-Spectrometry ( GC-MS ) analysis . Adults were transferred to paper cups , provided with cotton pads soaked with 10% sucrose solution and brought to the lab . Mosquito mortality at 24 h post-exposure was recorded . Gas-Chromatography Mass-Spectrometry analyses were carried out by Agilent 6850 II gas-chromatograph ( GC ) equipped with mass selective detector ( MSD ) Agilent mod . 5975C and capillary column Agilent HP-5 MS ( 60 . 0 m long x 0 . 25 mm i . d . , 0 . 25 μm film thickness ) . The column operated at 60°C ( hold 1 min ) to 170°C ( hold 0 min ) at 10°C/min , then to 280°C ( hold 5 min ) at 4°C/min . The split/splitless injector was maintained at 250°C , and transfer line at 280°C . Helium was used as carrier gas at 1 . 4 mL/min . The MSD was used in the single ion monitoring mode ( SIM ) . Insecticides were monitored by considering two ions for each compound , with the following masses ( m/z ) : permethrin = 127 and 183; tetramethrin = 123 and 164; piperonyl butoxide = 119 and 176 . After withdrawal filters left in cages during the insecticide space-spraying were transferred in a cylinder and extracted 3 times with 5 , 2 . 5 and 2 . 5 mL of hexane ( Sigma-Aldrich , USA ) , respectively . The organic extracts were collected in a vial , sealed and stored at -20°C until analysis . Analytical determinations were carried out by GC/MS with the external standard technique . Stock standard solutions of analysed insecticides at 100 . 0 ± 0 . 5 μg/mL were obtained by Ultra Scientific , USA . Working standard solutions ( w . s . s . ) for calibration were prepared daily and were obtained by diluting aliquots of the stock solution with hexane , to obtain working standard concentrations of 0 . 01 , 0 . 05 , 0 . 50 , 1 . 00 , 2 . 50 , 5 . 00 , and 10 . 00 μg/mL . All the glassware was in borosilicate class A . Calibration curves were obtained by injecting five 1 μL injections of each w . s . s . and calculating the average peak area for each different concentration . Linear responses were observed in the range of concentrations considered . Analytes concentrations were determined by three 1 μL injections of each sample extract , and average peak areas were considered for quantitation . Results were expressed as μg/cm2 . Whole procedure blank tests were performed in order to assess the absence of any contamination occurring from reagents and materials . A solvent blank was analysed every five samples to check the response of chromatography . Aedes albopictus adult population monitoring was carried out from June 17th to October 17th 2013 in treated and untreated sites . Monitoring of adult populations was conducted by means of Sticky-Trap ( ST ) consisting in a water container similar to a commonly used ovitrap equipped with an internal structure lined with adhesive films to which the mosquitoes approaching the trap , either to lay eggs or to rest , remain stuck[17] . Sticky-Trap catches have been shown to be correlated with catches by ovitraps ( i . e . the gold standard for Ae . albopictus monitoring ) , but collect eggs instead of adults [17] and have already been successfully exploited to assess the effectiveness of mosquito control interventions in Rome [14] . Sticky-Trap number and position was established subdividing an area within the treated site into a 24-cell grid and the untreated site into a 19-cell grid ( each cell = 40 x 40 m ) ( Fig 1 ) . One ST was located in each cell and equipped with sticky sheets and 500 ml tap water . On a weekly basis , mosquitoes stuck in ST were marked directly on sticky sheets after 72 hours ( day-3 ) ; after additional 72 hours , STs were removed and stuck mosquitoes identified and counted under a binocular stereo microscope ( day-6 ) . No STs were left in the field at day-7 , when insecticide spraying was performed if scheduled . Sticky-Traps equipped with freshly prepared sticky sheets were re-located in the same position at day-1 of each week . Water leftover was measured concomitantly to mosquito monitoring . Temperature and rainfall data were obtained from “Roma Macao” weather-station at 300 m distance from the treated site ( http://www . idrografico . roma . it/annali/ ) . All analyses were carried out using R version 3 . 1 . 0 [18] and lme4 , strucchange packages [19–21] .
The average effectiveness of the seven monitored insecticide sprayings assessed based on Henderson’s formula applied to caged mosquitoes was 77% ( Confidence Interval: 93%—61% ) at 10 m , 36% ( CI: 49%—22% ) at 30 m , 22% ( CI: 35%—8% ) at 50 m , 1% ( CI: 2%— 0% ) at 70 m from spraying ( S1 Table ) . Restricting the analysis to cages located at ≤50 m distance from spraying ( due to low mortality in the 70 m-distant cages ) , the average effectiveness of the treatments were as follows: T2 = 20 . 1% , T3 = 51 . 2% , T4 = 68 . 6% , T5 = 37 . 5% , T6 = 54 . 4% , T7 = 23 . 5% , T8 = 53 . 4% . Results from the binomial GLMM-1 carried out to test the effectiveness of insecticide spraying on caged adult Ae . albopictus either exposed or not-exposed to the adulticide treatments indicated an overall higher mortality in exposed cages ( Table 1; p = 0 . 002 ) . No differences in mortality were detected between genders . As expected , permethrin detection was positively associated with mortality ( S2 Table; p<0 . 001 ) . However , mortality was observed also in cages where permethrin was not detected ( concentration<0 . 0006 μg/cm2 ) . Tetramethrin values were not taken into consideration for data elaboration as they were below the limit of detection of the analytical procedure . Moreover , the second binomial GLMM-2—carried out to assess mortality in cages at different distances from the insecticide spraying in the treated site ( i . e . 10 , 30 , 50 and 70 m ) —showed lower mortality at increasing distances ( Estimated coefficient for Distance = -0 . 087; Z-value = -18 . 74; p<0 . 001 ) . Fig 2A shows expected adult mortality in treatment site modelled as a function of the distance between the cages and the insecticide spraying , as predicted by GLMM-2 . Overall , adult mortality was predicted to be higher than 0 . 75 in 29% of the area not occupied by buildings , and higher than 0 . 50 in 41% of the same area ( Fig 2B ) . Expected mortality obtained from GLMM-2 was validated by using mortality values observed in validation cages , located at 13m ( VC-1 ) and 41m ( VC-2 ) from spraying ( Fig 2A and 2B ) . Mortality rates were extremely variable among treatments , ranging from 5 to 100% in VC-1 and from 0 to 80% in VC-2 ( Fig 2A ) . In T7 observed mortality in VC-1 was even lower than in VC-2 . Mortality in VC-1 ( average observed value = 54% , predicted = 77% ) and in VC-2 ( average observed value = 29% , predicted = 22% ) was outside the 0 . 025 and 0 . 975 quantile of the expected mortality distribution in 5 and 3 out of 7 monitored treatments , respectively ( S1 Fig ) . Specifically , observed mortality was underestimated in 6 out of 8 of these cases ( i . e . values<0 . 025 ) , overestimated in 2 cases ( i . e . values>0 . 975 ) . Henderson’s formula computed for each single insecticide spraying showed a mosquito female and male adult reduction only for 4 out of 8 treatments ( i . e . T1 = 100% , T2 = 0%; T3 = 0%; T4 = 55 . 5%; T5 = 57 . 1%; T6 = 0%;T7 = 83 . 8%; T8 = 0%; S3 Table ) . However , the objective of the study was not only to evaluate effectiveness of single adulticide spraying , but also to assess the impact of the overall control strategy adopted ( i . e . adulticide sprayings and larvicide treatments of street catch basins ) taking into account the eco-climatic conditions in the two sites . In order to achieve this objective , water leftover inside ST was taken as a proxy for the specific eco-climatic conditions at ST level ( i . e . association between overall climatic conditions and ST exposure to sun-light ) . This was based on LMM results showing a negative relationship between water leftover in ST and temperature ( LMM-1; S4 Table; S2A Fig ) and a positive relationship with rainfall ( LMM-2; S5 Table; S2B Fig ) . Afterwards , measures of water leftover were included as explanatory variables in the Poisson GLMM-3 carried out to test how mosquito counts varied between treated and untreated sites . The result showed that mosquito counts were significantly higher in the untreated site ( N in treated site = 231; N in untreated site = 552; p<0 . 001 ) . However , while in the untreated site higher mosquito counts were observed in STs with lower values of water leftover , unexpectedly no relationship between mosquito counts and water leftover was observed in the treated site ( Table 2; Fig 3 ) . Finally , change point analysis was carried out to assess temporal variations of the impact of the control strategy adopted on the seasonal mosquito population dynamic ( Fig 4A ) . Results showed a sharp decrease in correlation ( Pearson’s coefficient from 0 . 77 to 0 . 47 ) between time series of adult mosquito mean counts in the treated and in the untreated site after T3 ( collection 15 , August 5th; Fig 4B ) . This change occurred when population in the untreated site was reaching its peak; afterwards , correlation between the two time series remained stable ( Fig 4B ) . Change point analysis was also applied to water leftover between ST-time series in treated and untreated sites to understand whether eco-climatic conditions were a major determinant of differences observed in mosquito abundance between the two sites . Results showed a sharp decrease in correlation coefficients between the two sites at collection 19 ( August 19th , after T4 ) . Afterwards , an increase of correlation along the season was observed ( Fig 4C and 4D ) .
The results obtained show that the effectiveness of sequential insecticide treatments on Ae . albopictus population dynamics may be assessed by coupling an intensive seasonal spatio-temporal monitoring of mosquito population dynamics and eco-climatic variations in treated vs untreated sites with the use of advanced statistical methods . These are necessary to disentangle the effect of the treatments from those of eco-climatic inter-site differences on mosquito population patterns . Thus , the proposed approach provides a reliable alternative to the need to have information on mosquito populations in treated and untreated sites in seasons/years before the effectiveness assessment . Moreover , it overcomes the difficulty in attributing inter-site differences in population patterns to the insecticide treatments rather than to site-specific eco-climatic variations . In fact , results of the temporal analysis showed that mosquito seasonal patterns were initially comparable in the two sites , diverged in the absence of diverging eco-climatic conditions and remained stable afterwards . This led us to attribute the lack of Ae . albopictus population expansion in the area of the main University hospital in Rome to the combined effect of multiple adulticide sprayings and regular larvicide treatments carried out during the whole season . In fact , a clear population expansion was observed in August in the untreated control site and it is known to typically occur in the same period in Rome[13 , 29] . The conclusion would have been very different if we would have speculated on the effectiveness of the treatments only based on Henderson’s formula results on caged mosquitoes and/or on field ST-collections before and after single sprayings in treated vs untreated sites . These results were variable and inconsistent . In the case of cage experiments , mortality was found negatively associated to distance from spraying and positively associated to Permethrin concentration , as expected . However , high variability in mortality was observed among cages within single treatments , as well as among treatments . Based on these results adult mortality was predicted to be higher than 50% only in 41% of the treated area . The high variability observed among caged mosquitoes was most likely due to variations in wind direction and/or strength ( not measured ) , as suggested by the variable concentrations of Permethrin detected in cages . In the case of the assessment based on ST-collections of wild mosquitoes after single insecticide sprayings , results showed an adult reduction with respect to the untreated area only after 4 out of 8 treatments . This high variability could be at least partially due to the fact that we did not sample the sites immediately before and after the insecticide spraying ( as implied by Henderson’s formula ) , but 3 days before and 3 days after each treatment , thus introducing the confounding factor of freshly adult emergence . Other factors intrinsic to field experiments may account for the inconsistency between results based on ST-collections and those based on cage experiments: e . g . i ) “controls” are affected by the mosquito population dynamics in the field , but not in the cages; ii ) mortality in cages is measured immediately after the treatment , thus reflecting the rapid knock-down effect , while assessment of treatment effectiveness in the field is based on ST-collection in the 72h following the treatment , thus reflecting both rapid knock-down and residual effect . The methodological approach here proposed to assess the effectiveness of seasonal-long mosquito control strategies can be applied to assess the effectiveness of various control methods , under the assumption that the major forces determining mosquito population dynamics are eco-climatic factors . The approach relies on the possibility to compare mosquito population dynamics in treated and in untreated control sites by sticky trap collections , even in the absence of prior information on mosquito abundance and eco-climatic situation in these sites . In fact , water leftover in sticky trap was shown to be correlated with temperature ( negatively ) and rainfall ( positively ) and can thus be taken as a good proxy for the eco-climatic conditions at sticky trap level , synthetizing the association between overall climatic conditions and sticky trap exposure to sun-light . Notably , water leftover can be easily measured during routine sticky trap monitoring activities without significant additional efforts in terms of time and costs . This allowed us to compare with great resolution changes in correlation between time series of adult mosquito mean counts and seasonal changes of eco-climatic conditions in the treated and untreated sites and to reach the conclusion that the lack of Ae . albopictus population expansion in the treated site was due to the insecticide treatments rather than to eco-climatic factors . In theory , the methodological approach here proposed could be carried out by ovitrap collections , a widely used method to indirectly assess adult abundance . However , complete water evaporation is frequently observed in ovitraps after <3 days in very hot sites/seasons , such as in Rome in August ( BC , personal observation ) , but not in STs which are supplied with a top lid . Moreover , ovitrap exploitation for assessing adult abundance based on number of collected eggs has been questioned [30] . On the other hand , it should be noted that monitoring STs is more laborious than ovitraps , due to the need to manipulate sticky-sheets . Overall , our results suggest that the combined effect of adulticide sprayings and larvicide treatments carried out in the study site had an effect in reducing Ae . albopictus abundance–and probably its nuisance—during the seasonal peak of the species . Larvicide treatments seem to have had a major role in determining the observed lack in the mosquito population expansion , as suggested by the apparent low impact of single adulticide sprayings assessed based on caged and wild mosquitoes . The latter could be due , among other factors , to the spraying time ( i . e . during the night to reduce human exposure to insecticides ) , when Ae . albopictus is believed to be less affected because of its diurnal activity . However , it should be mentioned that single night-time ULV adulticiding were shown to result in a significant percent of reduction in Ae . albopictus abundance in treated vs . untreated sites in the US [12 , 31] . Despite this study was carried out in a single location and replicated only once over one season , the conclusions are consistent with the preliminary indications on the effectiveness of a combined intervention based on IGR-treatments of catch basins and two insecticide sprayings carried out at the beginning of the major population expansion in Sapienza University campus in Rome [14] . This may suggest that interventions combining larvicide and adulticide treatments may have an effect even when sprayings are carried out only during the population expansion phase , thus allowing to reduce and optimize the use of insecticide ground spraying . Other studies are needed to confirm this hypothesis and to shed light on the relative contribution of larvicide and adulticide treatments . It is relevant to remind that despite the overall agreement that integrated control strategies–mostly based on public education , source reduction and larvicide application , with insecticide spraying restricted to specific situations—are needed to significantly reduce Ae . albopictus abundance and associated nuisance [32] , this is very rarely implemented . In fact , an integrated control strategy requires high level of public cooperation among local authorities , private companies , organized society , and communities and a continued support from both local authorities and communities . In practical terms , multiple calendar based adulticide sprayings associated to larvicide activities are offered by private companies to citizens in high Ae . albopictus infested areas , at least in Italy . Studies such as the present one are thus extremely important to provide information needed to optimize the planning of the treatments along the species reproductive season ( for instance restricting insecticide sprayings to the beginning of the season , as suggested by present results ) and more precisely assess their actual cost-benefits , also taking into account the environmental impact of adulticide ground spraying . | Due to the considerable nuisance created by the aggressive day-time biting behaviour of the “tiger mosquito , ” insecticide treatments are largely employed in urban areas of temperate regions where this tropical species has become a permanent pest . These include treatments of catch basins with lethal products for mosquito larvae and spraying of insecticides against flying adults . Despite the latter having a high environmental impact and not being recommended by health authorities to reduce the nuisance to citizens , they are perceived as the approach providing the greatest benefits . However , this is not fully demonstrated . We showed that a seasonal-long calendar-based control intervention carried out against mosquito larvae and adults in Rome ( Italy ) was effective in reducing the mosquito abundance in the months when highest densities and nuisance are known to occur . The novel methodological approach followed here facilitates the assessment of the actual effectiveness of control strategies against mosquitoes , which are very rarely assessed due to technical difficulties , high costs and lack of commitments , but are instrumental to optimize control strategies . Should our preliminary indications of a major effect of larvicide treatments be confirmed , the more harmful exploitation of insecticide spraying could be reduced or eliminated . | [
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| 2016 | Assessment of the Effectiveness of a Seasonal-Long Insecticide-Based Control Strategy against Aedes albopictus Nuisance in an Urban Area |
The human β2-adrenergic receptor ( β2AR ) , a member of the G-protein coupled receptor ( GPCR ) family , is expressed in bronchial smooth muscle cells . Upon activation by agonists , β2AR causes bronchodilation and relief in asthma patients . The N-terminal polymorphism of β2AR at the 16th position , Arg16Gly , has warranted a lot of attention since it is linked to variations in response to albuterol ( agonist ) treatment . Although the β2AR is one of the well-studied GPCRs , the N-terminus which harbors this mutation , is absent in all available experimental structures . The goal of this work was to study the molecular level differences between the N-terminal variants using structural modeling and atomistic molecular dynamics simulations . Our simulations reveal that the N-terminal region of the Arg variant shows greater dynamics than the Gly variant , leading to differential placement . Further , the position and dynamics of the N-terminal region , further , affects the ligand binding-site accessibility . Interestingly , long-range effects are also seen at the ligand binding site , which is marginally larger in the Gly as compared to the Arg variant resulting in the preferential docking of albuterol to the Gly variant . This study thus reveals key differences between the variants providing a molecular framework towards understanding the variable drug response in asthma patients .
G protein-coupled receptors ( GPCRs ) constitute a family of membrane proteins that serve as important communication mediators in cellular signal transduction [1] , [2] . GPCRs are thus critical in the modulation of several signaling related disorders [3] , [4] and constitute more than 25% of all human drug targets [5] . The human β2-adrenergic receptor ( β2AR ) is a member of the GPCR family that is abundantly distributed in smooth airway muscles of lung [6] . Endogenous catecholamine's such as epinephrine and norepinephrine act as agonists and bind β2AR causing smooth muscle relaxation and aiding respiration [7] . Agonists of β2AR such as albuterol , terbutaline ( examples of short acting drugs ) , salmeterol and formeterol ( long acting drugs ) , which cause respiratory smooth muscle relaxation are widely used in the treatment of asthma [8] . Agonist binding to β2AR triggers the activation of adenylyl cyclase via the Gs protein which leads to relaxation of the airway smooth muscles and relief from bronchospasm [9] . A number of genetic polymorphisms have been described in the gene ( ADRB2 ) encoding for the β2AR [10] . The non-synonymous single nucleotide polymorphism ( SNP ) Arg16Gly is common in the population with a minor allele frequency of about 50% [10] and has been implicated in variable response to albuterol treatment [11] , [12] . Clinical studies performed to investigate the association of this SNP with response to albuterol show results that vary between studies and across populations [13] . In a cell based assay , the 16th position variants were found to be expressed at similar abundances , but displayed dissimilar kinetics upon repeated agonist treatment implicating the N-terminal region in receptor activation [14] . Further , slight differences in the binding affinity of epinephrine were found between the variants in competition binding studies [14] . Down regulation of β2AR is seen in response to chronic exposure to agonists [15] , but is not likely to play a role in the differential drug response to albuterol which is used as a short acting drug . In summary , it was suggested that molecular level studies would help clarify the role of the N-terminal variants in albuterol binding and it is hypothesized that the differences in response to albuterol could arise due to varying dynamics of the N-terminal regions [14] . The β2AR is a 413 amino acid residue protein with three intracellular and three extracellular loops [16] , [17] . The N-terminal region of the receptor is 28 residues long ( as per β2AR crystal structures , where the 29th amino acid is the first helical residue ) [17] . Although several crystal structures of β2AR are now available in the public domain representing both active and inactive forms , the coordinates for the complete N-terminal region are missing in all the structures [16]–[23] . Various computational studies of β2AR have probed the mechanism of ligand entry , exit , binding and activation [24]–[27] . However , to the best of our knowledge , none of the studies have included the N-terminal region of β2AR . It still remains unclear how a variation at the 16th position might affect response to albuterol . The goal of this work was to explore the molecular mechanism that could lead to a differential response to albuterol . Towards this we modeled the N-terminal region and its variants at the 16th position ( Arg16Gly ) in conjunction with the available structure of the inactive receptor . We further performed six ( three each ) unbiased atomistic molecular dynamics ( MD ) simulations of the Arg and Gly variants totaling to 6 µs . We analyzed the differences in the local and global conformational dynamics arising from the N-terminal variations . Our results are an important first step towards understanding the role of the polymorphism in the molecular mechanism of β2AR .
Structural models of the 16th position Arg and Gly variants of β2AR were generated as discussed in the methods section . Of all the class A GPCRs at the GPCRDB [28] more than thirty structures have complete or partial N-termini which are all placed on top of the seven transmembrane ( TM ) helices ( S1 Table ) . Five class A GPCRs with N-termini of length similar to that of β2AR were chosen as templates for modeling . Although the sequence similarity of the β2AR with the templates in this region is poor , we chose to build our models based on available templates rather than ab-initio folding . Using related GPCR templates allows us to capture the structural characteristics of the family instead of the computationally daunting ab initio folding . Results from the Ramachandran plot analysis of the N-terminal regions indicate that for the Arg variant all the residues are in the allowed region while in Gly variant all but one residue are in the allowed region ( S1 Figure ) . In both the models , the placement of the N-terminal residues is on top of the receptor ( Fig . 1 ) . The overall secondary structure of the N-terminal region is mainly comprised of turns . In the Arg variant the N-terminal residues 5 to 13 , 15 to 19 and 21 to 24 adopt a turn conformation as defined by STRIDE [29] . The N-terminal residues 5 to 8 , 10 to 13 and 17 to 21 of the Gly variant adopt a turn conformation . The side chain of the arginine at the 16th position is cradled within residues 301 to 305 from the TM region and its guanidinium group is placed in close proximity of Glu 306 . In contrast , the glycine residue at that position is not predicted to interact with the TM region in the model ( based on contacts within 0 . 5 nm of the residue ) . MD simulations of the variants of β2AR embedded in a lipid bilayer were performed in triplicate to ensure adequate sampling . To characterize the structural variation during the course of the simulation , the RMSD of the entire protein , for each of the six simulations was calculated ( Fig . 2 A , B ) . The RMSD of the Arg variant , overall , is higher than the Gly variant . We further checked the RMSD of the TM helices , ICL3 and the N-terminal residues , separately to assess the contributions of the different regions of the receptor to the observed variation in RMSD for the entire protein . The TM helices were found to be stable over the simulation time ( S2 Figure ) . Interestingly , the RMSD of the ICL3 is slightly higher in the simulations of the Arg variant as compared to the Gly variant ( S2 Figure ) . The RMSD profiles of the N-terminal region ( residue 1 to 28 ) reveal significant differences across the variants ( Fig . 2 C , D ) . The three simulations of the Arg variant exhibit , on an average , a higher RMSD than the Gly variants suggesting enhanced dynamics . The large increase in the RMSD of the N-terminal region of the Arg variants contributes to the higher RMSD of the whole protein seen earlier . To understand the residue wise contribution of the N-terminal region to the conformational sampling we calculated the per-residue fluctuations over the simulations ( Fig . 2 E , F ) . Consistent with the RMSD plots , the fluctuations of the residues in the Arg variants are generally higher . The first ten residues of the Arg variants show higher fluctuations than the corresponding residues in the Gly variant , specifically large differences are observed between residues 5–10 . To visualize the differences in the N-terminal region between the variants , representative snapshots of the protein from the trajectories were analyzed ( Fig . 3 ) . In all the three simulations of the Gly variants the N-terminal region is found to stay on top of the TM helices . In contrast , the Arg variants show larger dynamics and tend to open up partially . To quantify the structural differences at the N-terminal regions of the variants , the residue wise secondary structure was plotted over time ( Fig . 4 ) . In all three simulations of the Arg variant , after comprehensive sampling ( 400 ns onwards ) , a conformation comprising of two turns separated by nine to twelve residues is observed . The location of the turn varies between the simulations . The first turn is between residues 5–12 whereas the second is from 19–27 . A turn that contains the 16th position arginine , in the initial model opens up in all three simulations . On the other hand , in the three simulations of the Gly variant the two turns are consistently present between residues 10 to 13 and 17 to 19 . In two simulations of the Gly variant , an additional turn is formed towards the N-terminal . The backbone of arginine at the 16th position in all three simulations of the Arg variant displays a lack of secondary structure character , whilst the glycine displays mainly a turn conformation . It is known that due to its small size , uncharged nature and thus unusual conformational ability , glycine is found in turns and can easily accommodate a turn in its vicinity . Further , side chain contacts of the N-terminal region with the receptor were calculated ( Fig . 5 ) . In addition , the contacts within 0 . 3 nm for at least 30% of the simulation time were calculated ( S2 Table ) . A striking difference is seen in the number of contacts of the first fourteen residues of the variants . The Arg variant has very few contacts with the remaining receptor , while the residues of the Gly variant have several contacts as a result of the differential placement of the N-terminal region . Interestingly , the 16th position arginine has several contacts with the receptor while the glycine at the 16th position in the Gly variant has none . Further , residues 21 to 26 in the Gly variant interact with Glu 306 anchoring the N-terminal region to the 7th helix . The residues 21 to 26 in the Arg variant on the other hand are closer to TM helix 2 . β2AR binds water soluble ligands which are hypothesized to enter from the extracellular face of the receptor and further migrate inward to the actual binding pocket that is cradled in the transmembrane ( TM ) region [17] . Previous MD studies have demonstrated that ligand entry occurs via two possible pathways on the extracellular face of the receptor [24] , [25] , [30] . The entry to the two pathways is separated by a salt bridge between residues Asp 192 and Lys 305 . The first pathway is on one side of the salt bridge and is formed by residues from TM helices 5 , 6 and 7 ( referred to as vestibule 1 ) . The second pathway on the other side of the salt bridge comprises of residues from TM helices 2 , 3 and 7 and is referred to as vestibule 2 . Vestibule 1 is suggested to be the dominant pathway by both studies but they differed on the frequency of ligand entry via vestibule 2 . In the crystal structure ( 2RH1 ) , the binding site cleft is entirely open and a salt bridge is seen to be formed between residues Asp 192 and Lys 305 . The opening from vestibule 1 ( defined in methods section ) is seen to be much larger than that of vestibule 2 . In the structural models that were built , the opening from vestibule 2 appears to be completely blocked while the opening from vestibule 1 stays partially open , due to the placement of the N-terminal residues on top of the receptor . The average volumes of the non-occluded grid points defining the vestibules was calculated and plotted against time ( Fig . 6 ) . Substantial fluctuations are observed in the volumes of the vestibule openings due to mobility of the side chains of the residues that line them . The plots indicate that the Gly variants on an average tend to have a more open vestibule 1 than their Arg counterparts . In the first 250 ns of the third simulation of the Gly variant , vestibule 1 opens up more due to lateral movement of the N-terminal region towards TM helix 6 and 7 . The increased size of the vestibule is comparable to that observed in the crystal structure ( 2RH1 ) . In the simulations of the Arg variants , the arginine residue at the 16th position interacts with residues lining vestibule 1 and in two out of three simulations partially blocks the vestibule with its bulky side chain . On the other hand , in the Gly variant the 16th position glycine is rarely seen to interact with the residues lining the vestibule , although residues 6 to 8 of the N-terminal region interact directly with these residues . The reverse trend is seen at the site of vestibule 2 . The Arg variants tend to have a more accessible vestibule 2 than the Gly variants , although there are large variations between the three simulations . The placement of the N-terminal region in the Gly variant in relation to the receptor , particularly the anchoring of the N-terminal region ( residues 21 to 26 ) to the 7th helix causes closure of the second vestibule . Since the N-terminal region stays on top of the receptor it leaves the first vestibule open . In the Arg variant , the N-terminal region ( residues 21 to 26 ) is closer to helix 2 allowing the second vestibule to be open . In continuation , we tracked the salt bridge dividing the two vestibules . We observe that the Gly variant has a greater tendency to form a salt bridge as opposed to the Arg variant ( S3 Figure ) . To analyze whether the vestibules 1 and 2 differed from each other with respect to their electrostatic potentials , electrostatics were calculated using Delphi implemented in DS 3 . 5 , for representative frames of the variants ( S4 Figure ) . In the crystal structure , the potential around the binding site cleft is negative and was suggested to facilitate ligand entry through electrostatic funneling . The Gly variant tends to have a negative potential around vestibule 1 in contrast to the Arg variant . The difference can be attributed to the positive charge of the arginine at the 16th position . In vestibule 2 of the Arg variant there is a large region at the entrance of the vestibule with a negative potential , however a small region of positive potential is also observed . The ligand binding pocket of β2AR is comprised primarily of residues belonging to TM helices 3 , 6 and 7 . Residues 113 , 203 , 289 and 312 define the topology of the binding pocket of β2AR ( Fig . 7 A ) . Out of these , residues 113 , 289 and 312 ( 3 . 32 , 6 . 51 and 7 . 39 ) have been observed to make consensus contacts with various ligands in the class A GPCRs [31] . We measured the distances between these residues across the six simulations of the two variants . We observe that the average distances of the residue 203 with 312 and 289 are about 0 . 5 nm larger for the Gly variant than that for the Arg variant ( Fig . 7 B , C ) . It thus appears that the overall binding pocket of the Gly variant in all three simulations is marginally larger than that of the Arg variant . To determine the effects of the change in the binding pocket on ligand binding , we docked carazolol and albuterol to the pocket . Since the second simulation of the Arg variant shows the largest entry point from vestibule 2 and the first simulation of Gly shows the largest entry point from vestibule 1 we chose the last frames from these simulations for the docking calculations . We initially docked S-carazolol to the 2RH1 structure and observed that Glide-XP [32] was able to reproduce the binding mode ( Fig . 7 D ) . We observed that the top ranking docking pose in the Arg variant is closer to the crystal structure pose than the top ranking pose ( or any other ) in the Gly variant . In the Arg variant the carbazole moiety is flipped by 180° as compared to the crystal structure pose , while in the Gly variant not only is the carbazole moiety tilted 90° in comparison to the crystal structure pose but also the polar side chain is at 90° in comparison to the crystal structure ( Fig . 7 E , F ) . To test the stability of the predicted poses we performed short ( 50 ns ) atomistic MD simulations on the complexes . Consistent with the docking studies , we observe that the carbazole moiety is stable in the Arg variant . In the Gly variant however , the carbazole moiety shows large dynamics and tilts between the different states ( S5 Figure ) . The difference in binding mode of the carazolol to the Gly variant can be attributed to the larger binding site . The docking score of S-carazolol with the Gly variant ( docking score −7 . 1 ) is comparable to that of the Arg variant ( −7 . 9 ) . Interestingly , the Gly variant has a much more favorable docking score for R and S Albuterol ( −8 . 5 , −9 . 5 ) as opposed to the Arg variant ( −2 . 9 , −5 . 7 ) ( S6 Figure ) . The ionic lock is a salt bridge formed between the two residues Arg 131 and Glu 268 , from adjacent transmembrane helices 3 and 6 . This salt bridge was implicated in the stabilization of the inactive state of GPCRs based on rhodopsin crystal structures . Consistent with previous studies [25] , [33] on β2AR we observe the ionic lock to form occasionally in both the variants ( S7 Figure ) . Overall , the Arg simulations showed an increased propensity to form the salt bridge . We further analyzed the consensus contacts as identified earlier in class A GPCRs [31] across the two variants and found that there are no significant differences except between residue 132 and 221 . The contacts are indeed conserved across the two variants as they are in the class A GPCRs ( S8 Figure ) .
GPCRs are important mediators in cellular signaling cascades and constitute a large percentage of current clinical drug targets [1] . The advent of next-generation sequencing tools has enabled the detection of polymorphisms in GPCRs that could be linked to disease and drug efficacy . The natural variant in β2AR at the 16th position has been implicated in a heterogeneous response to albuterol in asthma patients [10]–[13] . The N-terminal region of the β2AR that contains this variant is structurally unresolved [16]–[23] . The goal of our study was to provide a link between the variation in the structure and its functional implications . Towards the same we modeled the N-terminal region of the β2AR based on knowledge from related class A GPCRs and performed microsecond MD simulations of the receptor variants embedded in membranes . We observe from our simulations that the N-terminal region of the Arg variant is more dynamic in contrast to the Gly variant that displays limited positional sampling . Further , the Arg variants tend to open up and residues 1 to 14 display no contacts with the rest of the receptor . Interestingly , a few of the β2AR crystal structures that contain an unresolved N-terminal region were expressed using gene constructs that code for Arg at the 16th position[16] , [17] . Due to the restrained dynamics that we observe in the Gly variant , it is possible that this variant would be a better candidate for crystallization studies focusing on the N-terminal region . The difference at the N-terminal 16th position affects ligand accessibility and binding through long-distance effects . In particular , the ligand binding site is more accessible through vestibule 1 of the Gly variant as opposed to vestibule 2 of the Arg variant . Previous computational studies of β2AR although lacking the N-terminal region that caps the β2AR in our models have demonstrated that ligand entry and exit via vestibule 1 was the lowest energy pathway [24] , [25] . Thus , it seems that ligand entry that would occur via vestibule 2 in the Arg variant would be less favorable than ligand entry through vestibule 1 of the Gly variant . It has been previously proposed that differences in binding site accessibility could affect drug receptor kinetics [34] . The differences observed in the binding site accessibility in our simulations could thus affect β2AR activation kinetics by albuterol . Structural plasticity in GPCRs has been suggested to be critical [35] and sub-nm scale differences in the binding pocket of GPCRs have been implicated in altered receptor function [36] . In our studies , the ligand binding pocket is marginally larger in the Gly variant and shows more favorable docking to albuterol . The effects induced by the N-terminal variants on the TM region in our simulations , although subtle , affect the functionally-relevant structural plasticity of β2AR . A conformational coupling of the extracellular region ( extracellular loops 2 and 3 ) with ligand binding site has been previously observed by experiments [18] . A similar effect of the extracellular N-terminal region on the ligand-binding pocket is observed in our study . Thus , it is likely that the differential binding site size and accessibility between the variants could lead to the observed altered kinetics of albuterol in the cell based assay [14] . To check the statistical reliability of the major results of our study , we chose two additional models that differed maximally from the initial models in terms of RMSD of the N-terminal residues and performed 100 ns of atomistic simulations for each . In line with the previous results , the Arg variants showed a preference for a more accessible vestibule 2 while the Gly variants showed a preference for a more accessible vestibule 1 ( S9 Figure ) Further , the Gly variant showed a slightly larger distance between residues 203 and 289 although no differences were observed in the distance between residues 203 and 312 . Thus despite the limited sampling in these models , they nevertheless validate the main findings of the study . The influence of the lipid bilayer in GPCR structure and dynamics is currently being recognized as important [37]–[39] . Although , the N-terminal region modeled here does not directly interact with the lipid bilayer , we cannot rule out the possibility of indirect interactions of the N-terminal region with bilayer components in multi-component bilayers . In particular , negatively charged lipids have been implicated in influencing the conformational orientation of the juxtamembrane regions in receptor tyrosine kinases [40] , and could play similar roles in the charged Arg variants of β2AR . Our study is limited to microsecond time regimes and longer timescale studies might unveil further differences between the variants . Allosteric networks that lead to activation of β2AR have been demonstrated using microsecond time scale simulations [25] , [41] . These networks involve the coupling of the extracellular loops 2 and 3 with the G protein binding site but the study did not include the N-terminal region . In light of the coupling seen in our simulation between the N-terminal region and the ligand binding site , it would be interesting to recalculate these allosteric networks including the N-terminal region of the two variants . It would be further interesting to simulate ligand entry into the variants , which has not been carried out in this study . In conclusion , we probed the N-terminal polymorphism at the 16th position of β2AR , Arg16Gly , which is linked to variations in response to albuterol treatment . The N-terminal region is unresolved in all experimental structures of β2AR . Using structural models of the N-terminal region of the variants in conjunction with the rest of the receptor followed by 6 µs of atomistic simulations , we are able to observe molecular level differences between the variants in the timescales of our simulations . Most notably , the N-terminal region of the Arg variant is more dynamic than that of the Gly variant . Positional differences of the N-terminal regions are seen to affect the accessibility of ligand entry sites . While vestibule 1 is accessible and open in the Gly variants , vestibule 2 is accessible in the Arg variants . Further , we observe that the binding pocket of the Gly variant is slightly larger than the Arg variant . This difference in binding site size translates to a better docking score of Albuterol to the Gly variant . The differences between the variants arise due to both the charged and the bulky nature of the Arg side-chain compared to that of Gly . The charged side-chain moiety of Arg allows contacts with suitable partners towards vestibule 1 , causing the first fifteen residues of the N-terminal region to extend outwards from the receptor . The Gly variant on the other hand can accommodate a coil in the midst of the N-terminal region due to its small size and hence the first fifteen residues in this case remain coiled . We thus provide for the first time a molecular framework linking the differences in structural dynamics of the Arg16Gly variants to differences in binding albuterol , in microsecond timescale atomistic MD simulations . These results provide new insights towards understanding the variable response in asthma patients .
The primary sequence of human β2AR was retrieved from UniProt ( Accession number P07550 ) and edited to generate the variant sequence . The crystal structure PDB ID:2RH1 was chosen as the reference structure for the coordinates from residue 29 to 342 . For the N-terminal structure prediction , five class A GPCR templates were chosen ( 1U19 , 2ZIY , 2KS9 , 2L87 , 2K03 ) whose N-terminal region had a similar length to that of β2AR ( Table S3 ) . In addition , the third intracellular loop ( residues 231 to 262 ) was modeled using the templates 1U19 , 2ZIY and 2KS9 since it is absent in the 2RH1 structure . Models were built using the MODELER [42] program ( version 9 . 7 ) as implemented in Discovery Studio version 3 . 5 [43] . Fifteen models were generated for each variant of which three models including the one with the best energetics were chosen for further analysis . The structural models were taken as the initial structure for the simulations . The protonation state of ionisable residues was chosen as appropriate for pH 7 . 0 except for two residues , Glu 122 and His 172 which were protonated in accordance with the previous study of Dror et al . , [25] . The receptor was embedded in a fully hydrated POPC ( 1-palmitoyl-2oleoyl-sn-glycero-3-phosphocholine ) bilayer ( 256 lipids ) . GROMOS54a7 force field was used to represent the protein [44] and a compatible force-field was used to represent the lipid [45] . The Simple Point Charge ( SPC ) model was used to represent the water [46] . Counter ions were added to make the system neutral by replacing water molecules . The system was minimized and equilibrated . 100 ps NVT equilibration was followed by 25 ns NPT equilibration . All simulations were performed using the GROMACS version 4 . 5 . 5 package [47] . Periodic boundary conditions were applied . The system components were separately coupled to a temperature bath at 300 K with a coupling time constant of 0 . 5 ps [46] . For the short-range van der Waals and electrostatic cutoff , a distance of 1 . 2 nm was used . Long-range electrostatic interactions were calculated using the Particle-Mesh Ewald ( PME ) method [48] . Semi-isotropic pressure coupling was carried out using a Berendsen Barostat and the volume compressibility was chosen to be 4 . 5×10−5 bar−1 [46] . Three simulations of 1 µs each were performed for one of the Arg and Gly variant models and 100 ns simulations were performed for the remaining models . The structures were saved at a frequency of 1 ns . All analysis was performed using standard GROMACS [47] and VMD tools [49] . For the RMSD calculation , the protein structures were aligned to the first frame of the simulation with respect to the particular region for which the RMSD was being measured . The secondary structure was calculated using the STRIDE program as implemented in VMD . MDpocket [50] was used for detecting pockets along the course of the simulation . The algorithm is based on the principle of Voronoi tessellation . Residues 175–182 , 192–200 , 296–302 and 305 were chosen to define the opening of vestibule 1 . Residues 86–99 , 106–107 , 109 , 113 , 189–192 , 305–309 and 313 were chosen to define the opening of vestibule 2 . In the first step , all six trajectories were superimposed based on vestibule 1 or 2 separately and a grid was placed in voids between the residues defining the vestibules . The grid points best representing the opening of vestibule 1 and 2 were hand edited and saved . All six trajectories aligned by entire length along with the edited grid were submitted for the final calculation . The vestibule volume was calculated from the non-occluded grid points . Docking studies of the ligands albuterol and carazolol to the variants were performed using GLIDE-XP [32] . Albuterol and carazolol were saved in the SMILES format from Pubchem [51] and were prepared using the Ligprep module of Maestro version 9 . 4 [52] . Although both the R and S chiral forms of carazolol were generated only the S form was analyzed to match clinical use and crystallization studies ( 2RH1 contains carazolol in the S form ) . Furthermore , only the +1 protonation state of the aliphatic amine was considered . The β2AR frames chosen for docking were prepared using the Protein preparation wizard in Maestro . Residues that were within 0 . 7 nm of carazolol in the 2RH1 structure were used to define the centroid of the grid for docking in the corresponding variants . A grid was generated that encompassed the above mentioned residues for docking . The prepared ligands were docked into the β2AR binding site and their binding modes were analyzed . The docked structures were further simulated for 50 ns using the protocol of the unliganded receptor . Parameters for S-carazolol were generated and obtained from ATB [53] . | The human β2-adrenergic receptor ( β2AR ) is an important member of the GPCR family and a mutation at the 16th position , Arg16Gly , is commonly found in the population . This variation in asthma patients is linked to differential ( good/bad ) response to the drug albuterol , an agonist of the β2AR . To date , the coordinates of the N-terminal residues harboring the 16th position mutation have not been resolved . In our study we sought to glean insights into the dynamics of the variants that could address the differential response to albuterol . We used knowledge from class A GPCRs to build the N-terminal region of β2AR variants in conjunction with the available structure of the inactive receptor . This was followed by atomistic simulations in triplicate totaling to a sampling of 6 µs . We observe that the N-terminal region of the Arg variant is more dynamic than the Gly variant . Amongst the various differences between the variants , we observe long-range effects at the binding site leading to preferential docking of albuterol to the Gly variant . Our work is a first step to unravel the molecular mechanism linking the Arg16Gly variation to the differential response to albuterol in asthma patients . | [
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| 2014 | Molecular Insights into the Dynamics of Pharmacogenetically Important N-Terminal Variants of the Human β2-Adrenergic Receptor |
Mutations in the human LMNA gene cause muscular dystrophy by mechanisms that are incompletely understood . The LMNA gene encodes A-type lamins , intermediate filaments that form a network underlying the inner nuclear membrane , providing structural support for the nucleus and organizing the genome . To better understand the pathogenesis caused by mutant lamins , we performed a structural and functional analysis on LMNA missense mutations identified in muscular dystrophy patients . These mutations perturb the tertiary structure of the conserved A-type lamin Ig-fold domain . To identify the effects of these structural perturbations on lamin function , we modeled these mutations in Drosophila Lamin C and expressed the mutant lamins in muscle . We found that the structural perturbations had minimal dominant effects on nuclear stiffness , suggesting that the muscle pathology was not accompanied by major structural disruption of the peripheral nuclear lamina . However , subtle alterations in the lamina network and subnuclear reorganization of lamins remain possible . Affected muscles had cytoplasmic aggregation of lamins and additional nuclear envelope proteins . Transcription profiling revealed upregulation of many Nrf2 target genes . Nrf2 is normally sequestered in the cytoplasm by Keap-1 . Under oxidative stress Nrf2 dissociates from Keap-1 , translocates into the nucleus , and activates gene expression . Unexpectedly , biochemical analyses revealed high levels of reducing agents , indicative of reductive stress . The accumulation of cytoplasmic lamin aggregates correlated with elevated levels of the autophagy adaptor p62/SQSTM1 , which also binds Keap-1 , abrogating Nrf2 cytoplasmic sequestration , allowing Nrf2 nuclear translocation and target gene activation . Elevated p62/SQSTM1 and nuclear enrichment of Nrf2 were identified in muscle biopsies from the corresponding muscular dystrophy patients , validating the disease relevance of our Drosophila model . Thus , novel connections were made between mutant lamins and the Nrf2 signaling pathway , suggesting new avenues of therapeutic intervention that include regulation of protein folding and metabolism , as well as maintenance of redox homoeostasis .
The human LMNA gene exemplifies the rich source of genetic variation that exists in the human genome . Over 283 sequence variants and 460 disease-causing mutations have been identified to date . These mutations cause at least 13 distinct clinical diseases , called laminopathies , which have mainly tissue-restricted phenotypes , despite the fact that A-type lamins are expressed in nearly all cells [1] . For any given disease , mutations are scattered throughout the LMNA gene [2] . Furthermore , neighboring missense mutations can give rise to dramatically different disease phenotypes . These findings suggest that defined protein domains do not have tissue-specific functions . The LMNA gene encodes alternatively spliced mRNAs for lamin A and C that have a common domain structure [3] . The N-terminal region of lamins forms a globular domain , the central region forms a coiled coil domain , and the carboxy terminus contains an Ig-fold domain [4] . Lamins dimerize through the rod domain and form filaments via head-to-tail interactions of the dimers . Lateral interactions between lamin filaments are thought to generate higher order structures that form the network that underlies the inner membrane of the nuclear envelope . This network provides structural stability to the nucleus , serves as a scaffold for inner nuclear envelope proteins , and organizes the genome through contacts made with chromatin [5] . The mechanisms by which mutant lamins cause disease remain incompletely understood . It has been proposed that mutant lamins cause nuclear fragility , leading to nuclear deformation and breakage under mechanical stress [6] . This idea provides an explanation for the tissue-restricted phenotypes associated with muscular dystrophy and cardiomyopathy . However , sensitivity to mechanical stress does not explain why mutant lamins cause other diseases , such as lipodystrophy . For tissues that do not experience mechanical stress , mutant lamins are proposed to dysregulate gene expression [7] . While evidence exists for both the mechanical stress and gene expression models , it is also possible that lamins are required for adult stem cell homeostasis [8] . To gain novel insights into mechanisms by which mutant lamins cause disease , we previously developed a Drosophila model of lamin associated muscular dystrophy [9] . Mutations identified in patients are modeled into Drosophila Lamin C . Tissue-specific expression achieved by the Gal4/UAS system provides a means of expressing the mutant lamins in desired tissues [10] . Expression of the mutant lamins in larval body wall muscle causes larval locomotion defects and pupal death [9] . Here , we report in-depth structural and functional analyses of the mutant lamins identified in muscular dystrophy patients . Structural studies , which included NMR analysis , showed that the pathogenic mutations perturb the tertiary structure of the lamin Ig-fold domain . These structural perturbations are associated with cytoplasmic lamin aggregation , activation of the Nrf2/Keap-1 pathway , and reductive stress , yet have minimal effects on nuclear stiffness . These data lead to a novel hypothesis suggesting that cytoplasmic aggregation of nuclear envelope proteins causes Nrf2 target gene activation . Our findings provide new potential avenues for therapy involving protein metabolism and redox homeostasis .
To identify mechanisms by which LMNA mutations cause muscle disease , we performed an in-depth structural analysis on four mutations identified in patients with skeletal muscular dystrophy . Each patient possessed a single nucleotide substitution in LMNA that caused an amino acid substitution in the Ig-fold domain of A-type lamins . These amino acid substitutions ( G449V , N456I , L489P and W514R ) were dispersed throughout the Ig-fold domain and map to loop regions , making their effect on protein structure challenging to predict ( S1 Fig ) . To analyze the effects of these amino acid substitutions on Ig-fold structure , sequences encoding the wild type and mutant human A-type lamin Ig-fold domain were cloned into an expression vector , expressed and purified from E . coli ( S2A Fig ) . The peptides were analyzed by circular dichroism ( CD ) and NMR . The wild type A-type lamin Ig-fold domain contains eight anti-parallel and one parallel beta strands that form a beta barrel structure [11] ( S1 Fig ) . The CD spectra for the wild type and three of the mutant Ig-fold domains showed similar peak intensity at 220 nm ( S2B Fig ) demonstrating that the beta-sheet content of the wild type and mutant Ig-fold domains was comparable . Given the absence of obvious changes in beta sheet content between the wild type and mutant Ig-fold domains , we examined whether the amino acid substitutions altered the tertiary structure of the Ig-fold domain . Changes in tertiary structure often affect the thermal stability of a protein . We determined the T1/2 for denaturation of the wild type Ig-fold to be 55°C ( S2C Fig ) , which was slightly lower than the published value of 62°C [11] . This variation might be accounted for by slight differences in the size of the domain in the expression constructs . Our construct included amino acid residues 435–552 , whereas the published construct included amino acid residues 411–553 [11] . The T1/2 values for denaturation of G449V , N456I and W514R were reduced to 40 , 35 and 35°C , respectively ( S2C Fig ) . Thus , all three of the mutants analyzed had significantly lowered the thermal stability of the Ig-fold compared to that of the wild type Ig-fold domain . In the absence of changes in secondary structure , a lower T1/2 value for thermal stability for the mutant proteins suggested structural perturbations ( i . e . altered positioning of amino acids ) within the Ig-fold domain tertiary structure . The 15N/1H Heteronuclear Single Quantum Coherence ( HSQC ) NMR spectrum of the wild type Ig-fold domain of our construct showed well dispersed cross peaks , similar to those reported , indicating that the wild type protein is well folded and has similar tertiary structures as reported previously [11] ( S3A Fig ) . However , subtle differences are clearly observed between the two 15N/1H HSQC spectra , due to differences in the expression constructs used ( see above ) . To determine whether the amino acid substitutions caused changes in the tertiary structure of the Ig-fold domain , we assigned the backbone amide cross peaks in the 15N/1H HSQC spectrum of the wild type Ig-fold ( S3B Fig ) and compared it to that generated from the 15N/1H HSQC spectrum of each mutant ( Fig 1A ) . All mutants showed significant changes in the 15N/1H HSQC spectrum . In all cases , perturbations were observed in the loop in which the amino acid substitution occurred . In addition , G449V and W514R showed chemical shift perturbations throughout the beta sheets of the Ig-fold domain , indicating that these two mutations caused large structural changes . Among the four mutants tested , N456I exhibited a spectrum most similar to that of the wild type Ig-fold , while the L489P mutant showed a mixed population of folded and unfolded protein . Analysis of the chemical shift perturbation data revealed two clusters of commonly perturbed residues , one shared between G449V and W514R ( Fig 1B ) and other shared between N456I and L489P ( Fig 1C ) . These commonly perturbed residues are on opposite sides of the Ig-fold barrel ( Fig 1C ) . Taken together , our analysis identified two surfaces on the Ig-fold that are critical for the function of A-type lamins in muscle . Lamins A and C are contributors to nuclear stiffness [12 , 13] . Given the structural perturbations of the mutant Ig-fold domains , we tested whether full-length lamins possessing the amino acid substitutions within the Ig-fold altered nuclear deformation in response to mechanical stress . To accomplish this , we made use of a Drosophila model [9] . Drosophila Lamin C shows conservation of amino acid sequence and domain organization with human lamin A/C , including the amino acid residues under investigation [9] . Note that the amino acid numbering for the substituted residues is different between the human and Drosophila Ig-fold ( S1 Fig ) due to differences in the size of the domain between the two species . Importantly , the carboxyl sequence of Drosophila Lamin C is predicted to form an Ig-fold structure that is highly similar to that of human lamin A ( S4 Fig ) . In addition , the spatial and temporal expression pattern of Drosophila Lamin C is conserved with that of human lamin A/C [14] . Mutations identified in human LMNA were modeled into the Drosophila Lamin C gene , transgenic Drosophila were generated and the Gal4/UAS system [10] was used to express wild type and mutant lamins in Drosophila larval body wall muscle at levels comparable to endogenous Lamin C [9] . Expression of wild type Drosophila Lamin C in the larval body wall muscle caused no obvious phenotypes and did not affect viability [9] . In contrast , muscle-specific expression of mutant Lamin C caused larval locomotion defects and semi-lethality at the pupal stage [9] . Approximately 30% of the larval body wall muscles showed abnormally shaped and spaced nuclei , disorganization of the actin cytoskeleton , and cytoplasmic aggregation of nuclear envelope proteins including mutant Lamin C ( Fig 2A ) and nuclear pore proteins as shown previously [9] . None of these cellular phenotypes were observed in the wild type Lamin C control where lamin localization was confined to the nucleus ( Fig 2A ) . To examine the effect of mutant lamins on nuclear stability in muscle tissue , larval body wall muscle fillets were hand-dissected from transgenic Drosophila larvae and attached to a flexible silicone membrane for nuclear strain measurements [13] . Expression of wild type Lamin C in an otherwise wild type background did not alter nuclear stiffness relative to that of a non-transgenic stock ( Fig 2B ) . In contrast , expression of Lamin C in which the N-terminal head domain had been deleted ( ΔN ) showed larger nuclear deformation ( Fig 2A ) corresponding to decreased nuclear stiffness ( Fig 2B ) . These results are consistent with the dominant negative effects observed for headless lamin A in the cultured cells [13] . No statistical difference in nuclear tension was observed in myonuclei expressing wild type and mutant Lamin C . Thus , these show that the mutant lamins do not have dominant effects on the nuclear tension in Drosophila muscle fibers . In the absence of dominant changes in nuclear stiffness , we reasoned that mutant lamins might exert their pathogenic effects by changing muscle gene expression [15–17] . To minimize indirect effects on gene expression , we exploited the Drosophila model to capture changes in muscle gene expression 24–48 hours following induction of mutant Lamin C expression . Total RNA was isolated from larval body wall muscles and used for Affymetrix gene expression profiling . The analysis was performed on transgenic larvae expressing wild type , ΔN and G489V full-length versions of Lamin C . Lamin C ΔN was selected because it is known to have dominant negative effects on lamin assembly . Lamin C G489V was selected because it caused the greatest percent lethality ( 95% ) [9] . Using the Partek software suite and a two-fold cut off with a p value of 0 . 05 or greater , 28 genes showed changes in expression between muscle expressing wild type Lamin C and ΔN ( Fig 3A and S1 Table ) . A total of 87 genes showed changes in expression between wild type Lamin C and the G489V mutant ( Fig 3A and S2 Table ) , with 21 genes overlapping with those altered by the ΔN mutant ( Fig 3A and Table 1 ) . The majority of these genes were up-regulated in response to the mutant lamins , consistent with a repressive role of wild type lamins in gene expression [15 , 16] . The relatively small number of genes that changed expression is consistent with the idea that ‘first responder’ genes were captured by the analysis . Using Partek and Flybase gene annotations , we discovered that cellular detoxification genes , such as glutathione S transferase ( Gst ) genes , were enriched among those that changed expression ( Table 2 ) . These genes are typically activated in response to oxidative stress [18 , 19] . Other genes , including those involved in neuromuscular junction function ( Table 2 , S1 and S2 Tables ) might be activated to compensate for deterioration of the muscles at the neuromuscular junction . Thus , the gene expression analysis provided insights on the initial stages of pathogenesis . Cellular anti-oxidant genes are typically activated in response to a redox imbalance . Measurements of the levels of oxidized ( GSSG ) and reduced ( GSH ) glutathione were determined in extracts from body wall muscles from larvae expressing wild type , ΔN and G489V Lamin C transgene . This revealed similar levels of GSSG among all genotypes ( Fig 3C , left panel ) . In contrast , GSH levels were elevated in muscle expressing the mutant Lamin C relative to wild type ( Fig 3C , middle panel ) . Elevated levels of GSH and NADPH are hallmarks of a condition known as ‘reductive stress’ [20] . We found that NADPH levels were also elevated in the muscle of the larvae expressing ΔN and G489V , relative to wild type Lamin C ( Fig 3C , right panel ) . These findings demonstrate that mutant lamins cause reductive stress in muscle . To identify the source of the reductive stress , we measured the activity of NADPH-producing enzymes in larval body wall muscle . We discovered that the activity of glucose-6-phosphate dehydrogenase ( G6PDH ) and 6-phosphogluconate dehydrogenase ( 6PGH ) were similar between muscles expressing wild type and mutant Lamin C ( S5 Fig , left and middle panel ) . In contrast , the activity of isocitrate dehydrogenase ( IDH ) was elevated in muscles expressing mutant lamins , compared to that of wild type ( S5 Fig , right panel ) . Thus , the elevated IDH activity provides a potential explanation for the increased levels of NADPH . Genes involved in cellular detoxification , such as the Gst genes , are typically activated by the conserved Nuclear factor erythroid 2-related factor 2 ( Nrf2 ) /Kelch-like ECH associated protein 1 ( Keap-1 ) signaling pathway [21 , 22] . Under normal conditions , the antioxidant transcription factor Nrf2 is sequestered in the cytoplasm by Keap-1 . Under conditions of oxidative stress , cysteine residues within Keap-1 are oxidized , causing Nrf2 to no longer associate and translocate into the nucleus , where it activates target genes possessing anti-oxidant response elements ( AREs ) [21 , 22] . However , an alternative mechanism for Nrf2 target gene activation has been described for conditions of reductive stress [23] ( Fig 3B ) . This mechanism relies on the competition between Nrf2 and p62/SQSTM1 , an autophagy cargo acceptor , for the binding of Keap-1 . Increased levels of p62/SQSTM1 sequester Keap-1 , allowing Nrf2 to translocate into the nucleus and activate target genes . Our finding that cellular detoxification genes were upregulated in response to mutant lamins suggested that the Nrf2/Keap-1 pathway was activated . To determine if this was the case , we performed immunohistochemistry on larval body wall muscle expressing wild type and mutant Lamin C with antibodies to Cap-and-collar C ( CncC ) , the Drosophila homologue of Nrf2 [24] . In muscles expressing wild type Lamin C , we observed little to no staining , consistent with the fact that Nrf2/Keap-1 is rapidly turned over under normal conditions ( Fig 4A ) [25] . In contrast , we observed enhanced nuclear staining in muscles expressing each of the mutant Lamin C , relative to controls ( Fig 4A ) . Thus , mutant lamins cause nuclear accumulation of CncC ( Nrf2 ) , which is consistent with activation of the Nrf2/Keap-1 pathway and expression of many CncC target genes . To determine if the Nrf2/Keap-1 pathway is active in the human disease state , we stained muscle biopsy tissues from patients ( possessing the mutations that were modeled in Drosophila ) with antibodies to human Nrf2 [24] . In control muscle tissue , little to no staining was observed with the Nrf2 antibody ( Fig 4B ) . In contrast , enhanced staining within the myonuclei of the patient muscle biopsy tissue was apparent ( Fig 4B ) . Thus , expression of mutant lamins correlates with nuclear translocation of Nrf2 in both Drosophila and diseased human muscle . In mammalian systems Nrf2 and p62/SQSTM1 are co-regulated [21] . Given our findings of reductive stress and Nrf2 myonuclei enrichment , we hypothesized that levels of the autophagy cargo protein p62/SQSTM1 would be elevated in the muscles expressing mutant lamins relative to controls . To test this hypothesis , we stained Drosophila larval body wall muscles with antibodies to Drosophila p62/Ref ( 2 ) P , a homologue of human p62/SQSTM1 [26] . Muscles expressing wild type Lamin C showed hardly any staining for p62/Ref ( 2 ) P ( Fig 5A ) . In contrast , muscles expressing mutant lamins showed increased cytoplasmic foci of staining ( Fig 5A ) . The elevated levels of p62/Ref ( 2 ) P were validated by western analysis of protein extract from larval body wall muscles ( S6 Fig ) . Thus , mutant lamins cause elevated levels of cytoplasmic p62/Ref ( 2 ) P , which is consistent with the increased cytoplasmic aggregation of mutant lamins ( Fig 2A ) . To determine if the elevated levels of p62/Ref ( 2 ) P also occur in the human disease state , we stained muscle biopsy samples from the patients ( possessing the mutations that were modeled in Drosophila ) with antibodies to human p62/SQSTM1 [26] . Scoring muscle fibers positive for p62/SQSTM1 if they had ten or more visible foci containing p62/SQSTM1 , showed that the control patient muscle tissue had low levels of p62/SQSTM1; only 5/200 muscle fibers were positive ( Fig 5B ) . In contrast , 48–62/200 fibers were scored positive in the patient samples . Furthermore , the diameter of the p62/SQSTM foci in the patient tissues was larger than those in the controls ( Fig 5B ) . These findings strongly suggest that the Nrf2/Keap-1 pathway activation in both Drosophila and human muscle occurs through an alternative mechanism that is triggered by elevated levels of p62/SQSTM1 .
Our structural studies of the lamin Ig-fold demonstrated that single amino acid substitutions in the loop regions perturb the tertiary structure , leaving the secondary structure of the folded domain largely intact ( S2 Fig ) . These data were consistent with single-molecule force spectroscopy showing that the lamin Ig-fold possessing an R453W substitution required less force to unfold than the wild type Ig-fold domain [27] . Our structural data are consistent with in silico modeling in which amino acid substitutions in the Ig-fold that cause muscular dystrophy were predicted to alter the structure , more so than those that cause lipodystrophy or progeria [28] . Our NMR analysis of the mutant Ig-fold domains identified surfaces on opposite sides of the Ig-fold barrel that are critical for muscle function . This finding predicts that substitution of other amino acids that comprise these surfaces might result in muscular dystrophy . Consistent with this prediction , amino acid substitutions in eight of the 21 amino acids that make up these surfaces cause muscular dystrophy ( Leiden muscular dystrophy database http://www . dmd . nl ) . It is interesting to note that the largest structural perturbations were observed for the G449V and W514R mutants , which correspond to the most severe patient phenotypes . The corresponding amino acid substitutions in Drosophila Lamin C caused the greatest percentage of lethality [9] . The N456I mutant showed the least structural perturbations in the Ig-fold domain ( Fig 1 ) , though the relative severity of symptoms in this patient was not assertained [29] . Consistent with the structural data , the corresponding amino acid substitution in Drosophila Lamin C gave the least percentage of lethality [9] . Thus , our investigations showed an obvious correlation between the severity of the Ig-fold structural perturbations and phenotypic severity . The structural perturbations within the Ig-fold might generate novel interaction surfaces that promote lamin aggregation ( Figs 1 and 2A ) . Both nuclear and cytoplasmic aggregation of mutant lamins have been reported [30 , 31] , however , they are not commonly observed in human muscle biopsy tissue or tissue from a laminopathy mouse model [9 , 32] . Cytoplasmic aggregation was observed for a truncated form of A-type lamin that causes Hutchinson-Gilford progeria syndrome [30] . Lamin aggregation is supported by X-ray crystallography studies of a R482W substitition in the A-type lamin Ig-fold domain that causes lipodystrophy [33] . The R482W Ig-fold domain possesses unique interaction surfaces not present in the wild type Ig-fold that form a unique platform for tetramerization . The structural perturbations in the Ig-fold domain are likely to affect many functions of the mutant lamins . The lamin Ig-fold domain interacts with many partners to build the network that underlies the inner membrane of the nuclear envelope [5] . Mutant lamins can be inappropriately incorporated into the lamin network and function as dominant negatives [15 , 34 , 35] . This is the case for the headless lamin , which has dominant effects on nuclear shape and stiffness in both Drosophila muscle tissue and MEFs ( Fig 2 ) [13] . In contrast , the Ig-fold substitutions do not cause major dominant effects on stiffness , however , there might be undetected alterations in the lamina and/or nuclear organization . Changes in nuclear organization could explain the misregulation of gene expression that we observed in muscles expressing mutant lamins ( S1 and S2 Tables ) . In addition , the amino acid substitutions within the lamin Ig-fold domain might disrupt posttranslational modifications that occur on lamins , similar to what has been shown for familial partial lipodystrophy LMNA mutations that disrupt SUMOylation of Lamin A [36] . Changes in posttranslational modifications have the potential to alter interaction with partner proteins and/or affect aggregation properties . Cytoplasmic protein aggregation has been linked to reductive stress [37 , 38] . Here , we show that cytoplasmic lamin aggregation correlates with elevated levels of both GSH and NADPH , hallmarks of reductive stress [20] ( Fig 3 ) . Elevated levels of isocitrate dehydrogenase enzyme activity ( S5 Fig ) contribute to the additional NADPH . In a similar manner , dominant negative forms of alphaB-crystallin ( CryAB ) result in cytoplasmic CryAB misfolding/aggregation and reductive stress in the mouse heart , ultimately leading to dilated cardiomyopathy [39] . These findings suggest that reductive stress might contribute to the dilated cardiomyopathy in cases of lamin associated muscular dystrophy . Interestingly , mutations in the human CRYAB gene cause disease phenotypes that are strikingly similar to those observed for lamin associated muscular dystrophy , including skeletal muscle weakness and dilated cardiomyopathy in cases of lamin-associated muscular dystrophy . It is worthwhile to note that CryAB functions as a chaperone to prevent aggregation of intermediate filament proteins such as desmin , suggesting a common link between intermediate filament aggregation and reductive stress . An imbalance in redox homeostasis can provide an environment that promotes protein misfolding and aggregation . The redox state influences aggregation of lamins; aggregation has been observed under both oxidative and reductive conditions [33 , 40] . In fact , the formation of the novel tetramer generated by the R482W mutant Ig-fold domain ( see above ) required a reductive environment [33] . Reductive stress has also been observed in healthy individuals predisposed to Alzheimer disease , a disease of protein aggregation [41] . Alzheimer disease is typically accompanied by oxidative stress , however , lymphocytes from patients carrying an ApoE4 allele that predisposes them to Alzheimer disease show reductive stress . It is hypothesized that continual activation of antioxidant defense systems , such as Nrf2/Keap-1 signaling , becomes exhausted over time , particularly later in life , resulting in the inability to properly defend against oxidative stress . Our redox analysis in Drosophila muscle occurred 24–48 hours post expression of the mutant lamins . Our findings suggest reductive stress at the onset of pathology that could resolve into oxidative stress later in disease progression [42] . Typically lamins are thought to regulate gene expression from inside the nucleus , by interacting with transcription factors and organizing the genome [5 , 43] . Our data support a novel model in which genes are misregulated as a consequence of mutant lamin aggregation in the cytoplasm . Cytoplasmic lamin aggregates have been found in high molecular weight complexes in cases of liver injury [44] . Such complexes contain nuclear pore proteins , signaling mediators , transcription factors and ribosomal proteins , which are thought to disrupt the normal cellular physiology . Lamin aggregation might also serve a cytoprotective function by facilitating the coalescence of mutant lamin so that the contractile apparatus can properly function . A similar mechanism exists in Huntington’s disease , where sequestration of mutant huntingtin in inclusion bodies correlates with better neuron health [45] . Collectively , our findings continue to support this Drosophila model of laminopathies , as many of the phenotypes discovered here in Drosophila have been validated in human muscle biopsies ( Figs 4B and 5B ) [9] . It is now possible to use this rapid genetic model to ( 1 ) determine if mutations in other domains of lamin produce similar phenotypes and ( 2 ) if lamin mutations have similar effects in other tissues , such as the heart . Our data suggest that cytoplasmic lamin aggregation contributes to muscle pathology . Consistent with this idea , increased rates of autophagy suppress phenotypes caused by mutant A-type lamin in cultured cells and mouse models [32 , 46] . Furthermore , electron microscopy of skeletal muscle biopsies from patients with LMNA mutations showed large perinuclear autophagosomes [47] , similar to the localization of lamin aggregates and p62 foci in the Drosophila muscle ( Figs 2A and 5A ) . Thus , the regulation of autophagy , a process that removes both damaged organelles and proteins , might be central to the development of therapies . The Drosophila model will allow for genetic dissection of both the autophagy and reductive stress pathways to identify the key factors responsible for the muscle pathogenesis and its suppression .
LMNA mutations identified in patients were introduced into a wild type copy of the human LMNA gene in a pCR2 . 1 vector via site-directed mutagenesis ( Quick Change , Stratagene ) . The sequences of the PCR primers used to make these mutations are listed in S3 Table . DNA fragments encoding amino acids 435 through 552 of human lamin A were amplified using primers containing BamH1 and HindIII sites . The resulting PCR products were cloned into the pQE-30 Xa vector ( Qiagen ) and the constructs was expressed in M15[pREP4] E . coli cells . Expression was induced by IPTG overnight . Expression of wild type Ig-fold , G449V and W514R yielded protein that was purified by nickel column chromatography followed by Superdex-75 size exclusion chromatography . Expression of N456I and L489P yielded proteins that resided within inclusion bodies . Subsequent purification required denaturing conditions in 8 M urea during purification nickel and Superdex-200 size exclusion chromatography . Material from the monomeric peak eluted from the Superdex-200 column was dialyzed overnight to eliminate the urea and then re-purified on the Superdex-75 size exclusion column . Approximately 30 mg of wild type Ig-fold domain and approximately 8 mg of each mutant were purified per liter of cell culture , as determined spectrophotometrically ( Nanodrop , Thermo Scientific ) . Circular dichroism ( CD ) data was collected using 1 μM protein in 20 mM phosphate buffer with 100 mM NaCl and 0 . 1 mM DTT using a Jasco J815 CD Spectrophotometer . The spectral scan was performed between 190 nm and 280 nm . For T1/2 determination , melting curves were monitored under tryptophan absorbance at 230 nm; samples were heated at the rate of 2°C per minute . All CD experiments were performed in triplicate with independently prepared protein samples . Nuclear magnetic resonance ( NMR ) spectra were recorded at 20°C on a Bruker 500 or 800 MHz NMR spectrometer ( NMR Core Facility , University of Iowa ) . NMR data were processed using NMRPipe [48] and analyzed using Sparky [49] and/or NMRView [50] . For the wild type Ig-fold domain , 1H , 15N , and 13C resonances of the backbone were assigned using the triple resonance experiments [HNCA , HN ( CO ) CA , HNCACB , HN ( CO ) CACB , HNCO , HN ( CA ) CO , and C ( CO ) NH-TOCSY] with a 500 uM 15N/13C-labeled sample . All NMR experiments were conducted in a NMR buffer containing 20 mM sodium phosphate ( pH 7 . 0 ) , 100 mM NaCl , 2 mM DTT , 1 mM EDTA and 0 . 1 mM sodium azide . Drosophila stocks were cultured on standard corn meal media at 25°C [51] . Stocks with the wild type and mutant lamin transgenes were previously described [9 , 15 , 52] . All lamins were expressed using the Gal4/UAS system and the C57 muscle-specific Gal4 driver stock [10] . Western analysis of protein extract from MEFs was according to published procedures [13] . Western analysis using Drosophila muscle was performed by extracting protein from 10 muscle fillets hand-dissected from third instar larvae in 2X Laemmli grinding buffer ( 125 mM Tris HCL , pH 6 . 8 , 20% glucerol , 4% SDS , 0 . 005% bromophenol blue ) plus 10 mM DL-Dithiothreitol . Anti-Drosophila p62 ( 1:8 , 000; kind gift of G . Juhász ) and anti-Drosophila tubulin ( 1:300 , 000 , Sigma ) were used as primary antibodies and detected with anti-rabbit-HRP ( 1:400 , Sigma ) and anti-mouse-HRP ( 1:400 , Sigma ) . Nuclear strain analysis of modified and unmodified MEFs was performed as previously described [53] . Nuclear strain analysis of Drosophila muscle was previously described [13] . Total RNA was isolated from hand-dissected body wall muscle from 40 third instar larvae per sample . RNA was purified using Trizol ( Ambion ) followed by RNAeasy ( Qiagen ) . The RNA was used to generate labeled cRNA and hybridized to Drosophila 2 . 0 GeneChip arrays ( Affymetrix ) ( DNA Core Facility , University of Iowa ) . Triplicate biological samples were analyzed for each genotype . The microarray data were analyzed using the Partek Genomic Suite [54] . Differentially expressed genes were identified using analysis of variance ( ANOVA ) with a two-fold change in expression and a P value of 0 . 05 or higher used as a cut off . Immunohistochemistry of Drosophila larval body wall muscles and human muscle biopsy tissues were performed as previously described [9] . Drosophila larval body wall muscles were stained with affinity purified anti-CncC antibodies ( 1:100; gift from H . Deng and T . Kerppola ) [24] , anti-p62/Ref ( 2 ) P ( 1:3 , 000 , kind gift of G . Juhász ) [55] that was detected with Alexa Fluor 488 goat anti-rabbit ( 1:400 dilution; Invitrogen ) . Filamentous actin was detected with Texas Red Phalloidin ( 1:400 , Invitrogen ) . Human muscle biopsy cryosections were obtained from the Iowa Wellstone Muscular Dystrophy Cooperative Research Center and stained according to published procedures [9] with human p62/SQSTM1 ( 1:3 , 000 , Sigma ) and Nrf2 ( 1:300 , Santa Cruz Biotech ) , followed by Alexa Fluor 488 goat anti-rabbit ( 1:400 , Invitrogen ) . Filamentous actin was detected with Texas Red Phalloidin ( 1:400 , Invitrogen ) . Drosophila larval body wall muscles were hand-dissected from 15 larvae , placed in 200 μl of 5% 5-sulfosalicylic acid and quantitation of reduced glutathione ( GSH ) and oxidized glutathione disulfide ( GSSG ) was performed as published [56] . GSSG was determined by adding a 1:1 mixture of 2-vinylpyridine and ethanol to the samples and incubating for two hours before assaying as described previously [57] . Enzymatic rates were compared to standard curves obtained from control samples . GSH and GSSG amounts were normalized to the protein content of the insoluble pellet from the 5-sulfosalicylic acid treatment , dissolved in 2 . 5% SDS in 0 . 1N bicarbonate , using the BCA Protein Assay Kit ( Thermo Scientific ) . For measurements of NADPH , larval body wall muscles were hand-dissected from 15 larvae and immediately frozen in liquid nitrogen . Muscle samples were thawed in 170 μl of buffer [100 mM Tris HCl , 10 mM EDTA , 0 . 05% Triton X ( v/v ) , pH 7 . 6] and sonicated four times for 30 seconds each . Assays were performed according to previous published procedures [58] . Absorbance was read at 310nm using a DU670 Spectrophotometer ( Beckman ) and enzyme activity was expressed as micromoles of NADPH per milligram of total protein . For measurements of the NADPH-producing enzymes , larval body wall muscles were hand-dissected from 15 larvae and immediately frozen in liquid nitrogen . For assays of G6PD and 6PGD , diethylenetriaminepentaacetic acid ( DETAPAC ) was added to pellet and the mixture sonicated using a Sonics Vibra-cell sonicator with a cup horn at 20% amplitude . Enzymatic assays were performed according to previous published procedures [59] using 0 . 1 M Tris HCl-MgCl2 , 2mM NADP with either 0 . 034 grams of glucose-6-phosphate or 0 . 041 grams 6-phosphogluconic acid , pH 8 . 0 . Absorbance was read at 340 nm using a DU670 Spectrophotometer ( Beckman ) and enzyme activity was reported as milliunits per milligram of protein . IDH activity was measured according to published procedures [60] using 100mM Tris-HCl , 0 . 10 mM NADP , 0 . 84 mM MgSO4 , and1 . 37 mM isocitrate ( pH 8 . 6 ) . Absorbance was measured at 340 nm , every 9 sec , over 3 minutes at 25°C using a DU670 Spectrophotometer ( Beckman ) and enzyme activity was expressed as micromoles of NADPH produced per minute per microgram soluble protein X 10 , 000 . | Mutations in the human LMNA gene cause muscular dystrophy that is often accompanied by heart disease . The LMNA gene makes proteins that form a network on the inner side of the nuclear envelope , a structure that reinforces the cell nucleus . How mutations in the LMNA gene cause muscle disease is not well understood . Our studies provide evidence that LMNA mutations activate an intracellular signaling pathway and alter the redox homeostasis of muscle tissue . Thus , our results suggest that blocking the signaling pathway and maintaining the oxidative state of the diseased muscle are potential therapies for muscular dystrophy patients with LMNA mutations . | [
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| 2015 | Myopathic Lamin Mutations Cause Reductive Stress and Activate the Nrf2/Keap-1 Pathway |
It has been suggested that HIV-1 has evolved its set-point virus load to be optimized for transmission . Previous epidemiological models and studies into the heritability of set-point virus load confirm that this mode of adaptation within the human population is feasible . However , during the many cycles of replication between infection of a host and transmission to the next host , HIV-1 is under selection for escape from immune responses , and not transmission . Here we investigate with computational and mathematical models how these two levels of selection , within-host and between-host , are intertwined . We find that when the rate of immune escape is comparable to what has been observed in patients , immune selection within hosts is dominant over selection for transmission . Surprisingly , we do find high values for set-point virus load heritability , and argue that high heritability estimates can be caused by the ‘footprints’ left by differing hosts' immune systems on the virus .
Human immunodeficiency virus type 1 ( HIV-1 ) evolves under two levels of selection . On the one hand , there is within-host selection for immune escape . On the other hand , selection on the population-level acts on infectiousness and virulence . In this paper , we explore how these two levels of selection are intertwined , keeping in mind the massive heterogeneity of the hosts with respect to their cellular immune responses . A HIV-1 infection can be separated into three phases: the acute phase , the asymptomatic phase and the symptomatic ( or AIDS ) phase . During the acute phase , the virus establishes high virus loads ( the number of HIV-1 RNA copies per ml blood plasma ) [1] , until the CD4+ target cells are depleted [2] , and adaptive immune responses start limiting viral reproduction . The virus load then drops to a semi-stable level called the set-point . This marks the beginning of the asymptomatic or chronic phase , during which the partially restored CD4+ T-cell count gradually drops , and at some point patients develop AIDS . The set-point virus load ( spVL ) differs markedly between individuals . In untreated patients , spVL ranges from 102 to 106 copies/ml . The origin of this variation is an extensively researched topic , and explanations include host and viral factors . For instance , host factors incorporate the association between the set-point and the Human Leukocyte Antigen ( HLA ) haplotype , which is important for cellular immunity [3]–[6] . The observation that the spVL is to some extent heritable [7]–[14] , suggests that viral genetic factors sway the set-point too . The exact extent of this heritability is unknown , as estimates range from 6% to 59% . spVL is related to infectiousness and virulence . Patients with a higher spVL tend to be more infectious [15] , but also develop AIDS more rapidly [16] , resulting in a trade-off between infectiousness and the length of the asymptomatic phase . This life history trade-off was identified by Fraser et al . [17] , and opens the door for HIV-1 adaptation with respect to transmission by means of spVL evolution . Certain spVLs ( around copies/ml ) allow a HIV-1 strain to cause more secondary infections than strains with lower or higher set-points . A strain that establishes on average this optimal set-point should therefore become more abundant in the population . The striking observation is that , although large variation in set-points exists , most HIV-1 infected patients show a set-point close to the transmission-optimal value [17] . Moreover , mathematical models show that this adaptation can take place within realistic time scales [18] , given the heritability estimates of spVL [7] , and HIV-1's likely dates of origin [19] , [20] . In such mathematical models , HIV-1's population-level fitness ( measured in terms of the basic reproduction number ) is only constrained by the life history trade-off , and environment- and mutation-induced spVL-variation . It is therefore quite intuitive that in such a model evolution leads to intermediate levels of spVL [17] , [18] . The inclusion of directed within-host evolution in such models introduces an extra constraint on the population-level fitness; one which dominates the evolutionary outcome , unless within-host selection is exceedingly weak . For a homogeneous host population , this has been shown recently by Lythgoe et al . [21] , and they suggest that within-host evolution of traits affecting virus load must be slow . Below we argue that ‘short-sightedness’ [sensu 21 , 22] , i . e . , the life history trade-off has no apparent effect on the evolutionary outcome , can easily be understood when the host population is homogeneous . However , in a much more realistic situation where HIV-1 needs to escape from immune responses that vary markedly between individuals , the same intuition for the effect of directed within-host evolution can no longer be applied , and needs to be revised . In this study , we explicitly incorporate such immune selection and massive host-heterogeneity with respect to immune responses in a nested epidemiological model . We investigate whether spVL evolution of HIV-1 is influenced by the virus' life history trade-off . Our model predicts that within-host immune selection has a major influence on population-wide spVL evolution . Thus , both Lythgoe's and our model predict short-sighted spVL evolution . However , we do not agree that within-host evolution must therefore be slow . Throughout the paper , we use the term ‘between-host adaptation’ for evolutionary dynamics where HIV-1's life history trade-off notably affects the evolution of spVL . The term ‘within-host selection’ refers to selection for immune escape and reversion of deleterious mutations . At the same time , we use our model to investigate spVL heritability . We argue that high heritability can be a result of HIV-1 rapidly escaping immune responses , and the between-individual variation of these responses . We emphasize that spVL heritability caused by such a mechanism does not provide support for between-host adaptation .
Our approach combines a caricature model for immune escape with a susceptible-infectious ( SI ) model for HIV-1 transmission . Both the within-host and the between-host simulations are discrete-event and individual based . The technical details are given in Methods . Here we give an intuitive exposition . Cytotoxic T-Lymphocyte ( CTL ) responses are arguably important for controlling HIV-1 virus load [23] , [24] . Human cells notify the cellular immune system about their proteome by presenting peptides on HLA molecules . On infected cells , a subset of these peptides originate from viral proteins . If a CTL clone detects such a foreign peptide , it can kill the infected cell , and the peptide ( in its proper HLA context ) is called an epitope . Not all peptides can be presented by the HLA molecules of a host , and HIV-1 can escape from CTL recognition by mutating amino acids in its peptides to prevent presentation by the host's HLA molecules [25]–[27] . Due to HLA-polymorphism , the particular subset of all peptides that can be presented by a host's HLA molecules ( the binding repertoire ) differs strongly between individuals [28] . In our model we incorporate this by assuming that a wild-type virus has n peptides that can be presented in the population . A particular host can present a subset of size k of these n peptides . During infection , we assume that mutations in the n potentially recognized peptides occur according to a Markov process . Some of these mutations will result in CTL escape ( escape mutations ) . In this case , the mutant takes over the viral population in that host . Naturally , if two hosts have have a common peptide in their binding repertoires , the mutated peptide is a CTL escape for both hosts . In line with evidence , we assume that escape mutations in HIV-1 come with a fitness cost [29] , [30] . The total fitness effect of an escape mutation , resulting from immune escape and its fitness cost , must be positive before the escape mutant can replace the dominant HIV-1 strain in the host . In order to model this , we use the virus load in the asymptomatic phase as a measure for within-host fitness . An immune response causes a reduction in the log10 virus load , and a fitness cost of any mutation reduces the log10 virus load by . The total fitness effect of an escape mutation is then a increase in the log10 virus load . In the simulations , we choose and so that lies within estimated ranges [31] , [32] . Qualitatively , our results do not depend on these particular choices for and , as long as ( results not shown ) . Certain hosts have an efficient immune response to HIV-1 . This can partially be explained by HLA-type . For instance , HLA-B*57 , B*27 , B*58 and B*18 are associated with a low spVL . HIV-1 is able to escape immune responses in hosts with these HLA-types , but the associated fitness costs tend to cripple the virus [25] . When such a crippled virus is transmitted to the next host , lacking the protective HLA-type , the virus load in this secondary host can remain low for a long time [29] . After a while , the crippled virus reverts the deleterious mutations , since the immune pressure causing these crippling mutations is not present in the secondary host [33] . We propose that this effect is not only restricted to known protective HLA-types , but holds more generally [e . g . , see 34] . We model this similar to immune escape . As a result of immune escape in previous hosts , a viral strain may carry a number of deleterious mutations . These mutations can revert to the wild-type , again according to a Markov process . In summary , our model for the log10 virus load V is [cf . 35]where Vmax is the log10 virus load of a HIV-1 strain without deleterious mutations in the absence of CTL-responses ( ) , e . g . , the high virus load observed in a CD8+ T cell depleted individual [23] , [36] , [37] . The integer e represents the number of escape mutations in a host ( and hence , equals the number of immune responses ) , and f denotes the number of deleterious mutations . In other words , f equals the number of mutated peptides outside the current host's binding repertoire . We assume that escapes and reversions appear at a rate proportional to the number of immune responses and deleterious mutations , respectively . Hence where and are the ‘per-peptide’ rate of escape and reversion , respectively . We will refer to and as ‘mutation rates’ . Keep in mind , however , that our model of escape and reversion is quite phenomenological . The rates and are a combination of many factors , such as the error rate during reverse transcriptase and the fixation rate . Moreover , the rates and should in reality depend on the virus load . We simplify this dependence by assuming that the rates differ only between disease phases . In the acute and AIDS phase the per-peptide rates are high and in the asymptomatic phase , these rates are lower . Instead of and , we therefore take distinct parameters and for the per-peptide mutation rate in the acute ( ) , asymptomatic ( ) and AIDS ( ) phase . We choose with , meaning that reversion is slower than escape ( see Table 1 for the exact parameterization ) . This is in line with the assumption that the total fitness benefit of an escape mutation is greater than the benefit of a reversion ( i . e . ) . As mentioned earlier , a HIV-1 strain infecting a new host carries a history of mutations acquired in previous hosts [29] , [38] . In the context of the new host , many of these mutations will not be beneficial . Some of them may be advantageous , because HLA molecules can share epitopes [39] , [40] , and individuals share HLA molecules . To keep our model simple , we assume that a random host's binding repertoire is a random subset of size k of the set of all n possible HIV-1 epitopes . In reality , HLA haplotypes , and hence binding repertoires , are less regularly distributed . However , our simpler distribution provides us with the advantage that we only have to keep track of the number of mutated peptides . Namely , when a host transmits a virus with e escape mutations and f deleterious mutations ( denoted as an -virus ) , then in the secondary host the virus will have phenotype with . We find the number of escape mutations e′ by choosing a new random binding repertoire of size k′ . Since every peptide is part of the new binding repertoire with equal probability , the number of a priori escape mutations is drawn from the hypergeometric distribution ( ) . An example of how a virus' phenotype can differ between hosts is given in Figure 1 . By default , we choose and ( see Table 1 ) , such that about 10% of HIV-1's peptides can serve as an epitope . The number k is chosen such that hosts have a realistic number of responses , also when many of the n peptides are mutated . We model the three phases of a HIV-1 infection based on Fraser et al . [17] and Hollingsworth et al . [41] . The acute phase has a fixed length D1 , and in this phase individuals have a fixed infectiousness β1 . After D1 years , the asymptomatic phase starts and infectiousness β2 ( V ) and the average length of the asymptomatic phase D2 ( V ) depend on the virus load V . The functions β2 and D2 are Hill functions with coefficients as estimated by Fraser et al . [17] . When the asymptomatic phase ends , the AIDS phase starts . This AIDS phase has , similar to the acute phase , a fixed length D3 and fixed infectiousness β3 . We do not incorporate any correction for serial monogamy on infectiousness . As an illustration of the within-host model , we have simulated a large number of within-host processes for two different parameter settings ( Figure 2 ) . Stochasticity and host-heterogeneity cause large variation in the within-host evolution of the virus ( the thin step-wise lines in Figure 2 ) . As deleterious mutations are reverted and CTL responses are escaped , the virus load increases during the infection . If the mutation rate is high , almost all escapes happen during the acute phase of the infection . The cohort-average virus load ( the heavy blue line in Figure 2 , bottom panels ) can then even decrease , since individuals with a high set-point develop AIDS more rapidly . When these fast progressors die , we exclude them from the calculation of the cohort's mean virus load . Notice that during the acute phase , the variable V does not reflect the peak virus loads observed in patients , but is merely a measure of the virus' fitness . For the between-host model , we explicitly model a population of infected individuals ( of size I ) , and assume a frequency-dependent contact process with susceptibles [42] . Super-infection and co-infection are ignored . We keep the total population size ( N ) constant , and only keep track of the susceptibles' number ( S ) . Because of within-host evolution , an individual may transmit different viral strains during the course of an infection . When the virus load increases due to within-host adaptation , the infection rate also increases . We verified that a model with a non-constant population size does not give different results ( not shown ) . Since in our model the virus load can increase during the asymptomatic phase , we need to specify what we mean with set-point virus load . We define the spVL ( in log10 scale ) as the geometric average of the log10 virus loads in the asymptomatic phase , i . e . , , where the integral is taken over the chronic phase , which lasts L years , and V ( t ) denotes the virus load at time t . We often write to indicate the population-wide arithmetic average spVL . Bracket notation is also used for other population-wide averages . When we choose the mutation rate low and run the agent-based model , the mean spVL converges to 4 . 52log10 copies/ml; the value optimal for transmission ( Figure 3A ) . However , this takes many centuries , depending on the maximal virus load Vmax and the initial number of mutations . By increasing the mutation rate , we make the evolutionary dynamics faster , but lose between-host adaptation ( Figure 3B ) . In fact , the mean spVL is approximately 1 . 3 log10 copies/ml higher than 4 . 52 . By keeping the mutation rate equally high , but lowering Vmax , the HIV-1 quasi-species can be given a population-level fitness ( ) that is about 17% higher than what is reached in Figure 3B . Apparently , selection for spVL values that are optimal for transmission is overruled by within-host selection at high mutation rates . Both simulations in Figure 3 are approaching different steady states . Thus , to investigate between-host adaptation further , we now look at the properties of the model in population-level steady state for many different parameter combinations ( Figure 4 ) . To make the analysis computationally feasible , we stochastically approximate the next-generation matrix ( NGM , see Methods ) . We fix all parameters except for the mutation rates ( and ) , and the maximum virus load ( Vmax ) . We keep the ratios and between the mutation rates constant ( see Table 1 for the parameters chosen ) . Apart from the standard model described above , we also consider two modifications that serve as controls . In the first control , we take out the effect of population-level selection for transmission . In the second control , we make the population homogeneous . Between-host adaptation is only possible if spVL is inherited from one person to the next . If the speed of within-host adaptation is intermediate or fast , our model does not predict population-level adaptation for transmission . Here we show that the absence of between-host adaptation is not due to lack of spVL heritability ( h2 , see Methods ) . To this end , we compute heritability during an epidemic ( see Figure 3 , bottom panels ) , and in the steady state of the ( standard ) model for many different parameter combinations ( see Figure 5C ) . During a simulated epidemic , we use all transmissions that take place within a time span of a year to compute heritability . This means that the sample size for computing heritability equals the ( yearly ) incidence . The median incidence in the simulation with a low mutation rate ( , Figure 3B ) equals 2335 yearly infections ( 2 . 5th , 97 . 5th percentiles: [1839 , 4085] ) . For the simulation with a faster mutation rate ( , Figure 3A ) , more virulent viruses evolve , and the median incidence equals 3209 infections per year ( 2 . 5th , 97 . 5th percentiles: [241 , 3586] ) . Even with these large sample sizes , heritability fluctuates substantially . In Figure 3B the median of h2 is 3 . 12% ( 2 . 5th , 97 . 5th percentiles: [−1 . 31 , 8 . 29] ) , and in Figure 3A the median of h2 is 17 . 1% ( 2 . 5th , 97 . 5th percentiles: [10 . 8 , 21 . 8] ) . The rapid fluctuation in h2 might explain why different experimental studies to HIV-1 spVL heritability that use transmission couples [7] , [11]–[14] often give quite varying results [cf . 9] . The NGM approach allowed us to produce an even larger number of transmission couples , and hence , to estimate heritability more accurately . Overall , heritability lies between 0% and 30% , and is for realistic parameter combinations . In our model , one can think of two mechanisms that cause heritability . The first mechanism applies when mutation rates are not too high . If variation in the number of mutations exists and the mutation rate is low , the spVL of transmitting and recipient hosts are correlated , although this correlation will not be perfect due to the variation in the breadth of the immune response ( k ) . If the mutation rate increases , viruses adapt to their host more rapidly and , according to this first mechanism , the correlation vanishes . The second mechanism is related to transmission of crippled viruses . If a host controls the infection well because of a broad immune response , the virus will escape more CTL responses and , when transmitted , becomes crippled in the average new host . In the primary , controlling host , the set-point virus load is low due to good initial immune responses and the virus' fitness cost of escape , and in the average secondary host the virus load will again be low due to the high number of deleterious mutations . Vice versa , in hosts with a narrow immune response , transmitted strains will have few new escape mutations and this will lead to few deleterious mutations in the recipient . We can most clearly see the effect of the second mechanism when both mutation rate and Vmax are high ( the contour in Figure 5C ) . In this part of the parameter space , most immune responses are escaped in the acute phase ( cf . the solid graph in Figure 5B ) . Rapid escape causes variation in the number of deleterious mutations in the transmitted virus , because the size of the binding repertoire ( k ) varies among individuals . However , when , not all deleterious mutations can be reverted in the acute phase ( cf . the dashed graph in Figure 5B ) . For high Vmax , the asymptomatic phase is short , resulting in few reversions during this phase and a ‘footprint’ of the transmitting host's immune responses on the receiving host's spVL [11] . Notice that the second mechanism does depend on the reasonable assumption that reversion is a slower process than escape ( and , not shown ) , and that the size of the binding repertoire ( k ) differs between individuals . As the above evidence for the second mechanism—or ‘footprint effect’ as we like to call it—is only circumstantial , a quantification of this mechanism is needed . To quantify the footprint effect we analyze the simulations using a structural equation model ( SEM ) . The model estimates heritability , and takes the fitness costs ( m = e+f ) and breadth of the immune response ( k ) into account . Heritability of spVL is the sum of two effects; one mediated by viral fitness , and the other by the breadth of the immune response of the transmitting host . Figure 7 shows a graphical representation of the model , and details of the analyses are given in the Methods section . For realistic parameter values , approximately half of the observed heritability is due to the footprint effect ( Figure 5C ) . When we lower the rate of escape , the footprint effect , and therefore also the heritability , decreases . On the contrary , when within-host evolution is extremely fast , almost all of the heritability is due to the footprint effect , although the total heritability decreases . Our model predicts that heritability of the set-point virus load and host-heterogeneity are related . When within-host evolution is fast enough , approximately half of the observed heritability may be explained by the immunological footprint . Also , when we lower heterogeneity in our model , heritability decreases . An intuitive measure for heterogeneity in the host population is the expected similarity of hosts’ binding repertoires . This tells us how much adaptation to one host remains beneficial in the next . As a measure of the similarity of two binding repertoires K1 and K2 ( of size k1 and k2 , respectively ) we use the Jaccard index , the overlap between binding repertoires , divided by the the number of ( wild-type ) epitopes that at least one of the hosts can recognize . Figure 8A shows the relation between the expected similarity between hosts ( ) and the heritability of the set-point ( h2 ) . We modulated heterogeneity by varying n , the total number of potential epitopes , between 30 and 300 , corresponding to low and high host-heterogeneity , respectively . The mutation rate equals 3y−1 , such that the number of escape mutations during the acute phase lies within a realistic range . The figure shows that heritability indeed decreases when the population becomes more homogeneous , which indicates that high heritability relies on host-heterogeneity . HLA-heterogeneity differs between human populations . If our model prediction holds , then this variation could affect the heritability of the set-point measured in these populations . An unpublished study by Hodcroft et al . [44] , shows that heritability in measured for HIV-1 clade C in a Sub-Saharan African population is higher than heritability for HIV-1 clade B in a European cohort [10] ( 30% vs . 5 . 7% ) . Keeping our model in mind , we are able to understand this , if the European population with respect to clade B , is less heterogeneous than the Sub-Saharan population with respect to clade C . Using the peptide-MHC binding predictor NetMHCpan [45] , we compared the two populations and circulating viruses ( see Methods ) . Again , we measured heterogeneity in terms of similarity between binding repertoires . We sampled from the HLA-haplotype distributions of the European and Sub-Saharan populations , and calculated how similarity ( again measured in terms of the Jaccard index ) within these populations is distributed . Figure 8B shows two of these distributions . The black bars correspond to the European population , and the gray bars to the Sub-Saharan population . Although small , these populations do show a difference in heterogeneity: The Sub-Saharan population is more heterogeneous than the European population , as European binding repertoires tend to be more similar . The difference in heterogeneity is statistically significant ( see Methods and Figure 8C ) .
In this paper , we model HIV-1 transmission and within-host adaptation by means of immune escape in a HLA-heterogeneous host population . In comparison to what data suggests , we do not find that HIV-1's life history trade-off determines or influences spVL evolution . For realistic mutation rates , the evolutionary outcome is mostly determined by within-host selection for escape and reversion . Without HLA-heterogeneity , viruses would evolve to be within-host optimal in every host . Due to HLA-polymorphism , however , deleterious mutations accumulate , and the environment changes at each transmission . This causes virulence to evolve to intermediate levels for most hosts . Incomplete adaptation at the individual level is not exploited by the virus in order to improve it's transmission potential . Although set-point virus loads are expected to be lower in a heterogeneous population , spVL evolution remains short-sighted . As we will point out below , our model is limited in the sense that we only incorporate immune escape and reversion as a means for within-host and between-host adaptation . Nevertheless , since population-level adaptation does occur when within-host adaptation is slow , the model's limitations do not necessarily revoke our conclusions . In our model , we do find that spVL is heritable , even when the mutation rate is high . spVL heritability is needed for between-host adaptation . However , for realistic mutation rates , high heritability , as measured using transmission couples , is over-estimated; it mostly results from a ‘footprint’ left by the transmitter's immune system on the receiver's spVL . This novel explanation calls the validity of the use of high heritability as support for between-host adaptation in question . During real HIV-1 infections , immune escape sometimes requires compensatory mutations . Such escape variants need more time to revert to the wild-type in hosts lacking the escaped CTL response [46] . Such a mechanism is not incorporated in our model , but is likely to cause even higher heritability compared to what we find . Given previous results on the effects of transmitted CTL escape mutations on a receiver's virus load [29] , [33] , and the sharing of HLA alleles [11] , [47] , we think the footprint effect provides a sound explanation for the experimentally observed high heritability of the set-point . Importantly , if this explanation were to be found true , and if spVL evolution and heritability are indeed strongly influenced by CTL escape , reversion and compensatory mutations , finding SNPs in HIV-1's genome that control spVL might be a fool's errand , unless this pursuit would be restricted to known CTL-epitope sites . Our claims concerning the footprint effect , and the dependency of heritability on host-heterogeneity are not just speculative . We show that the model can make testable predictions , and we give an example of how such a test can be performed , i . e . , by comparing host-heterogeneity in different human populations . In our example , we compared Sub-Saharan and European populations with respect to the viruses circulating in these populations , and showed that host-heterogeneity is higher in the African population , which is consistent with our novel explanation , and estimates of the heritability in these populations . Of course , we would not suggest that this isolated finding is evidence for the footprint effect , although we do want to stress that heritability estimates are expected to be correlated with host heterogeneity . Moreover , the heritability estimates that were used in this example were obtained using a phylogenetic analysis [10] , [44] , while our explanation only holds for studies that use transmission couples . In future work , we plan to investigate whether an immunological footprint can also affect heritability that has been estimated using phylogenies or pedigrees . Intuitively , the fact that within-host adaptation overrules between-host adaptation can be understood by considering that many viral generations separate the founding virus and a transmitted strain , while transmission only takes one generation . In the homogeneous model , this results in full within-host adaptation ( throughout the population all epitopes are escaped ) , except when within-host adaptation is extremely slow . This result was also shown recently by Lythgoe et al . [21] . The intuition mentioned above works best for homogeneous populations . Adaptations to a primary host are beneficial again in a secondary host , and if within-host adaptation is fast , this leads to population-wide within-host adaptation and not between-host adaptation . This part of the intuition fails for a heterogeneous host population , where within-host adaptations in the form of immune escapes , are most likely not beneficial in the next host . Therefore , one could argue that homogeneity obstructs between-host adaptation . Here , we attempt to remove that obstruction by adding host-heterogeneity to a multi-level HIV-1 model . We find that in a heterogeneous population , HIV-1 also fails to evolve a mean spVL that maximizes the transmission potential , as shown by our sensitivity analysis and controls . Of course , when we make within-host adaptation trivial by choosing a very low mutation rate , population-level adaptation occurs . Apparently , host-heterogeneity does not solve the within- versus between-host adaptation paradox . Our models tell us that within-host adaptation overrules between-host adaptation , and yet HIV-1 appears to have adapted with respect to the life history trade-off [17] , or at least is evolving its mean spVL towards the value that maximizes the transmission potential [48] . Several mechanisms that can serve as a solution for the paradoxical observation have been proposed [49]–[51] . One of these mechanisms is referred to as ‘store and retrieve’ [49] . It is hypothesized that latently infected memory CD4+ cells occasionally produce virus , and that these virions are preferentially transmitted . Preferential transmission is backed up by the observation that evolutionary rates are higher at the within-host than at the between-host level [52] , and recently by a very interesting study into HIV-1's transmission bottleneck [53] . However , transmission of CTL escape mutants within transmission couples [29] , and even the spread of CTL escape mutants through populations has been observed [38] , [54]–[57] . These observations indicates that ‘store and retrieve’ is not absolute , and in order for this mechanism to solve the paradox , we expect it to rely on getting the population-level evolutionary rate below a threshold; one which may not be reached . This premise could limit the robustness of the ‘store and retrieve’ model . Furthermore , when the population-level evolutionary rate is slowed down because of a mechanism like ‘store and retrieve’ , the rate of between-host adaptation is also decreased , which could conflict with the short time scales at which adaptation must have been taking place for HIV-1 [18] , [20] . Another possible mechanism is a heritable viral trait that influences spVL , but that is not under within-host selection . This was recently examined by Hool et al . [50] . An example of such a trait could be target cell activation rate [58] , [59] . In short , if a viral trait influences target cell activation , and a mutant strain manages to increase the activation rate , then this additional activation is a ‘common good’ for the entire within-host viral population ( activated cells produce more virions ) . Hence , the mutant does not have an advantage and will not be preferentially selected . Drift creates within-host variation in activation rate , and the transmission bottleneck leads to variation of the target cell activation rate at the population-level . This hypothesis could be challenged by other traits that affect spVL , since these may still be under within-host selection , and are likely to interfere with the within-host neutral one . We finish with a novel suggestion for solving the paradox , one which is based on our modeling formalism , and was recently also put forward by Fraser et al . [51] . One point of criticism on our model could be that we limit the evolutionary capabilities of our in-silico viruses . Strains can only evolve their number of deleterious mutations in order to approach population-level favorable spVLs . Unfortunately , in the current framework , it is not sensible to allow for mutations in other parameters , in particular Vmax , since then Vmax would only increase during within-host evolution , and hence , during the course of the epidemic . This is because we assume that no two strains simultaneously reside a single host , and that mutants with a higher fitness go to fixation rapidly . In reality , fixation of mutants within a host can take a considerable amount of time [60] . An obvious—but technically challenging—fix for this problem is to abandon the assumption that the within-host evolutionary dynamics is memoryless , and allow for multiple mutants to compete for fixation , i . e . , allow for clonal interference [61]–[65] . These mutants can then carry negative fitness effects ( e . g . , Vmax decreasing mutations ) along with beneficial escape mutations or reversions ( genetic hitchhiking ) . Additionally , mutants with a small Vmax increasing effect , but that are otherwise equal to the wild-type , may have a long fixation time and can easily be out-competed by , e . g . , escape mutants . This makes within-host Vmax evolution more selectively neutral , and hence more sensible in our model . In future work , we aim to test if these speculations are valid , and whether a more detailed within-host fitness and selection model can unify within-host evolution and population-level adaptation .
In general , whenever a new event E is created during the simulation at time t , the exact moment when E will take place is unknown . Therefore , we assign to E a threshold and a load . The threshold is sampled from some probability distribution with non-negative support and mean 1 . We first compute the waiting time , while conditioning on E being the first event to take place: Here , is the ‘rate’ or ‘hazard’ at which E takes place , which can depend on time . Notice that the ( conditional ) waiting time could be infinite ( e . g . , when the number of susceptibles equals zero , the first event to take place can never be a transmission ) . When we perform this computation for all future events E , we find the event F that must take place first , and also the time at which it takes place , i . e . , . We then perform the following steps: First , we update the time . Then , for all future events E , we update the load as follows: Finally , we let the event F act on the current state and remove F from our event list . For instance , if F happens to be a transmission event , we should initiate a new host and decrease the number of susceptible individuals . Additionally , new transmission events should be created for the transmitter and recipient . Hereafter , we re-compute the waiting times for all events E and repeat the above steps . In most cases , the computation of wE and the updating of αE is simple . For instance , if E is a reversion event and f>0 , then . For updating the load , we replace αE with . A transmission event requires more effort , because the rate of transmission varies during an individual's infectious lifetime . The model can now be described by specifying for the events E listed in Table 2 , their threshold-distribution , their ‘rates’ , and the precise actions on the state ( see Table 3 ) . In order to study the steady state of the above described model , we developed a faster and more accurate method . In deterministic ( e . g . ODE-based ) models with multiple viral strains , one can compute the next-generation matrix ( NGM ) , using the model's rate equations [67] . Given a ‘generation’ ( i . e . , a distribution of strains in a cohort of newly infected individuals ) , the NGM gives the ‘next generation’ after mutation and selection in a discrete generation-based model . The steady states of the original ( continuous time ) and generation-based model coincide . This steady state can be computed by finding the dominant eigenvector of the NGM . The dominant eigenvalue equals ( by definition ) [68] . Our model is not deterministic , but we can approximate the NGM using a Monte-Carlo method . We start with a virus that has m1 mutations . We then infect a large cohort ( of size N ) of individuals . These individuals may have different binding repertoires ( of diverse size k ) , so we first sample pairs ( e1 , f1 ) with and . Then we run a within-host simulation for each of the virus-host pairs . Finally , we sample strains ( e , f ) that would be transmitted by the hosts at the start of an epidemic , and we count the number of transmitted stains that have mutations . The vector with approximates the m1-th column of the NGM . If the sample size N is large enough , the dominant eigenvalue and corresponding right eigenvector of the matrix approximate , respectively , and the steady state distribution of prevalent viral strains in our agent-based model . By sampling strains from the steady-state distribution , and simulating infections with these strains , we can compute statistics as in equilibrium . This method is not based on formal arguments , but below we put forward some heuristic evidence for its correctness . For the statistic heritability ( h2 ) , the above scheme is insufficient . However , we do have a cohort of potential transmitters , and hence we can create transmission couples by first sampling transmitted strains from the cohorts’ individuals , and then infecting recipients . The statistic h2 is computed as the slope of the regression between the spVL of transmitters and receivers . Classically , heritability of a trait x is defined as the proportion of variance in x that is caused by inherited genetic factors [see e . g . 18] . Hence , if we write , where is a genetic , and an environmental factor , then . The slope of the regression mentioned above is an estimator for this quantity , but only when the transmitted quantity in the recipient is independent of the the transmitter's environmental factor . Below we will see that such an independence assumption does not hold for our model , and that the use of transmission couples results in an over-estimate of spVL heritability . To quantify the effect of the immunological footprint on heritability , we use a structural equation model ( SEM ) , depicted as a directed , acyclic graph ( DAG ) in Figure 7 . In our model , the actual inherited quantity is the number of mutated peptides . During an infection this quantity can change due to escapes and reversions , so we will only incorporate the number of mutations at the moment of infection ( mtra for a transmitting host , and mrec for the corresponding receiver ) in our statistical model . The set-point virus load of the receiver ( spVLrec ) depends on mrec , and the breadth of the immune response against the wild-type virus ( krec ) . Of course , more factors determine the set-point virus load , such as the initial number of escape mutations , and stochastic effects such as mutations and progression to AIDS , but the simplified SEM only contains the variables spVL , m and k . Apart from ktra , the breadth of the transmitter's immune response and mtra , the set-point virus load of the transmitter ( spVLtra ) depends also on mrec . This is because the set-point is an average over the chronic phase , and hence , the transmitted virus co-determines the set-point of the transmitter . In Figure 7 , this is indicated by the arrow . During infection of the transmitter , the virus escapes a number of immune responses , and this number is dependent on ktra . This means that ktra influences the number of mutations of the transmitted virus mrec . This ‘immunological footprint’ is represented by the arrow in Figure 7 . The breadth of the immune response ktra has no direct effect on mtra , since mtra corresponds to the transmitter's founder virus . Likewise , there is no direct effect of krec on mrec . We use the the R package lavaan [69] to fit the model to ( standardized ) simulated data , that were produced using the NGM method and the standard model's parameters . As an example , the result of one of such fits is given in Figure 7 . In this graph , the numbers above the arrows indicate the estimated weights . The maximal virus load Vmax equals copies per ml , and the mutation rate equals 3y−1 , such that the mean set-point for this population is 4 . 51 log10ml−1 ( cf . Figure 4A ) . Despite the large sample size of 25690 transmission couples , and the fact that the SEM has 4 degrees of freedom , the model describes the data quite well ( the -test's p-value equals 0 . 81 , and the root mean square error of approximation ( RMSEA ) equals 0 with a 90% CI of [0 , 0 . 01] ) . In the context of our SEM , the statistic h2 equals the correlation between spVLrec and spVLtra . This correlation can be computed as the sum of the contributions of all paths that connect spVLtra with spVLrec . The contribution of each path equals the product of the coefficients along the path . The 3 paths that connect spVLtra with spVLrec are: where P3 is responsible for the immunological footprint . In the example of Figure 7 , the contribution of P3 equals 0 . 082 , which is about half ( 49 . 7% ) of the total correlation between spVLrec and spVLtra ( i . e . , of the heritability ) . We refer to the contribution of the path P3 as the “contribution of the immunological footprint to heritability” . We downloaded representative sequences for clades B and C from LANL's HIV sequence database ( www . hiv . lanl . gov; four sequences for each clade , as described in [70] ) . Then , we downloaded the HLA-A and HLA-B distributions for Europe and Sub-Saharan Africa from the NCBI database dbMHC ( www . ncbi . nlm . nih . gov/projects/gv/mhc , [71] ) . Using the MHC binding predictor NetMHCpan ( version 2 . 4 [45] ) , we computed binding affinities of all 9-mers from the representative strains for the most common HLA alleles ( covering 95% of the populations ) . For each HLA molecule , the binding threshold was chosen such that the top 1% of a set of 105 naturally occurring peptides would be considered a binder ( as described in [72] ) . For our analysis , we sample pairs of HLA haplotypes from the HLA distributions of one of the populations ( ignoring linkage disequilibrium ) , each haplotype consisting of two HLA-A alleles and two HLA-B alleles . For each two haplotypes , we then compare the similarity of the binding repertoires with respect to one of the four representative strains . As a measure of similarity , we use the Jaccard index ( J ) : the size of the intersection , divided by the size of the union of the two binding repertoires . This gives us the distribution of similarity scores of a population with respect to a strain . Figure 8B depicts two of these distributions . The black bars correspond to the European population with respect to a clade B virus , and the gray bars to the Sub-Saharan population with respect to a clade C virus . By comparing the similarity distributions of a Sub-Saharan with a European population ( Figure 8B ) , we can assess the difference in heterogeneity between the two populations and clades . The right panel of Figure 8C depicts the medians ( one value for each representative strain ) . The European medians are significantly higher than the Sub-Saharan medians . For a better comparison between two distributions , we use a U-statistic , defined as , where Jeur and Jafr are distributed as the European and Sub-Saharan similarity distributions , respectively ( cf . the Mann-Whitney U-test ) . Hence , U equals the likelihood that a random haplotype pair in the European population shows more similarity than a random pair in the Sub-Saharan population . We have four clade B strains and four clade C strains , and hence we can compute 16 probabilities U ( Figure 8C , right panel ) . They turn out to be significantly higher than 0 . 5 , meaning that the European population , subject to clade B strains , is less heterogeneous than the Sub-Saharan population and clade C strains . We model within-host escape and reversion with two Markov chains: Let and denote the probability at time t that during infection phase i the host is infected by a virus with e escape mutations and f deleterious mutations , respectively , given that phase i started with an -virus at time . These probabilities satisfy the Kolmogorov forward equations [see e . g . 73] . Closed-form expressions for and are given by The probability that the host is infected with an ( e , f ) -virus only makes sense if we condition on the infection still being in phase i . We want to get the expected number of transmitted virus of a specific type , and in order to make the calculations possible , we take exponential distributions for the length of the phases . We tested that this assumption is not crucial by considering Erlang distributions . The rate at which phase i ends is given by . We also assume that mutation during the asymptomatic phase is slow and that the spVL is determined by the virus at the end of the acute phase ( which is of type ) . This means that and can be kept constant . Furthermore , the fraction of susceptibles ( ) can be kept constant , either because the population is in a steady state , or because the epidemic has just started ( ) . Consider the probability generating function [cf . 74] for the number of transmitted virus of type during phase : Assuming that , we can write this integral in terms of the Beta function ( ) . First we substitute the above given expressions for and and when we now assume that , we can get which equals by definition . Since one of the arguments in this Beta function is an integer , the function is rational: where we use the ( rising ) Pochhammer symbol . Now that we have this expression for , we can exploit the probability generating function's useful properties . The number equals the probability that at the end of phase , the host is infected with an -virus . The expected number of transmitted -strains during phase equals . We use the following notation: If we now take into account that a transmitted -virus has a different phenotype in the receiver ( with probability given by the hypergeometric distribution ) , we can find the NGM for the case . We verified that for this part of the parameter space ( i . e . , ) , the deterministic and stochastic computation give the same results ( not shown ) . When the host population is homogeneous ( ) , we find a threshold in the parameter space across which between-host adaptation is no longer possible . Here we will make this precise and show that this threshold is caused by a transcritical bifurcation . In a homogeneous population , we lose deleterious mutations . In the above introduced notation , we may ignore and we write for instance . Let denote the NGM , then we get the following formula in terms of and : The matrix is triangular , since the number of escape mutations , which equals the total number of mutations , can only grow during an infection . The diagonal elements of are the eigenvalues of , and the dominant eigenvalue equals ( by definition ) of the quasi-species . The diagonal elements can be written as If is dominant , then population-level evolution will result in strains that have escaped all CTL responses . If another eigenvalue with is dominant , then not all viruses have escaped all CTL responses and this is due to selection for transmission on the population-level . If we now fix and let approach from the right , then for high the eigenvalue is dominant . The mentioned bifurcation occurs when equals one of the ( with ) for the first time . We first give simple expressions for and that occur in the expression for : These expressions and the formula for enable us to ( numerically ) find the curves for . These curves and the resulting threshold are shown in Figure 6 . | HIV-1 is a relatively young virus , being introduced in the human population somewhere between 1884 and 1924 . Yet , previous studies suggest that the virus has already evolved to be efficiently transmitted among humans . Efficient transmission occurs when the set-point virus load , the semi-stable number of virus particles in the blood during the asymptomatic phase , is intermediate ( approximately particles/ml ) . At this virus load level , individuals remain asymptomatic for a long period ( 7 . 0 years on average ) , and still remain sufficiently infectious . In this study , we model the combined immunological and epidemiological dynamics of HIV-1 to explore whether population-level adaptation is feasible . We show that strong selective forces within the host are expected to dominate the much weaker population-level selection , unless the within-host dynamics of immune escape becomes exceedingly slow . Surprisingly , our analyses yield high levels of set-point virus load heritability , as observed in human populations . In the model , heritability of set-point virus load partially results from an immunological ‘footprint’ of the host-virus interaction in transmitting hosts , affecting the receiving hosts' virus load . | [
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| 2014 | Immuno-epidemiological Modeling of HIV-1 Predicts High Heritability of the Set-Point Virus Load, while Selection for CTL Escape Dominates Virulence Evolution |
A major goal of genetics is to define the relationship between phenotype and genotype , while a major goal of ecology is to identify the rules that govern community assembly . Achieving these goals by analyzing natural systems can be difficult , as selective pressures create dynamic fitness landscapes that vary in both space and time . Laboratory experimental evolution offers the benefit of controlling variables that shape fitness landscapes , helping to achieve both goals . We previously showed that a clonal population of E . coli experimentally evolved under continuous glucose limitation gives rise to a genetically diverse community consisting of one clone , CV103 , that best scavenges but incompletely utilizes the limiting resource , and others , CV101 and CV116 , that consume its overflow metabolites . Because this community can be disassembled and reassembled , and involves cooperative interactions that are stable over time , its genetic diversity is sustained by clonal reinforcement rather than by clonal interference . To understand the genetic factors that produce this outcome , and to illuminate the community's underlying physiology , we sequenced the genomes of ancestral and evolved clones . We identified ancestral mutations in intermediary metabolism that may have predisposed the evolution of metabolic interdependence . Phylogenetic reconstruction indicates that the lineages that gave rise to this community diverged early , as CV103 shares only one Single Nucleotide Polymorphism with the other evolved clones . Underlying CV103's phenotype we identified a set of mutations that likely enhance glucose scavenging and maintain redox balance , but may do so at the expense of carbon excreted in overflow metabolites . Because these overflow metabolites serve as growth substrates that are differentially accessible to the other community members , and because the scavenging lineage shares only one SNP with these other clones , we conclude that this lineage likely served as an “engine” generating diversity by creating new metabolic niches , but not the occupants themselves .
Illuminating the laws that produce Darwin's “tangled bank” remains one of the great challenges of biology , one that requires understanding how differences among forms are selected for , and how interdependence among forms is generated . In natural environments , meeting this challenge is complicated by the fact that selection pressures often vary widely over both space and time . Laboratory evolution experiments , in particular those using microbes , offer an attractive alternative by which to study , under controlled conditions , both the dynamic interplay between genotype and phenotype as well as the interactions among phenotypes in simple communities . Classical models of large asexually evolving populations led to the expectation that in simple environments complexity should be transient and limited in scope [1] , [2] . Experimental evidence now suggests otherwise . Multiple genotypes that arise from a single ancestral clone can coexist over evolutionary time; in other words , ex uno plures ( out of one many ) . This phenomenon has been documented in spatially and temporally unstructured chemostats [3] , [4] , in temporally-structured batch cultures [5]–[9] and in spatially-structured microcosms [10] . In each setting , the emergence and persistence of polymorphism in the absence of sexual recombination requires that cohabitants opportunistically exploit unoccupied niche space , and/or accept trade-offs between being a specialist and a generalist [11]–[13] . In serial dilution batch culture , multiple growth parameters can come under selection [14]: different clones may arise having reduced lag time , increased maximum specific growth rate , or enhanced capacity to survive at high cell densities in the presence of low nutrients [15] . Periodic changes in population density and nutrient levels may bring balancing selection to bear on these different phenotypes ( e . g . , [8] ) , especially if mutations are antagonistically pleiotropic [16] , [17] . In spatially structured environments selection may favor mutants better adapted to particular regions or better able to colonize microhabitats formed at the boundaries between such regions . By contrast , in continuous nutrient-limited environments , theory predicts that selection will favor clones better able to scavenge the limiting resource or more efficiently convert that resource to progeny [18] , [19] . Ultimately , the outcome of the ‘evolutionary play’ in any of these ‘ecological theaters’ ( sensu [20] ) will depend on founder genotype , mutation rate , the complexity of genetic pathways leading to different adaptive strategies , as well as pleiotropy [21] and epistatic interactions [22] . Increasing evidence points to the possibility of not one , but three potential outcomes when asexual microbes evolve in simple environments: clonal succession , where a population is successively swept by clones of higher fitness arising in the dominant lineage [23] , [24]; clonal interference , where fixation of a single fittest clone is deferred because independent beneficial mutations arise in multiple , independent clones that compete with one another and reduce each other's fitness [25]–[27] , and what we propose to call clonal reinforcement , where the emergence of one genotype favors the emergence and persistence of other genotypes via cooperative interactions . Because cooperation is now recognized to be at least as important as competition in driving biological innovation and in structuring communities [28] , [29] , we investigated the genetic and environmental factors that foster interdependence in an evolving lab population . Previously [30] , we examined the transcriptomes of an experimentally evolved E . coli community in which three different strains that arose from a common ancestor stably coexisted in a simple , unstructured environment through metabolic cross-feeding [3] , [31] . This system is unusual in that it clearly involves symbiotic interactions among ecotypes that co-evolved in the absence of spatial or temporal variation . Remarkably , the community's expression profile did not proportionately represent the sum of each strain's expression profile grown singly under evolutionary conditions , indicating that the whole was not the sum of its parts . Instead , expression data suggested that consumption of one ecotype's overflow metabolites by the others relieved feedback inhibition , adding a layer of complexity to the evolved clones' interactions . Using a candidate gene approach we identified two mutations in the ancestral strain that may have predisposed clonal reinforcement [30] . To better understand how physiological performance and expression profile map onto genotype , and to identify the molecular basis of community interactions we sequenced the genomes of each of the three community members and their common ancestor . We found unexpectedly high levels of genetic variation in the three-membered community as a whole and the numerically dominant strain in particular , as well as a strong mutational bias due to specific lesions in DNA repair . The dominant community member is a hypermutator that excels at acquiring glucose , but does so at the expense of carbon excreted as overflow metabolites to which it has limited access; this trade-off effectively opens up new niches for other genotypes . We discovered a set of adaptive mutations in this clone that have not been previously reported to co-occur in other E . coli evolution experiments , specifically those that enhance glucose acquisition and may serve to maintain redox balance , but may also increase net flux to overflow metabolites under aerobic conditions . Because this community member releases growth substrates that are differentially accessible to other community members , and because it shares only one SNP mutation with these strains , we suggest that it acts as an engine generating biodiversity , creating new metabolic niches , but not necessarily the occupants themselves .
In previous publications [3] , [30] , [31] we described certain features of a stable polymorphism that arose from a single clone during the course of 765 generations of aerobic , glucose-limited culture at 30°C ( D≈0 . 20 hr−1 ) ( Table 1 ) . The four clones isolated from this population ( CV101 , C103 , CV115 and CV116 ) were originally distinguished by their differences in antibiotic resistance and colony morphology , and later shown to exhibit strain-specific rates of glucose uptake , residual metabolite concentrations and expression profiles . In reconstruction experiments performed under evolutionary conditions CV101 , CV103 and CV116 were shown to stably coexist at frequencies of approximately 0 . 10 , 0 . 65 and 0 . 025 , respectively . Because CV115 was not stably maintained , the interactions among CV101 , CV103 and CV116 were studied more intensively . Based on differences in glucose uptake and residual substrate concentrations , and the fact that strains' steady state frequencies could be predictably altered by exogenously increasing the concentrations of residual metabolites , we concluded that this community was sustained by positive density-dependent interactions in the form of cross-feeding , in which the numerically dominant strain CV103 best takes up the limiting resource glucose , but excretes acetate and glycerol ( and/or a closely-related compound , glycerol 3-phosphate ) ( J . Adams unpublished results ) . These two overflow metabolites are then scavenged by CV101 and CV116 , respectively . Transcriptional profiling of each clone in monoculture relative to the common ancestor , JA122 , revealed gene expression differences among the evolved isolates related to carbon metabolism and expression differences specific to CV103 consistent with activation of stress response pathways and loss of motility . As summarized in Table 2 , 584 mutations ( 580 SNPs , two insertions and two deletions ) were identified among the four evolved clones . This number is substantially higher than has been previously reported for laboratory evolution experiments of similar duration conducted under similar conditions [32] . A majority of these mutations , 504 ( 86% ) , is in coding regions and 374 ( 64% ) are non-synonymous , while 428 ( 73% ) , are unique to CV103 . Almost all ( 99 . 7% ( 578 ) ) SNPs are GC→TA transversions , suggesting defects in DNA repair among all of the evolved isolates . Strong transversion bias has been noted in other evolution experiments , and is likely due to defective repair of oxidatively damaged G:C base-pairs [32] , [33] . Phylogenetic analyses of the whole genome sequences [30] show that the dominant clone CV103 is part of a highly divergent lineage , while CV115 and CV116 are very closely related to one another , and share a common ancestor with CV101 ( Fig . 1 ) . Indeed , as CV115 and CV116 differ by only two SNPs , one a synonymous substitution ( gutQ , A221A ) , the other C-terminal ( ycaO G546V ) , and because only CV101 , CV103 and CV116 could be stably maintained at steady state , we restrict most of our discussion to these three clones . Remarkably , the evolved clones all share only one SNP . Thus , it is likely that the CV103 and CV101/CV115/CV116 lineages diverged from one another early in the experiment and that stable co-existence of more than one lineage was an early feature of the population . In fact , previous work documented a variant similar to CV103 ( small colony , ampicillin-resistant ) had risen to appreciable frequency by generation 340 [3] . This result is not unexpected , as subsequent to the observations of Helling et al . 1987 , a number of other reports appeared showing that multiple genotypes can coexist in nutrient-limited chemostats [34]–[38] . An unanticipated finding in our experiments was the large number of SNPs that accumulated along the CV103 branch . Fluctuation analyses performed on the ancestral strain , JA122 , the canonical E . coli strain K12 MG1655 , and the four co-evolved clones revealed that the founder strain , JA122 , is a mutator whose mutation rate is approximately thirty-fold higher than E . coli K12 MG1655 ( Table 1 ) . This observation can be explained by a nonsense mutation in JA122 affecting the adenine glycosylase mismatch repair enzyme MutY ( L299* ) . Defects in MutY are known to cause a GC→TA transversion bias [33] , [39] and have been observed by others in glucose limited chemostat experiments [40] , [41] . Evolved strains CV101 , CV115 and CV116 all had mutation rates similar to JA122 , while CV103's mutation rate was almost 10-fold higher again ( Table 1 ) . This difference can be attributed to a second mutation resulting in an amino acid substitution ( A56D ) in the base-excision repair pathway DNA glycosylase , MutM . Increased mutation rate has been shown to be selected for in microbial evolution experiments performed under a variety of conditions [33] , [41]–[45] where it can strongly influence evolutionary dynamics [46] . The fitness of mutators need not decrease over time; Maharjan et al . [47] recently showed that mutators arising under glucose limitation gain fitness relative to their ancestor as a result of adaptive mutations in , for example , rpoS and mglD . Mutator lineages persisted because most of the other mutations accumulating in their backgrounds were neutral in their effect on fitness , deleterious mutations being purged as the lineages evolved . A similar mechanism may be at work in our experiment , with an appreciable number of mutations in CV103 being neutral . Given the large number of SNPs , we sought to determine whether certain genes or transcription units carried multiple mutations , either because they had had been selected for or because their inactivation allowed neutral mutations to accumulate . Overall , 52 transcription units and 37 individual genes contained more than one SNP ( Table S1 ) . Twenty-five of the single/multiple gene hits occur exclusively in strain CV103 ( Table S1 ) . Four of these ( eno , maeA , malG and ptsI ) are directly involved in glucose uptake/metabolism suggesting that they contribute to the superior glucose uptake kinetics of this clone . Five CV103-specific substitutions affect flagellar synthesis: three SNPs occur in fliM , which encodes a flagellar motor switch protein , one occurs in fliF , which encodes the flagellar M ring protein , and one occurs in fliH , which encodes another flagellar biosynthesis protein . These findings are consistent with the previously observed down-regulation of flagellar genes in CV103 and the fact this strain is non-motile [30] . When all of the evolved isolates are considered , it is perhaps surprising that CV116 , thought to salvage excreted glycerol/glycerol phosphate from CV103 , has a number of mutations ( 10 ) in transcription units that are also mutated in CV103 ( Table S1 ) . The same is not true for CV101 , the strain that salvages overflow acetate ( Table S1 ) . While most of these SNPs are silent mutations , they could plausibly affect RNA stability or translation . Of the non-silent changes , particularly noteworthy are four mutations ( three in CV103 and one in CV116 ) that affect NADH:ubiquinone oxidoreductase I , as well as additional mutations affecting genes involved in anaerobic formate production and electron transfer ( Table S1 ) . Because both CV103 and CV116 show enhanced glucose uptake relative to CV101 and the ancestral strain ( Table 1 and [3] ) , these changes may be shared features of an adaptive response to rapid glucose consumption and concomitant overproduction of NADH ( discussed below ) . Because we previously observed significant differences in gene expression among evolved isolates grown in monoculture [30] , we sought to identify SNPs that could explain these differences . Because the great majority of these transcriptional differences were manifest in CV103 , we focused on genes that could directly or indirectly regulate loci differentially expressed in this strain and on the 428 genes associated with a SNP in CV103 . The intersection of these two gene lists yielded three global regulators that could explain the majority of transcriptional differences between CV103 and the other evolved isolates: rpoD , csrA and sdiA . RpoD , the housekeeping sigma factor , controls transcription of over 2 , 300 genes . The ancestor of the four evolved isolates , JA122 , has an amber nonsense mutation ( E26* , GAG→TAG ) in rpoD that likely leads to reduced translation of RpoD , but not necessarily its complete absence , as JA122 also carries the supE44 amber suppressor . CV103 has an additional silent mutation in rpoD ( T459T , ACC→ACA ) that does not affect protein sequence but may affect translation , as the mutant codon ACA is 30% less common than the wild-type codon ACC [48] . The regulatory protein CsrA favors gluconeogenesis and glycogen synthesis over glycolysis . While the CV103 mutation in csrA is also silent ( G27G , GGC→GGA ) , the resulting codon change is from one that is common ( f = 0 . 40 ) to one that is rare ( f = 0 . 09 ) . Finally , sdiA encodes an N-acylhomoserine-L-lactone receptor that functions in quorum sensing . In CV103 , a SNP is located 27 base-pairs upstream of the sdiA transcriptional start site in a region that encodes a small RNA , RNA0-361 . The function of this sRNA is unknown , but it has been repeatedly identified in screens for small RNAs that interact with the global RNA chaperone , Hfq [49] , [50] , in which CV103 has a missense mutation ( see below ) . ( Fig . 2 ) In earlier work we showed that all evolved isolates were better able to scavenge limiting glucose than their common ancestor [31] , and that strain CV103 had a significantly higher rate of glucose uptake than the other evolved isolates [3] . We later attributed these observations to increased expression of LamB glycoporin in all four strains relative to JA122 [51] , and significantly higher transcription of lamB in CV103 relative to CV101 , CV115 and CV116 [30] . In other studies of adaptation under glucose limitation elevated lamB expression has repeatedly been tied to mutations that affect the regulation of the Mal operon , specifically mutations that affect expression of the lamB regulators Mlc and/or MalT [35] , [51] , [52] . While we previously found that CV101 , CV115 and CV116 share a mutation in malT that could up-regulate lamB expression , this mutation did not occur in CV103 [30] , the strain that best scavenges limiting glucose . Whole genome sequencing uncovered two other mutations in CV103 that help explain higher transcript levels of lamB mRNA and , by extension , its superior ability to scavenge limiting glucose . The first mutation affects the gene for MalK , a negative regulator of MalT [53] . Null mutations in malK promote constitutive mal/lamB gene expression [54] , [55] , presumably by disrupting the MalK-MalT interaction . The malK SNP in CV103 causes an amino acid change ( D297E ) in the C-terminal portion of the protein that contains an important part of the MalT interaction domain . Replacement of aa 297 is known to diminish the ability of MalK to inhibit MalT [56] , and thus the D297E SNP likely leads to increased lamB transcription during glucose-limited growth [57]–[60] . The second CV103 mutation that may influence lamB expression occurs in the gene for the RNA chaperone Hfq . Hfq is a global regulator that facilitates binding of small regulatory RNAs ( sRNAs ) to mRNAs and by doing so affects translation and/or degradation of those transcripts [61] , [62] . Details are scant as to how most of these sRNAs regulate their targets and how Hfq enhances or attenuates their activity , but the profound effect these interactions can have on key cellular processes is being increasingly recognized [61] , [63] . Hfq interacts with the mRNA of the E . coli stationary phase sigma factor , RpoS , and is required for efficient translation of RpoS mRNA [64] . Reduced RpoS expression has been extensively studied as a key adaptation to continuous glucose-limitation [40] , [65] , [66] , and Hfq has recently been identified as an alternative mutational target under glucose limitation in rpoS+ strains [67] , [68] . In these isolates , a missense mutation in Hfq enhances glucose uptake via PtsG , increases levels of LamB , and apparently reduces the amount of functional RpoS , resulting in lower biomass yield . The hfq mutation in CV103 , which results in a Q52H substitution , could have similar effects: CV103 has increased uptake of the glucose analog α-MG ( Table 1 ) , which occurs exclusively via PtsG , increased LamB gene expression , and decreased biomass yield relative to the other evolved clones [3] , [30] . Interestingly , the genomic context of the Hfq mutation in CV103 differs from those characterized by others in which defects in Hfq have evolved in an rpoS deficient background [67] , [68] . CV103 and our other evolved isolates share an ancestral rpoS ( Am ) mutation which likely allows some RpoS translation . ( Fig . 2 ) After entry into the periplasm , glucose is actively transported across the inner membrane using the glucose-specific sugar phosphotransferase system ( PTS ) . Mutations that upregulate an alternative high-affinity glucose transporter , the galactose transporter MglBAC , are frequently observed following prolonged growth under glucose limitation [35] , [51] , [52]; in this regard , our system is no exception . In fact , the only SNP shared by all of the evolved isolates is in the operator sequence for the MglD repressor ( mglO , C→A , +3 bp relative to the end of mglD ) , consistent with increased expression of the mglBAC transcription unit [30] , [35] . Because the rate of glucose uptake in CV103 exceeds that of the other clones , we looked for mutations that might affect expression or activity of other inner-membrane glucose transporters . CV103 has two mutations in ptsI , which encodes enzyme I of the E . coli sugar phosphotransferase system . This protein is active in its dimeric form and participates in glucose uptake by accepting a phosphate group from phophoenolpyruvate ( PEP ) and passing it via phosphocarrier protein Hpr to sugar-specific enzymes such as PtsG , which then use phosphate to “charge” incoming sugars ( Fig . 2 and 3 ) [69] , [70] . The mutations in ptsI result in two amino acid substitutions ( A328S , M518I ) , both of which occur in the C-terminal region responsible for binding PEP and dimerization . These may be reasonably expected to alter PtsI activity [71] , [72]; aa 328 is close to two residues that are part of the PEP binding site ( R332 and D335 ) , while aa 518 is close to C502 , which is required for phosphotransfer [73] , [74] . When ∼PO4 is not passed along by PtsI , there can be regulatory consequences: unphosphorylated PtsG can bind the transcriptional regulator Mlc and prevent it from negatively regulating expression of its targets which include ptsG itself , manXYZ and malT [75]–[77] . Thus , these mutations could also explain the up-regulation of manXYZ in CV103 , and lead to higher expression of the LamB glycoporin though derepression of malT transcription [78] . PtsI has also been shown to interact directly with other phospho-enzymes , notably acetate kinase ( ackA ) , thus mutations in this protein may also affect acetate excretion [79] . Although glucose transport is increased in all evolved isolates relative to their common ancestor , and especially so in CV103 , expression of many glycolytic genes is decreased ( Fig . 4 ) . This trend has also been observed in yeast evolution experiments carried out under glucose limitation , and may reflect the selective advantage of an energy conservation strategy under low nutrient conditions [80] . Also , the hypermutator lineage that gave rise to CV103 accumulated a number of mutations predicted to impact conversions in glycolysis , fermentation , and the TCA cycle . Certain of these mutations likely underlie CV103's superior glucose uptake kinetics , but some may also favor excretion of and restricted access to overflow metabolites , which opens up new niches for other genotypes . In CV103 , transcript levels of pfkB , which encodes PfkII , a secondary enzyme that converts fructose-6-phosphate into fructose-1 , 6 , -bisphosphate , is more highly expressed in CV103 than in the other strains; CV103 also carries a missense mutation in this gene ( Q201H ) ( Fig . 4 ) . It is unclear whether this mutation is beneficial , as PfkII is thought to be responsible for less 5% of the phosphofructokinase activity in E . coli [81] . However , PfkII can also use tagatose-6-phosphate as a substrate [82] . CV103 has a surprising number of differences compared to the other clones in the expression and sequence of other genes in the galactitol/tagatose-6-P glycolytic pathway , including downregulation of gatZY ( encoding tagatose-1 , 6-bisphosphate aldolase 2 ) and gatABC ( galactitol PTS permease ) as well as a mutation in the gene for GatY ( G49* ) ( Table S2 ) . As in a number of other K12-derived strains , the gat operon is likely constitutively expressed in JA122 due to IS3E element insertion in the galactitol regulator ( GatR ) gene [83] . Interestingly , increased expression of gat genes has been observed in experiments where E . coli has been evolved under lactulose and/or methyl-galactoside limitation [12] , [84] , [85] . Enolase , responsible for the conversion of 2-phosphoglycerate into phosphoenolpyruvate ( PEP ) , has two mutations in CV103 , one silent and one missense ( L60L , A37S ) ( Fig . 4 and Table S2 ) . Aside from the regulatory role played by its product , PEP , enolase participates in the degradation of certain RNAs as part of the degradosome [86] . In particular , enolase is needed to degrade ptsG mRNA when intracellular levels of G-6-P are high ( i . e . , during phosphosugar stress ) [86] , [87] . This interaction also involves the sRNA SgrS , Hfq and Pnp ( polynucleotide phosphorylase ) , in which CV103 also has a substitution ( P104Q ) [50] , [87]–[89] ( Table S2 ) . ptsG mRNA is not degraded when Hfq is mutated [90] , thus in CV103 the combined action of mutations in enolase , Pnp and Hfq may increase longevity of ptsG transcripts . Pyruvate is a major metabolite that sits at the branch point between glycolysis , the TCA cycle and fermentation ( Fig . 4 ) . Measurements of intracellular pyruvate indicated that all of the evolved isolates had significantly less intracellular pyruvate than their common ancestor under glucose-limited conditions , although no significant differences in pyruvate concentration could be detected among the evolved strains ( Table S3 ) . The primary route for oxidation of pyruvate during aerobic growth is pyruvate dehydrogenase ( PDH ) , a three-enzyme complex that catalyzes the conversion of pyruvate into acetyl-CoA and contributes to the redox burden by transferring electrons to NAD+ ( Fig . 4 and 5 ) . Protein profiling of CV103 suggested the presence of a mutation in one of the three PDH enzymes , lpd , severe enough to eliminate the corresponding spot on a 2D gel [51] . Whole genome sequencing confirmed a missense mutation in the CV103 lpd gene that results in an amino acid substitution ( F76L ) in the N-terminal portion of the translated protein ( Table S2 ) . Because this substitution occurs in the FAD binding domain , it likely affects electron transfer from the reduced co-factor FADH2 to NAD+ . PDH specific activity in CV103 was previously shown to be 2–3 fold lower than that in the other strains , indicating that this mutation does indeed have a negative effect [51] . Loss of lpd activity can lead to phenotypic changes consistent with many of the unique characteristics of CV103 . In a screen for E . coli knockouts with extended lifespans , lpd null mutants were identified that had extended survival compared to wild-type E . coli K-12 MG1655 [91] . This enhanced survival was accompanied by reduced growth rate , reduced stationary phase cell density , reduced oxygen consumption , reduced respiration and increased accumulation of extracellular acetate , many of which phenotypes are exhibited by CV103 ( Table 1 , [3] ) . Given that CV103 rapidly consumes glucose while excreting acetate , it seems unusual that a mutation affecting the conversion of pyruvate into acetyl-CoA would be retained in this strain . An lpd knockout can still grow on glucose and produce acetate , but it does so more slowly by using an alternate route to acetate , pyruvate oxidase ( PoxB ) [92] . In an effort to determine whether CV103 might be using an alternate enzymatic pathway to convert pyruvate into acetate , we compared gene expression and sequence data for the three alternative pyruvate oxidation pathways: pyruvate oxidase ( POX ) , pyruvate:flavodoxin oxidoreductase ( PFOR ) and pyruvate formate-lyase ( PFL ) ( Fig . 5 ) . The POX pathway directly converts pyruvate into acetate without concomitant ATP generation ( Fig . 5 ) . Nevertheless , this pathway is active during growth on glucose and can substitute for PDH if highly expressed [93] . In our system , expression of the gene for POX ( poxB ) is downregulated in all four evolved isolates relative to their common ancestor . This is not unexpected , as other glycolytic genes are downregulated . However , CV103 has a nonsense mutation at amino acid 14 of poxB ( E14* ) which , given its position at the extreme N-terminus , is likely to completely inactivate the protein . While the ancestral supE44 amber suppressor might allow limited poxB translation to occur , it is unlikely that the POX pathway produces an appreciable amount of acetate in CV103 . A second gene involved in acetate excretion , ydbK is expressed under both anaerobic and aerobic ( albeit at very low levels ) conditions ( Fig . 5 ) , [94] , [95] . ydbK encodes PFOR , which catalyzes the conversion of pyruvate into acetyl-CoA with concomitant reduction of flavodoxin or ferredoxin [96] . Interestingly , both of these reduced molecules can be used to re-activate oxygen sensitive PFL ( see below ) [94] . CV103 carries a missense mutation of unknown effect ( R539L ) in ydbK; however , because this ORF was not represented on our expression array , its transcription level is unknown . Finally , acetyl Co-A can also be produced by the cleavage of pyruvate by pyruvate formate-lyase ( PFL ) . PFL activity is primarily associated with the pflB gene but is also encoded by tdcE , ybiW and pflD [97]–[99] . PFL is oxygen sensitive and thus the primary route to acetyl-CoA under anaerobic conditions , but it is transcribed , active and useful during microaerobiosis [100]–[103] . Moreover , production of PFL requires a smaller anabolic investment than production of PDH , and may thus be preferred under conditions of nutrient stress [104] . Functional PFL requires both transcriptional ( regulated by FNR and ArcA/B ) and post-translational activation by the activating enzyme PflA or the alternate activator YfiD [103] , [105] . Consistent with the downregulation of many glycolytic genes , transcript levels of both the primary PFL ( pflB ) and its activator ( pflA ) are also downregulated across all of the evolved strains relative to the ancestor JA122 . The gene for pflB is unchanged , and pflA has a silent substitution in CV103 ( L205L ) . By contrast , transcript levels of the three alternate PFLs ( tdcE , ybiW , pflD ) and their alternate activator yfiD are not altered in any of the evolved strains relative to JA122 , though CV103 has SNPs in the genes that encode two of the alternate PFLs: YbiW ( A469S ) and PflD ( G513G ) ( Fig . 5 ) . Perturbations in pyruvate transformation are known to impact a key state variable in central metabolism: cellular redox balance . NADH accumulates in cells with high glycolytic flux because it cannot be re-oxidized as fast as it is generated . This increased redox ratio ( NADH/NAD+ ) creates a cellular response reminiscent of anaerobiosis , stimulating the cell to direct pyruvate toward overflow metabolites and leading to repression of TCA cycle genes such as isocitrate dehydrogenase ( icd ) and citrate synthase ( gltA ) [106] . Given the relatively rapid uptake of glucose and higher rate of acetate production by CV103 , stress in the form of high redox ratio may have influenced the evolution of this strain . NAD+ regeneration typically occurs downstream of pyruvate either via the TCA cycle or fermentation , but could also occur via the conversion of DHAP into glycerol ( see section on glycerol metabolism below ) . ( Fig . 6 ) The TCA cycle consists of eight steps beginning with the conversion of acetyl CoA to citrate and ending with the conversion of malate to oxaloacetate . Expression profiling of each evolved isolate showed that relative to their common ancestor , levels of transcripts for proteins involved in three of these steps ( α-ketoglutarate dehydrogenase , succinate dehydrogenase and fumarase ) were upregulated across all evolved strains . At two other steps , aconitase ( acnB ) and isocitrate dehydrogenase ( icdA ) , transcript levels were elevated in three of four strains , but reduced in CV103 . Whole-genome sequencing uncovered intergenic SNPs in CV103 that could impact flux at the icd branch point connecting the TCA cycle with the glyoxylate bypass . In the ancestral strain , JA122 , the negative regulator of the glyoxylate bypass , iclR , has a promoter mutation that likely affects iclR negative autoregulation , leading to higher expression and concomitant repression of the glyoxylate shunt genes aceA , aceB and aceK . AceK negatively impacts flux through isocitrate dehydrogenase by phosphorylation , diverting carbon through isocitrate lyase ( aceA ) and malate synthase ( aceB ) ( Fig . 6; [107] ) . Thus , in the ancestor increased IclR can reasonably be expected both to decrease transcription of glyoxylate bypass enzymes and to prevent inactivation of Icd by AceK . While CV101 and CV116 exhibit increased relative expression of icdA and acnB , CV103 shows decreased expression of both , perhaps owing to a C→A mutation that lies between the aceA and aceK open reading frames . Cellular pyruvate levels have long been thought to modulate TCA cycle flux , principally by modulating isocitrate deydrogenase activity ( Fig . 6 , [108] , [109] ) . The CV103-specific mutations described above may also affect the pool of pyruvate . lpd mutants have been shown to increase Entner-Doudoroff and glyoxylate shunt activities and to decrease TCA cycle activity [92] , while poxB null mutations have been shown to repress citrate synthase and malate dehydrogenase , and to activate acs [110] . CV103 contains mutations in the lpd FAD binding domain , which likely accounts for the 2–3 fold reduction of its pyruvate dehydrogenase activity , as well as a nonsense mutation at amino acid 14 of poxB ( E14* ) that likely inactivates this protein ( Table S2 ) . Together these mutations could be expected to impede flux through the TCA cycle in CV103 . Conspicuous among genes differentially expressed between evolved strains and their ancestor , and among the evolved strains themselves , were those encoding respiratory proteins that have a high H+/O coupling ratio . When each was grown by itself in glucose-limited chemostats , transcript levels for genes in the cyoABCDE operon that encode cytochrome oxidase subunits were significantly increased in CV101 and CV115/116 , but significantly decreased in CV103 , relative to their common ancestor JA122 [30] . Finally , CV103 also shows gene expression differences and mutations affecting the respiratory chain . Under conditions of nutrient stress , metabolic cost-benefit analyses predict E . coli will shift from the more efficient but anabolically expensive NADH:ubiquinone oxidoreductase I ( nuoABCEFGHIJKLMN ) /cytochrome bo oxidase ( cyoABCD ) chain to the anabolically inexpensive lower-yield NADH:ubiquinone oxidoreductase II ( ndh ) /cytochrome bd oxidase ( cydABX ) pairing [104] , [111] , [112] . Compared with JA122 , expression of nuoGHIJKL is 1 . 1-fold lower in CV103 but 1 . 3 to 1 . 5-fold higher in CV101 , CV116 and CV115 . Similarly , transcript levels of cyoABC are 2 . 1-fold lower in CV103 and 1 . 3 to 1 . 9-fold higher in the other evolved strains . As noted , CV103 also has missense mutations that affect NuoM ( L336F ) and NuoI ( R93L ) as well as a silent substitution in NuoE ( L14L ) . The effect of these differences in expression and sequence of NDH-I and cytochrome bo oxidase is unknown , but they may impact cellular redox balance . In summary , by considering the whole genome sequencing data in the light of transcriptome [30] , proteome [51] , and phenotype data [31] a coherent picture emerges of the dominant clone , CV103 , being highly fermentative but impaired in aerobic pathways , resulting in the production of overflow metabolites , which in the case of acetate creates a redox imbalance , but in the case of glycerol/glycerol phosphate , may provide a means to correct this imbalance . Because the other consortium members , like their common ancestor , are respiro-fermentative they are capable of exploiting the new biochemical niches created by CV103 . ( Fig . 3 ) We previously showed that differential production and scavenging of acetate , glycerol and/or glycerol phosphate explained stable coexistence of multiple genotypes under glucose limitation [31] , [36] . Concerning acetate , whose excretion by CV103 supports growth of CV101 , whole genome re-sequencing confirmed previously identified mutations in CV101 that up-regulate acetyl CoA-synthetase ( Acs ) and the acetate transporter ActP , as well as an ancestral mutation shared by the other clones that prevents efficient re-uptake of acetate by disrupting a CRP regulatory site in the acs promoter [30] . No additional mutations affecting acetate excretion or uptake were found in any of the evolved strains' genomes , so it is likely that these two alone explain the ability of CV101 to cross feed on the acetate produced by CV103 . Regarding glycerol and/or glycerol phosphate , previous experiments had shown that chemostat-grown CV116 assimilate radiolabeled glycerol 50% faster than all other strains , and that when CV116 and CV103 were co-cultured under glucose limitation , the addition of exogenous glycerol [31] or glycerol 3-phosphate caused the frequency of CV116 to increase . All of the evolved strains carry an ancestral mutation ( G55A ) in the DNA-binding transcriptional repressor , GlpR , that renders it constitutively inactive [113] . We also discovered a CV116-specific SNP in glpK encoding glycerol kinase , the first step in glycerol assimilation ( Table S2 ) , though this is a silent substitution at a site ( G232G; GGC→GGA ) not currently known to be associated with glpK regulation . Transcriptional profiling of GlpR regulated genes showed that the transcription unit containing glycerol kinase ( glpFKX ) remained at ancestral levels , while genes for the glycerol-3-phosphate:phosphate antiporter GlpT ( glpT ) , and the divergently transcribed anaerobic glycerol-3-phosphate dehydrogenase genes ( glpABC ) were upregulated across all isolates relative to their common ancestor [30] . The absence of differences in glp gene expression between CV103 and CV116 led us to explore the possibility that glycerol and/or glycerol phosphate cross-feeding might be mediated by other mechanisms controlling the assimilation and production of these metabolites . For example , post-transcriptional regulation in CV103 could explain why JA122 , CV101 and CV116 have three-fold higher specific activity of glycerol kinase , and two-fold higher specific activity of glycerol-3-phoshate dehydrogenase than CV103 [31] . GlpK is known to be post-transcriptionally inactivated by the unphosphorylated version of the glucose-specific PTS enzyme IIAGlc ( crr ) , or by an excess of the effector molecule fructose-1 , 6-bisphosphate [114]–[116] . As described above , IIAGlc is phosphorylated via its interaction with the sugar non-specific EI ( ptsI ) /HPr ( ptsH ) ( Fig . 3 ) . When glucose is transported through the inner membrane by the Enzyme IIGlc complex ( ptsG/crr ) , IIAGlc ( crr ) transfers its phosphate to IIBCglc ( ptsG ) , which then phosphorylates glucose to yield intracellular glucose-6-phosphate ( reviewed in [114] ) . High levels of unphosphorylated IIAGlc signal glucose abundance , and inhibit enzymes needed for catabolism of alternate carbon sources such as glycerol and glycerol phosphate . Specifically , unphosphorylated IIAGlc is known to inhibit glycerol kinase activity [114] , [115] , [117] . We have already noted two mutations in CV103 ptsI , which , by impairing EI , could lead to excess unphosphorylated IIAGlc , which would inhibit glycerol kinase ( Fig . 3 ) , and thereby restrict CV103's access to glycerol . CV103 may also have an excess of another potent glycerol kinase inhibitor: fructose-1 , 6-bisphosphate ( FBP ) [116] , [118] . We previously noted that relative to JA122 and the other evolved isolates , CV103 has enhanced expression of pfkB , which encodes the minor FBP creating enzyme , and lower levels of fbp , which encodes the reverse enzyme fructose bisphosphatase [30] . Whole-genome sequencing revealed a missense mutation of unknown effect in pfkB ( Q201H ) . These distinctive features of CV103 , combined with its demonstrated capacity for enhanced glucose transport and assimilation , may produce elevated levels of the GlpK inhibitor , FBP , further impeding CV103's ability to assimilate glycerol . To understand why CV103 might release glycerol and/or glycerol phosphate as metabolic by-products , we examined the transcript levels and sequences of genes encoding proteins involved in their production ( Fig . 3 and Fig . 7 ) . Glycerol can be generated by E . coli as either ( 1 ) a by-product of phospholipid synthesis from sn-glycerol-3-phosphate , ( 2 ) an end-product of the detoxification of dihydroxyacetone phosphate/methylglyoxal , or ( 3 ) via hydrolysis of glycerol-1-phosphate [119] . CV103 has multiple mutations that affect nearly every step of phospholipid biosynthesis ( Fig . 7A ) . While the number of SNPs suggests CV103 reaps some benefit from altering phospholipid production , these mutations should also have the effect of restricting glycerol and phospholipid formation from glycerol-3-phosphate . The second route to glycerol , detoxification of DHAP/methylglyoxal , is catalyzed by the reversible glycerol dehydrogenase GldA ( Fig . 7B ) . This reaction is likely to be useful for a strain such as CV103 that is consuming glucose at a high rate , because it prevents the buildup of DHAP and subsequent production of toxic methylglyoxal and , significantly , because it re-oxidizes NADH [119] , [120] . CV103 may also have higher amounts of intracellular DHAP and methylglyoxal as a consequence of a non-synonymous mutation ( A6D ) in the gene for glycerol-3-phosphate dehydrogenase GpsA , which favors the production of DHAP from G3P ( Fig . 3 and 7 ) . No SNPs affecting GldA were found , and gldA transcript levels were not up- or downregulated in any of the isolates . The third mechanism for the production of glycerol is via the activity of the enzyme YfbT . YfbT catalyzes the conversion of glycerol-1-phosphate into glycerol and its gene is truncated in CV103 ( E22* ) , resulting in a defect likely expected to increase the pool of glycerol phosphate but not glycerol [121] . In short , there are multiple , non-mutually exclusive reasons to explain why CV103 has both a diminished capacity to assimilate glycerol as well as an increased propensity to produce glycerol and/or glycerol phosphate as overflow metabolites . Biocomplexity , here defined as stable co-existence of multiple genotypes , can emerge in clonal populations evolving in environments that are spatially structured with respect to their physical features [10] , [13] or temporally structured with respect to availability of limiting nutrients [8] , [11] , [122] . Under these conditions , the emergence of complexity can be explained in terms of classical niche theory [123] . Biocomplexity can also emerge in clonal populations evolving in simple , unstructured environments where reproduction is continuously limited by a single resource [3] , [25] , [47] , [124] . There , population genetic complexity can be maintained by clonal interference , wherein competitive interactions preclude fixation of a fittest genotype [25] , or by clonal reinforcement where one clone supports growth by others via the excretion of metabolizable substrates [3] . Our results bear out certain predictions arising from Mazancourt and Schwartz's resource ratio theory of cooperation [125] . In their model , two species initially competing for two resources can evolve towards a cooperative trading relationship one of whose emergent properties is enhanced resource utilization . Conditions that favor this outcome include low mortality , low resource levels and differential efficiency between species at depleting limiting resources . Our community evolved under similar conditions: clonal lineages persisted for many generations under chronic resource limitation and gained differential access to available resources . Two differences between their model and our system are that our population was founded by a single clone initially limited on one resource , and that the mutations which gave one of its descendants preferential access to that resource have pleiotropic effects that favor production of secondary resources on which other clones can thrive . As for when this genotype arose , we know by PCR analysis that key CV103-specific mutations at malK , mutM , ptsI , hfq and lpd are present in the earliest Helling et al . [3] population sample archived ( ∼350 generations ) ( data not shown ) . A category of mutations certain to prove beneficial under nutrient limitation is one that favors increased uptake of the limiting nutrient , in this case glucose . Levels of residual glucose in steady state CV103 monocultures are significantly lower than in those of the other evolved strains and their common ancestor [31] , consistent with CV103's more rapid uptake of the non-metabolizable glucose analogue α-MG [3] and its higher expression of LamB glycoporin [51] . The amino acid substitution in MalK ( D297E ) likely diminishes its ability to deactivate MalT and , by extension , activates lamB expression . A mutation in Hfq ( Q52H ) , that may negatively affect translation of the stress response global regulator RpoS , would enhance glucose scavenging via LamB and PtsG [126] , [127] . Because glucose consumption in most organisms , including E . coli , is greater under anaerobic than under aerobic conditions ( e . g . , [128] and refs within ) , and because enhanced glucose consumption is an adaptive strategy selected for in glucose-limited chemostats , it is perhaps not surprising that , relative to the other evolved clones and their common ancestor , the metabolic profile of CV103 appears to be fermentative . Multiple lines of evidence support this interpretation: ( i ) TCA cycle genes encoding aconitase ( acnB ) and isocitrate dehydrogenase ( icd ) are downregulated ( Fig . 6 ) , ( ii ) expression of the cyoABCD operon encoding cytochrome oxidase is reduced [30] , ( iii ) multiple mutations suggest CV103 is dealing with excess formate production ( Table S4 ) , and ( iv ) steady state chemostats of CV103 contain appreciable residual concentrations of overflow metabolites [3] , [31] . While we found no CV103-specific mutations that would constitutively repress aerobic pathways , missense mutations at lpd and maeA , and a nonsense mutation at poxB could be expected to diminish TCA cycle flux . Moreover , increased glucose consumption by E . coli under glucose limitation has been shown to repress both respiration and the TCA cycle via changes in global regulators such redox balance and pyruvate [106] . High glucose consumption also strongly represses transcription of acs encoding acetyl CoA synthetase [106] , which helps to explain why no activity of this acetate scavenging enzyme can be detected in CV103 monocultures [31] , and why extracellular acetate is present at a dilution rate ( D = 0 . 2 h−1 ) where none is expected . We therefore conclude that CV103's fermentative metabolism arises as a consequence of selection for enhanced glucose consumption . The CV103 lineage's response to selection for enhanced glucose consumption results in trade-offs that critically determine its role in niche construction . At lower dilution rates E . coli growing under aerobic glucose-limitation usually carries out a high-yield metabolism that converts all available glucose to CO2 ( [129] and refs therein ) . Indeed , under aerobic conditions the switch from respiratory to respiro-fermentative metabolism typically occurs at high growth rates and glucose concentrations , resulting in the production of overflow metabolites such as acetate . ( Under anaerobic conditions , E . coli typically ferments all glucose to CO2 , acetate and ethanol [130] . ) The excretion of overflow metabolites under conditions of restricted TCA flux creates an imbalance in cellular redox , because acetate production in E . coli , unlike ethanol production in yeast and lactate production in animals , fails to regenerate NAD+ from the NADH formed by glycolysis . In CV103 this problem may be further exacerbated by mutations affecting pyruvate transformation , which would place an additional premium on NAD+-generating processes , including reactions that lead to glycerol and glycerol-3-phosphate production . Concerning the apparent pleiotropic effects arising from constitutively high glucose consumption in the CV103 background , it interesting to note that a recently discovered mechanism to limit the production of overflow metabolites is to overexpress the small RNA SgrS , which effectively reduces the rate of glucose consumption [131] . SgrS , complexed with its binding partner , diminishes cells' ability to create new sugar transporters , in particular PtsG [132] . In CV103 the binding partner of SgrS , Hfq , has a missense mutation ( Q52H ) , which may contribute to its rapid uptake , but incomplete assimilation of glucose . The glucose scavenging adaptations seen in CV103 , which contribute to its persistence and its ability to create new niches , contrasts with metabolic adaptations commonly observed in experimentally evolved yeast [21] . Yeast cultured at similar dilutions rates adapt to aerobic glucose limitation by reversing the Pasteur effect , switching from fermentative to respiratory metabolism , which results in an enormous gain in their reproductive output . While yeast evolution experiments typically begin with populations that produce appreciable amounts of overflow metabolites , mainly ethanol , strains quickly evolve a metabolic strategy that produces essentially no residual carbon [21] , [133] . This difference in the adaptive trajectory followed by the two species is grounded in fundamental differences in their metabolism , which may preclude evolution of cross-feeding in yeast cultured under glucose limitation . Superior glucose consumption by the largely fermentative strain CV103 helps to create multiple resources in an environment where initially only one was limiting , thereby providing a selective advantage to genotypes that can access those secondary resources . The mechanism by which strain CV101 gains preferential access to acetate is straightforward and relates to insertion of an IS30 element in the upstream regulatory region of acs encoding the acetate-scavenging enzyme acetyl CoA synthetase [36] . The acs locus is misregulated in the ancestral background due to a mutation in the CRP binding site . Indeed , we observed appreciable amounts of acetate in JA122 monocultures at steady state [31] , making it likely that selection pressure for acetate scavenging existed at the outset of these experiments . This pressure only increased with the advent of the CV103 lineage , which evolved a superior mechanism for glucose consumption , but which produces even more acetate because it retains the ancestral defect , and because increased flux through fermentative pathways represses acs transcription . The basic CV103 phenotype is additionally supported by mutations , in particular those related to pyruvate oxidation ( e . g . lpd ) , that accentuate changes in redox potential typical of fermentative cells whose overflow metabolite is acetate . These changes likely favor the formation of glycerol through side-reactions whose dehydrogenase steps help regenerate NAD+ . CV116 has preferential access to this other metabolite because it retains two ancestral mutations: one in acs that impairs acetate scavenging , and another in glpR that constitutively derepresses enzymes required for glycerol and glycerol phosphate assimilation . By contrast , CV103 likely has limited access to these resources because it has accumulated mutations in ptsI and pfkB , whose downstream effects include inhibition of glycerol kinase , the rate-limiting step in glycerol assimilation . The evolution and persistence of multiple genotypes in a population of asexual organisms supported by a single resource seems to violate the principles of competitive exclusion [134] , [135] and periodic selection [1] , [136] . Yet this phenomenon has now been observed in a variety of simple experimental systems , in particular those that are temporally [8] , [16] or spatially structured [10] , [13] . Although continuous nutrient-limited chemostats are unstructured in both respects , a number of studies have now shown that multiple bacterial lineages , some of them mutators , can arise and coexist in a single chemostat vessel [3] , [35] , [38] , [47] , [52] , [137] . However , none of these studies have provided evidence that biocomplexity can arise and be sustained by means of the trading relationships we call clonal reinforcement . We contend that this mechanism may be quite common , as it is formally analogous to and may sometimes be a precursor to syntrophy , a ubiquitous feature of natural bacterial communities [138] . Clonal reinforcement may also be at work in clinically relevant environments . For example , the dominant clones in tumors are often fermentative , differing markedly from normal ( ancestral ) tissue in their demand for O2 and nutrients , their production of CO2 ( see [139] and refs therein ) , and in their release of overflow metabolites that acidify the local environment [140] . Such cells may create opportunities for subpopulations to follow independent evolutionary trajectories that lead to further genetic differentiation , perhaps even to changes in their contact inhibition and drug resistance phenotypes . Chronic bacterial infections are also genetically heterogeneous [141] , and can even be supported by syntrophic interactions [142] . We may therefore reasonably ask: to what extent does clonal reinforcement enable subpopulations in tumors and chronic infections to differentiate and become more resistant to chemotherapy and/or less visible to the immune system ? The answers to these questions have far-reaching implications .
Escherichia coli JA122 , CV101 , CV103 , CV115 and CV116 were maintained as permanent frozen stocks and stored at −80°C in 20% glycerol . Davis Minimal medium was used for all liquid cultures with 0 . 025% glucose added for batch cultures and 0 . 0125% for chemostats [143] . Chemostat cultures were initiated using colonies picked from Tryptone Agar ( TA ) plates and outgrown in Davis minimal batch medium overnight . For transcriptional profiling , total protein and metabolite assays chemostats were maintained at 30°C with a dilution rate of ≈0 . 2 h−1 for approximately 70 hours ( ∼15 generations ) . At the end of each chemostat run , three aliquots of 50 mL culture were rapidly filtered onto 0 . 2 µm nylon membranes , flash-frozen in liquid nitrogen and stored at −80°C . Strains were streaked to colonies on LB agar overnight . Twelve colonies were picked in their entirety for each strain and inoculated into 3 mL of liquid LB , then grown overnight at 37°C . Subsequently , a portion of the liquid cultures was spread on LB plates containing 300 µg mL-1 rifampicin . Colonies were counted 48 h after spreading . To determine the titer , three cultures for each strain were spread on LB agar plates at 10−7 , 10−8 and 10−9 dilutions . Colonies were counted and titers determined as the average of the three 10−8 dilutions . Mutation rates per cell per generation were calculated using maximum likelihood calculator FALCOR ( http://www . keshavsingh . org/protocols/FALCOR . html ) [144] . Following fast filtration and disruption by sonication intracellular pyruvate was determined on chemostat-grown cells using the Pyruvate Assay kit and PicoProbe ( Biovision , Milpitas , CA , K609 and K317 ) as directed by the manufacturer's guidelines . Estimated values were normalized to cell protein , which was determined via the Pierce BCA Protein Assay Kit ( Cat . # 23227 , Thermo Scientific , Rockford , IL ) using BSA as standard . Global gene profiling was described in detail in a previous publication [30] . Briefly , total RNA was extracted from triplicate cultures of chemostat-grown JA122 , CV101 , CV103 and CV116 ( D = 0 . 2 h−1 , 0 . 0125% glucose ) and hybridized to full-length open reading frame PCR products spotted onto aminosilane-coated slides . Raw data was analyzed using TIGR MIDAS and MeV software pipelines ( www . tm4 . org ) and Significance Analysis of Microarrays ( SAM ) [145] was used to examine expression differences between strains using a multi-class comparison consisting of four groups . Similarities among strains were identified using one-class SAM and differences between the strains were examined using a 4-class SAM . δ cutoffs were set at the 0% FDR threshold ( i . e . the highest δ value that gave a median false discovery rate of 0% ) . Average ( mean ) log2 ratios were calculated after SAM analysis using Microsoft Excel and represent the relative expression ratios of each evolved isolate compared to their common ancestor . Genomic DNA for Illumina sequencing was extracted from cells grown in batch culture using a modification of methods described by Syn and Swarup [146] . Subsequent to DNA precipitation , spun pellets were re-suspended in 1XTE ( 10 mM Tris , 1 mM EDTA , pH 8 . 0 ) containing 50 µg/mL DNAse-free RNAse A and incubated at 37°C for 30 minutes . Samples were re-extracted once with phenol:chloroform ( 3∶1 ) , once with phenol:chloroform ( 1∶1 ) and twice with chloroform and then precipitated with EtOH using standard techniques . Following re-precipitation , DNA was dissolved in TE . Single-end 36 bp sequencing libraries were created using the Illumina Genomic DNA Sample Prep Kit according to manufacturer's instructions ( 5 µg input genomic DNA ) , and sequencing flow cells were prepared using the Illumina Standard Cluster Generation Kit . Samples were sequenced on the Illumina Genome Analyzer II , and image analysis and data extraction were performed using Illumina RTA 1 . 5 . 35 . 0 . Reads ( with qualities ) were aligned to the K12 reference genome ( gi|49175990|ref|NC_000913 . 2 ) using BWA v0 . 5 . 8 [147] with default parameters . Whole-genome pileup files were generated using SAMtools v0 . 1 . 8–18 [148] and single-nucleotide polymorphisms due to the evolution were called using custom perl scripts that compared each evolved strain with the original ancestor . Briefly , SNPs passed the filter if they were represented in at least 40% of reads in the evolved strain and at most 10% in the reference strain , with at least 5 reads covering the position in both strains . Additional heuristic filters included a confirming read from both strands , and no more than one ambiguous SNP call ( “N” ) or deletion ( “*” ) at that position . Insertions and deletions relative to the K12 reference sequence were identified using Breseq v . 0 . 18 with default parameters ( http://barricklab . org/breseq; [32] ) . Thirty-seven SNPs identified by Illumina sequencing were verified by Sanger sequencing . PCR products were generated using Fermentas DreamTaq PCR Master Mix ( Thermo Scientific , Waltham , MA ) following manufacturer's instructions , treated with ExoSAP-IT ( Affymetrix , Santa Clara , CA ) and sequenced on an ABI3730XL machine by High Throughput Genomics Center ( Seattle , WA ) ( Table S5 ) . | The variability of natural systems makes it difficult to deduce how organisms' genotypes manifest as phenotypes , and how communities of interacting organisms arise . Using laboratory experimental evolution we can control this variation . We previously showed that a population of E . coli that originated from a single clone and was cultured in the presence of a single limiting resource , evolves into a stable , three-membered community , wherein one clone excretes metabolites that the others utilize as carbon sources . To discern the genetic factors at work in producing this outcome and to illuminate the community's physiology , we sequenced the genomes of the ancestral and evolved clones . We identified in the ancestor mutations that may have predisposed evolution of cross-feeding . We found that the lineages which gave rise to the community diverged early on , and that the numerically dominant lineage that best scavenges limiting glucose does so as a result of adaptive mutations that enhance glucose uptake but favor fermentative over respiratory pathways , resulting in overflow metabolites . Because this clone produces secondary resources that sustain other community members , and because it shares with them only one mutation , we conclude that it is an “engine” generating diversity by creating new niches , but not the occupants themselves . | [
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| 2014 | Ex Uno Plures: Clonal Reinforcement Drives Evolution of a Simple Microbial Community |
The morphology of synapses is of central interest in neuroscience because of the intimate relation with synaptic efficacy . Two decades of gene manipulation studies in different animal models have revealed a repertoire of molecules that contribute to synapse development . However , since such studies often assessed only one , or at best a few , morphological features at a given synapse , it remained unaddressed how different structural aspects relate to one another . Furthermore , such focused and sometimes only qualitative approaches likely left many of the more subtle players unnoticed . Here , we present the image analysis algorithm ‘Drosophila_NMJ_Morphometrics’ , available as a Fiji-compatible macro , for quantitative , accurate and objective synapse morphometry of the Drosophila larval neuromuscular junction ( NMJ ) , a well-established glutamatergic model synapse . We developed this methodology for semi-automated multiparametric analyses of NMJ terminals immunolabeled for the commonly used markers Dlg1 and Brp and showed that it also works for Hrp , Csp and Syt . We demonstrate that gender , genetic background and identity of abdominal body segment consistently and significantly contribute to variability in our data , suggesting that controlling for these parameters is important to minimize variability in quantitative analyses . Correlation and principal component analyses ( PCA ) were performed to investigate which morphometric parameters are inter-dependent and which ones are regulated rather independently . Based on nine acquired parameters , we identified five morphometric groups: NMJ size , geometry , muscle size , number of NMJ islands and number of active zones . Based on our finding that the parameters of the first two principal components hardly correlated with each other , we suggest that different molecular processes underlie these two morphometric groups . Our study sets the stage for systems morphometry approaches at the well-studied Drosophila NMJ .
Normal brain function relies on functional neuronal networks in which neurons connect and communicate with one another . Communication primarily takes place at chemical synapses , where neurotransmitters are released from the presynaptic compartment of a neuron and activate receptors at the postsynaptic compartment of its target cell . Abnormal synaptic development and function have been found to underlie cognitive disorders such as intellectual disability , autism spectrum disorder and schizophrenia [1–5] . The morphology and function of synapses are highly intertwined [6–9] and morphological aspects have therefore been studied extensively to gain further insight into the regulatory networks underlying synaptic function . Mammalian dendritic spines change shape upon maturation and plasticity from long , thin filopodia-like structures to typical stubby and mushroom-shaped postsynaptic compartments of increased efficacy [10 , 11] . In Drosophila , synaptic structure and activity is modulated according to circadian timing [12–15] or upon experienced-dependent or stimulated activity [16 , 17] , to name only three examples . Despite the central interest in synapse morphology in neuroscience -studied at different developmental stages , upon genetic or environmental perturbation and in different organisms- it is still largely unknown how different structural aspects relate to one another and adapt in a coordinated manner when changes are induced . Systematic synapse morphometry could shed light on these poorly understood relationships . In genetically unperturbed conditions , such insights would be crucial to understand the developmental design principles that shape the synapse . This in turn would provide a basis to identify the genetic players that drive the required coordinated structural changes during synaptic development and plasticity with higher sensitivity . As an initial step into quantitative , correlative synapse morphometry , we have turned to an identifiable , methodologically and genetically accessible synaptic terminal: the Drosophila larval neuromuscular junction ( NMJ ) . The Drosophila NMJ is an extensively studied and well-established in vivo model for glutamatergic synapse biology [18 , 19] . The synaptic terminal , a branched chain of synaptic boutons , is formed by the motor neuron and gets surrounded by the subsynaptic reticulum ( SSR ) as it invades its target muscle [20] . Boutons are periodic enlargements [21] that host the presynaptic release sites , ‘active zones’ [20] , at which the synaptic vesicles dock to the presynaptic membrane to release their neurotransmitters . Together with the exactly opposed postsynaptic receptor complex , the active zone forms the chemical synapse [20] . Large scale genetic screens at the NMJ have been very successful in identifying genes and molecular mechanisms of synapse development [22–31] . However , so far , these screens have largely relied on visual inspection and semi-quantitative scoring of a limited amount of morphometric features . While this has uncovered main determinants of NMJ morphology , it is likely that the extent of the regulatory networks remained undiscovered . In this study , we developed a macro in Fiji ( an open-source image analysis software [32] ) to quantitatively assess nine morphometric features in a large number of glutamatergic NMJs based on high-content fluorescence microscopy images . We found the macro to accurately assess eight of them ( NMJ area , perimeter , total length , longest branch length , number of islands , number of branches and branching points and number of active zones ) , making it suitable for high-throughput analyses of synapse morphology . Here , in preparation for reverse genetic approaches , this method was applied to two isogenic host strains of genomewide RNAi libraries ( VDRC [33]; see Methods ) to build large wt-like control datasets . Using these data , we followed a systems biology approach by using the differences in gender , abdominal body segment and genetic background as natural sources of biological variation to gain insights into the ( in ) dependencies and correlations of the measured morphometric NMJ features . Our study is the first to investigate the systems properties of the well-studied Drosophila NMJ , providing new insights into the design principles of a synapse .
We generated a large collection of NMJ images in two different genotypes , the isogenic host strain for the GD and KK RNAi libraries of the Vienna Drosophila Resource Center [33] crossed to a panneuronal elav promotor line ( see Methods ) . The obtained larvae were dissected and stained with antibodies against two key components of the NMJ , the discs large 1 protein ( Dlg1 –the ortholog of mammalian PSD-95 ) and bruchpilot ( Brp—sole ortholog of human ELKS/CAST/ERC proteins [34] ) , to visualize general synapse morphology and active zones [35] , respectively . We focused on abdominal segments A2-A5 , which are best accessible in larval ‘open book’ preparations . In total we acquired microscopic images of 1576 NMJs in 397 larvae . It is a laborious undertaking to measure NMJ features ( semi ) manually , especially when several NMJ features are of interest . To support high-throughput analyses and achieve objective quantification , we set out to develop a macro for computer-assisted morphometry that can accurately quantify high-content , non-confocal images . The macro ‘Drosophila_NMJ_morphometrics’ , was developed using the open source Fiji platform [32] and is made available via figshare , a public repository where users can make their research outputs available: https://figshare . com/s/ec634918c027f62f7f2a [36] . For usage , Fiji needs to be installed and the macro has to be downloaded and saved with the extension ( . ijm ) into the Fiji Plugins folder . The macro will appear in the Plugins menu under the name Drosophila_NMJ_Morphometrics . Upon running the macro a graphical interface displays the default settings of the macro , which can be adjusted according to the customer’s needs . The ‘help’ option offers additional information . A point-by-point protocol for using the macro is provided in the supplementary material ( S1 Protocol ) . The macro consists of three sub macro’s that can be used separately or run in a consecutive manner to analyze and process images ( via checkbox options of the macro interface ) . The first sub macro “Convert to stack” identifies all image files available and creates stacks and maximum intensity projections of both channels . The second sub macro “Define ROI” presents the projections to manually delineate the region of interest ( ROI ) . As we were interested in type 1b NMJs on muscle 4 , this manual step was required to exclude type 1s synaptic terminals on the same muscle , and occasionally exclude synapses on nearby muscles that are present in the images . The third sub macro “Analyze” applies fully automated analysis through all stacks within the limits of the ROI . For each NMJ , nine morphological parameters are measured ( described in more detail below ) and processed to an ( . txt ) output file . Images are processed to a result picture , in which the delineation of the automatically recognized NMJ features is presented . During image analysis , from each NMJ three structures were derived: 1 ) NMJ outline , 2 ) NMJ skeleton and 3 ) number of Brp-positive active zones . Technical details underlying each derived structure are described in the Methods section Image Analysis . The NMJ outline is used to determine the NMJ area and its perimeter and a subsequent watershed separation provides the number of boutons ( Fig 1A , NMJ outline indicated in yellow ) . From the skeleton ( Fig 1A , indicated in blue ) five NMJ features are deduced: the total NMJ length , the sum of the length of the longest continuous path connecting any two end points ( longest branch length ) , the number of unconnected Dlg1-stained compartments per NMJ ( referred to as ‘islands’ ) , the number of branches and the number of branching points ( one branching point connects three or more branches ) . The number of active zones was determined by counting Brp-positive spots in the Brp-channel ( Fig 1A , indicated by white foci ) . Taken together , the macro determines three derivatives per NMJ , from which it deduces nine morphological NMJ features . Eight of the nine features are based on the Dlg1- and one on the Brp-channel . Fig 1A provides a schematic overview of all nine NMJ features . The Fiji macro was used to process our dataset of 1576 NMJ images . We checked the quality of our images at low magnification ( Fig 1B , input checkpoint 1 ) and found that for 33 images ( 2 . 09% ) the NMJ was not fully captured in the acquired stack . Furthermore , a second checkpoint at high magnification ( Fig 1B , input checkpoint 2 ) revealed 35 images ( 2 . 22% ) in which the NMJ was partially out-of-focus , 25 images ( 1 . 59% ) with weak staining and 21 images ( 1 . 33% ) from which we could not guarantee the specificity of type 1b . For the remaining 1468 NMJ images ( 93 . 15% ) ( Fig 1C ) , the macro-annotated images were used to evaluate the obtained NMJ outline , skeleton and Brp-positive active zones per NMJ image ( Fig 1B , output checkpoint 3 ) . Variability in staining intensity led to a relatively high amount of images from which part of the NMJ outline was not recognized ( n = 90 , 6 . 13% from 1576 ) , certain areas that lack Brp-positive active zones ( n = 121 , 8 . 24% ) or a combination of these two events ( n = 95 , 6 . 47% from 1576 ) . Images with skeleton misannotations ( n = 186 , 12 . 67% from 1576 ) were manually corrected . In summary , after three rounds of quality checks , we remained with a NMJ dataset of 1295 images for the NMJ-outline features ( 82 . 17% from initial; 86 . 62% from 1468 ) , 1383 images for the NMJ-skeleton features ( 87 . 75% from initial; 92 . 51% from 1468 ) and 1259 images for the active zones ( 79 . 89% from initial; 84 . 21% from 1468 ) . In total , we obtained 1163 NMJ images from which all features past the three quality rounds ( 73 . 79% from initial , 79 . 22% from 1468 ) . In absence of truly objective NMJ measures , we compared the results obtained with the macro to the manual counts of two experienced experimenters for 30 NMJ images ( S1 Table ) . We first investigated the sample distributions to determine the deviation between manual and macro assessment over the complete set of images . The 95% confidence intervals largely overlapped with each other for the parameters NMJ area , perimeter , length , longest branch length , islands , branches , branching points , and active zones ( S1 Fig ) . Thus , no significant differences were found between the distributions of macro and manual assessment for these NMJ features ( Table 1 ) . However , the macro resulted in a significantly lower amount of bouton counts ( macro: 16 boutons per NMJ; manual; 25 per NMJ; p<0 . 0001 ) ( Table 1 ) . The 95% confidence interval widths were highly comparable between macro and manual counts , indicating that the macro does not add additional noise to the outcome ( S1 Fig ) . Secondly , we investigated the deviation between manual and macro evaluation per given sample , expressed as %deviation or sensitivity and specificity . The %deviation per given sample was often negative for the NMJ perimeter , length and longest branch length ( S1 Table ) , indicating that the macro measures somewhat higher absolute values as compared to the manual counts . On average , the boutons showed a six times higher % deviation between macro and manual counts compared to the NMJ area , perimeter , length and longest branch length ( Table 1 ) . Sensitivity ( the proportion of positive results that is indeed a true positive ) and specificity ( the proportion of true positives that is identified as such ) was determined per NMJ image for the discrete NMJ features islands , branches , branching points and active zones ( S1 Table ) . On average , all four parameters scored >91% on sensitivity and >92% on specificity ( Table 1 ) . Finally , Lin’s concordance correlation coefficient ( ccc ) , which describes the reproducibility between two evaluation methods , was calculated to determine the deviation of the acquired macro data from the perfect concordance ( x = y ) ( Table 1 , Fig 2 ) [37] . On a scale from 0 . 00 to 1 . 00 , the macro scored ccc’s ≥0 . 84 for all NMJ features but bouton count . Bouton count resulted in a ccc of 0 . 22 ( C . I . 95% 0 . 10–0 . 32 ) , which indicates that macro and manual performance are discordant . In summary , the macro assessed nine NMJ features , eight of which were successfully validated with high concordance correlations . We therefore mainly focused on these eight features in all subsequent analyses . To further validate the macro , we tested the reproducibility of published findings on mutants with altered synaptic parameters for each of the three principal image segmentation procedures performed by our macro ( NMJ outline , skeleton and active zones ) . We and others have shown that Ankyrin 2 ( Ank2 , CG42734 ) mutant [38 , 39] or knockdown [40] flies present with fused boutons and smaller NMJs . Here , we used panneuronal Ank2 knockdown NMJs as a positive control to validate the macro’s NMJ outline ( Fig 3A and 3B ) . The NMJ area was significantly smaller upon Ank2 knockdown by two independent RNAi strains ( Ank2-RNAiKK107238 339μm2 , padj = 2 . 18E-08; Ank2-RNAiKK107369 361μm2 , padj = 1 . 20E-05 ) , compared to our genetic background control dataset ( 452 μm2 ) . The NMJ perimeter was only significantly smaller for the stronger RNAi strain ( control 289μm; Ank2-RNAiKK107238 238μm , padj = 1 . 82E-03 ) . Highwire ( hiw , CG32592 ) is a known regulator of NMJ length and the extent of branching and mutants typically present with long , highly branched NMJs [41] . Our macro reproduced the mutant phenotype in NMJs that have a panneuronal knockdown of Highwire , again by using two independent RNAi strains ( Fig 3C and 3D ) . The NMJ skeleton-derived parameters length ( Hiw-RNAiGD28163 197μm , padj = 3 . 10E-25; Hiw-RNAiGD36085 147μm , padj = 7 . 31E-07; control 122μm ) , longest branch length ( Hiw-RNAiGD28163 154μm , padj = 2 . 02E-13; Hiw-RNAiGD36085 122μm , padj = 4 . 62E-04; control 106μm ) , number of branches ( Hiw-RNAiGD28163 9 . 33 , padj = 2 . 10E-04; Hiw-RNAiGD36085 7 . 69 , padj = 2 . 52E-02; control 5 . 74 ) and number of branching points ( Hiw-RNAiGD28163 3 . 13 , padj = 6 . 74–04; Hiw-RNAiGD36085 2 . 73 , padj = 3 . 31E-02; control 1 . 79 ) are all significantly higher ( 120–180% ) compared to the genetic background control dataset . Lastly , the GTPase Rab3 is required for proper bruchpilot distribution and the mutant ( rup ) presents with a reduced number of Brp-positive active zones ( 81 compared to 298 in control NMJs on muscle 4 ) [42] . The macro reproduced this phenotype upon panneuronal knockdown ( Rab3-RNAiKK100787 138 Brp-positive active zones; control 290 Brp-positive active zones; p = 4 . 43E-29 ) ( Fig 3E and 3F ) . The large collection of objectively quantified NMJ data offered the possibility to look at systematic differences in NMJ morphometry for gender , genetic background and body segment . For this purpose we restricted the dataset to images from which we obtained data for all nine features , including muscle measurements ( due to the latter requirement an additional 62 NMJ images were excluded ) . We divided the dataset into male- ( n = 724 ) and female-specific ( n = 377 ) data and evaluated the differences between both sexes . We found that six features significantly differed from each other ( padj < 0 . 05 ) , with males showing lower average values than females: active zones ( ♂ 281; ♀ 303; padj = 1 . 51E-08 ) , NMJ area ( ♂ 429μm2; ♀ 464μm2; padj = 8 . 43E-09 ) , perimeter ( ♂ 289μm; ♀ 306μm; padj = 7 . 26E-06 ) , NMJ total length ( ♂ 124μm; ♀ 130μm; padj = 1 . 65E-04 ) , longest branch length ( ♂ 107μm; ♀ 114μm; padj = 8 . 30E-05 ) and muscle area ( ♂ 61377μm2; ♀ 66976μm2; padj = 1 . 98E-15 ) ( Fig 4 ) . In contrast , gender did not significantly impact the number of branches ( ♂ 5 . 5; ♀ 5 . 4; padj = 1 . 00 ) , branching points ( ♂ 1 . 7; ♀ 1 . 7; padj = 1 . 00 ) and Islands ( ♂ 2 . 1; ♀ 2 . 1; padj = 1 . 00 ) . Taken together , this suggests that the branching geometry is similar for both sexes , whereas size is not . Next , we aimed to determine the influence that the genetic background might have on our NMJ features , focusing on two genetic backgrounds relevant for large scale reverse genetic screening . We divided our dataset , considering males only , into two genetic backgrounds , deriving from GD ( n = 311 ) versus KK VDRC RNAi libraries ( n = 413 ) , and compared these between each other for each NMJ feature ( Fig 5 ) . Three of the nine features showed a significant difference between the two tested genetic backgrounds: active zones ( GD 267; KK 292; padj = 2 . 21E-08 ) , NMJ area ( GD 396μm2; KK 453μm2; padj = 1 . 98E-15 ) and length ( GD 121μm; KK 126μm; padj = 3 . 78E-02 ) . No significant differences were observed for the other six features: longest branch length ( GD 105μm; KK 109μm; padj = 1 . 32E-01 , Islands ( GD 2 . 1; KK 2 . 0; padj = 1 . 32E-01 ) , branches ( GD 5 . 7; KK 5 . 3; padj = 3 . 96E-01 ) , branching points ( GD 1 . 8; KK 1 . 7; padj = 8 . 38E-01 ) , muscle area ( GD 60765μm2; KK 61838μm2; padj = 3 . 29E-01 ) and NMJ perimeter ( GD 288μm; KK 289μm; padj = 9 . 57E-01 ) . This data shows that the genetic background can be a significant source of “variance” at the NMJ . The literature reports data for abdominal body segments in the range of A2-A5 , whereby studies report on evaluated NMJ data at one segment or a combination of different segments [19 , 43] . We aimed to quantitatively determine whether among these segments NMJs show considerable differences in one or several features . Consequently , we divided the dataset into four groups , each representing one segment . We did find differences among features across the 4 evaluated segments , following different spatial patterns . The number of active zones , branches and branching points showed a relative decrease from anterior to posterior ( Fig 6A–6C ) . However , only segment A2 showed significant differences to ( some of ) the other segments ( Table A in S2 Table ) . The number of islands followed the same pattern , but the values were not significantly different over the different segments ( Fig 6D; Table A in S2 Table ) . The total length and longest branch length showed the opposite pattern; segment A2 NMJs were significantly shorter than NMJs of segments A3-A5 ( Fig 6E and 6F; Table A in S2 Table ) . The muscle area of segment A2 was also significantly smaller compared to segment A3 . The size peaks in segment A3 and significantly decreased in the segments A4 and A5 . The muscle size of segment A5 was significantly smaller than that observed for A2 ( Fig 6G; Table A in S2 Table ) . The NMJ area formed the fourth category that showed significant increase from segment A2 to A3 and a significant decrease to the same level from A4 to A5 . The NMJ perimeter behaved very similar , although values were not significantly different from one another ( Fig 6H and 6I; Table A in S2 Table ) . An overview of the number of cases , mean , confidence intervals en p-values is provided in S2A Table . Although not always significant , consistent patterns were observed in each group of gender and genetic background ( Tables B-E in S2 Table ) . Taken together , the NMJ features could be subdivided in four groups with different patterns over the abdominal segments A2-A5: i ) active zones , branches , branching points and islands , ii ) length and longest branch length , iii ) muscle area and iv ) NMJ area and perimeter . Finally , we used our morphometric dataset to determine which NMJ features might correlate with each other and which features appear comparatively independent , to reveal coordinated aspects of NMJ morphology . A pair wise correlation analysis was performed , in which the correlations of all possible feature pairs were determined and ordered accordingly ( Fig 7A ) . As one might have expected , the strongest positive correlation was found between branches and branching points ( R = 0 . 92 ) , indicating that these features can almost predict each other . The other group of moderately-to-strongly correlating features included the size-related features NMJ area , perimeter , length and longest branch length ( 0 . 45<R<0 . 82 ) . The number of active zones correlated to a lesser extent with this group ( 0 . 36<R<0 . 47 ) . We only observed a weak correlation between the NMJ area and the muscle area ( R = 0 . 35 ) . Both the features muscle area and number of islands seemed to behave as independent features , lacking any moderate ( 0 . 4<R≤0 . 7 ) or strong ( R>0 . 7 ) correlation with any of the other NMJ features . We applied a principal component analysis ( PCA ) , a statistical method to reduce the dimensionality of a dataset , and summarized our data in five different components . This aggregation was the most acceptable because it explained 91% of the variance of our data and classified each of the measured NMJ features on one of these components ( Fig 7B; Table A in S3 Table ) . The size-related features NMJ area , perimeter , length and longest branch length constitute the first principal component , which explained 38 . 2% of the total variance . The features branches and branching points contributed most to the second principal component ( 21 . 7% of the total variance ) , thus the second component mainly accounted for NMJ geometry . The first two components explained almost 60% of all variance . The angles between the features contributing to the first versus the second principal components were around 90° , indicating that the variables NMJ area , perimeter , length and longest branch length hardly correlated with the variables branches and branching points . This is in agreement with the above reported correlation coefficients ( Fig 7B and 7C ) . The features islands , muscle and active zones contributed most to the third ( 13 . 4% ) , fourth ( 10 . 4% ) and fifth ( 7 . 7% ) principal component , respectively . Based on these results , we defined five morphometric groups with a variety of mutual kinship: 1 ) NMJ size ( NMJ area , perimeter , length and longest branch length ) , 2 ) geometry ( branches and branching points ) , 3 ) islands , 4 ) muscle area and 5 ) number of active zones . Important to note is that the active zones also showed a moderate correlation and contribution to the NMJ-size features underlying most of the first component . This suggests that the number of active zones is at least partially coordinated with NMJ size . We obtained comparable results when applying PCA on datasets specific to one combination of gender and genetic background library ( Tables A-E in S3 Table ) or datasets specific for one abdominal body segment ( Tables F-I in S3 Table ) . In summary , our data showed that synaptic size varies the most within ( natural ) populations , followed by the branching geometry . It is remarkable that the size- and geometry-related features hardly correlated , suggesting that these features are differentially regulated during larval NMJ development . Our macro was designed to cope with the challenges of high-throughput images with limited resolution and quality . However , to ensure wide applicability we also tested our macro on confocal images ( S3 Fig ) . Following a similar strategy as above , manual and macro counts were compared between n = 15 NMJ confocal images , co-labeled for Dlg1 and Brp ( Tables A-I in S4 Table ) . No significant differences and ccc scores ≥0 . 83 were found for the NMJ features NMJ area , perimeter , length , longest branch length , number of islands , number of branches , number of branching points and number of active zones when manual measurements were compared to the macro assessment ( Table 2 ) . We found , however , a significant difference between manual and macro bouton count ( p = 0 . 01; ccc = 0 . 55 ) , which indeed confirmed that the marker and not the technique is causing this difference . It generally applied that the better the quality of the image , the better the macro performed . We further tested the applicability of our software to other synaptic markers . Horseradish peroxidase ( Hrp ) is a neuronal membrane marker , commonly used to stain NMJ presynaptic terminals . Visual inspection of macro-annotated images still revealed errors in bouton counting for boutons that lacked a discernible interbouton space . All other NMJ features were displayed correctly , as is shown in a representative NMJ image ( Panel A in S2 Fig ) . Neither the pre- or postsynaptic marker tested ( Dlg1 and Hrp ) where suitable for bouton counting with our macro . Since the number of boutons is a frequently assessed parameter in studies of NMJ morphology , we further optimized the macro in order to reliably recognize and count the boutons using the synaptic markers Synaptotagmin ( Syt ) and Cysteine string protein ( Csp ) , two presynaptic vesicle-associated proteins . Both proved to be very suitable markers to distinguish and count even closely positioned boutons , probably because of the complete lack of staining in interbouton regions ( Panels B-C in S2 Fig ) . For appropriate segmentation of bouton numbers a second macro was created: Drosophila_NMJ_Bouton_Morphometrics . It is available via the same public figshare repository: https://figshare . com/s/ec634918c027f62f7f2a [36] . Drosophila_NMJ_Bouton_Morphometrics allows users to accurately count boutons on Syt or Csp immunostaining , co-labeled with Brp The working procedure is the same as described previously for Drosophila_NMJ_Morphometrics ( S1 Protocol ) . It provides a result file where the NMJ features: number of boutons , NMJ bouton area , NMJ length , NMJ longest branch length , number of islands , number of branches , number of branching points and number of active zones are assessed . To prove the reliability of Drosophila_NMJ_Bouton_Morphometrics bouton counts , the same validation procedure as described previously was used to evaluate manual versus macro bouton counts in n = 26 NMJ confocal images , labeled with Syt ( S5 Table and S4 Fig ) . No significant differences in number of boutons and a ccc score of 0 . 96 between manual and macro counting were found ( Table 3 ) .
Our knowledge on molecules and mechanisms that shape synapses has greatly expanded in the last decade , and genetic screens in Drosophila have made an important contribution . Synapse morphology is a frequently used readout to discover genes required for proper synaptic function . Most studies have used ( semi- ) quantitative analyses , using e . g . the selection- and line-options of Fiji/ImageJ , but often only upon initial visual detection and performed by hand . Although this has proven to be sufficient to identify genes that if mutated grossly disrupt synapse development , this strategy likely has left more subtle modulators unidentified . Thus , the extent of the synapse regulome , including players that ensure proper orchestration of synapse coordinates , still awaits discovery . Their comprehensive identification needs a sensitive readout and a thorough understanding of how different morphological aspects relate to one another . Sutcliffe et al . created a publically available ImageJ plugin called “DeadEasy Synapse” , which measures the total voxel size of Brp-positive active zones per NMJ [44] , and is thus complementary to our macro that instead achieves active zone counts and quantitative assessment of eight further morphometric NMJ features . In addition to Fiji/ImageJ , Cellprofiler is another open-source system for high-throughput image analysis [45] . It has been proven very useful for cell image analysis [46–48] , but also for morphological phenotypes measured in C . elegans [49] . Cellprofiler lacks options to trace branch-like skeleton structures , required to measure NMJ length and branching pattern . In this study , we developed such a tool . We show that our Fiji-based macro is semi-automated , sensitive , and objective , whereas manual counting is laborious and can be assumed to be subject to interpersonal differences . The macro generates output files that allow the user to evaluate accuracy of the image segmentation and to correct or exclude ( depending on the nature of the limiting feature ) annotated images from further analysis . A low quality of the immunohistochemistry resulted in less correctly assessed NMJ images . Whenever the input quality was guaranteed ( input checkpoints 1 and 2 ) , we retained 84–92% of NMJ images , depending on the feature of interest . In this study where we took a specific interest in the correlations among all features , we exclusively used the NMJ images in which all features could be assessed with high accuracy . Staining variability is responsible for most of the excluded images . However , it similarly influences manual evaluation and can therefore not be linked to the macro performance . Based on literature and our experimental observations , we aimed at quantifying nine morphological NMJ features . The macro performance was assessed for both wide field high-content and confocal muscle 4 NMJ images by investigating the deviation between manual and macro evaluations at three different levels: ( 1 ) sample distribution , ( 2 ) per given sample , and , most importantly , ( 3 ) for concordance . We deliberately chose the word ‘deviation’ over ‘error’ to underline that neither method can be considered as objectively true . Whereas successful for eight of these features , we were not able to optimize the bouton count in a satisfying manner for Dlg1 , our marker of choice . The successful features were scored objectively and accurately , given the equal confidence interval widths and high concordance correlation coefficients ( ≥0 . 84 , from which seven features even scored above 0 . 90 ) . Whereas the macro measurements resulted in somewhat higher absolute values for length-related parameters , the high ccc scores demonstrate that this is a consistent proportional difference compared to the manual evaluation . Consequently , when both mutant and control samples are assessed by the same method , the difference ( mutant:control ) is equal for both methods . The macro counts somewhat higher absolute values , because it continuously thresholds between fore- and background , whereas manual measurements -in this case- are based on straight lines . The NMJ area is a two dimensional NMJ feature , a small difference in area evaluation therefore has a larger effect , explaining the somewhat lower ccc . This phenomenon is intrinsic to the nature of this parameter , as is also illustrated by a similar deviation between both manual experimentors . We conclude that our methodology shows accuracy and sensitivity comparable to manual evaluation for eight NMJ features . Manual versus macro assessment of bouton number was not comparable , given the low concordance correlation coefficient of 0 . 22 . We thus excluded the number of boutons from further analysis on this control dataset in this study . The macro uses a watershed transform -an algorithm that separates touching or slightly overlapping particles by identifying their local maxima in the distance function within these objects- on the segmented NMJ outline to distinguish individual boutons on NMJ outline invaginations , characteristic for interbouton regions [50] . We show that Dlg1 is not the optimal marker to determine bouton number , as this ( mainly ) postsynaptic marker presents with poorly pronounced interbouton constriction . We developed a second macro “Drosophila_NMJ_Bouton_Morphometrics” to assess the number of boutons using anti-Syt and anti-Csp , which successfully segmented boutons . We carefully validated the reliability of this second macro as done previously for our markers of interest ( Dlg1 and Brp ) . Taken together , we developed 2 Fiji-based macros ( Drosophila_NMJ_Morphometrics and Drosophila_NMJ_Bouton_Morphometrics ) that perform objective and sensitive quantification of nine morphological NMJ features in a high-throughput manner . Beyond the sensitive read-out that is required to detect subtle differences in synaptic morphology , a thorough understanding of natural variation and contributing factors is required to limit the calling of false positive phenotypes . We therefore quantified the effect of the main variables in our study -gender , genetic background and abdominal body segment- on the eight muscle 4 NMJ features acquired by our macro and the manually acquired muscle area . Males and females showed considerable differences in size-related NMJ features and in the number of active zones . The female synaptic terminal was almost 5% longer and 8% bigger compared to males and it contained 7% more active zones . In agreement with the bigger size of female flies [51] , the area of muscle 4 was 9% bigger in females than in males . Therefore , one might generate false positive results when comparing two datasets with each containing an uncontrolled amount of males-females . Interestingly , mutant screens or gene focused studies do not always report on gender selection or control , although gender can easily be selected for analyses [52] . Furthermore , gender selection is not mentioned in NMJ protocols that are often referred to by these studies [53–58] . Sex-specific differences at specific NMJ terminals were already described: Lnenicka , et al reported that muscle 5 produced larger excitatory postsynaptic potential in females and that type 1s motonerves on muscle 2 and 4 showed a greater charge transfer [59] . Interestingly , except for muscle size , no sex-specific difference in electrophysiology was detected for type 1b neurons on muscle 4 , the synaptic terminal we focused on . Synapse morphology of 4-1s and 5-1b was measured , but no differences between males and females were found in this study for NMJ length , number of branches or number of boutons , which led the authors to suggest that the observed differences in transmitter release are due to ultra-structural and/or biochemical differences [59] . Our highly sensitive morphometry on 4-1b uncovered sex-specific NMJ properties that can potentially underlie the reported physiological differences . To our knowledge , no sex-specific regulators of NMJ morphology have been described yet . Many of the NMJ-size related features were also significantly different between the two isogenic host strains ( GD and KK RNAi libraries ) we tested . Unquestionably , genetic differences can influence larval growth [60] , but since no significant difference was observed in muscle 4 size , we did not find indication of overall differences in animal growth in the two investigated genotypes . A traceable difference between both strains is the yellow+ marker in a yellow mutant background carried by the KK host strain . No NMJ abnormalities have been reported in yellow mutants , but yellow mutant alleles affect male courtship and mating behavior [61] , suggesting a potential role in or effect on the nervous system . Finally , we also demonstrated a significant impact of the abdominal body segments on muscle 4 NMJ morphometry . The observed patterns per morphological feature could be divided into four categories , with the greatest variance in geometry-related features and active zones . In general , muscle 4 type 1b synapses were shorter , more branched and have the highest number of active zones anteriorly . In summary , all three tested variables in our NMJ analysis—gender , genetic background and abdominal segment- had a significant effect on at least some of the assessed morphological NMJ features on muscle 4 . For quantitative evaluations , if aiming at high sensitivity , it is therefore important to take these features into account . So far , Drosophila NMJ studies were often focused on a particular aspect of NMJ morphology , rather than assessing morphology more comprehensively . Consequently , the interdependencies of morphological features at this synapse , and at others , remained unknown . Carefully evaluating these relationships in unperturbed conditions , we here shed light onto which features are to what extent correlated , and thus provide first insights into the system properties of this important model synapse . We identified five morphometric groups based on a pair wise correlation and principal component analysis . Interestingly , the features underlying the two groups explaining most of the variance hardly correlated , which led us to speculate that different biological processes underlie NMJ size and geometry . In agreement , both groups behaved differently between sexes and over the abdominal segments . Whereas NMJ geometry showed a decreasing number of branches and branching points from anterior to posterior , size features seemed to increase from abdominal segment 2 to 3 . Surprisingly , the muscle 4 area only correlated to a minor extent with NMJ size ( R = 0 . 349 ) and very weakly with the number of active zone ( R = 0 . 160 ) . Although a strong correlation was observed between bouton number and muscle 6/7 size during embryonic and larval growth [62] , the features seem to be less correlated within third instar larval stage . Whereas we cannot exclude differences between different NMJs/muscles , it seems more likely that the earlier observed correlation over a developmental time period reflect a general trend in growth to which all underlying mechanisms are subject to , rather than a tight causal relationship . Our result is in agreement with an earlier observation in which muscle size only partially ( ~50% ) explained the variation in bouton number when comparing different Drosophila species [60] . Our finding provides an argument not to normalize synaptic size by muscle size , as has been practiced by some studies in third instar Drosophila larvae . These conclusions are applicable to NMJ morphological analyses of muscle 4 with the two markers of choice . Although we validated our macro and carefully evaluated its performance in each NMJ image , our findings are still subject to technical variation such as variations in specimen dissections , immunolabeling intensities and the genome constitution . However , we have shown that the macro does not produce more variance than a manual counter does and the consistent correlations among gender and genetic background subgroups show the reproducibility of our data and support our conclusions . In our study , we evaluated three natural sources of variation to study the relation between nine synapse parameters: sex , abdominal segment identify and two selected genetic backgrounds . Additional sources causing NMJ structural variation have been demonstrated , such as larval and induced synapse activity [16 , 17] . We raised larvae under controlled conditions and therefore do not expect a significant variation in our data linked to such mechanisms . In summary , we developed a sensitive , accurate and semi-automated Fiji-based macro that permits high-throughput systems morphometry on the well-studied Drosophila larval NMJ on muscle 4 . This method has the ability to handle several commonly used NMJ markers and microscope techniques . Here , we used a systems biology approach on two comprehensively generated , multiparametric datasets to start to understand how different morphological aspects of the synapse are coordinated . We showed how the nine measured morphometric features relate to one another and defined five morphometric groups in which features showed higher intra- than inter-correlation ( Fig 8 ) , suggesting that different molecular mechanisms are at work . The macro that we have developed and the here reported results have shed first light onto the design principles of an important model synapse , paved the way to quantitative synapse morphometry and can be applied to identify genes that couple features within a one morphometric group or that orchestrate the relations between different morphometric groups .
Fly stocks were maintained using standard Drosophila diet ( sugar/cornmeal/yeast ) . Virgins from a w1118; UAS-Dicer-2; elav-Gal4 promotor line were crossed to males from either w1118 ( VDRC stock 60000 –genetic background of the GD library ) or y , w1118;P{attP , y[+] , w[3`] ( VDRC stock 60100 –genetic background of the KK library ) and maintained at 28°C , 60% humidity . Crosses were performed with consistent amounts of flies and food . Positive control RNAi strains were in a similar manner crossed to virgins of the promotor line . The following RNAi strains were obtained at the Vienna Drosophila research center: Ankyrin2 ( FBgn0261788; CG42734 ) P[KK104937]VIE-260B ( VDRC stock KK107238 ) and P[KK106729]VIE-260B ( VDRC stock KK107369 ) ; highwire ( FBgn0030600; CG32592 ) w1118; P[GD14101]v28163 ( VDRC stock GD28163 ) and w1118; P[GD14104]v36085 ( VDRC stock GD36085 ) and Rab3 ( FBgn0005586; CG7576 ) P{KK108633}VIE-260B ( VDRC stock KK100787 ) . Panneuronally induced knockdown conditions were compared to progenies from the driver crossed to the respective library host strain ( GD60000 or KK60100 ) . Wandering L3 larvae were labeled for their gender and dissected , fixed in 3 . 7% paraformaldehyde for 30 min and co-labeled for bruchpilot ( Brp ) and discs large 1 ( Dlg1 ) . Brp was revealed using the primary antibody nc82 ( 1:125 ) ( Developmental Studies Hybridoma Bank ) applied overnight at 4°C and the secondary Alexa 488-labeled goat-anti-mouse antibody ( 1:125 ) ( Invitrogen Molecular Probes ) . Discs large was visualized using primary antibody anti-Dlg1 ( 1:25 ) ( Developmental Studies Hybridoma Bank ) conjugated with the Zenon Alexa Fluor 568 Mouse IgG1 labeling kit ( Invitrogen ) , applied according to the manufacturer’s protocol . For Hrp , Syt and Csp labeling , larvae were blocked for 1 . 5h on 5% NGS-PBS-T ( 0 . 3% Triton X-100 in PBS ) . Primary antibody anti-Hrp ( rabbit , 1:750 ) ( Jackson ImmunoResearch ) , anti-Syt ( rabbit , 1:100 ) ( kindly provided by H . Bellen ) or anti-Csp ( mouse , 1:25 ) was applied overnight at 4°C , followed by the Alexa 568- or 488-labeled secondary antibodies ( 1:500 ) ( Invitrogen Molecular Probes ) . NMJ images were obtained of type 1b NMJs at muscle 4 using an automated Leica DMI6000B high-content microscope . Individual NMJs were imaged at 10x ( snapshot; Dlg1 only; 1 . 096 pixels/μm ) and 63x magnification ( stack; both channels; 6 . 932 pixels/μm ) . A fixed stack size was used , comprising 42 images per channel with a z-step size of 0 . 3μm and a z-volume of 12 . 152μm . The 2x42 images were saved as separate tiff files , encoding the NMJ number , z-plane and channel number in the file name . The area of muscle 4 was manually assessed via the segmented line option in Fiji at the lower magnification . Confocal NMJ images were obtained of type 1b NMJs at muscle 4 using the Olympus FV1000 microscope . Individual NMJs were imaged at 60x ( stack; both channels; 4 . 83 pixels/μm ) with a z-step size of 0 . 91μm and a z-volume adjusted to the depth of the NMJ . The macro , written in ImageJ macro language , is compatible with the open source Fiji platform [32] . The entire analysis procedure consists of three steps , for which three separate sub-macros were written . This setup was chosen to allow maximum flexibility in the workflow . Sub macros can be executed from the main macro through a graphical user interface ( GUI ) . The first sub macro “Convert to stack” traverses a directory structure selected by the user in the GUI . Detected unprocessed images belonging to the same NMJ are recognized based on their z-plane and channel number and subsequently converted and saved to a hyperstack ( containing all image data in a single tiff file ) and maximum-intensity based Z-projection ( referred to as ‘flat stack’ ) . In our setup , the macro takes as input two channel z-stacks where the individual z-planes are stored as separate tiff files . The macro can however be adapted to deal with other types of input images . The second part of the macro ( sub macro 2 –“Define ROI” ) is a semi-automated step where flat stacks are detected and opened automatically in a consecutive manner . Only images are opened that have not been processed by sub macro 2 before . In every flat stack , the region of interest ( ROI ) is manually defined by the user using the free hand selection tool . A binary ROI image is created and stored in the image source directory . The third sub macro ( “Analyze” ) identifies hyperstacks for which the ROI image is present . NMJ image analysis is performed throughout each stack , within the limits of the image-specific ROI . Macro-annotated images are stored , containing a delineation of the analyzed NMJ . Additionally , a text file containing nine quantified features per NMJ is stored in the main directory . To enable high-throughput image acquisition and analysis , we used wide field fluorescence imaging to develop the macro . Out-of-focus fluorescence around the NMJ terminal , inherently present in these images , necessitates pre-processing to distinguish foreground signal from background noise . Therefore in the first step of the macro images are filtered applying a rolling ball background subtraction algorithm with a radius of 20 pixels . This algorithm is considered effective and fast for suppression of a non-uniform background with objects of rather constant diameter [63] . The outline of the entire NMJ is defined by an auto-threshold selection based on Renyi’s Entropy algorithm , applied to the Dlg1 or Hrp staining . This algorithm was shown to outperform several other entropy-based threshold selection methods [64] , and resulted in consistent and adequate segmentation on a series of test images in the present study . Constriction of the synaptic terminal between boutons provided the basis for the analysis of bouton counting’s . A watershed separation is performed on the binary NMJ outline . Resulting objects exceeding an ( empirically determined lower bound ) area threshold of 100 pixels are considered to represent boutons . To filter against background noise as for example present in Hrp staining , an optional filter ( “Remove small particles” ) was implemented to remove particles smaller than 100 pixels . To measure NMJ length and branching geometry , a binary skeleton for the NMJ is determined . The skeleton is a one-pixel thick axis along the center of the NMJ , calculated using mathematical morphology on the binary image . We found that the auto-threshold described above , used to accurately determine the NMJ outline , and was sometimes too restricted for accurate determination of the skeleton when using Dlg1 , Syt or Csp staining . The macro therefore uses auto-threshold selection based on Li’s Minimum Cross Entropy for this purpose . This algorithm generally results in somewhat wider segmentation results , as previously witnessed by the results of Sengur et al . [65] . The Renyi’s Entropy algorithm was used for NMJs stained with Hrp . From the NMJ skeleton five features ( length , longest branch length , number of branches , branching points and islands ) are calculated . Subsequently , the number of active zones is counted in the Brp-channel by finding local intensity maxima in the 3D image stack . To reduce the effect of intensity variations over individual active zones , stacks are first filtered applying a 3D grey closing with a small circular structuring element . Identified local maxima are considered to represent one active zone if they do not touch other local maxima ( either horizontally/vertically or diagonally ) and exceed a minimum intensity level ( automatically determined using Huang's fuzzy thresholding method ) , to prevent background fluctuations to be counted as active zones . Confocal NMJ images were processed in a similar manner , with maxima noise tolerance ‘100’ and Brp-puncta lower threshold ‘250’ . The nine NMJ features measured by the macro were in parallel manually quantified , blind to the macro results , independently by two experimenters in 30 NMJ images . Images were processed with Fiji . For each channel a projection was created from in-focus planes . The subtract background algorithm was applied to the Dlg1 channel , followed by 3 consecutive applications of the standard FIJI smooth filter ( 3x3 average filter ) , and area and perimeter were determined by manually thresholding the NMJ terminals . All length related NMJ features were measured using the freehand lines tool . Active zones were visually assessed in the Brp channel . Macro counts were plotted against averaged manual counts , and Lin’s concordance correlation coefficients was calculated in R , using the epi . ccc function of the epiR package [66] . The % deviation between manual and macro count is calculated as ( average manual result—macro result ) / average manual result x 100% . Sensitivity is true positives / ( true positives + false positives ) , specificity is true positives / ( true positives + false negatives ) . Active zone results are compared to experimentor #1 . Confocal NMJ images ( n = 15 ) were validated in a similar manner by one experimenter . Picture one was excluded from Brp-analysis because of low staining quality . Statistics were performed in R ( R Development Core Team , 2008 ) [67] . For comparisons between manual and macro counts , gender and genetic backgrounds , independent 2-group t-tests were applied for normally distributed features ( area , active zones , boutons , length , longest branch length , perimeter and muscle ) and Mann-Whitney U tested for not normally distributed features ( branches , branching points and islands ) . Whenever required , p-values were adjusted by a Holm-Bonferroni correction and indicated as padj . Anova-Tukey method was used for body segment analysis followed by Tukey’s honest significance test ( Tukey’s HSD ) . Pearson correlations were calculated for the different feature combinations and visualized in an adjusted scatter plot matrix [68] . Principal component analysis [69] was used to study the relationship among different aspects of synapse morphology . Drosophila_NMJ_Bouton_Morphometrics was specifically developed to assess the number of NMJ boutons of Syt- or CSP- immunostained NMJs . The macro processing and macro analysis follow the same steps as described previously in this section , except that the outline of the entire NMJ is defined by an auto-threshold selection based on the algorithm moments and a dilating step prior to the watershed separation . These increase efficiency of bouton segmentation . The algorithm area in this macro assesses bouton area after the bouton segmentation , and the algorith NMJ perimeter is obsolete and has been removed . Resulting objects exceeding an ( empirically determined lower bound ) area threshold of 10 pixels are considered to represent boutons . The filter against background noise ( “Remove small particles” ) should always be activated when running this macro and was implemented to remove particles smaller than 10 pixels . The validation of bouton counts for the Drosophila_NMJ_Bouton_Morphometrics macro was performed using confocal images of NMJs ( n = 26 ) immunolabeled with anti-Syt antibody . The number of boutons were counted by two experimenters blind to the results of the macro , and where compared with the macro counts using the same procedures as described in macro validation . Two picture where excluded from Syt-analysis because of low staining quality . | Altered synapse function underlies cognitive disorders such as intellectual disability , autism and schizophrenia . The morphology of synapses is crucial for their function but is often described using only a small number of parameters or categories . As a consequence , it is still unknown how different aspects of synapse morphology relate to each other and whether they respond in a coordinated or independent manner . Here , we report a sensitive and multiparametric method for systematic synapse morphometry at the Drosophila Neuromuscular Junction ( NMJ ) , a popular model for mammalian synapse biology . Surveying a large NMJ image repository , we provide insights in the natural variation of NMJ morphology as a result of differences in gender , genetic background and abdominal body segment . We show which synapse parameters correlate and find that parameters fall into five groups . Based on our findings , we propose that two of them , NMJ size and geometry , are controlled by different molecular mechanisms . Our study provides insights into the design principles of a model synapse and tools that can be applied in future studies to identify genes that modulate or co-orchestrate different aspects of synapse morphology . | [
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| 2016 | A New Fiji-Based Algorithm That Systematically Quantifies Nine Synaptic Parameters Provides Insights into Drosophila NMJ Morphometry |
Directional mechanoreception by hair cells is transmitted to the brain via afferent neurons to enable postural control and rheotaxis . Neuronal tuning to individual directions of mechanical flow occurs when each peripheral axon selectively synapses with multiple hair cells of identical planar polarization . How such mechanosensory labeled lines are established and maintained remains unsolved . Here , we use the zebrafish lateral line to reveal that asymmetric activity of the transcription factor Emx2 diversifies hair cell identity to instruct polarity-selective synaptogenesis . Unexpectedly , presynaptic scaffolds and coherent hair cell orientation are dispensable for synaptic selectivity , indicating that epithelial planar polarity and synaptic partner matching are separable . Moreover , regenerating axons recapitulate synapses with hair cells according to Emx2 expression but not global orientation . Our results identify a simple cellular algorithm that solves the selectivity task even in the presence of noise generated by the frequent receptor cell turnover . They also suggest that coupling connectivity patterns to cellular identity rather than polarity relaxes developmental and evolutionary constraints to innervation of organs with differing orientation .
Two of the most highly conserved sensory-neural maps in vertebrates are those underlying postural control during locomotion and rheotaxis [1–8] . These sensory-motor processes originate from the action of mechanoreceptive hair cells residing , respectively , in the inner ear and in the lateral line [9–16] . Mechanosensitive ion channels in hair cells open or close according to the direction of deflection of the cells’ apical hair bundle [4 , 17] . Consequently , the planar orientation of the hair bundles endows the sensory epithelium with directional excitability . The mammalian ear contains 3 vestibular receptive organs—the semicircular canals that detect angular shifts of head position , the sacculus that senses vertical movements , and the utricle that detects horizontal movements [18] . Utricular and saccular hair cells are divided into 2 groups of opposing hair bundle orientation , each situated across a line of polarity reversal [19 , 20] . Similarly , the superficial lateral line in fishes senses velocity fields in the surrounding water [3 , 6 , 7 , 13–15] . This system is formed by a collection of discrete mechanosensory organs called neuromasts , which are distributed across the body of the animal [11] . In zebrafish , each neuromast contains 16 to 20 mechanoreceptive hair cells that occur in 2 equally numbered populations with opposite planar polarity [21] . However , unlike the utricular and saccular epithelia , hair cells of both polarities intermingle in each neuromast ( Fig 1A and 1B ) . In addition to this local epithelial polarity , the global orientation of the hair cells varies according to the position of the neuromast . For example , the posterior lateral line of larval zebrafish contains neuromasts whose axis of planar polarity is oriented either parallel ( horizontal neuromasts ) or perpendicular ( vertical neuromasts ) to the anteroposterior body axis ( Fig 1C ) [21] . Horizontal neuromasts , therefore , are formed by hair cells that are activated by water flowing in the rostrocaudal direction ( rcHCs ) and by hair cells of caudorostral directional tuning ( crHCs ) ( Fig 1D ) . Each branch of the lateral line in larval zebrafish contains around 60 bipolar lateralis afferent neurons ( LANs ) that are postsynaptic to the hair cells ( Fig 1C–1E ) . Typically , each neuromast is innervated by the peripheral axons of 4 LANs , but each axon exclusively synapses with hair cells of identical planar polarity ( Fig 1D–1G ) [22 , 23] . Because the transmission content of a sensory neuron is determined by all the inputs that it receives [24] , planar-polarity–selective connectivity renders each LAN tuned to a single direction of mechanical stimulation to form a labeled-line sensory pathway from the periphery to the brain ( Fig 1C and 1D ) . How planar-polarity–selective synapses are established remains unknown [5 , 8 , 22 , 25] . Understanding this process is important because it will reveal the basic rules that direct the development of neural circuits that enable directional mechanosensation , as well as how they are maintained despite continuous turnover of receptor cells [26–31] . Previous work has led to two models to explain polarity-selective synaptic connectivity in the lateral line . The biased-selector model suggests that synaptogenesis is inherently promiscuous and that selectivity occurs by the continuous rectification of mismatched synapses through a biased interaction between hair cells and the axonal population [32] . By contrast , the scaffold model presents constitutive basal projections from the hair cells as short-range processes that provide instructive polarity-specific scaffolds to the axons [33] . This idea is attractive in its simplicity , but it is based exclusively on phenomenological descriptions that have not been experimentally tested . Although both models are consistent with most results , they do not satisfy some observations . For example , the biased-selector model is difficult to reconcile with the invariable ratio of half of the LANs innervating every identically polarized hair cell . Conversely , the scaffold model cannot explain the loss of selectivity by solitary axons . Furthermore , two model-independent basic aspects of the selectivity mechanism remain unsolved: first , whether synaptic connectivity is defined locally in each neuromast independently of its global orientation and second , whether axons synapse with hair cells of identical polarity regardless of their local orientation ( synaptic fidelity ) or with hair cells of a specific orientation ( synaptic partner matching ) . This distinction is important because the rules underlying fidelity ( stochastic ) and partner matching ( deterministic ) are likely to be mechanistically different .
We began by experimentally testing the scaffold model using high-resolution live imaging in transgenic zebrafish expressing different fluorescent markers in LANs , hair cells , and their presynaptic active zone ( Fig 2A–2C ) . We first confirmed previous findings that only immature hair cells produce basal projections , up to 15 hours after they are born ( Fig 2A and 2B ) [33] . Synaptic connections between LANs and hair cells correlate with the stable juxtaposition of presynaptic puncta containing the active-zone constituent protein Ribeye and postsynaptic neurites ( Fig 2C ) [32 , 34–36] . Next , we assessed synapses by individualized axons marked by mosaic expression of the red fluorescent protein mCherry in single LANs , in transgenic embryos co-expressing beta1-actin-enhanced green fluorescent protein ( EGFP ) and ctbp2a ( Ribeye ) -Kusabira in every hair cell ( Tg[myo6b:actb1-EGFP; pou4f3:ctbp2l-mKOFP] ) ( Fig 2D–2G’” and S1 Video ) [22 , 26 , 37 , 38] . In less than one-half of the hair cells that were analyzed ( N = 40 ) , basal projections and axonal terminals physically interacted ( 16/40 ) , but only a small minority of these interactions resulted in stable synapses ( 3/16 ) . Moreover , although Ribeye ( + ) puncta were readily evident in hair cells ( arrows in Fig 2D–2E’” ) , we could never detect them in basal projections ( N = 31 ) ( arrowheads in Fig 2D–2E’” , and S1 Video ) . These observations suggest that apposition of presynaptic projections and postsynaptic neurites correlates poorly with synaptic stabilization but does not rule out a function of these projections in synaptic selectivity . To test this possibility , we assayed synaptogenesis by regenerating axons . To this end , we severed axons using an ultraviolet laser delivered through the imaging objective ( Fig 2H–2K ) [39] . We found that cut axons regenerated to re-innervate preexisting mature hair cells that did not produce basal projections ( Fig 2L–2O and S2 Video ) . To directly assess synaptic selectivity by regenerating axons , we marked single LANs with mCherry in the transgenic lines Tg[myo6b:actb1-EGFP] or Tg[myo6b:actb1-EGFP; nkhgn39d] , which reveal hair cell planar polarization in vivo ( Fig 1F and 1G ) [22 , 40] . First , we corroborated that individually marked axons selectively synapse with hair cells of identical orientation before severing ( Fig 3A , 3B , 3E and 3F ) . Two days after severing , every regenerated axon selectively synapsed with identical-oriented hair cells despite the conspicuous absence of basal projections ( N = 35 ) ( Fig 3C , 3D , 3G and 3H ) . Together , these results indicate that , under our experimental conditions , presynaptic scaffolds do not form the bases of polarity-selective connectivity in the lateral line . During the course of the previous experiments , we made the surprising discovery that regenerating axons nearly always re-innervate hair cells not only of identical but also of the original planar orientation , recapitulating neuronal directional tuning ( Fig 3I , right bar ) . In most of these cases , when axons were cut immediately below the neuromast , they also re-innervated the original neuromast ( Fig 3J , right bar ) . Thus , we hypothesized that a potential memory of previous connectivity could rely on 2 possible sources of information: a cell-autonomous molecular code , or a nonautonomous cue that may include the neuronal extracellular basal lamina that remains around preexisting hair cells . Such nonautonomous blueprint of previous connectivity is reminiscent of the neuronal extracellular matrix that directs the repair of mammalian neuromuscular junctions after nerve crush and that is found in the piscine electric organs [41 , 42] . To test this possibility , we cut axons and , subsequently , killed hair cells pharmacologically by incubating specimens in a solution of neomycin , which eliminates local sources of information about previous synapses . Yet this manipulation did not alter the recovery of LAN directional tuning after the concurrent regeneration of axons and hair cells ( N = 8 ) ( Fig 3K–3N ) . In a separate set of experiments , when axons were cut furthest from the neuromasts ( near the lateralis ganglion ) , they often did not re-innervate the original neuromast ( Fig 3J , left bar ) . Nevertheless , these axons formed selective synapses with identical-oriented hair cells in new neuromasts and almost always with those whose orientation was the same as the original ( Fig 3I , left bar ) . Moreover , we found a remarkable correlation between horizontal and vertical neuromasts in that , when regenerating axons that originally innervated crHCs in horizontal neuromasts ( Fig 3O–3Q ) innervated a vertical neuromast , they always synapsed with ventrodorsal hair cells ( vdHCs ) ( N = 4 ) ( Fig 3O , 3O” , 3R and 3S ) . Conversely , rcHC-innervating LANs switched to re-innervate dorsoventral hair cells ( dvHCs ) in vertical neuromasts after regeneration ( N = 3 ) . Put together , these data reveal that LANs consistently recognize hair cells of identical polarity and of a specific orientation . These results are important because they suggest that hair cells of either orientation must have distinct marks that can be recognized by the axons during development and regeneration . It has been shown that planar-polarity–selective synapses occur independently of evoked activity [23 , 32] . Therefore , we hypothesized that a cell-autonomous molecular cue differentiates hair cells of either orientation in each neuromast and identifies as “same” the horizontal rcHCs and vertical dvHCs , as well as the crHCs and vdHCs . While considering this possibility , we noted a previous study in mice showing that a mutation in the transcription factor Emx2 eliminates the line of hair cell polarity reversal in the utricular and saccular maculae without affecting coherent hair cell orientation , and that Emx2 is exclusively expressed on one side of this line in wild-type animals [19 , 43] . A more recent report revealed that gain- and loss-of-function of Emx2 is sufficient to instruct hair cell orientation along a conserved planar polarity axis in zebrafish neuromasts [44] . These observations raise the possibility that Emx2 is part of a code underlying polarity-selective synaptogenesis . To test this hypothesis , we used a previously validated antibody to Emx2 to immunostain Tg[myo6b:actb1-EGFP] transgenic fish bearing individually labeled neurons . We first corroborated that Emx2-positive hair cells in wild-type fish were always rcHC and dvHC orientation , respectively , in horizontal and vertical neuromasts ( Fig 4A–4H ) [44] . Next , we found that each axon synapsed with either Emx2 ( + ) or Emx2 ( − ) hair cells ( N = 6 ) ( Fig 4I–4L and S5 Video ) . To test whether innervation determined the expression of Emx2 in hair cells , we examined zebrafish carrying a mutation in neurogenin1 , which lack all innervation to the lateral line but have normal planar polarity in neuromasts [32] . We found that Emx2 was normally expressed in about half of the hair cells in mutant specimens ( Fig 4M–4P ) . The above results , together with the strict correlation between hair cell orientation and synaptogenesis , support the long-standing idea that synaptic selectivity and coherent epithelial planar polarity are mechanistically linked [33] . However , to examine this relationship experimentally , we took advantage of trilobite zebrafish , which harbors a loss-of-function mutation in the core planar-polarity protein van gogh-like 2 ( Vangl2 ) [45] and displays a fully penetrant and strongly expressive randomization of hair cell orientation in the lateral line ( Fig 5A and 5B ) [46] . We reasoned that , if planar polarity and polarity-selective synaptogenesis are functionally linked , loss of coherent hair cell orientation will either induce neurons to innervate all , none , or random numbers of hair cells or to affect synaptic stability . First , we performed immunostaining for Emx2 in Vangl2 mutants and saw that , similarly to the wild type , approximately half of the hair cells in neuromasts were Emx2 ( + ) , indicating that neither the core planar-polarity pathway nor coherent hair cell orientation determine the expression pattern of Emx2 ( Fig 5C–5E ) . Second , we quantified hair cell innervation in Vangl2 mutants and found that , as in wild-type specimens , selective innervation correlated with Emx2 expression ( Fig 5F–5K ) and that approximately half of the hair cell population was stably innervated by each axon ( Fig 5L–5O ) . Together , these data reveal that Emx2 expression correlates with synaptic partnership and , crucially , that selective synaptogenesis is separable from coherent planar polarity . To test the function of Emx2 in synaptic partnership , we used clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 ( CRISPR/Cas9 ) -mediated genome editing to mutate Emx2 . Specifically , to facilitate this analysis by live imaging in zebrafish carrying multiple transgenes , we used the CRISPant approach for somatic mutagenesis [47] [48] . We first corroborated that Emx2 loss-of-function renders nearly every hair cell identically oriented , always dorsally in vertical neuromasts ( Fig 5P ) and rostrally in horizontal neuromasts ( Fig 5Q ) [44] . Second , we found that in Emx2-deficient animals , individually marked axons synapsed with nearly every hair cell in neuromasts ( Fig 5L and 5R–5T ) or with a small minority to none ( Fig 5L and 5U–5W ) . Some specimens presented partial expressivity of the Emx2 mutant phenotype , with neuromasts bearing a majority of hair cells oriented in one direction and a small minority in the opposite . Yet in these cases , individually marked axons also synapsed exclusively with hair cells of identical orientation regardless of their absolute or relative number ( Fig 5L ) . These results reveal that loss of Emx2 homogenizes hair cell orientation without altering the ability of axons to recognize identically oriented hair cells and , importantly , that the Emx2 expression status in hair cells determines whether they will be innervated by a specific axon .
Similarly to the mammalian utricular and saccular maculae , the hair cells in piscine neuromasts are divided into 2 populations with opposing planar orientation [11 , 21] . Utricular and saccular afferent neurons do not co-innervate hair cells across the line of polarity reversal [49–51] . Identical observations have been made in the vestibular apparatus of amphibians [51] and birds [52] . Several studies have revealed that the afferent neuronal pathway of the lateral line is organized in a similar manner [8 , 22 , 23 , 33 , 52] . This is remarkable given the absence of anatomical compartmentalization of epithelial planar polarity in neuromasts . To shed light on the synaptic selectivity mechanisms , we experimentally tested the scaffold model . We show that hair cell basal projections are dispensable for polarity-selective innervation , suggesting that they act as haptotactic cues that facilitate or accelerate the innervation of nascent hair cells in a nonselective manner , possibly by increasing the cell’s surface area during the search-and-find period that precedes synaptic maturation [22 , 53] . The above results raise 4 interwoven questions . What is the source of the selectivity cues ? Do selectivity cues act locally in each neuromast or globally along the entire lateral line system ? Does a synaptic selectivity code exist ? If so , what is its identity ? We investigated whether planar polarity per se represents a selectivity cue by using mutant zebrafish lacking the core planar-polarity component Vangl2 , in which hair cells assume random orientations . We reasoned that if coherent planar polarity instructed selectivity , its loss would randomize or destabilize synapses . In Vangl2−/− animals , connectivity patterns are stable and nonrandom because , as in wild-type specimens , each LAN axon synapses with one-half of the hair cells . Furthermore , our analysis of de novo synaptogenesis by regenerating axons shows a remarkable robustness in the lateral line system in that axons invariably re-innervate hair cells of the same orientation as their original . This remains true even when regenerating axons target a neuromast different from the original . We also found a near-perfect correlation of synaptogenesis when regenerating axons re-innervate neuromasts of differing global orientation because LANs that innervate rcHCs in horizontal neuromasts synapse with dvHCs when switching to innervate vertical neuromasts . Together , these results demonstrate that synaptic selectivity is separable from the local and global orientation of the hair cells and that it is determined independently in each neuromast . It is tempting to extrapolate these results to mammals to explain the puzzling observation that , while the core planar-polarity proteins Frizzled6 and Prikle-like2 distribute identically in the entire epithelium of the sacculus and the utricle , neurons never co-innervate hair cells across the line of polarity reversal [19 , 51 , 54] . Therefore , it is likely that both in the mammalian inner ear and in the piscine lateral line , planar polarity per se does not determine polarity-selective synaptogenesis . Assessing hair cell innervation in the vestibular system of Vangl2 mutant mice may directly test the likelihood of such evolutionary conservation . The above results suggest the existence of a label that identifies as same caudal-oriented and ventral-oriented hair cells , as well as rostral- and dorsal-oriented cells . They also indicate that the process of planar-polarity–selective synaptogenesis can be framed as a problem of synaptic partner matching rather than of synaptic fidelity . The transcription factor Emx2 is expressed in half of the hair cells in each neuromast and correlates with their global orientation in that Emx2 is exclusively expressed in rcHCs and dvHCs . We found that Emx2 expression patterns are not affected by the loss of coherent planar polarity and that axons make strict connections according to the hair cells’ Emx2 expression status , even when they are not coherently oriented . Moreover , we show that loss of Emx2 affects hair cell innervation in a predictable manner because it enables some axons to synapse with nearly every hair cell in the neuromast while remaining selective as well as simultaneously prevents other axons from making any synapse . These findings demonstrate that Emx2 establishes an identity code for synaptic partners and represents the link between planar polarity and synaptic selectivity . It transpires that there must be differences between the neurons that innervate each hair cell polarity class because , if neurons were identical , regenerating axons should not be able to regain directional tuning by recognizing previous partners or re-innervate matching populations of hair cells when switching from horizontal to vertical neuromasts . Although we currently lack the tools to directly test this hypothesis , we believe that it is likely given some evidence from the mouse , in which a common pool of Neurogenin-1 ( + ) neuronal progenitors produces auditory afferent neurons with segregated connectivity in the inner ear [55] . We wonder how a transcription factor may mediate polarity-selective synaptic connectivity . Genetic perturbation experiments indicate that Emx2 does not control local or global planar polarity because the global axis of polarization of the hair cells is not altered in the inner ear of mice and in neuromasts of zebrafish lacking Emx2 . Instead , we interpret the collective data as suggesting that Emx2 instruct hair cell identity , which in turn enables cells to implement the global polarity cue in one of the two possible directions . One possibility is that Emx2-dependent expression of high-affinity complementary transmembrane proteins determines synaptic partnership between matching subpopulations of LANs and hair cells . This will enable neurons to recognize synaptic partners locally and independently of their relative of absolute number and to regain directional tuning after recovery from injury . It also explains how individual neurons synapse with hair cells of differing global orientation when they co-innervate multiple neuromasts during the expansion of the lateral line in juvenile and adult zebrafish [56 , 57] . This can occur because synaptic partnership is controlled locally by Emx2 , independently of the specific orientation of the hair cells . Taken together , our data strongly support the conclusion that Emx2 establishes an identity code that governs synaptic partnership between hair cells and afferent neurons . How may the rules governing synaptic partner choice be implemented ? In other words , what is the underlying wiring algorithm [58] ? Our data suggest a simple structure for the interaction among synaptic partners that can explain the development of planar-polarity–selective synapses , the regeneration of neuronal directional tuning , and the long-term maintenance of labeled lines of directional mechanosensation ( Fig 6A ) . Crucially , it also unifies all previous results leading to the biased-selector and the scaffold models [32 , 33] . The components of the selectivity process are 2 subpopulations of hair cells and t2wo subpopulations of LANs ( Fig 6B ) . In hair cells , this assumption is supported by polarity-associated expression of Emx2 as well as Tmc2b [59] . Hair cells attract axons nonselectively [32] . However , the Emx2 expression status in hair cells determines the outcome of this attraction because synapses will preferentially form when the appropriate axonal subclass contacts hair cells in an Emx2-matching manner . Once formed , stable synapses repulse incorrect partnerships ( Fig 6A ) . This explains the promiscuous synaptogenesis by solitary axons , which become doubly tuned because the absence of other axons prevents the formation of the converse correct synapses , eliminating the repulsive signals ( Fig 6C ) . A combination of attractive and repulsive signals also explains the recovery of selectivity by the reintroduction of additional axons , which reestablish synapses with the correct partners based on Emx2 expression status , regenerating the repulsive signal that eventually pushes the promiscuous solitary axon to exclusively synapse with hair cells of identical polarity ( Fig 6D ) [32] . Previous work has shown that chronic loss of hair cell innervation does not affect epithelial planar polarity in neuromasts . This has led to the prediction that innervation does not determine Emx2 expression patterns , which we have now confirmed experimentally . This explains how regenerating axons are able to synapse with the original hair cells ( Fig 6E ) . It occurs because Emx2 expression in denervated hair cells does not change , thus serving as an indelible mark of previous partnership . Our model also explains the recovery of selectivity and neuronal directional tuning after hair cell death and regeneration because the system continuously self-organizes by reenacting the developmental program that assigns different identities to sibling hair cells , in turn determining the likelihood of synaptic stabilization of specific axons ( Fig 6F ) . Upon loss of Emx2 , the entire hair cell population becomes homogeneously oriented [44] . In this case , the architecture of the wiring algorithm becomes asymmetric , directing one axonal population to synapses with every hair cell and inhibiting the other axonal population from establishing any synapse ( Fig 6G ) . Furthermore , because loss of mechanoreception does not affect hair cell planar polarity , hair cell activity cannot affect Emx2 expression , explaining why synaptic selectivity is normal in fish with nonfunctional hair cells [23 , 32] . Finally , selective synaptogenesis is maintained in the absence of coherent epithelial planar polarity because Emx2 remains expressed in half of the hair cells in Vangl2 mutants ( Fig 6H ) , and thus the architecture of the algorithm remains intact ( Fig 6A ) .
Three questions in mechanosensory biology are the current focus of intense attention . First , what is the mechanism that establishes the connectivity patterns between distinct mechanoreceptors and ascending afferent neurons [1 , 32 , 60] ? Second , how are the resulting labeled lines of directional mechanosensation maintained in the face of continuous turnover of receptor cells [1 , 32 , 60] ? Third , how much has the anatomical blueprint for directional mechanosensation diverged during the 600 million years of evolution since hair cell–based mechanosensation appeared in the common ancestor of agnathans and mammals [50 , 54 , 61 , 62] ? We found that selective connectivity between LANs and hair cells of identical orientation occurs independently of presynaptic projections and that it is separable from the coherent planar polarity and global orientation of the hair cells . We demonstrate a wiring algorithm that locally disambiguates hair cell identity , links identity to orientation , and establishes the synaptic partnership rules that generate neuronal directional tuning . Despite its simplicity , this algorithm readily solves the selectivity task even in the presence of constant noise generated by frequent death and regeneration of hair cells , and independently of scale differences between the receptive organ and the neuronal population . It also operates robustly upon evolutionary changes in epithelial polarity , or when neurons co-innervate organs with differing global orientation , which naturally occurs during the expansion of the lateral line that accompanies animal growth [56 , 57] . The striking similarities between the zebrafish lateral line and the mammalian vestibular apparatus , including their dependence on Emx2 for hair cell orientation , suggest that the wiring mechanism that we have unveiled may be ancient and conserved across vertebrates .
Experiments with wild-type , mutant , and transgenic embryos of undetermined sex were conducted under a protocol approved by the Ethical Committee of Animal Experimentation of the Parc de Recerca Biomedica de Barcelona ( Spain ) , and protocol number Gz . :55 . 2-1-54-2532-202-2014 by the “Regierung von Oberbayern” ( Germany ) . Naturally spawned zebrafish eggs were collected and cleaned , and embryos were maintained in system water under standardized conditions at 28 . 5 °C , at a maximum density of 50 individuals per 85-mm Petri dish . The transgenic and mutant lines used in this study have been previously described: Tg[pou4f3:GAP-GFP] [38] , Tg[pou4f3:ctbp2l-mKOFP] [26] , Tg[myo6b:actb1-EGFP] [40] , Tg[hsp70l:mCherry-2 . 0cntnap2a] [32] , Tg[nkhgn39d] [22] , and Et ( krt4:EGFP ) sqet4 [46] . The neurogenin1 mutant line was neurog1hi1059 [32] , and the trilobite/Vangl2 mutant was Df ( Chr07:stbm ) vu7 [45 , 46] . The hsp70:mCherry-SILL ( SILL:mCherry ) construct was generated using the Tol2 kit . Entry vectors were generated as described in the Invitrogen Multisite Gateway manual . PCRs were performed using primers to add att sites onto the end of DNA fragments , using Platinum Pfx ( Invitrogen ) . The pEntry vectors containing the UAS sequence , hsp70 minimal promoter , mCherry , and polyA are from the Tol2 kit , and the pEntry vector containing the SILL enhancer was previously generated in our laboratory [32] . For sparse labeling of LANs , we injected 15 to 20 pg of a DNA plasmid containing the construct hsp70:mCherry-SILL ( SILL:mCherry ) in 1– to 4–cell-stage embryos . Resulting embryos at 3 to 4 dpf were anaesthetized and screened for red fluorescence in single neurons using a Zeiss stereomicroscope . Targeted somatic mutagenesis of Emx2 was done using the CRISPant strategy [47] . To this end , a solution containing sgRNA ( 160 ng/μl ) , Cas9 ( 760 ng/μl ) , and SILL:mCherry DNA ( 20 ng/μl ) was incubated for 5 minutes at 37 °C and injected in 1–cell-stage eggs of the transgenic lines Tg[Myo6b:actb1-EGFP; pou4f3:ctbp2l-mKOFP] and Tg[myo6b:actb1-EGFP] . sgRNA was designed using the online tool CCTop—Crispr/Cas9 target online predictor ( crispr . cos . uni-heidelberg . de ) . The selected target sequence used in this study was GGAGGAGGTACTGAATGGACTGG . Larvae were fixed overnight at 4 °C in a solution of 4% PFA . After fixation , fish were washed with PBST and permeabilized in acetone at −20 °C for 5 minutes . Then , samples were washed with MiliQ water for 5 minutes and incubated for 1 to 2 hours at room temperature with blocking solution ( 1% BSA , 2% NGS , 1% DMSO ) . After blocking , larvae were incubated overnight with primary Ab ( Ctbp2 1:100 ) or for 48 hours ( Emx2 1:250 ) at 4 °C . The next day , fish were washed with PBST 5 to 6 times and incubated overnight at 4 °C with secondary Ab ( GaRb 633 , GaRb 555 ) . Then , samples were washed with PBST and mounted for imaging . The Ctbp2 primary antibody was obtained from Proteintech ( Manchester , UK ) and the Emx2 antibody from TransGenic ( Fukuoka , Japan ) . For in vivo imaging , laser-mediated axotomy , and some Emx2 immunostaining , we used a custom-built inverted spinning disc microscope ( Zeiss Axioscope ) . Emx2 immunostainings in wild type with single neurons labeled were imaged using a Zeiss inverted confocal microscope with a 40× water immersion objective . Embryos and larvae used for in vivo imaging were anesthetized in MS-222 ( tricaine ) 0 . 16 g/L and mounted in 1% low–melting-point agarose on the cover slip of a glass-bottom dish ( MatTek , Ashland , MA ) . Imaging dishes were bathed in Danieau’s with MS-222 0 . 16 g/L , except for time-lapse imaging where concentration was reduced to 0 . 08 g/L . Acquisition was performed at 28 . 5 °C using a 63× water immersion objective . Synaptic connectivity was assessed in transgenic fish expressing different fluorescent markers in all hair cells and in individual axons . Synapses were identified as bulged postsynaptic endings adjacent to the base of hair cells . This was always done by progressing through individual focal planes of Z-stacks , from the apical end of the epithelium ( to assess the planar polarization of each hair cell ) to the most basal aspect of the epithelium ( where apposition of neuronal endings and hair cells are found ) . Examples are shown in 2 supplemental videos ( S3 and S4 Videos , associated with Fig 3 ) . The total number of hair cells is not uniform across different neuromasts . Thus , quantification of the innervation in wild-type , Vangl2 , and Emx2 mutant specimens was normalized to the total number of hair cells in each neuromast . We compared the obtained fractions in the different groups using an ANOVA test . For the analysis of Emx2-positive cells in wild-type and Vangl2 mutants , we also normalized samples by dividing the number of positive hair cells by the total number of hair cells . We then compared the wild-type and Vangl2 mutant groups using an unpaired t test . Specimens were incubated in MS-222 ( 3-aminobenzoic acid ethyl ester ) in E3 medium ( 5 mM NaCl , 0 . 17 mM KCl , 0 . 33 mM CaCl2 , and 0 . 33 mM MgSO4 , in deionized water [pH 7] ) and mounted in 1% low-melting agarose on a glass-bottom Petri dish . They were positioned using a hair loop under light from a xenon arc lamp passed through the appropriate filters to reveal green or red fluorescence . Axon severing wad done using a computer-controlled iLas-Pulse laser system ( Roper Scientific SAS , Evry , France ) consisting of a pulsed ultraviolet laser ( 355 nm; 400 ps/2 . 5 μJ per pulse ) . The axons were transected with a focused laser beam coupled to a spinning-disk inverted microscope . The laser power was set to 35 mW at the sample plane . To sever axons , a region of interest ( ROI ) was drawn over the nerve , and a train of laser pulses was repeatedly applied until all fluorescence disappeared within the ROI . After severing , the axons were observed repeatedly to evaluate the success of the cuts . Subsequently , specimens were removed from the agarose and left to recover in fresh E3 embryo medium in individual Petri dishes . For regeneration experiments , larvae are remounted as the above for time-lapse imaging . Z-stacks were made using ImageJ ( Plugins → Segmentation → Simple Neurite Tracer ) . Hair cells were pharmacologically ablated by incubation of specimens in a 250 μM solution of neomycin for 45 to 60 minutes at room temperature and then rinsed with E3 medium [26 , 39 , 46] . | Mechanosensory systems are essential for maintaining posture , gaze , and body orientation during locomotion . Such stability requires a coherent representation in the brain of the location and movement of mechanical stimuli . In fishes , mechanical stimuli at a given position activate direction-sensitive receptors called hair cells that are oriented with polarized directionality . These hair cells stimulate neurons that selectively connect with them based on polarity . We have addressed how neurons target hair cells based on polarity during development of the mechanosensory lateral line system in zebrafish . We show that neurons selectively connect based on the expression pattern of the transcription factor Emx2 in hair cells . We find that the lateral line can maintain directionality after damage and regeneration . Our data suggest a cellular mechanism that controls the formation , maintenance , and regeneration of labeled lines to enable directional mechanosensation . | [
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| 2018 | Hair cell identity establishes labeled lines of directional mechanosensation |
Most chronic viral infections are managed with small molecule therapies that inhibit replication but are not curative because non-replicating viral forms can persist despite decades of suppressive treatment . There are therefore numerous strategies in development to eradicate all non-replicating viruses from the body . We are currently engineering DNA cleavage enzymes that specifically target hepatitis B virus covalently closed circular DNA ( HBV cccDNA ) , the episomal form of the virus that persists despite potent antiviral therapies . DNA cleavage enzymes , including homing endonucleases or meganucleases , zinc-finger nucleases ( ZFNs ) , TAL effector nucleases ( TALENs ) , and CRISPR-associated system 9 ( Cas9 ) proteins , can disrupt specific regions of viral DNA . Because DNA repair is error prone , the virus can be neutralized after repeated cleavage events when a target sequence becomes mutated . DNA cleavage enzymes will be delivered as genes within viral vectors that enter hepatocytes . Here we develop mathematical models that describe the delivery and intracellular activity of DNA cleavage enzymes . Model simulations predict that high vector to target cell ratio , limited removal of delivery vectors by humoral immunity , and avid binding between enzyme and its DNA target will promote the highest level of cccDNA disruption . Development of de novo resistance to cleavage enzymes may occur if DNA cleavage and error prone repair does not render the viral episome replication incompetent: our model predicts that concurrent delivery of multiple enzymes which target different vital cccDNA regions , or sequential delivery of different enzymes , are both potentially useful strategies for avoiding multi-enzyme resistance . The underlying dynamics of cccDNA persistence are unlikely to impact the probability of cure provided that antiviral therapy is given concurrently during eradication trials . We conclude by describing experiments that can be used to validate the model , which will in turn provide vital information for dose selection for potential curative trials in animals and ultimately humans .
To date , cure of most chronic viral infections has remained an impossible goal . Replicating forms of hepatitis B virus ( HBV ) , Herpes Simplex Virus ( HSV ) and Human Immunodeficiency Virus ( HIV ) can be targeted with potent small molecule therapies , thereby decreasing the burden of disease associated with these pathogens [1]–[4] . However , latent , non-replicating viral genomes persist within reservoirs for each of these infections , and high levels of viral replication typically resume soon after cessation of antiviral therapy , even after years of treatment [5]–[8] . Lifelong therapy is therefore often required , resulting in enormous costs to the healthcare system [9] . In addition , therapy can be complicated by lack of compliance , drug toxicity and resistance . Curative approaches to these infections will need to target persistent , non-replicating viral genomes . DNA cleavage enzymes , including homing endonucleases ( HE ) or meganucleases , zinc-finger nucleases ( ZFN ) , transcription activator-like ( TALEN ) effector nucleases and CRISPR-associated system 9 ( cas9 ) proteins represent a promising new therapeutic approach for targeting these viral forms [10] . These enzymes can be designed to target specific segments of either episomal DNA for HBV and HSV , or integrated viral DNA for HIV , which are vital for replication [11] , [12] . When viral DNA is cleaved , it is quickly repaired , allowing for repeated binding of the cleavage enzyme . DNA repair occurs by non-homologous end joining ( NHEJ ) , an error prone process . The enzyme binds to the target site if no mutation occurs during repair . Eventually , target DNA incurs a deletion or insertion that prevents subsequent enzyme binding as well as translation of essential viral proteins . The remaining viral DNA is thereby rendered replication incompetent . Zinc-finger nucleases are currently being used successfully ex vivo as a tool to modify the HIV entry receptors CCR5 and CXCR4 on CD4+ T-cells; altered cells which are resistant to HIV entry or replication have been transplanted back to infected animals as a form of adaptive immunotherapy with resultant decreases in viral load [13] , [14] , and this method is being tested in human clinical trials [15] . Similar modification of CCR5 in hematopoietic stem cells may allow reconstitution of the full immune system with exclusively HIV-resistant cells [16] . In contrast , our approach here is to use DNA cleavage enzymes that directly target latent viral genomes , rather than host cell viral entry receptors , to be delivered to infected cells as transgenes within viral vectors [10] , [17] . However , numerous fundamental questions remain regarding such a gene therapy approach: which vectors are most appropriate for gene delivery ? How many doses will be necessary for viral eradication ? Can vector delivery be limited to decrease the probability of toxicity ? If multiple doses are needed , how will immunity to the delivery vector impact the likelihood of cure ? Does reconstitution of latently infected cells occur rapidly enough to necessitate a narrow interval between successive gene therapy doses ? Is there benefit to delivering multiple transgenes per vector and should enzymes be engineered to target several regions of viral DNA [10] ? Mathematical models are crucial tools for identifying dynamics of active infection , and for designing antiviral regimens that maximize potency and avoid drug resistance [18] , [19] . However , despite twenty years of experience with antiretroviral therapy , a unifying mathematical theory of pharmacodynamics , that contrasts different antiviral agents according to potency , likelihood of resistance , and therapeutic synergy has only recently been developed [20]–[25] , and exists only for HIV-1 targeting agents . To maximize the probability that viral inactivation can be achieved , we believe that key quantitative components of gene therapy should be established early during development of DNA cleavage enzyme technology . With these fundamentals in place , dosing regimens can be designed rationally rather than blindly . To this end , we developed theoretical models that capture different critical components of viral cure approaches with DNA cleavage enzymes . Our initial models and analyses focus on HBV infection . HBV infects hepatocytes , which are highly accessible to gene therapy delivery vectors and which can be assessed serially for clearance of non-replicating virus . For this reason , HBV may be the most promising initial target for cure . However , the model is easily expanded to account for parameters that govern HIV-1 and HSV infections . We describe the mathematics of viral vector delivery to hepatocytes , and enzyme - substrate kinetics in the setting of heterogeneous density of episomal infection per hepatocyte . The theoretical problem of de novo resistance to DNA cleavage enzyme is also addressed . To this end , we consider concurrent vectorization of multiple enzymes that target separate DNA regions within the HBV episome . Finally , we incorporate simple differential equation models that capture dynamics of HBV persistence between gene therapy doses , and estimate how these dynamics may impact dosing strategies . Our simulations suggest that therapeutic outcome is likely to hinge on four key factors: percent vector delivery to target cells per dose ( which in turn depends on what proportion of vectors are removed by humoral immune mechanisms ) , enzyme-DNA target binding affinity and cleavage efficiency , degree of binding cooperativity between cleavage enzymes and target DNA , and number of transgenes delivered per vector . We predict that re-accumulation of the latent pool of HBV is unlikely to occur rapidly enough to overcome weekly dosing of delivery vectors , provided that viral replication is concurrently suppressed with available antiviral therapy . If cleavage enzymes that target single regions within the viral genome are used , de novo enzyme resistance could develop rapidly such that nearly all remaining episomes are therapy resistant following only a few doses of effective therapy . However , resistance to cleavage enzymes can be effectively mitigated if different DNA cleavage enzymes that cleave different regions of HBV episomes are dosed sequentially , or if single vectors can concurrently deliver several of these transgenes . As this model has yet to be confronted with empirical data , we also discuss potential cell culture and animal model experiments to help identify values for key model parameters , and to better inform future iterations of the model .
The HBV genome can exist in various states within a cell according to stage of replication . The persistent viral form is covalently closed circular ( cccDNA ) , which is maintained with a half-life of months to years in cells [26] , but is also a fundamental intermediate in the HBV replication cycle [27] . HBV is notable for an extraordinarily high burden of infection with most of the ∼2×1011 human hepatocytes harboring multiple HBV cccDNA episomes [28] , as well as other replication intermediates including HBV that may be integrated into host chromosomal DNA [29] , [30] . If fully suppressive antiviral therapy is given , the balance of remaining viral molecules is shifted in favor of cccDNA which remains in >95% of cells even a year after HBV DNA becomes undetectable in serum [31] . In model simulations , unless otherwise stated we assume that 99 . 67% of 2*1011 hepatocytes are infected based on a median of five infectious genomes per cell [32] , [33] . We replicate the wide distribution of viral burden between cells for HBV with a Poisson distribution . The goal of gene therapy-delivered DNA viral cleavage enzymes will be to functionally disrupt all , or the vast majority of cccDNA , such that viral reactivation is impossible . While we believe that parameters of gene therapy vector delivery and intracellular pharmacodynamics described in our models can ultimately be precisely identified , the parameter values that govern dynamics of HBV cccDNA persistence are likely to remain undetermined when our therapies are tested in animal and human trials . With these uncertainties in mind , the goal of our models is not to prove or disprove competing dynamical hypotheses of HBV cccDNA persistence [34] , but rather to incorporate any possible features of reservoir maintenance that may challenge the effectiveness of gene therapy . As a practical matter , our assumptions are weighted to favor HBV persistence during treatment . We favor “pessimistic” models of latency to ensure that selected gene therapy regimens exceed thresholds for viral cure by a comfortable margin . Because it is generally agreed that cccDNA probably decays slowly even without eradicative therapies , we make the simplest pessimistic assumption , that cccDNA levels remain stable between doses , unless otherwise noted . Gene therapy vectors can be delivered intravenously , allowing random dispersion to target hepatocytes . Entry into target cells can be achieved by utilizing vectors that are engineered to preferentially bind chosen cell surface receptors , such as sodium taurocholate cotransporting polypeptide , which are specific to HBV target cells [35] . Alternatively , viral vectors , which naturally target cell surface receptors that are ubiquitously expressed on the cell surface of target hepatocytes , such as the laminin receptor , heparan sulfate proteoglycans ( HSPGs ) , sialic acids and other glycans , can be used [36]–[41] . During a single dose , all hepatocytes are equally susceptible but delivery of multiple vectors to one cell may occur allowing for multiple transductions . We make the assumption that entry of multiple vectors is not impaired following prior entry of a single vector . In addition , the model is structured such that gene product , or DNA cleavage enzyme concentration in the cell nucleus is assumed to be directly proportional to the number of delivery vectors with successful entry and gene expression . Recombinant adeno-associated virus ( AAV ) can be produced in high titers in plasmid transfected cells [42] although non-infectious capsids exceed infectious DNA containing particles 10–30 fold [43] . A total of 60 copies of the capsid protein VP3 are needed to produce a single infectious particle during wild type AAV infections [44] , suggesting that generation of a single double-stranded replicating form of AAV vector DNA can correspond with amplified transgene expression . The heterogeneous distribution of gene therapy vectors to cells can be captured with an adjusted multiplicity of infection formula Pv = [ ( σ*m ) v * e− ( σ*m ) ]/v ! , where m is the ratio of delivery vectors to target cells in the human body ( including uninfected hepatocytes which presumably also take a high number of viral vectors ) , v is the number of vectors delivered per target cell , and Pv is probability of v transduced vectors per cell ( Fig . 1a ) . Viral vectors such as AAV have been developed for delivery to the human liver and have successfully deployed at high does ( 2*1012 particles per kilogram ) with success in human clinical trials for metabolic disorders [45] . This dose equates roughly to m = 1200 , or 1200 particles per hepatocyte in a 60 kg adult . Parameter σ is included to account for the fact that most vectors will not transduce their intended target cells . As a function of the development process , >90% of vector capsids lack viral DNA [43] . Other vectors may be removed by humoral immune mechanisms , enter cells which are not targets for HBV infection , or degrade due to shear forces or chemical stress in the blood . Finally , vector entry into a target cell's cytosol does not guarantee successful transduction , as viral nuclear localization sequences are required to bind nuclear transport receptors for nuclear entry [46] . Therefore the ratio of transduced vectors per target cells ( σ*m ) , which we refer to as the functional MOI ( fMOI ) , is likely to be far lower than the value for m which is ∼1200 . σ will take on a value of one if transduction of all dosed vectors occurs , and zero if no gene expression is achieved . This parameter value may be lower for infections such as HIV where latently infected cells potentially exist in anatomic sanctuaries such as the nervous system , as compared to HBV where vectors encounter the liver during first pass metabolism . Certain delivery vectors such as adenovirus ( ADV ) are immunogenic and delivery of identical serotypes will decrease with successive doses [47] . Enhanced neutralizing antibody response can prevent efficient delivery , thereby decreasing σ with successive doses , even when using less immunogenic vectors such AAV [48]–[54] . The delivery equation reveals a wide distribution of vector delivery and transduction when σ*m >1 . If HBV infection is modeled with 2*1011 hepatocytes , even if 1012 vectors are delivered successfully ( m = 1200 , σ = 0 . 004 , σ*m = 5 ) , there is no transduction within a small percentage ( Fig . 1b ) , but relatively large absolute number ( ∼109 ) , of infected cells . If σ = 0 . 167 ( σ*m = 20 ) is assumed , then a majority of hepatocytes will have multiple vector delivery ( Fig . 1b ) . When σ*m<1 , σ*m approximates proportion of cells with delivery and the majority of targeted cells contain only a single delivery vector ( Fig . 1c ) . The latter condition is unlikely to promote complete eradication of HBV cccDNA: if we make the simplifying and overly optimistic assumptions that delivery of one or more vectors automatically leads to lethal mutation of all viral genomes within a target cell , that no immunity to the viral vector or enzyme develops with successive doses of delivery vectors , and that there is no replenishment of infected hepatocytes or HBV cccDNA between doses , then the number of doses prior to eradication can be estimated with the formula Nn = N0 * ( 1−P ( v>0 ) ) n where N0 is initial number of infected cells , Nn is the remaining number of infected cells following n doses , and cure occurs when Nn<1 . The number of necessary doses increases dramatically if 50% delivery is not achieved while delivery greater than 99% dramatically decreases number of doses needed for cure ( Table 1 ) . In this analysis , we include HIV and HSV , which have lower numbers of total body latently infected cells ( high estimates are 107 and 106 , respectively ) [55] , [56] , to highlight that large infectious burden necessitates considerably more doses for elimination of HBV than HIV or HSV ( Table 1 ) . This analysis highlights the importance of high vector to target cell ratio , even under favorable assumptions regarding intracellular pharmacodynamics . Because the value of parameter m will be known as a function of dose , the key unknown parameter of delivery is σ , the proportion of vectors that enter target cells and are transduced . Two factors will drive outcome of an infected cell following delivery of transgene-carrying vectors: the number of viral vectors transduced in the cell and the strength of the enzyme-substrate interaction . The critical biophysical interactions are the binding affinity between enzyme and substrate , the efficiency of enzyme cleaving following binding and the efficiency of precise DNA repair . These processes are captured indirectly with constant d in the formula λo = 1/ ( 1+ ( v/d ) ) where v is number of vectors transduced in the cell and λo is probability that the genome will remain uncleaved . In this formula , d is scaled according to vector gene expression value per cell under the assumption that intracellular enzyme concentration is directly proportional to v [44] . The value of d determines whether one or multiple vectors will need to be delivered to the nucleus to ensure terminal mutation of most viral episomes . If d<<1 , then transduction of one vector is likely to predict episomal cleavage . Alternatively , if d>1 , then multiple vectors per nucleus will be necessary to disrupt all viral DNA . A possible hurdle to disruption of latent genomes is resistance to the cleavage enzyme in question . cccDNA molecules may contain pre-existing mutations . The HBV mutation rate is relatively low [57] , and pre-existing mutations to cleavage enzymes are likely to be relatively rare . DNA cleavage enzymes may also induce de novo mutations that render the site resistant to subsequent enzyme binding but do not incapacitate the virus . For example , if an enzyme repair event results in a 3 base pair mutation within the open reading frame , the ensuing loss of a single amino acid may theoretically not impair activity of the viral protein . However , if the DNA cleavage site is no longer recognized by the cleavage enzyme then this site has effectively become “enzyme resistant” . Presumably , this process will occur at a relatively low rate . For each cleavage and mutation event , the maximum probability of resistance is 33% as a deletion or insertion with a multiple of 3 is a pre-requisite for this event . However , addition or removal of one or several amino acids from the viral gene product will prove fatal to the virus on most occasions . Based on preliminary data using a target site in the N-terminus of a green fluorescent protein in a non-functional region , an absolute upper possible estimate is that ∼5% of cleavage/mutations events may result in de novo resistance [11] , though we expect the actual rate to be considerably lower . If probability of cleavage is Pc = ( 1−λo ) , then the probability of resistance is Pr = Pc * Ψ where Ψ is the frequency of induced mutations that prevent further enzyme binding despite being non-lethal to the viral episome . Therefore , in our model , development of resistance is assumed to increase proportionally with amount of DNA cleavage . To isolate the more important effects of induced de novo mutations , we include no pre-existing mutations in model simulations . Most persistent HBV exists as multiple non-replicating episomes within infected cells . For this reason , outcomes for a cell with intra-nuclear cleavage enzyme expression include only partial inactivation of genomes , as well as development of de novo resistance to cleavage enzymes in some but not all remaining viral molecules . The number of possible transition states of an infected cell following delivery of a vector is a function of the number of genomes within the cell ( Fig . 2a ) : all or a portion of episomes can be disrupted by DNA cleavage , while all or a portion of disrupted episomes can develop de novo resistance . Each transition state has a certain probability following delivery of a certain number of delivery vectors , including the probability that the infected hepatocyte will undergo no change in its state . In general , development of resistance is less common than successful disruption and elimination of viruses ( Fig . 2a ) . The total number of cells undergoing each transition is estimated by multiplying individual transition probabilities , by the number of cells with a certain number of cccDNA molecules , and amount of vector delivered . Enhanced cooperative binding between HIV directed antiviral agents and their multivalent viral enzyme targets has been demonstrated as a key determinant in antiviral agent potency . For example at equivalent drug concentrations , HIV protease inhibitors can be 100 , 000 times more potent than HIV nucleoside reverse transcriptase inhibitors [20] . Similarly , enzyme binding to a single viral episome may enhance or impair binding of subsequent enzymes to neighboring episomes in the nucleus ( Fig . 2b ) . Moreover , if multiple enzymes that target distinct genomic regions within a single cccDNA episome are dosed simultaneously , then there may be enhanced or impaired binding to these multiple episomal sites ( Fig . 2c ) . The mechanism to determine whether cooperative binding is present is generation of log-converted dose response curves with a particular emphasis on the slope of the curve , which translates to Hill coefficient ( h*z ) in the formula λo = 1/ ( 1+ ( v/d ) h*z ) . Parameter h represents enhanced binding of one enzyme product to multiple intranuclear episomes ( Fig . 2b ) . A value of parameter h greater than one implies positive cooperative binding and will favor cleavage of multiple episomes ( Fig . 2b ) , while a value less than one implies binding competition and will favor cleavage of only a single episome per transduction event . Under extreme conditions of negative binding cooperativity , the number of gene therapy doses will need to be equivalent to the maximum number of genomes per cell . Parameter z represents the possibility of enhanced or impaired binding of multiple enzymes products to one viral genome at separate binding sites ( Fig . 2c ) . If only one episomal DNA sequence is targeted , then z = 1 and the Hill coefficient is reduced to parameter h alone . The presence of multiple cleavage enzyme targets may be necessary to avoid resistance: while only one successful cleavage event will usually be required to neutralize replication activity of the episome , if cleavage at a certain site induces de novo resistance , then a different enzyme will need to bind a separate site to terminally disrupt the episome ( Fig . 3 ) . Under this set of rules , enhanced binding to secondary sites may prove advantageous . An episome that becomes resistant to all available enzyme products and maintains replicative capacity is termed fully resistant ( Fig . 3 ) . To reflect that parameters h and z may have opposing or complementary effects , they are included as a product in the equation . Also of note , parameter z may take on different values for different enzymes that are concurrently dosed , though for the purpose of theoretical simulations , we assume a single value . Assuming that potency of a single DNA cleavage enzyme ( z = 1 ) on an individual cccDNA episome level is captured with the equation λo = 1/ ( 1+ ( v/d ) h ) where λo is probability of the episome remaining uncleaved , total cleavage enzyme activity within a single cell is represented by Pc ( i ) = ( Si ) * ( 1−λo ) i * ( λo ) ( S−i ) where a cell has S enzyme susceptible cccDNA genomes and Pc ( i ) represents the probability of cleaving i episomes . At high levels of v/d , the probability of cleaving all episomes within a cell , or ( 1−λo ) S , increases . Resistance to cleavage enzymes occurs as a function of cleavage events . Given i cleaved episomes within a cell , k episomes will become resistant according to formula: Pr ( k ) = = ( ik ) ( Ψ ) k ( 1−Ψ ) ( i−k ) . To synthesize these concepts for HBV infection , we created a three-dimensional matrix . This model tracks total number of cells occupying different states over time . Between cleavage enzyme doses , the numbers of cells with every possible combination of replication competent enzyme susceptible ( S ) and enzyme resistant ( R ) genomes are measured . A third dimension is incorporated following each infusion of therapy , and accounts for different doses of vector transduction: each item within the matrix represents the total number of infected cells with a certain value for S , R and v . Transition probabilities are calculated for each cell according to Pc ( i ) and Pr ( k ) . The matrix is updated accordingly following each dose ( Fig . 2a ) . Initial data suggest that enzyme activity and DNA mutations accrue over a week following vector delivery [11] . In practice , delayed enzyme activity following vector entry into target cells would prove problematic only if cccDNA levels reconstitute in a meaningful way during the time period between doses . Otherwise , dosing interval can simply be prolonged to wait for enzymes to exert their full effect , and this would not impair the probability of therapeutic efficacy . In the simulation model , for simplification purposes , DNA cleavage is assumed to occur instantly following delivery of vectors with a dosing interval of one week . In later model realizations , the possible effects of cccDNA reconstitution and slower enzyme onset are explored . Strategies to bypass enzyme resistance will be analogous to those employed for antiviral therapy , namely design of cleavage enzymes that target separate regions within episomal HBV cccDNA ( Fig . 2c , 3 ) . Several possible dosing schemes exist ( Table 2 ) . Smaller vectors such as AAV can probably only carry 1–2 open reading frames , though different serotypes can theoretically be given with each successive dose with the goal of avoiding a strong humoral immune response . If AAV is employed , then multiple enzymes that target separate sites must be divided between separate vectors . These vectors can be dosed concurrently ( thereby decreasing the delivery dose of each vector/enzyme combination ) . While this strategy will theoretically increase the proportion of genomes targeted with two enzymes , the overall number of eradicated genomes may decrease due to overlapping targeting within the genome leading to a lower overall number of targeted episomes: we term this hypothetical problem “antagonistic potency” ( Fig . 4 ) . While a very high effective fMOI ( >10 in Table 1 ) may overcome antagonistic potency , another approach would be to dose separate cleavage enzymes within AAV successively rather than concurrently . With sequential delivery of enzymes targeting different regions , the vector delivery equation remains unchanged . The equation λ0 = 1/ ( 1+ ( v/d ) h ) again describes the probability of a genome remaining uncleaved . If q enzymes are available , then all remaining replication competent episomes are assumed to remain sensitive to subsequent doses through the first q doses ( assuming a different enzyme is used with each dose ) . In other words , resistance to the first delivered enzyme will not impact activity of the second enzyme and so on . Transitions are mediated by Pc ( i ) = ( Stoti ) ( 1−λ0 ) i ( λ0 ) ( Stot−i ) , where Stot is the number of total episomes in a cell ( either susceptible or resistant to prior delivered enzymes ) . Enzyme resistance is again captured with Pr ( k ) = ( ik ) ( Ψ ) k ( 1−Ψ ) ( i−k ) and generation of single and multiple mutants is tracked following each dose . Each cell within the liver may harbor different numbers of episomes with zero , single and multiple resistant sites ( Fig . 5 ) . We add a new dimension to the matrix with each delivery of a new cleavage enzyme such that the matrix contains q+2 dimensions given q total enzymes . For instance , a simulation with q = 3 ( 3 sequentially dosed enzymes with different DNA target sequences ) will include the following dimensions: S ( non-resistant episomes ) , R1 ( single resistant episomes ) , R2 ( double resistant episomes ) , R3 ( triple of fully resistant episomes ) and v ( vectors ) . Transitions to a newly resistant state are mediated by prior resistant state of the episome: with development of de novo resistance , S transitions to R1 , R1 transitions to R2 , and R2 transitions to R3 . Stot , defined above , is the sum of S , R1 and R2 . The model output is constructed in one of two ways: either the number of episomes with any resistance ( Stot−S ) are plotted against number of fully susceptible episomes ( S ) ; or the number of episomes with total resistance to all episomes ( SRtot ) are plotted against number of remaining episomes without total resistance ( Stot−SRtot ) . Only after q doses are given is it possible to have SRtot>0 due to totally resistant episomes to each of the q available enzymes . Following q doses , our model assumes repeated dosing of the finally dosed enzyme , as the least number of resistant episomes will exist against this enzyme . If vectors such as ADV with higher gene payload capacities are utilized , then two or more separate enzymes can be delivered and transduced within the same vector . This approach has the theoretical advantage of increasing per cell dose of cleavage enzyme , and has the potential to increase the proportion of targets that receive multiple cleavage enzymes , a process we term “synergistic potency” ( Fig . 4 ) . Moreover , if z>1 due to enhanced binding cooperativity between enzymes ( Fig . 2c ) , then cccDNA cleavage will be augmented in this fashion as well . Unfortunately , ADV is highly immunogenic and may only achieve high delivery following the first dose , and would need to be followed with AAV or other smaller delivery vectors , or ADV of different serotypes . Our model allows for analysis of potential benefits gained from delivery of multiple transgenes within a single ADV vector . The delivery equation is unchanged from prior simulations , as the number of vectors and therefore proportion of cells with no vector transduction ( Pv = 0 ) remain the same . If a vector carries q enzymes , then intracellular concentration of cleavage enzyme increases by a factor q ( Fig . 4 ) . We isolate the compounded effects of multiple enzymes , as well as the possible accrual of multiple enzyme resistant mutants by sequentially evaluating the activity of individual enzymes within a cell using Pc ( i ) = ( Stoti ) ( 1−λ0 ) i ( λ0 ) ( Stot−i ) , where Stot is again equal to the number of total episomes in a cell ( either susceptible or resistant to prior evaluated enzymes ) . Resistance is captured with Pr ( k ) = ( ik ) ( Ψ ) k ( 1−Ψ ) ( i−k ) and generation of single and multiple mutants is tracked following each dose . The matrix again contains q+2 dimensions . The critical difference between sequential dosing and multiple enzyme delivery simulations is that for the latter , delivery is not updated between successive evaluation of enzyme activity . Only after all of the q enzymes are evaluated , do we sum the number of totally resistant ( SRtot ) , partially resistant ( Stot−SRtot−S ) and susceptible ( S ) episomes in liver cells to update infectious burden within the entire liver . To demonstrate characteristics of the model , we conducted simulations under different assumptions of vector delivery ( fMOI ) , enzyme-substrate binding avidity/cleavage efficiency ( binding dissociation constant or d ) , and cooperative binding of enzymes to multiple episomes ( Hill coefficient or h ) . Initial simulations assumed a single transgene per vector and ignored de novo resistance . Pre-therapy conditions assumed fully suppressive antiviral therapy , a median of 5 episomes per cell , no inherent decay of infected cells or HBV cccDNA over time , and a total of 10 weekly doses . We defined infected cells as any cell with at least one remaining replication competent HBV cccDNA molecule . In initial simulations , we also assumed that the effect of each dose occurred instantaneously . First , we performed a multi-parameter sensitivity analysis with parameter values drawn randomly from a pre-determined wide range ( fMOI 0 . 5–5 , binding dissociation constant 0 . 008–5 , and Hill coefficient 0 . 2–5 ) using Monte Carlo selection methods . We generated 200 parameter sets and simulated the model to identify parameter effects on therapeutic outcome . Increasing fMOI ( R2 = 0 . 50 ) , and decreasing binding dissociation constant ( R2 = 0 . 24 ) predicted lower remaining numbers of infected cells to a greater extent than increasing the Hill coefficient ( R2 = 0 . 03 ) . To obtain a more mechanistic understanding of how model parameters interact to impact the extent of episome disruption , we created 80 parameter sets derived from 4 possible values for fMOI , 4 possible values for the Hill coefficient , and 5 possible values for binding dissociation constant . Model simulations were stochastic but produced equivalent results for repeat experiments with each parameter set . At low fMOI ( m*σ = 0 . 5 ) , decreasing the dissociation constant and/or increasing the Hill coefficient only allowed for a slight relative decrease in number of infected cells following 10 doses; at higher levels of vector delivery , each 5-fold decrease in the dissociation constant ( change in color in Fig . 6a ) resulted in a substantial decrease in infected cells following 10 doses . Increasing the Hill coefficient from 1 to 5 had a similar effect ( change in shape in Fig . 6a ) , though this effect was absent at the highest simulated dissociation constants ( all red lines in Fig . 6a ) , because a threshold of intracellular enzyme density was not surpassed to allow enhanced cooperative binding . At high fMOI and very low dissociation constants , episome binding saturated with or without the presence of enhanced cooperative binding ( blue line under fMOI = 5 in Fig . 6a ) . Residual replication competent genomes during simulations with low dissociation constant and high binding cooperativity resulted from lack of vector delivery to a subset of cells ( fMOI = 0 . 5 or 1 . 0 ) rather than lack of enzyme activity within infected cells . If we assumed that humoral immunity removed an increasing proportion of vectors prior to delivery with each dose ( successive decreases in parameter σ ) , then a greater number of cells retained replication competent episomes following 10 doses even with a potent regimen ( Fig . 6b ) . However , pre-treatment burden of infection as measured by median number of cccDNA episomes per cell prior to initiation of gene therapy , had only a small impact on remaining number of infected cells ( Fig . 6c ) and total replication competent episomes ( Fig . 6d ) following 10 equivalently potent doses of therapy . If de novo enzyme resistance developed at a fixed rate per cleavage event and single enzyme therapy was assumed , then resistant genomes rapidly predominated following dosing with parameter combinations that would constitute potent regimens . If we assumed high delivery , avid enzyme – DNA substrate binding and positive binding cooperativity , and that the resistance rate was 5% or 1% per cleavage event , then only 2 or 3 doses were needed respectively prior to infected cells containing resistant genomes becoming the predominate infected cells . In addition , the set point of number of cells with resistant genomes was >0 . 5 log higher with an assumed resistance rate of 5% versus 1% ( Fig . 7a ) . More potent regimens lead to more rapid predominance of cells with resistant HBV cccDNA but if enough doses were given , the set point of number of infected cells with resistant HBV was equivalent between more and less potent regimens with lower fMOI and higher dissociation constant , assuming equal probability of resistance per cleavage event ( Fig . 7b ) . With potent regimens and a resistance rate of 5% , cells with multiple HBV episomes harbored a combination of susceptible and resistant forms , though many cells developed multiple resistant episomes , even after a single dose ( Movie S1 ) . Therefore , a key parameter to deduce experimentally will be rate of resistant mutants generated per cleavage event . To avoid cleavage enzyme resistance , we next considered sequential delivery of 1 , 2 , 3 , 4 , or 5 enzymes in separate , weekly doses . A new enzyme was given each week until no new enzymes remained ( at the sixth dose for the 5 enzyme condition , for example ) . At this point , the final enzyme was repeatedly redosed . Simulations assumed favorable potency parameters and a resistance rate of 1% . The addition of extra enzymes increased the time until enzyme resistant forms predominated , and lowered the steady state of cells retaining replication competent HBV cccDNA by ∼0 . 5 log with addition of each enzyme ( Fig . 7c ) . Simulations with multiple successive enzymes resulted in lower numbers of infected cells than simulations with a single enzyme ( dotted lines Fig . 7a , red line Fig . 7c ) . Yet , high numbers of enzyme resistant episomes still remained even following sequential dosing of five different enzymes ( blue line , Fig . 7c ) . We next simulated trials with a single dose of a multi-payload vector such as ADV carrying 1 , 2 , or 3 transgenes concurrently under different assumptions of fMOI and cooperative binding of enzymes to multiple episomes . A favorable enzyme-substrate binding avidity/cleavage efficiency was assumed for each simulation . Results from simulations with 36 pre-selected parameter sets ( all following a single dose with assumed resistance rate = 1% ) show that total remaining cccDNA genomes decreased with increasing fMOI , and that maximizing the transgene payload ( blue line , Fig . 8a ) increased effectiveness under high delivery conditions , especially in the presence of positive cooperative binding ( circles , Fig . 8a ) , or lower dissociation constant ( not shown ) . Even under lower delivery conditions ( fMOI = 2 ) , increasing the number of enzymes per vector dramatically decreased the total burden of infected cells containing viral genomes with at least one de novo enzyme resistance mutation ( Fig . 8b ) as well as the total number of infected cells containing HBV cccDNA molecules that were fully resistant to all of the q available delivery enzymes ( Fig . 8c ) . Concurrently delivered DNA cleavage enzymes therefore are predicted to exhibit synergistic potency and decrease both the overall burden of infection and de novo resistant genomes ( Fig . 4 ) . Delivery remained a critical parameter for HBV cccDNA disruption and at low fMOI , most remaining cccDNA episomes were susceptible to the cleavage enzymes ( Fig . 8d ) . Alternatively , delivery of multiple enzymes generally decreased percent of remaining episomes that were resistant . Positive binding cooperativity between enzymes generally increased the proportion of enzyme resistant episomes by virtue of its overall positive impact on cleavage: a similar effect occurred with lowering the binding dissociation constant ( data not shown ) . If cccDNA levels reconstitute at a meaningful rate and several day intervals are required between doses to allow effects of DNA cleavage enzymes to accrue , then this may imply the need for more prolonged therapeutic courses . We therefore examined the effects of underlying dynamics of HBV cccDNA survival , as well as the possible delayed effects of DNA cleavage enzymes following target cell entry . Several factors may drive changes in levels of HBV cccDNA during suppressive antiviral therapy . Hepatocytes with HBV cccDNA molecules periodically die at a rate equivalent to that of an uninfected hepatocyte ( Fig . 9a ) . Decay of individual episomes at a slow rate is possible ( Fig . 9b ) but has not been explicitly documented and may be counterbalanced by low-level replication despite antiviral therapy , which may also allow spread to uninfected cells ( Fig . 9c ) . Indeed , many patients do not achieve full virologic suppression [6] . Finally , most evidence supports division of nuclear cccDNA between daughter cells during homeostatic proliferation ( Fig . 9d ) [33]: as a result , patients on antiviral therapy appear to have a slow decay in levels of cccDNA over time though this decline is not rapid enough for viral eradication [58] . A less optimistic assumption for the standpoint of achieving cure would be that episomes divide along with human chromosomal DNA during cell division , limiting cccDNA decay ( Fig . 9e ) . In all simulations in Fig . 10 , enzyme dosing occurred every two weeks . However , enzyme activity was assumed to accrue continuously over a week rather than instantaneously . We first assumed baseline conditions with high potency ( Fig . 10 ) with no change in cccDNA levels between doses . A simulation with homeostatic proliferation of cccDNA ( Fig . 9d ) , revealed a marginally lower level of remaining viral episomes after 10 doses , while episomal death concurrent with hepatocyte death ( Fig . 9a ) augmented episomal decay more substantially . If poor control of cccDNA replication was assumed due to incomplete suppression by antiviral drugs ( Fig . 9c ) , then therapy was less potent .
We describe mathematical models that aim to capture critical features of DNA cleavage enzyme therapy for eradication of HBV . Our results identify potentially critical parameters that will determine whether cure will be feasible with available vector cleavage enzyme constructs . In particular , successful vector delivery to the majority of target cells with each infusion , and favorable intracellular binding kinetics between enzymes and DNA target sites appear to be pre-requisites for successful regimens . Cooperative binding of enzymes between multiple episomal targets could also potentially limit the number of doses needed prior to cure , particularly if enzyme concentration in cells only marginally exceed binding coefficient values . While multiple doses of gene therapy will likely be required for cure , the first dose appears to be particularly critical . In order to enhance potency and limit resistance , this dose should have a high vector to target cell ratio , and if possible , multiple enzymes should be packaged within each delivery vector . Sequential use of different enzymes appears to be another useful strategy to avoid de novo resistance if only low-payload delivery vectors such as AAV are available . While our integrated therapeutic model is relatively complex , its individual components ( vector delivery , intracellular pharmacodynamics , resistance ) are quite manageable . In total , the model contains only five unknown parameter values including 1 ) proportion of vectors removed prior to entry into target cells , 2 ) enzyme-DNA binding coefficient , 3 ) vector-DNA cleavage dose response slope ( Hill coefficient ) , 4 ) resistance rate per DNA cleavage event and 5 ) dose response slope within a single episome if multiple enzymes are present in the cell nucleus . Each of these parameter values can be identified via specific experimental approaches for all vectors and cleavage enzymes of interest , which will allow for testing and refining of the model . Vector delivery to target cells is best estimated initially in animal model studies . Humanized mouse models of HBV hold promise for this indication [59] , [60] . Flow cytometry of liver biopsy tissue can be employed to quantify proportion of target cells without vector delivery following different doses of vector; the effective multiplicity of infection ( σ*m ) can be back calculated using Pv ( 0 ) = [ ( σ*m ) v * e− ( σ*m ) ]/v ! . This effective delivery dose will represent a fraction of the pre-determined vector to target cell ratio ( m ) , which in turn will allow for an estimate of proportion of vectors lost prior to target cell entry ( 1−σ ) . Ultimately , these experiments will need to be conducted in humans , as the human immune response to delivery vectors cannot be predicted from animal models . However , animal model parameters will serve as useful initial estimates that may be used within a Bayesian framework to assist in human clinical trial design . A critical caveat of the functional MOI ( fMOI ) is that the vector to target cell ratio assumed in parameter m is inclusive of all cells that may serve as targets for vector entry , rather than only HBV infected cells . If a particular delivery vector also efficiently enters other intrahepatic cells such as Kupffer cells , endothelial cells or cells in other organs , then the fMOI will decrease accordingly . In effect , these cells will serve as vector sponges and will decrease the probability of high vector delivery to infected cells containing HBV cccDNA . Therefore , vector receptor specificity is critical not only to avoid untoward toxicity , but also to ensure that precious vector is not wasted . A key experimental goal should be to determine which enzymes achieve avid binding and DNA cleavage activity ( low values for d ) and positive cooperative binding ( h>1 or z>1 ) to their DNA targets . Dose response curve slope and enzyme-substrate binding coefficients can be obtained from cell culture models of HBV cccDNA infection in which infected cell lines are exposed to delivery vectors dosed at different multiplicities of infections . Using high throughput sequencing of the DNA target site , it will be possible to measure the proportion of target genomes with terminally disrupted DNA for each vector dose . Experimental dose response curves can be tested against our models describing enzyme DNA binding kinetics . If multiple enzymes are delivered concurrently in a single vector , then similar curves can be used to assess cooperative binding between several sites within a single episome . To obtain a conservative upper limit for resistance rate per cleavage event , it will first be necessary to identify cells with confirmed vector delivery and HBV cccDNA cleavage . One possibility is to sort for vector transduced cells that are HBV e antigen positive and then look for mutation events within the cleaved open reading frame . For the purposes of informing clinical trial dose design , this estimate will be useful to ensure that doses exceed predicted thresholds for viral persistence . When all unknown parameter values are estimated and a model structure is selected that best represents available data regarding vector delivery , enzyme/DNA substrate kinetics and resistance rate , then it will be possible to design regimens that maximize probability of cure while limiting excess dosing and possible toxicity . While it will be necessary to characterize all available delivery vectors and cleavage enzymes prior to predicting likelihood of therapeutic success , certain strategies are promising based on in silico simulations . For instance , if multiple transgenes targeting different viral DNA regions can be packaged within the same delivery vector , at least during the first dose , this may augment potency and decrease resistance when compared to multiple transgenes split among vectors . Ensuring high delivery during the first dose will maximize this effect . A key challenge will be measuring therapeutic outcome . For HBV , it is difficult to take serial quantitative measures of episomal reservoirs of infection . While active viral replication can be tracked with quantitative PCR , burden of quiescent viral episomes can only be assessed with liver biopsy and tissue quantitation of uncleaved HBV cccDNA using sequencing . Even a tiny number of latently infected cells may theoretically be enough to reactivate and repopulate the reservoir . Because serial biopsies are likely to be feasible only in animal models of infection , therapeutic efficacy will ultimately need to be evaluated with close clinical follow up after cessation of antiviral therapy . For this reason , we make conservative assumptions in our model , so that the dosing schedule exceeds the presumed threshold for cure . While we have focused on eradication of HBV , our model is easily adjusted to account for potential cure of other chronic viral infections such as HIV or HSV-2 . The burden and properties of non-replicating viral stores differ dramatically between HBV , HIV and HSV [10] . As such , each infection presents a unique set of challenges for eradicative approaches . While latent HIV integrates as viral DNA into the human genome , HIV-1 DNA is present in only ∼107 cells during chronic infection , typically as a single genome per cell [61] , [62] . However , the HIV-1 reservoir may be anatomically difficult to target with delivery vectors; while memory CD4+ T-cells are the central population of cells within the latent reservoir , the possibility that other immune cells form important reservoirs has not been completely excluded and if target receptors on these cells differ , then they may serve as sanctuaries from therapeutic cleavage enzymes [63] . Finally , due to rapid HIV-1 intra-host evolution in the context of ongoing immunological pressure , the HIV-1 reservoir is populated with diverse quasispecies , which may lead to pre-existing resistance to certain cleavage enzymes [64] . Therefore , phylogenetic techniques may be necessary to explore for bottleneck effects if a majority , but not all viral strains , are eliminated following repeated dosing of DNA cleavage enzymes . HSV latency exists within a relatively low number of neuronal cell bodies in either the trigeminal or dorsal root ganglia [56] , which may represent a therapeutic sanctuary where delivery of vectors is poor . For HSV-2 , sampling of the dorsal root ganglia , the site of latency , is not feasible . Close clinical follow up following gene therapy will be necessary to evaluate for cure . As with HIV-1 , the possibility of re-infection will need to be considered using phylogenetic sampling of pre and post-treatment positive PCR samples , as inactivation may not ensure protective immunity from re-exposure . In summary , we present a model to capture the effects of gene therapy with DNA cleavage enzymes for chronic HBV infection . The model helps identify key therapeutic parameters that will be necessary for cure , and outlines appropriate experimental steps to identify dosing regimens that are most likely to disrupt all latent viral DNA following a minimal number of gene therapy doses .
Simulations were performed on C++ and using Microsoft Excel . | Innovative new approaches are being developed to eradicate viral infections that until recently were considered incurable . We are interested in engineering DNA cleavage enzymes that can cut and incapacitate persistent viruses . One hurdle is that these enzymes must be delivered to infected cells as genes within viral vectors that are not harmful to humans . In this paper , we developed a series of equations that describe the delivery of these enzymes to their intended targets , as well the activity of DNA cutting within the cell . While our mathematical model is catered towards hepatitis B virus infection , it is widely applicable to other infections such as HIV , as well as oncologic and metabolic diseases characterized by aberrant gene expression . Certain enzymes may bind DNA more avidly than others , while different enzymes may also bind cooperatively if targeted to different regions of viral DNA . We predict that such enzymes , if delivered efficiently to a high proportion of infected cells , will be critical to increase the probability of cure . We also demonstrate that our equations will serve as a useful tool for identifying the most important features of a curative regimen , and ultimately for guiding clinical trial dosing schedules to ensure hepatitis B eradication with the smallest number of possible doses . | [
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| 2013 | Predictors of Hepatitis B Cure Using Gene Therapy to Deliver DNA Cleavage Enzymes: A Mathematical Modeling Approach |
Functional recovery from cutaneous injury requires not only the healing and regeneration of skin cells but also reinnervation of the skin by somatosensory peripheral axon endings . To investigate how sensory axon regeneration and wound healing are coordinated , we amputated the caudal fins of zebrafish larvae and imaged somatosensory axon behavior . Fin amputation strongly promoted the regeneration of nearby sensory axons , an effect that could be mimicked by ablating a few keratinocytes anywhere in the body . Since injury produces the reactive oxygen species hydrogen peroxide ( H2O2 ) near wounds , we tested whether H2O2 influences cutaneous axon regeneration . Exposure of zebrafish larvae to sublethal levels of exogenous H2O2 promoted growth of severed axons in the absence of keratinocyte injury , and inhibiting H2O2 production blocked the axon growth-promoting effects of fin amputation and keratinocyte ablation . Thus , H2O2 signaling helps coordinate wound healing with peripheral sensory axon reinnervation of the skin .
Successful wound repair and regeneration requires coordination between the various cell types that make up the injured tissue . For example , following injuries that damage both epidermis and sensory endings , wounded epidermis promotes the regeneration of nerve fibers [1] , [2] . Conversely , complete epidermal wound healing requires the presence of sensory axons [1] , [3] . In amphibians , innervation of the wound epidermis by nerve fibers is also essential for limb regeneration and correlates with the establishment of signaling centers [4]–[6] . These observations imply that coordination between wound epidermis and sensory axons during healing and regeneration is regulated by molecular interactions between these cell types . In mammals , peripheral axon regeneration is generally more robust than axon regeneration in the central nervous system . Nonetheless , reinnervation in the periphery can be slow or incomplete , depending on the extent of axonal injury and on interactions with surrounding cells [7] , [8] . Because nerve injury is often associated with damage of not only the nerve but also neighboring tissues , it has been difficult to separate autonomous and non-autonomous factors influencing axon regeneration in vivo . Recent studies in C . elegans and zebrafish have utilized laser axotomy to precisely damage single axons in the peripheral nervous system , making it possible to assess the influence of non-neuronal tissues on axonal regeneration [9] , [10] . Tissue damage triggers a complex cascade of signals that activate inflammatory responses and promote tissue repair [11] . In fruit flies and zebrafish , the recruitment of immune cells to wounds is mediated by the small reactive oxygen species ( ROS ) hydrogen peroxide ( H2O2 ) , which emanates from the injury [12] , [13] . The role of H2O2 in oxidative stress has been well studied , as high levels can have deleterious effects on the maintenance of cell homeostasis [14] . In the nervous system , H2O2 can induce neurodegeneration through activation of pro-apoptotic pathways [15]–[17] . More recently it has come to be appreciated that H2O2 can act as a signaling molecule with specific developmental and physiological functions . H2O2 is thought to signal by oxidizing cysteine residues on target proteins , most notably phosphatases [18] , [19] . The larval zebrafish tail fin provides an accessible setting for investigating how peripheral axon regeneration is coordinated with the healing of injured tissue and for testing whether H2O2 plays a role in these interactions . During larval stages , zebrafish fins consist of a folded two-layered epithelium , surrounding muscle cells ( Figure S1A ) . Zebrafish tail fins regenerate after amputation , both during larval development [20] , [21] and in adults [22] , but sensory reinnervation of regenerated fins has not been explicitly assessed . Somatosensation at larval stages in zebrafish is accomplished by two populations of neurons: trigeminal neurons , which are located in ganglia outside the hindbrain and innervate the skin of the head , and Rohon-Beard ( RB ) neurons , which are located in the dorsal spinal cord and innervate the skin of the trunk and tail ( Figure 1A ) . The peripheral axons of somatosensory neurons arborize between the two epithelial layers that make up the larval skin , the outer periderm and inner basal cell layers [23] . Precisely severing a trigeminal peripheral axon after arborization is complete ( ∼36 h post-fertilization , hpf ) promotes some regenerative growth , but regenerating axons avoid their former territories and undamaged neighboring axons never sprout into these newly denervated areas [24] . We have investigated the relationship between tissue damage and peripheral axon regeneration , using injury to the larval zebrafish tail fin as an experimental paradigm . Amputating the fin promoted peripheral sensory axon growth , allowing the robust reinnervation of the newly regenerated fin . This axon regeneration-promoting effect could also be elicited by ablating a few keratinocytes anywhere in the body . H2O2 exposure mimicked the axon growth-promoting effect of keratinocyte damage , and morpholino-mediated knockdown of the H2O2-generating enzyme Duox1 inhibited axon growth-promotion by fin amputation . Thus , H2O2 produced by damaged keratinocytes promotes the reinnervation of healing skin by sensory axons .
The caudal fins of larval zebrafish regenerate completely within a few days after amputation [20] , implying that RB peripheral axons must also regenerate to provide sensory function to the new fin . To directly assess whether RB axons in the tail can regenerate , we imaged GFP-labeled RB arbors in the islet2b:GFP transgenic line [25] after caudal fin amputation at 3 d post-fertilization ( dpf ) . Amputation caused immediate degeneration of axon branches near the wound ( Figure 1B , brackets ) , creating a denervated zone that regenerating axons would need to traverse to fully innervate the regenerating fin . Despite this potential barrier , the fin was always reinnervated by RB arbors at 120 h post-amputation ( hpamp ) ( Figure 1B ) . Three days after fin amputation , there was no detectable difference in the total amount of sensory axons in regenerated fin tips and fin tips of age-matched animals ( 6 dpf ) that were never injured , indicating that reinnervation of regenerated fins was complete ( Figure 2A ) . Sensory reinnervation of regenerated fins was functional , since 6 dpf fish with regenerated fins responded to touch at the tip of the tail as often as uninjured control fish ( Figure 2B ) . The observation that RB axons robustly reinnervate larval fins within a few days after amputation , despite the fact that trigeminal axon regeneration is limited after precise axotomy [24] , could be explained in either of two ways: ( 1 ) fin injury and healing promote peripheral axon growth or ( 2 ) RB neurons innervating the tail possess greater structural plasticity than trigeminal neurons . To assess the intrinsic plasticity of RB axon arbors , we monitored axon behavior after precise laser axotomy with time-lapse imaging for 12 h ( see Figure S1B for experimental procedures ) [10] and traced the position of individual axon tips every 30 min . Axotomy of RB neurons induced a 2-fold increase in axon activity ( axon tip displacement , including both growth and retraction ) compared to uninjured axons ( 54 . 92±2 . 72 µm , n = 24 versus 32 . 47±2 . 53 µm , n = 13 axon tip displacement , * p<0 . 05; compare Figures 1C and 3A; quantification in Figure 3D , Videos S2 and S1 , respectively ) , but , like trigeminal axons , regenerating RB axons avoided denervated territory ( Figure S2 ) [24] . Notably , axon growth was balanced by retraction , so that total arbor size did not substantially increase ( Figure 3F; see Video S2 ) . Like trigeminal axons [24] , the ability of RB axons to reinnervate former territory in the fin was improved by inhibiting Rho kinase ( unpublished data ) . Thus , the ability of RB axons to regenerate after fin amputation is likely not due to intrinsic regenerative capacity but is probably a specific response to tissue damage . To further investigate the influence of tissue injury on RB axon regeneration , we compared the behavior of uninjured axon arbors ( Figure 1C ) , precisely axotomized arbors ( Figure 3A ) , and injured arbors in amputated fins ( Figure 1D ) . Fin amputation ( Video S3 ) increased total axon activity ( growth and retraction ) more than axotomy alone ( 77 . 40±4 . 03 µm , n = 26 , *** p<0 . 001 ) . Measuring the linear distance between an axon tip's position just after amputation and its position 12 h later revealed that fin amputation promoted productive axon growth , since axon tips traveled farther after amputation than after precise axotomy ( 29 . 62±2 . 50 µm , n = 8 , versus 8 . 42±3 . 09 µm , n = 8 , ** p<0 . 01; Figure 3E ) . Combining fin amputation with subsequent laser axotomy of a nearby RB axon branch increased the axon activity ( 83 . 74±3 . 09 µm , n = 26 , ** p<0 . 01 ) and total growth ( 46 . 54±4 . 92 µm , n = 13 , *** p<0 . 001 ) even further ( Figure 3B , D , E , Video S4 ) , but the amount of retraction was not dramatically altered ( Figure 3F ) . Amputating fins significantly improved the ability of regenerating axons to innervate denervated areas ( 14 . 11±7 . 02 µm , n = 8 versus 60 . 24±13 . 06 µm , n = 10 , * p<0 . 05; Figure S2 ) , which is likely important for allowing regenerating arbors to traverse the denervated zone that forms just proximal to the wound after amputation ( Figure 1B , brackets ) . Thus , fin injury increases sensory axon activity , promotes growth ( but not retraction ) , and allows axons to overcome their avoidance of denervated territories . To determine the effective range of axon growth-promoting signals from injured tissue , we axotomized axons distant ( >50 µm ) from the amputation site ( Figure 3C , Video S5 ) . These axons did not grow significantly better than precisely severed axons in uninjured tissue , since neither axon activity nor linear growth distances were increased by distant amputation ( axon activity: 38 . 67±2 . 85 µm , n = 10 , p = ns>0 . 05; linear distance = 15 . 63±4 . 42 µm , n = 9; p = ns>0 . 05; Figure 3D , E ) . Thus , growth-promoting signals emanating from injured tissue likely function at short range . To define the time window during which axons can respond to regeneration-promoting signals , we axotomized RB arbors at different time points after amputation . Axon activity was most enhanced when arbors were axotomized at 3 h post-amputation ( 114 . 6±7 . 04 µm , n = 10 , ** p<0 . 01 ) , but axotomy at 6 h post-amputation did not increase axon activity ( 61 . 20±6 . 45 µm , n = 10 , p = ns>0 . 05; Figure S3A ) , as compared to axotomy alone . This observation suggests that axon growth-promoting signals are transiently emitted from the wound , rather than continuously from regenerating fin tissue . To assess whether the size of the severed arbor fragment influenced the amount of axon activity induced by amputation , we traced degenerated fragments in three dimensions to measure their total length and plotted length as a function of axon activity . Size of the axotomized arbor did not correlate with axon activity ( Figure S3B ) . To identify the origin of axon growth-promoting signals , we ablated individual muscle cells or keratinocytes in the fin of larvae expressing cell type-specific reporter transgenes that highlight each tissue [26] , [27] . Ablating muscle cells did not promote axon growth ( 25 . 72±3 . 65 µm , n = 10; Figure 4A , E ) , but ablating ≥3 keratinocytes prior to axotomy provoked robust axon regeneration in both the fin ( 70 . 75±6 . 14 µm , n = 11 , Figure 4B , E ) and head ( 73 . 81±20 . 95 µm , n = 4; Figure 4C , D , E ) . However , ablating a single keratinocyte in either the fin ( 44 . 34±2 . 35 µm , n = 10 , *** p<0 . 001 ) or the head ( 27 . 62±1 . 94 µm , n = 14 , ** p<0 . 01 ) did not promote axon regeneration . This result suggests that a threshold of injury-induced signals is required to promote growth and reinnervation by RB and trigeminal axons . The recently reported observation that zebrafish larval fin amputation produces high levels of hydrogen peroxide ( H2O2 ) at the wound margin [13] prompted us to investigate whether H2O2 contributes to the promotion of axon regeneration by keratinocyte injury . By monitoring H2O2 with a chemical sensor ( pentafluorobenzenesulfonyl fluorescein ) , we first verified that , like fin amputation ( Figure 5A ) , laser ablating several keratinocytes produced detectable levels of H2O2 around the wound ( Figure 5B ) . Ablation of 1–2 keratinocytes did not produce levels of H2O2 sufficient to detect with the chemical sensor , but ablating ≥3 keratinocytes generated detectable levels of H2O2 at the wound margin ( Figure 5C ) , indicating that the severity of the injury correlates with the amount of H2O2 produced . To test whether H2O2 can promote axon regeneration , we added 3 mM H2O2 ( 0 . 01% ) to the larval media ( the highest concentration of H2O2 at which most embryos survived and developed normally , see Figure S4 for survival rates ) ( Figure 6A , Video S6 ) . The addition of H2O2 to uninjured larvae significantly promoted some axon activity ( untreated , uninjured: 32 . 47±2 . 53 µm versus H2O2 uninjured: 72 . 30±1 . 94 µm , *** p<0 . 001; Figure 6D ) . Adding H2O2 for 3 or 12 h to larvae in which RB axon arbors had been axotomized increased axon activity variably but significantly , compared to axotomy in untreated animals ( 3 h H2O2: 122 . 1±8 . 81 µm , n = 6; 12 h H2O2: 101 . 4±3 . 09 µm , n = 10 , versus untreated 54 . 92±2 . 72 µm , n = 24 , ** p<0 . 01 each; Figure 6D ) . The linear growth distances of axotomized arbors were also increased by H2O2 ( 3 h: 43 . 58±6 . 06 µm , n = 5; 12 h: 30 . 04±2 . 25 µm , n = 8 , versus untreated: 5 . 46±3 . 78 µm , n = 5 , ** p<0 . 01 each; Figure 6E ) . Thus , H2O2 is sufficient to promote axon regeneration and does not need to be present in a gradient for this effect , as has been proposed for its role in leukocyte recruitment [13] . To test whether H2O2 is required for injury-induced axon growth , we blocked H2O2 production and monitored axon regeneration after fin amputation by downregulating the primary enzyme generating H2O2 in the larval fin , Dual oxidase 1 ( Duox1 ) , using a previously characterized morpholino ( duox1-MO ) ( Figures 5A , S5A ) [13] . Injecting this morpholino into embryos prevented the promotion of axon activity by fin amputation ( 23 . 89±3 . 29 µm , n = 11 , ** p<0 . 01 ) ( Figure 6B , F , Video S7 ) . Interestingly , fin regeneration was also compromised in duox1 morphants , potentially reflecting a role for axon innervation in fin regeneration , similar to limb regeneration in amphibians [4] . Treating amputated morphant larvae with 1 . 5 mM H2O2 for 12 h rescued the deficit in axon reinnervation observed in the morphants ( 102 . 3±5 . 6 µm , n = 5 , ** p<0 . 01 ) ( Figure 6C , F , Video S8 ) . Due to the toxicity of prolonged H2O2 treatment , we unfortunately could not assess whether such rescued morphants also regenerated their fins . Blocking H2O2 production with the duox1-MO did not affect growth and retraction induced by axotomy alone ( Figure 6D ) , suggesting that cell-intrinsic mechanisms through which axotomy induces axon activity may be regulated by different pathways . To minimize the possibility that duox1-MO toxicity inhibited axon growth following axotomy , we repeated this experiment with co-injection of a morpholino targeting p53 , which inhibits apoptosis [28] , as was done in a previous study with the duox1-MO [13] . Like in larvae injected with duox1-MO alone , axon growth promotion by amputation was blocked in larvae injected with both p53-MO and duox1-MO , compared to larvae injected with p53-MO alone ( Figures 6F , S5B–D ) , supporting the idea that the duox1-MO's effect on regeneration is not due to cellular toxicity . Together these results indicate that Duox1-mediated H2O2 production is necessary for the promotion of injury-induced axon growth . To determine where Duox1 is required to promote axon regeneration , we created genetic chimeras by transplanting cells at the blastula stage from donor embryos injected with duox1-MO into uninjected host embryos ( Figure 7A ) . Donor embryos were transgenic for a somatosensory GFP reporter ( sensory:GFP ) and host embryos were transgenic for a keratinocyte RFP reporter ( Krt4:RFP; previously termed Krt8 ) [27] , [29] . At larval stages , we ablated wildtype RFP-labeled keratinocytes in these chimeras and axotomized nearby duox1 morphant peripheral sensory arbors ( n = 7 RB neurons , Figure 7B , Video S9 ) . Keratinocyte ablation in these animals significantly promoted axon regeneration , demonstrating that Duox1 is required non-autonomously to achieve the full level of axon growth promotion by keratinocyte ablation ( Figure 7C ) . This result also verified that the ability of the duox1-MO to block regeneration was not due to morpholino toxicity in the neuron . The promotion of axon growth by H2O2 could in principle result from the direct activation of axon growth or the repression of axon growth inhibitors , such as those that arise after initial growth stages to stabilize axonal structure [24] . To address this issue , we examined regeneration at 30 hpf , when axons are developing and repellants are presumably not present ( Figure S6 ) . Axotomy at this stage increased axon activity ( ** p<0 . 01 ) , but the addition of 3 mM H2O2 to developing 30 hpf larvae did not further increase activity when compared to untreated larvae . Conversely , knockdown of duox1 did not significantly change axon activity after fin amputation in 30 hpf larvae ( WT versus duox1-MO , p = ns>0 . 05 ) . These results support the notion that H2O2 acts by blocking axon growth inhibition , since it only influences regeneration at stages when inhibitors are present . Interestingly , a study in chick showed that axon growth promotion by skin wounds was also only effective at late developmental stages [1] . H2O2 promotes immune cell recruitment to wounds in developing fruit fly embryos [12] and during early inflammatory responses to fin amputation in larval zebrafish [13] . To test whether inflammation and axon growth are linked or independent effects of H2O2 signaling , we assessed axon growth in homozygous cloche mutants , which lack blood cells [30] , and macrophage recruitment in larvae injected with ngn1-MO , which lack somatosensory neurons [31] . Amputation promoted axon growth in the absence of blood ( 91 . 16±10 . 44 µm , n = 9; Figure 6F , Video S10 ) and macrophages homed to the wound in the absence of sensory neurons ( 1 . 5±0 . 42 macrophages expressing lysC:GFP at the wound margin within 1 h of amputation , n = 6 ) , similar to wildtype ( 1 . 0±0 . 43 macrophages at the wound margin within 1 h of amputation , n = 7 , p = ns>0 . 05; Figure S7 ) , indicating that these two processes are independent of each other . Our results demonstrate that skin injury promotes the growth of axons near the wound , an effect that is mediated by H2O2 ( Figure 8 ) . Keratinocyte ablation and genetic chimera experiments suggested that the axon growth-promoting effects of H2O2 require its production in keratinocytes . Similarly , in axolotl and chick , wound epidermis attracts axons [1] , [2] and damage to human skin co-cultured with rat dorsal root ganglia promotes regeneration of axons at the dermal/epidermal interface [32] . It will be interesting to determine whether H2O2 also plays a role in these phenomena . Intriguingly , H2O2 improves hippocampal neurite outgrowth in culture [33] . In C . elegans , a mutation in pxn-2 , which encodes an extracellular peroxidase , improves regeneration of mechanosensory axons [34] . In zebrafish , H2O2 may be signaling directly to axons , altering the extracellular matrix , or eliciting a second signal from keratinocytes to promote axon growth , but does not require leukocytes ( Figure 8 ) . Assessing whether application of H2O2 to somatosensory neurons in culture can improve axon growth , as has been reported for hippocampal neurons in culture [33] , could help resolve whether H2O2 acts directly or indirectly on axons to influence their regeneration . In summary , we have found that wounded epidermis promotes somatosensory axon regeneration in zebrafish larvae and that H2O2 is a critical mediator of this effect . Since this effect does not require the presence of leukocytes , we propose that H2O2 plays two independent roles during wound healing: promoting axon growth and mediating leukocyte recruitment . Thus , one signaling molecule emitted from injured tissue helps coordinate wound healing with functional recovery of skin .
Zebrafish embryos were obtained from Nacre [35] , AB ( wildtype ) , Line Mü4435_64 [26] , cloche ( clom39 ) [30] , lysC:GFP [36] , sensory:GFP [29] , and islet2b:GFP [25] fish . Embryos and larvae were treated with 0 . 15 mM Phenylthiourea ( PTU ) to prevent pigment formation . TEM was performed according to Rieger & Köster , CSH Protocols Vol . 2 ( doi:10 . 1101/pdb . prot4772 , 2007 ) . Zebrafish larvae were anesthetized in 0 . 01% Tricaine and mounted in a sealed chamber in 1 . 2% low-melting agarose ( Sigma , St . Louis , MO ) . Details of the mounting and imaging techniques are described elsewhere [10] . Larvae were imaged for 12 h using a 20× air objective . Stacks were scanned every 30 min in 3 µm intervals . Imaging was performed with 6–10 larvae per session on an LSM 510 confocal microscope ( Zeiss ) with an automated stage and Multitime software . Larvae were maintained at 28 . 5°C using a stage heater . Maximum intensity projections of confocal stacks were compiled using Zeiss software and further processed using Adobe Photoshop , NIH open source software Image J 1 . 34S ( Abramoff , NIH Open Source software ImageJ , 2004 ) , and Quick Time Player 7 Pro . For time-lapse imaging of peripheral sensory axon regeneration in H2O2 solution , 0 . 005%–0 . 01% H2O2 ( 1 . 5–3 mM ) was added to the larval media 1 h prior to axotomy . Larvae were maintained in H2O2 solution for 3 or 12 h of time-lapse imaging . See also Figure S1B for timeline of experiments . All transgenes were constructed using the Gateway ( Invitrogen ) tol2kit created by the lab of Chi-Bin Chien [37] . Tol2CREST3-Gal4VP16-14xUAS-EGFP: The somatosensory neuron-specific CREST3 enhancer [38] was cloned into the 5′ Gateway vector ( p5E ) , Gal4VP16-14xUAS [39] into the middle vector ( pME ) , and EGFP-SV40pA into the 3′ vector ( p3E ) . Elements were recombined together with the Tol2 destination vector ( pDESTTol2 ) . Tol2CREST3-LexA-LexAop-EGFP: LexAVP16-SV40pA and four copies of the LexAop [40] were cloned into the middle Gateway vector ( pME ) and recombined with p5E-CREST3 and p3E-EGFP-SV40pA to generate Tol2CREST3-LexA-4xLexAop-EGFP . Approximately 15 pg of CREST3-Gal4VP16-14xUAS-EGFP or CREST3-LexA-LexAop-EGFP plasmids were co-injected with ∼240 pg of Tol2 [41] transposase mRNA into 1-cell stage embryos of wildtype AB or Nacre strains or into the Gal4-UAS muscle reporter line Tg ( Mü4435_64 ) [26] , respectively . A similar amount of CREST3-Gal4VP16-14xUAS-EGFP was co-injected with 10 pg of Krt4:RFP and Tol2 transposase mRNA for keratinocyte ablations . To knock down expression of p53 [28] , duox1 [13] , and ngn1 [31] , 50 nM of each modified antisense oligonucleotide was injected into 1-cell stage embryos . Knockdown of duox1 by morpholino injection was verified with RT-PCR , using published primers [13] . Ten larvae at 3 dpf were pooled for RNA isolation and subsequent RT-PCR ( see also Figure S5A ) . To determine the sublethal concentration of H2O2 ( Fisher Biotech , 30% in water ) to use in larval experiments , we identified the maximum concentration at which 100% of larvae were viable for a minimum of 12 h . Groups of five larvae were incubated in serial dilutions of H2O2 from 0 . 003% to 30% and viability was assessed 12 h later . The EC50 was determined to be ∼0 . 03% ( Figure S4 ) . Larvae survived without any morphological abnormalities at 3 mM H2O2 ( 0 . 01% ) or less . For rescue experiments , we used a lower concentration of H2O2 ( 1 . 5 mM ) to maintain optimal viability . To detect the presence of H2O2 after amputation or ablation , 5 µM of the H2O2 sensor pentafluorobenzenesulfonyl fluorescein ( Santa Cruz Biotechnology ) was added 1 h prior to injury . Larvae were exposed to the sensor throughout the imaging procedure up to 12 h . Fluorescence was detected at 488/505 nm . To create chimeras between wildtype and duox1-morphants , a few blastula cells ( 1 , 000–cell stage ) were transplanted from sensory:GFP transgenic embryos injected with duox1 morpholino into Krt4:RFP wildtype transgenic embryos . Larvae were screened for sensory-specific GFP expression ( Duox1-negative neurons ) and red fluorescence in keratinocytes ( H2O2-positive skin ) . Axons were axotomized and imaged as described above . Macrophages were imaged for 12 h in lysC:GFP [36]/islet2b:GFP [25] double-transgenic zebrafish larvae ( 78 hpf ) , which were either wildtype or injected with 50nM of ngn1 morpholino [31] to inhibit sensory neuron development . New macrophages that arrived within 1 h after amputation at the amputation margin were counted and compared between both groups , similar to [13] . Axon activity was measured by tracing the movements of the 10 axon tips that grew most over a 12 h time window using Image J 1 . 34S and the Image J Manual Tracking software plugin ( F . Cordelires , Institut Curie , Orsay , France ) . Projected images were adjusted for movement of the specimen , using the Image J StackReg plugin ( P . Thévenaz , Swiss Federal Institute of Technology , Lausanne , Switzerland ) . Measurements were made from projections of 24 time points recorded at 30 min intervals , assuming that axon tips move in a two-dimensional plane . A minimum of 10 axon tips per arbor and specimen were traced . The linear distances of axon growth were quantified using the Zeiss LSM 510 software and ImageJ analysis tool by measuring the distance between the growth cone position in the first ( 1 h ) and last ( 12 h ) stack . To quantify reinnervation of denervated territory , NeuroLucida software ( Microbrightfield , Williston , VT ) was used to generate tracings of individual Rohon-Beard axons in the fin skin from confocal stacks at 30 min ( first recorded time point ) and 12 h ( last recorded time point ) . These tracings were overlaid and length measurements were used to quantify the percentage of the axon that entered denervated territory . To minimize distortion caused by developmental growth , images were aligned at the closest shared branch point proximal to the site of axotomy . Statistical analyses were performed using Prism 4 ( GraphPad Software Inc . ) . Unpaired , two-tailed Student's t-tests were used for comparisons of two groups ( Figures 1E and S2 ) . One-way ANOVA and Dunnett's ( comparing groups to a control group ) or Bonferroni's ( comparing groups to one another ) post-tests were performed as indicated in each figure . Significance was set to p<0 . 05 . All graphs show the standard error of the mean . Confocal images were loaded into ImageJ software and converted to 8-bit images . A binary image was created and the mean pixel values in a 50×50 µm field in the distal fin portion measured to determine the axon density . Images were exported as tiff files from the LSM software ( Zeiss ) and loaded into the ImageJ software . Axon tips were traced as described above and individual movements were designated as growth or retraction within each 30 min interval . The total length of growth and retraction for each arbor was calculated for a 12-h period and a mean value of all traced axon tips derived ( n = 4 axon tips/4 axons = 16 tracings total ) . The detached distal portions of axotomized axons were traced using Neurolucida software ( MBF Bioscience ) to determine the total combined length of all the branches in the detached arbor . The length was plotted against axon activity of the parent axon during the regeneration phase ( 12 h ) . Larvae were placed in a petri dish and tapped with an insect pin at the distal tip of the caudal fin and escapes were recorded . Two groups were compared: wildtype uninjured larvae at 6 dpf and age-matched wildtype larvae whose fins were amputated 3 dpf . | Touch-sensing neurons project axonal processes that branch extensively within the outer layers of skin to detect touch stimuli . Recovering from skin injuries thus requires not only repair of damaged skin tissue but also regeneration of the sensory axons innervating it . To study whether skin wound healing is coordinated with sensory innervation , we compared the regeneration of severed sensory axons innervating larval zebrafish tail fins with and without concomitant injury to surrounding skin cells . Severed axons regenerated more robustly when nearby skin cells were also damaged , suggesting that wounded skin releases a short-range factor that promotes axon growth . The reactive oxygen species hydrogen peroxide ( H2O2 ) is known to be produced by injured cells , making it a candidate for mediating this signal . We found that adding exogenous H2O2 improved the regeneration of severed axons . Conversely , blocking H2O2 production prevented the axon growth-promoting effect of skin injury . Thus , H2O2 promotes axon growth after skin damage , helping to ensure that healing skin is properly innervated . | [
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| 2011 | Hydrogen Peroxide Promotes Injury-Induced Peripheral Sensory Axon Regeneration in the Zebrafish Skin |
Prediction of compounds that are active against a desired biological target is a common step in drug discovery efforts . Virtual screening methods seek some active-enriched fraction of a library for experimental testing . Where data are too scarce to train supervised learning models for compound prioritization , initial screening must provide the necessary data . Commonly , such an initial library is selected on the basis of chemical diversity by some pseudo-random process ( for example , the first few plates of a larger library ) or by selecting an entire smaller library . These approaches may not produce a sufficient number or diversity of actives . An alternative approach is to select an informer set of screening compounds on the basis of chemogenomic information from previous testing of compounds against a large number of targets . We compare different ways of using chemogenomic data to choose a small informer set of compounds based on previously measured bioactivity data . We develop this Informer-Based-Ranking ( IBR ) approach using the Published Kinase Inhibitor Sets ( PKIS ) as the chemogenomic data to select the informer sets . We test the informer compounds on a target that is not part of the chemogenomic data , then predict the activity of the remaining compounds based on the experimental informer data and the chemogenomic data . Through new chemical screening experiments , we demonstrate the utility of IBR strategies in a prospective test on three kinase targets not included in the PKIS .
Early-stage drug discovery involves a search for pharmacologically active compounds ( hits ) that produce a desired response in an assay of protein function or disease-related phenotype . The active compounds serve as starting points for further structural optimization , with the ultimate goal of developing therapeutic agents . Virtual screening ( VS ) can be an effective strategy for prioritizing compounds that can lower high-throughput screening costs by reducing the experimental search to smaller , active-enriched compound subsets . This process can be cheaper and more effective than exhaustive , unguided testing of entire compound libraries [1] . VS may also allow us to evaluate much larger physical or virtual compound libraries . As on-demand synthetic capabilities expand , a VS-guided approach might obviate costs associated with purchasing and on-site storage/maintenance of large general libraries in favor of growing smaller , project-focused compound sets [2] . The choice of which VS methodology to deploy depends on the types of information available at the start of this effort [3] . Structure-based VS methods ( such as docking ) require specific , structurally-characterized biomolecular targets , but these target structures might only be approximated by homology models [4] , or might not be available at all . Phenotypic endpoints like cell death or tumor shrinkage are not amenable to structure-based approaches because specific target structures and sites of action may not be known . Furthermore , structure-based VS performance varies substantially across targets , where failures are difficult to predict [4 , 5] . Ligand-based VS approaches can provide more consistent levels of enrichment and are independent from any target structure , but they depend strongly on the quality and abundance of training data in the form of measured compound activities on the target of interest [6] . Such approaches , especially those using topological features for compound representations ( such as graph-based fingerprints ) , may also suffer from high prediction uncertainty when presented with compounds whose chemotypes/scaffolds are outside the scope of the training set [6 , 7] . The key issue , however , is that training data are usually scarce in early stages of the screening process , making it difficult to generate a predictive model . For some well-studied target classes ( for example , kinases or GPCRs ) , rich chemogenomic data are available in the form of compound activity profiles across many members of a target class . These data can be structured as a targets-by-compounds matrix of functional interactions , which we term the bioactivity matrix . Though sometimes sparse , incomplete , or limited in compound and target coverage , such matrices hold valuable information that can be leveraged to make predictions on new targets or compounds . Predictions of compound activities are routinely made using machine learning algorithms to relate a selection of chemical features to the previously measured bioactivities of a training set of compounds . In many cases , these features are chemical fingerprints that describe the presence and proximity of chemical substructures in each compound [6 , 7] . Alternative compound fingerprints have been developed on the basis of prior chemogenomic data [8–13] . In these cases , the bioactivity profile of a compound across a series of assays is used as a fingerprint , referred to as a “High Throughput Screening FingerPrint” ( HTS-FP ) , based either on continuous bioactivity values or on a binary quantity representing activity/inactivity . HTS-FPs enable a useful expression of compound relationships through distances derived among standardized bioactivity profiles in much the same manner as chemical fingerprints . HTS-FPs have limited extensibility in that the wide array of assays/target responses that confers a rich pharmacological representation cannot be readily generated for new molecules . However , looking beyond compound representations , arrays of standardized bioactivity data , even when incomplete , can help to establish target relationships . Given a new target with little or no prior structure–activity relationship information , building an effective ligand-based VS model requires training data acquired through preliminary screening . For the virtual screening model to be cost effective , the library subset providing training instances should be as small as possible . However , preliminary unguided screens constrained to only 100s to 1000s of compounds are likely to produce insufficient training data with few active training instances of limited potency and structural diversity . Motivated by the need for batch selection strategies to enable effective iterative screening efforts , there has been significant recent effort in developing compound prioritization models from minimal data [14–18] . These methods prioritize additional compounds for testing based on an initial increment of screening data , but the selection of the initial subset of compounds to be screened is often random , pseudo-random , or based on chemical diversity . A recent effort by Paricharak et al . [18] uses an active learning process to select an informer set from the most active and least active compounds across a series of PubChem assays . Their work removes specific assay labels from the chemogenomic data to create a balanced data set , and selects compounds on the basis of uncertainty from previous predictive models . However , their optimal informer set is too large to be useful as an initial screening set in most HTS settings . Our emphasis in this paper is on the selection of the informer set—the initial set of compounds to be assayed . The experimental data for these compounds may then be used to train initial models or to select additional compounds as the initial ( 0th iteration ) set of compounds to be assayed in a multi-phase scheme . We refer to approaches based on informer sets as Informer-Based Ranking ( IBR ) methods . This is different than the focus of the studies cited above that focus on model-guided or heuristic selection of compounds for multiple phases of screening . Our approaches are analogous to earlier chemometric experimental design approaches like chemical cluster sampling [19] , but leverage chemogenomic data instead . The proposed IBR methods each involve two steps; see Fig 1 . In the first step , they select an informer set of compounds to evaluate experimentally for bioactivity on the new target . Importantly , this selection is guided by the bioactivity matrix . The second step involves prioritization of the compounds outside the informer set , according to their bioactivity against the new target . This prioritization may make use of both the bioactivity data on other targets as well as the new data obtained on the informer compounds on the new target . We describe algorithms both for the selection of the informer compounds and for the prioritization of other compounds after screening data are obtained for the informer compounds . We propose three novel IBR strategies: Regression Selection ( RS ) , Coding Selection ( CS ) , and Adaptive Selection ( AS ) . Each strategy consists of ( i ) an informer set selection method that chooses a small number of compounds to be tested , based only on characteristics of the bioactivity matrix , and ( ii ) a compound ranking method that leverages returned informer data to predict which of the untested compounds is active against a new target . Underlying all three strategies is the premise that targets may be naturally organized according to patterns in their bioactivity profiles across compounds . This organization leads to a clustering of targets as well as to the identification of informer compounds that are predictive of the cluster identity of a novel target . The strategies leverage advances in optimization and statistical analysis , and they differ in how patterns are recognized and computations are deployed . We apply the proposed IBR strategies in the context of two public human kinase chemogenomics matrices: PKIS1 [20] and PKIS2 [21] . We demonstrate the strategies prospectively , by prioritizing PKIS1 and PKIS2 compounds for activity against three distinct protein kinase targets of potential therapeutic importance: Mycobacterium tuberculosis PknB [22] , Epstein-Barr virus BGLF4 , and Toxoplasma gondii ROP18 [23] . We also apply the strategies retrospectively , in a cross-validation study of each chemogenomics matrix , leaving out one target at a time and prioritizing compound activity against the left-out target . The performance of each new IBR strategy was assessed prospectively by inspection of the successful activity predictions , and retrospectively using common VS metrics , including: Area Under the Receiver-Operator Characteristic Curve ( ROCAUC ) and enrichment factor ( EF ) . We also assessed each strategy’s ability to retrieve structural diversity among active compounds by computing the fraction of active scaffolds identified in the top of the ranking . For benchmarking purposes , we compared the proposed IBR methods to a set of baseline models that make use of the compound structures , and include the commonly used diverse selection as well as a selection of the most frequently active compounds in the bioactivity matrix .
We describe IBR strategies that require experimental testing of some new target of interest on a small fraction of the compound library—the informer subset—with a view to effectively prioritizing the remaining compounds for subsequent testing for activity with the target . The complete IBR strategy thus has two parts: a scheme to identify the informer subset and a scheme to prioritize the remaining compounds after assay data have been obtained for the informer compounds . Initially , we may have no assay data on the new target , though we typically have some such chemogenomic data on related targets that populate a related sector of chemical space , in some sense . Ideally , a successful IBR strategy might be applied in target-agnostic drug development settings ( for example , phenotypic targets or incompletely featurized targets ) , so we intentionally exclude from each IBR strategy target-specific features , such as protein sequences or structural information . We described three novel IBR strategies that use statistical patterns in the bioactivity matrix that is available prior to informer-set assay testing . Regression Selection ( RS ) , Coding Selection ( CS ) , and Adaptive Selection ( AS ) all treat the target space as being partitioned into clusters of targets so that , within each cluster , there is some relevant similarity of the bioactivity profiles of the targets across the space of tested compounds . These three strategies also posit that a small number of compounds ( the informer subset ) have bioactivity profiles that are predictive of the cluster label appropriate to any target , including the novel target of interest . RS , CS , and AS differ in how they evaluate clusterings and potential informer subsets . For example , RS and AS involve kmeans clustering of targets followed by regularized multinomial regression to learn the relationship between compounds and cluster labels , but they differ in how the regression is regularized and how the informer compounds are identified . In contrast , CS forms a single objective function that simultaneously scores clustering strategies and potential informer compounds . Computationally simpler baseline IBR strategies are useful to consider , as they may approximate practical experimental design scenarios . Baseline Chemometric strategies ( BCs , BCl , and BCw ) use chemical features for both informer selection and non-informer ranking . Three different chemometric ranking strategies are used for the non-informer ranking , as denoted by subscripts s , l , and w ( described in detail in the Methods ) . Here , clustering is applied on the compound space using the known chemical structure ( fingerprints ) of the compounds ( not used in RS , CS , or AS ) in order to identify informer compounds . Then , prioritization of the non-informers makes use of various ways of ranking the chemical distance between bioactive informers and non-informers . Alternatively , a Baseline Frequent-hitters strategy simply takes as informer compounds those that show the highest rate of activity within the initial target set ( BFs , BFl , BFw ) . Prioritization of non-informers uses chemical distance , as in the chemometric methods . To simplify , we only report baseline results for each of our top chemometric and frequent-hitters baseline strategies ( BCw and BFw ) . Outcomes for the full set of baselines are available in the supplemental information . Performance of the IBR strategies was evaluated using two virtual screening metrics that reflect successful prioritization of active compounds: ROCAUC and Normalized Enrichment Factor in top 10% of ranking ( NEF10 ) ) . An additional metric Fraction of Active Scaffolds Retrieved ( FASR10 ) assesses the diversity of the active chemical structures that were prioritized in the top 10% of the ranking . Also , standard classification metrics F1 score and MCC were applied . We applied the IBR strategies on three novel kinase targets outside of the PKIS1 and PKIS2 target sets . These microbial targets are phylogenetically distant from most of the human protein kinases in the PKIS data sets , with relatively low kinase domain sequence identities to the nearest neighbors in the PKIS1/2 sets in comparison to kinase domain sequences ( S1 Fig ) . For Mycobacterium tuberculosis kinase PknB ( UniProt ID: P9WI81 ) , the nearest neighbors were the human serine/threonine kinases MARK2 ( 16 . 1% kinase domain sequence identity ) in PKIS1 and BRSK1 ( 16 . 1% ) in PKIS2 ( UniProt IDs: Q7KZI7 and Q8TDC3 , respectively ) . For Epstein-Barr virus kinase BGLF4 ( UniProt ID: I1YP37 ) , the most similar kinase domain sequences were from human protein tyrosine kinase ( PTK2 or FAK2 ) ( 13 . 8% ) in PKIS1 and human serine/threonine-protein kinase ( LRRK2 ) ( 14 . 2% ) in PKIS2 ( UniProt IDs: Q14289 . 2 and Q5S007 ) . For Toxoplasma gondii ROP18 ( UniProt ID: Q2PAY2 ) , the most similar kinase domains are NEK7 ( UniProt ID: Q8TDX7 . 1 ) ( 20 . 2% ) in PKIS1 and aurora kinase C ( AURKC , UniProt ID: Q9UQ89 . 1 ) ( 20 . 7% ) in PKIS2 . To prioritize which PKIS compounds might be active on PknB , BGLF4 , or ROP18 , each IBR strategy selected 16 informer compounds from PKIS1 and 16 informer compounds from PKIS2 . PknB and BGLF4 were obtained and screened in-house while ROP18 data were collected from an external collaborator [23] . The screening data were held separately from the IBR and baseline method operators prior to informer selection . After selecting PKIS1 and PKIS2 informer compounds , screening data only for those compounds were provided to each IBR and baseline method . Informer set selections by each IBR for PKIS1 are shown with their associated experimental bioactivity measurements in S1 Table . The assay results for the informer compounds selected by each of the IBR strategies were used to rank the remaining non-informers in PKIS1 or PKIS2 . To evaluate the performance of the different methods , all of the available PKIS1 and PKIS2 compounds were assayed . Experimental active/inactive labels were assigned using μ + 2σ percent inhibition ( activity ) thresholds in PKIS1: PknB = 13 . 4% , BGLF4 = 20 . 2% , and ROP18 = 43 . 8% and PKIS2: PknB = 8 . 7% , BGLF4 = 12 . 5% , and ROP18 = 33 . 4% based on screening results from the PKIS compound sets . The RS and CS approaches were the only methods that recovered multiple hits and active scaffolds in their top 10% of ranked compounds for all three kinase targets and both PKIS datasets ( Table 1 and S2 Table ) . RS managed to recover actives for PknB even though it did not include any active compounds in its PKIS1 or PKIS2 informer sets . The RS method was also the best overall for BGLF4 on PKIS2 and tied as the best method for PknB on PKIS1 . CS was the best approach for PknB on PKIS2 . AS and the three BF baseline methods ( BFw shown in Table 1 ) struggled for PknB and BGLF4 with the PKIS2 compounds , each identifying only a single hit . However , AS was the best approach for BGLF4 on PKIS1 compounds and performed better on ROP18 with PKIS2 compounds . The three purely chemometric baseline approaches ( BC ) ( BCw shown in Table 1 ) were the worst overall , in many cases failing to recover any hits . Nevertheless , BCs and BCl were the top methods on ROP18 with PKIS2 compounds ( S2 Table ) . The best methods were the same when evaluated with NEF10 or FASR10 , but varied slightly for ROCAUC ( S3 Table ) . The PknB , BGLF4 , and ROP18 results demonstrate that the IBR methods perform reasonably well even in a challenging setting where the new targets have low kinase domain similarity with the targets used to construct the informer set . For a more comprehensive quantitative assessment of the IBR methods , we conducted retrospective leave-one-target-out ( LOTO ) analysis for each of the m = 224 targets in PKIS1 . This involved m = 224 separate applications of all the IBR strategies applied to reduced chemogenomics matrices ( m − 1 rows ) , again using an informer size of 16 compounds . Each time , the bioactivity profile of the left-out target was predicted in the sense that compounds were prioritized for activity against this one left-out target . Results from PKIS1 LOTO cross validation are summarized in Table 2 . With respect to the ROCAUC metric ( Fig 2 ) , the purely bioactivity-based RS model provides the best rankings with a median ROCAUC value of 0 . 92 ± 0 . 11 ( ± one standard deviation ) . RS and AS methods both had better performance than the top chemocentric and frequent-hitter baseline approaches , BCw ( 0 . 67 ± 0 . 22 ) and BFs ( 0 . 83 ± 0 . 14 ) . The improvements in ROCAUC of RS and AS over BCw ( p = 5 . 5E-31 , 2 . 0E-23 ) and BFs ( p = 1 . 3E-21 , 4 . 9E-6 ) were statistically significant . All p-values were obtained from a 2-sided , pairwise Wilcoxon sign-rank test with S̆idák multiple comparison correction for 6 hypotheses ( 6 baselines ) . This correction increases the stringency of the statistical threshold applied on each of the 6 individual tests from α = 0 . 05 to α = 0 . 0085 . The CS method also had statistically better ROCAUC performance than all baseline models except BFl ( p = 0 . 037 ) and BFs ( p = 0 . 032 ) . A complete set of p-values from a pairwise comparison of the IBRs is available in S5 Table . The hybrid baseline approaches , which use compound bioactivity profiles to select the most broadly active compounds as informers , performed much better than the chemometric approaches that use chemical features for informer selection . We also compared strategies using enrichment factor ( EF ) as an alternative VS metric that , like ROCAUC , reflects retrieval of active compounds ( Fig 3 ) . The maximal EF value that could be achieved on a target , however , depends on the active fraction in the set . To address the variation in the extent of the class imbalance across kinase targets ( active fractions ranging from 0 . 01-0 . 12 in PKIS1 ) ( S2 Fig ) , we apply the normalized EF metric NEF10 . The EF cutoff was also extended from a typical 1% threshold out to 10% , due to the small number of compounds considered ( n = 366 ) . To simplify comparison with the ROCAUC metric , we scale NEF10 such that a value of 0 . 5 reflects a random classifier ( equivalent to random ranking or no enrichment ) and a value of 1 . 0 represents a perfect classifier , in which the top 10% has been maximally enriched . Over the 224 targets considered in PKIS1 , the three bioactivity-based models ( RS , CS , and AS ) are statistically superior to all of the baseline approaches ( all p <0 . 0085 ) . The AS method had the strongest enrichment for active compounds with a median NEF10 of 0 . 85 ± 0 . 13 . This was better than the top frequent hitters model , BFl , which had a median NEF10 of 0 . 74 ± 0 . 13 ( p = 5 . 7E-14 ) . The enrichment is even better compared to the chemometric models , the best of which is BCw , providing a median NEF10 of 0 . 60 ± 0 . 13 ( p = 6 . 7E-28 ) . Another key characteristic of robust virtual screening performance is the recognition of diverse active compound structures rather than retrieval of only a subset of the active chemotypes . Because of the high rate of failure for hits in follow-up hit-to-lead or optimization efforts , we value methods that can retrieve as many active scaffolds as possible , even at some expense to predictive accuracy reflected by ROCAUC and NEF metrics . Across PKIS1 targets we assessed the diversity among the known active chemotypes prioritized by each model by monitoring the Fraction of Active Scaffolds Retrieved among the top ranking 10% of compounds ( FASR10 ) ( Fig 4 ) . The bioactivity-based IBR methods outperform the top hybrid and chemocentric baseline models , according to this metric . The median FASR10 for the AS model 0 . 75 ± 0 . 23 exceeded the top hybrid model , BFl ( 0 . 52 ± 0 . 21 , p = 2 . 1E-18 ) , and chemocentric model , BCw ( 0 . 29 ± 0 . 21 , p = 3 . 7E-32 ) . Although the IBRs were developed for compound ranking and not necessarily as classifiers , classifier metrics F1 score ( F1 ) and Matthew’s Correlation Coefficient ( MCC ) were also evaluated for the methods across the PKIS1 targets . The scores/ranks returned by each method were converted to binary classifications using a threshold based on the median active fraction for PKIS1 ( 5 . 5% ) . Over the 224 targets considered in PKIS1 , RS , CS , and AS are statistically superior to all of the baseline approaches ( all p <0 . 0085 ) . The full set of metrics evaluations on PKIS1 are provided in S4 Table with the corresponding p-values from pairwise comparisons in S5 Table . To assess the robustness of IBR performance , we stratified the PKIS1 targets into four equi-sized subsets and compared IBR methods on all performance metrics separately on each subset . This stratification was based on target hit rate and was obtained by binning targets after ranking by hit rate . S7 , S9 and S11 Figs stratify Figs 2–4 , and indicate very little effect on performance of the target hit rate . To examine further , we used linear regression to decompose each target-method performance metric into a target effect and a method effect; S8 , S10 and S12 Figs plot estimated method effects and multiplicity-adjusted 95% confidence intervals . AS , CS , and RS are all robust to the target hit rate , having quite similar performance in all strata . By contrast , BFw is relatively sensitive to the target hit rate . Considering that the hit rate of a novel target is unknown prior to testing , marginal features such as in Figs 2–4 , reflect relevant operating characteristics of the proposed IBR methods . All IBR strategies require choosing the number of elements nA to include in the informer set . Larger nA allows more information to be gleaned from intermediate screening data , and therefore improved prioritization of non-informer compounds . Marginal improvements in performance as a function of nA are expected to diminish as nA increases , because of redundancies in the information acquired as more activity data accrues . Larger nA also leads to higher assay costs . The experiments reported above used nA = 16 , about 4% of the compounds in the chemogenomics matrix . To examine the relationship of informer set size to prioritization performance , we applied IBR strategies on a range of informer set sizes . First , we considered AS , our best performing IBR strategy . S4 Fig shows ROCAUC and NEF10 metrics from the LOTO retrospective analysis of PKIS1 for nA varying from 9 to 28 . Performance did not vary greatly over this range . We also tested a wider range of informer set sizes ( nA = 1 to 48 compounds ) on PKIS1 target predictions using LOTO cross validation , and examined ROCAUC and NEF10 using baseline IBR methods BCw ( S5 Fig ) and BFw ( S6 Fig ) . Over this range , we observe performance degradation with diminishing informer set sizes . These experiments indicate that our preferred value nA = 16 strikes a reasonable balance between size and performance for this particular data set .
Chemogenomic assay data have been used through inductive transfer or transfer learning approaches to make successful predictions on compound-target interactions in several contexts [24 , 25] . Reker et al . [16] and Cichonska et al . [26] placed chemogenomic predictions into 4 classes: ( 1 ) filling in missing elements within a relatively complete chemogenomic matrix ( bioactivity imputation ) , ( 2 ) predicting interactions for a target on matrix compounds ( virtual screening ) , ( 3 ) predicting interactions for a compound on matrix targets ( drug re-purposing or off-target effects ) , and ( 4 ) predicting interactions for non-matrix compounds on a non-matrix target ( virtual screening ) . Wasserman et al . [27] showed that simple kernel approaches using nearest proxy targets could be used to rank compounds effectively for a query target ( class 2 ) , as long as it was possible to identify proxy targets closely related to the query target . For kinase targets , Cichonska et al . [26] explored a wide range of ligand and target kernels to address class-1 and class-3 problems . For focused target sets ( kinases and GPCRs ) , Janssen et al . [28] recently applied nearest-neighbor approaches to ligand and targets mapped on t-SNE projections to address class-2 and class-3 problems . The methods we report differ from prior chemogenomic methods for addressing the class-2 problem by involving strategic but limited data acquisition on the query target . Determination of the responses of targets to key informer compounds shifts a relatively difficult class-2 problem into the more tractable class-1 problem of imputation . Unlike chemogenomic kernel-based approaches [26 , 27] , we did not use target features , focusing instead on target-agnostic strategies for compound ranking that could be used in the future for cell-based or phenotypic assays . Our focus on limited , strategic data acquisition on the target of interest frames the problem in a more practical context akin to compound prioritization in early , low-data stages of an iterative screening effort [14 , 18 , 29 , 30] . Lack of active compound instances can stall implementation of supervised models for compound selections [15] . Our bioactivity-based IBR methods overlap hit expansion methods using chemogenomic data , as applied by groups at Novartis for guiding molecule selection in iterative screening [18 , 29] . In agreement with their findings , IBR methods that use compound bioactivity profiles , rather than chemical features , provided broader active scaffold retrieval [29] . Previous implementations of HTSFP , however , define compounds by normalized bioactivity vectors from an independent reference assay set , whereas our IBRs use compound bioactivity profiles derived directly from the available chemogenomic matrix . We tested targets only from the same target class , namely , protein kinases . The IBR-based informer sets could be applied in the same way that Paricharak et al . used their Mechanism-of-Action Box ( MoABox ) of probe compounds for testing in “iteration zero” of their iterative screening procedure [29] . The IBR strategies described here could enable iterative screens either on orphan members of a target class or on targets on which very few compounds have been tested . Data returned on each screening iteration would then be used as new training instances to refine the model , potentially in an active-learning framework that also considers relevance of training instances for subsequent compound selection . To promote efficiency of an iterative approach , initial compound batches are often limited in size , with compounds are often being selected at random or to achieve chemical diversity . Initial screens chosen in this way are likely to return few active compounds , thus stalling effective implementation of a supervised activity prediction model . The IBR strategy reported here can be deployed for compound prioritization in early rounds of batch selection; the informer set could be tested to obtain preliminary compound rankings in the low-data phase of iterative screens . Due to class imbalance being skewed towards inactive compounds in drug discovery tasks , IBR methods could enable rapid identification of relatively rare but important active instances necessary for training the activity-prediction model until it can score compounds accurately for prioritization . Moreover , the bioactivity-based IBR methods exhibited diverse active-scaffold recognition properties , yielding positive training instances with greater structural diversity for supervised compound prioritization models . The FASR10 results indicate that bioactivity-based IBR approaches generalize better over different compound structures than chemometric IBRs , so they should exhibit a greater tendency to scaffold hopping [29 , 31 , 32] . In contrast , all of the baseline IBR methods use Morgan fingerprint-derived distances to active informers , thus confining their perspective to those active regions of chemical space identified with the informer set . Different chemotypes , however , can exhibit strong activity on the same target . Plots of PKIS1 compounds projected into their three major principal components of chemical feature space ( Morgan fingerprints ) frequently show active regions that are non-adjacent ( S3 Fig ) . While active compounds tend to cluster in specific regions of chemical space , many targets elicit multiple , sometimes distantly separated regions of active chemical space . There are several potential uses for IBRs in drug discovery . This work demonstrates the possibility of effective prediction of activities for new targets within the same target class ( kinases ) from an extensive chemogenomics data matrix representing many targets within that class . A future direction of research is to quantify the amount of chemogenomic data needed to enable robust prediction within the same target class . It appears that low-rank structure in the chemogenomic matrix used in the IBR methods helps to enable reliable predictions of a target’s compound preferences . Statistical models that faithfully represent variation and dependence in bioactivity data also could be leveraged to guide the development of alternative IBR strategies beyond RS , CS , and AS . Of greater interest is the development of a more general informer set from a broader collection of chemogenomic data . To investigate the generalizability of the methods , we plan to apply them to a wider range of novel targets ( or held-out targets ) using an expanded chemogenomic data set with broader target and compound coverage . We do not know how well IBRs will perform on new targets that are unrelated to those within the matrix . We are encouraged by the prospective predictive performance on query kinases ( PknB , BGLF4 , and ROP18 ) that are dissimilar from kinases in the chemogenomic data but note that these targets are still similar structure and chemical function as protein kinases . More comprehensive data matrices tend to be incomplete , with many missing data values , but they should be useful in testing whether these methods are effective in extended pharmacological spaces . The size of the informer set may well have a dramatic impact on overall performance . It may be possible to use IBR methods for prioritizing non-matrix compounds on a new target ( a class-4 problem ) . Chemogenomic matrices enable pharmacological mapping of a given new target ( query ) to matrix targets that exhibit similar bioactivity profiles ( proxy targets ) . Associations between query targets and proxy targets can be made on the basis of full-compound bioactivity profile in the matrix , or potentially just informer assay results . Given that certain proxy targets are likely to be more extensively screened ( tested with compounds outside the matrix set ) , it might be possible to use non-matrix screening data on proxy targets to infer activities for additional compounds and thus prioritize them for testing on some query target .
Most of the IBR strategies developed here leverage chemogenomics data matrices for activity predictions on compounds against selected kinase targets . The matrices were derived from two public human kinase chemogenomics data sets PKIS ( PKIS1 ) [20 , 33] and PKIS2 [21] . Prior to development and testing of methods , these sets were processed as described below . ( Links to our processed PKIS datasets are provided below ) . PKIS1 . The original PKIS data set ( PKIS1 ) was downloaded from https://www . ebi . ac . uk/chembldb/extra/PKIS/PKIS_screening_data . csv Each row in this data set contains an assay result on a specific compound . Each row lists several identifiers for each compound and the target , assay conditions , and the assay read-out ( percent inhibition ) . For nearly every compound , kinase activity was tested independently at 0 . 1 μM and 1 . 0 μM concentrations . For this work , only the inhibition values obtained at 1 . 0 μM were used , in order to match the PKIS2 concentrations . PKIS1 contains 366 unique compounds with unique SMILES and ChEMBL IDs that were tested on 200 unique parent kinases having unique target ChEMBL IDs . When we include mutants/variants of the parent isoforms , there is a total of 224 targets with unique ChEMBL ASSAY IDs . Our processed PKIS1 data was therefore arranged as a matrix of 224 kinase targets by 366 compounds . PKIS2 The original PKIS2 was downloaded from https://doi . org/10 . 1371/journal . pone . 0181585 . s004 . This set comprises 641 unique compound SMILES and 406 target columns . However , only of these 415 compounds were available to us from the original set for testing . We included only these compounds from the PKIS2 data set , so our bioactivity matrix has 406 targets by 415 compounds . PKIS2 activity values represent percent inhibition values observed at inhibitor concentrations of 1 μM . Mycobacterium tuberculosis PknB . Recombinant bacterial kinase ( PknB ) and bacterial substrate ( GarA ) were purified from E . coli following published procedures [22] . The kinase inhibition assay was done using the Kinase Glo ( R ) kit from Promega similar to published procedures . PknB was added to plated kinase inhibitor libraries ( the available compounds from PKIS 1 and 2 ) and incubated at room temperature for 10 minutes , after which ATP and GarA ( protein substrate ) were added . The final concentrations were: PknB 0 . 25 μM , GarA 40 μM , ATP 100 μM , inhibitors 2 μM , DMSO 1% in a final volume of 5 μL . The kinase reaction proceeded at room temperature for 30 minutes and quenched by the addition of 5 μL of Kinase Glo ( R ) reagent . The plate was allowed to develop for 10 minutes and luminescence was detected on a BMG PheraStar multiplate reader . Luminescence was converted to μmol/minute of ATP consumed using a standard curve of ATP from 100 to 0 μM . A negative control ( no inhibitor ) was used to determine percent activity . A positive control ( GSK690693 ) was used to ensure a baseline and compare plate-to-plate variation . Data were analyzed using CDD Vault ( Collaborative Drug Discovery , Inc . ) to determine plate Z′ > 0 . 5 and report percent inhibition for each compound . Epstein-Barr virus BGLF4 . Viral kinase BGLF4 was provided by the laboratory of Professsor Yongna Xing . BGLF4 was expressed with an N-terminal His8-MBP-dual-tag in insect cells , and purified over Ni2+-NTA resin ( Qiagen ) and then Maltose resin ( Qiagen ) , followed by ion exchange chromatography ( Source 15Q , GE Healthcare ) and gel filtration chromatography ( Superdex 200 , GE Healthcare ) to more than 95% homogeneity . The purified BGLF4 was then used for kinase inhibition assays using the C-terminal fragment peptide of retinoblastoma protein ( RB ) as substrate ( Millipore Sigma cta# 12-439 ) . The remaining assay parameters were the same as those applied for PknB except for the following changes . The final concentrations in the reaction medium were: BGLF4 0 . 004 μg/μL , RB 0 . 04 μg/μL , ATP 500 μM , inhibitors 3 μM , DMSO 0 . 3% in a final volume of 5 μL . As a positive control , K252a ( 5 μM ) was used . The reaction proceeded at room temperature for 20 minutes and was then quenched by the addition of 5 μL of Kinase Glo ( R ) reagent . ADP depletion proceeded for 40 minutes , followed by addition of 10 μL of kinase detection reagent . The reactions were incubated for 1 hour prior to luminescence detection . Toxoplasma gondii ROP18 . Inhibition data for the PKIS compounds on the Toxoplasma gondii kinase ROP18 was provided by the University of North Carolina Structural Genomics Consortium and Professor L . David Sibley at Washington University in St . Louis . Their assay measured phosphorylation of a substrate peptide by purified ROP18 using microfluidic capillary electrophoresis [23] . Metrics . To evaluate model performance , we applied three different virtual screening metrics , ROCAUC , NEF10 , and FASR10 . Standard classification metrics , F1 score ( F1 ) and Matthew’s Correlation Coefficent ( MCC ) , were applied as well . ROCAUC and NEF10 measure the extent to which a model prioritizes the active compounds in its ranking . ROCAUC is a standard metric in virtual screening [41] and applied generally in machine learning to evaluate classifiers . Enrichment Factor ( EF ) ( Eq 17 ) is another commonly used metric for assessing virtual screening performance . EF reflects the fold increase in active compounds over that expected from random compound selection , for a subset of a compound library taken from some top ranking portion of a prioritized compound list . EF 10 i = ∑ j ∈ B z i , j | B | / ∑ j = 1 n z i , j n , ( 17 ) where B is the set of compounds among the top 10% of those ranked by a method applied to target i , and zi , j is as in ( 1 ) . However , the number of active compounds for each left-out target i varies from target to target ( S1 Fig ) . We apply a scaling scheme on EF at the top 10% ( Eq 18 ) , which enables better comparisons across targets exhibiting significant differences in active:inactive ratios . NEF 10 i = 1 + EF 10 i - EFbase EF10max i - EFbase 2 , ( 18 ) where EFbase is 1 , which corresponds to random guessing; EF10maxi is the maximum theoretical EF10i , which means all actives are ranked at the top and depends on the number of actives for each target . Our NEF metric returns a value between 0 . 5 and 1 , where a NEF10i larger than 0 . 5 shows better ranking performance than random guessing–similar to ROCAUC . We selected the 10% threshold with consideration of the sizes of our informer ( nA = 16 ) and full compound sets ( n = 366 and n = 405 ) . This threshold includes the 16 informers and 21 noninformer compounds in our PKIS1 evaluation . For the ROCAUC and NEF10 metrics , experimental percent inhibition ( activity ) data were binarized using a target-specific μ+2σ threshold based on the activity distribution of the PKIS1 compounds for the kinase target . Actives were defined as compounds with greater than twice the standard deviations above the mean , as noted in ( 1 ) . When applying the metrics , active informer compounds were counted as true positives , whereas inactive informers did not count against the models as false positives . It should be noted that the main purpose of the informer set is to facilitate accurate activity ranking on the non-informers . However , since informer compounds represent the highest priority compounds for testing , we reward models for retrieving active informers but refrain from penalizing models for choosing inactive informers . Some baseline models that rely upon binary compound labels occasionally failed to evaluate the noninformer compounds in cases where no active informers are returned . In such cases , metric scores reflecting random ranking were assigned to the model: ROCAUC and NEF10 of 0 . 5 and a FASR10 score of 0 . 0 . FASR10 assesses a model’s capacity to recognize different active chemotypes among the the top 10% of ranked compounds . The metric reflects the fraction of all active scaffolds identified on a given target within the compound set . Again , zi is the Boolean vector of compound binary activity labels on target i for compound set J . Let OJ be the vector of chemical scaffold identifiers for compounds in J . The scaffold identifiers are arbitrary integer scaffold indices assigned to each of the generic Bemis-Murcko scaffold presented in J , as obtained using the MurckoScaffold module in RDKit [39 , 42] . Bemis-Murcko scaffolds were made generic by stripping hydrogens , converting all bonds to single , and setting all atom types to aliphatic carbon . The unique active scaffold identifiers are the set of all non-zero values in the Hadamard product vector: C J = { z i ∘ O J } ( 19 ) If we then let z i 10 and O J 10 be the binary activity labels and scaffold IDs for the top 10% ranked compounds , the subset of unique active scaffolds recognized just among the top 10% of compounds is: C J 10 = { z i 10 ∘ O J 10 } ( 20 ) The fraction of active scaffolds recognized in the top 10% is: FASR 10 = | C J 10 | | C J | ( 21 ) Note , active scaffolds were not considered retrieved unless an experimentally observed active member from that chemotype was in the top 10% . Cases arise where only inactive members of an active scaffold were obtained in the top 10% of the compound ranking . In such cases , the FASR10 metric does not count the chemotype as recognized . Although the IBRs were developed for compound ranking and not necessarily as classifiers , standard classification metrics F1 and MCC were also applied for IBR performance evaluations . The scores/ranks returned by each method were converted to binary classifications using a fixed threshold across targets based on the median active fraction of the chemogenomic data: 5 . 5% for both PKIS1 and PKIS2 . This amounts to assigning an active classification to the top 20 and top 23 scoring compounds in PKIS1 ( 366 ) and PKIS2 ( 415 ) , respectively . As in the other metrics , inactive informers were not counted as false positives and were removed before metric calculations . Active informers , however , were counted as true positives . Model evaluations . Performance of the models was evaluated in two stages . The first stage follows a retrospective leave-one-target-out ( LOTO ) evaluation scheme . Each of the 224 kinase targets in the PKIS1 target set is removed and treated as a new target of interest i . The PKIS1 compound activities are hidden for this target . An informer set Ai is selected for this new target , the activities are revealed for the informers , and then the model rank orders the remaining noninformers A i c using the informer data and in some cases data from the other 223 targets . The 9 models were evaluated in this stage using the 3 metrics described above . The second stage is a prospective evaluation of the 9 models as applied on three novel , non-human , kinase targets . In these evaluations , informer sets were generated twice for each model–once on each of the training matrices , PKIS1 and PKIS2 . The remaining compounds ( noninformers ) from the corresponding matrix are then ranked on the two novel kinase targets using data returned for the informer sets and data within the corresponding PKIS1 or PKIS2 training matrix from which the informer set was selected . As in the retrospective PKIS1 LOTO evaluation , each model was assessed using the 3 metrics described above . However , in this prospective test on the new targets , each model was applied twice , using each of the PKIS data matrices , and therefore a total of 6 evaluations were performed on each model . We attempted to build a larger PKIS matrix by merging the PKIS1 and PKIS2 data matrices . The structure of the merged matrix , was however problematic in that the compound sets were nearly disjoint between PKIS1 and PKIS2 . The resulting incomplete matrix lacks a structure that enables accurate imputation of the missing activity elements . PknB and BGLF4 screening data obtained at the UW-Carbone Cancer Center’s Small Molecule Screening Facility , ROP18 data , formatted PKIS1 and PKIS2 datasets , and a Python implementation of the baseline IBR methods , evaluation metrics , and plotting procedures are available here: https://github . com/SpencerEricksen/informers . Matlab code and documentation involving the RS method is available here: https://github . com/leepei/informer . An R package for running CS and AS methods is available here: https://github . com/wiscstatman/esdd/tree/master/informRset . | In the early stages of drug discovery efforts , computational models are used to predict activity and prioritize compounds for experimental testing . New targets commonly lack the data necessary to build effective models , and the screening needed to generate that experimental data can be costly . We seek to improve the efficiency of the initial screening phase , and of the process of prioritizing compounds for subsequent screening . We choose a small informer set of compounds based on publicly available prior screening data on distinct targets . We then collect experimental data on these informer compounds and use that data to predict the activity of other compounds in the set for the target of interest . Computational and statistical tools are needed to identify informer compounds and to prioritize other compounds for subsequent phases of screening . We find that selection of informer compounds on the basis of bioactivity data from previous screening efforts is superior to the traditional approach of selection of a chemically diverse subset of compounds . We demonstrate the success of this approach in retrospective tests on the Published Kinase Inhibitor Sets ( PKIS ) chemogenomic data and in prospective experimental screens against three additional non-human kinase targets . | [
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| 2019 | Predicting kinase inhibitors using bioactivity matrix derived informer sets |
Autologous transplantation and engraftment of HIV-resistant cells in sufficient numbers should recapitulate the functional cure of the Berlin Patient , with applicability to a greater number of infected individuals and with a superior safety profile . A robust preclinical model of suppressed HIV infection is critical in order to test such gene therapy-based cure strategies , both alone and in combination with other cure strategies . Here , we present a nonhuman primate ( NHP ) model of latent infection using simian/human immunodeficiency virus ( SHIV ) and combination antiretroviral therapy ( cART ) in pigtail macaques . We demonstrate that transplantation of CCR5 gene-edited hematopoietic stem/progenitor cells ( HSPCs ) persist in infected and suppressed animals , and that protected cells expand through virus-dependent positive selection . CCR5 gene-edited cells are readily detectable in tissues , namely those closely associated with viral reservoirs such as lymph nodes and gastrointestinal tract . Following autologous transplantation , tissue-associated SHIV DNA and RNA levels in suppressed animals are significantly reduced ( p ≤ 0 . 05 ) , relative to suppressed , untransplanted control animals . In contrast , the size of the peripheral reservoir , measured by QVOA , is variably impacted by transplantation . Our studies demonstrate that CCR5 gene editing is equally feasible in infected and uninfected animals , that edited cells persist , traffic to , and engraft in tissue reservoirs , and that this approach significantly reduces secondary lymphoid tissue viral reservoir size . Our robust NHP model of HIV gene therapy and viral persistence can be immediately applied to the investigation of combinatorial approaches that incorporate anti-HIV gene therapy , immune modulators , therapeutic vaccination , and latency reversing agents .
Timothy Brown , known as the Berlin Patient , has recently reached 11 years of HIV-free remission in the absence of combination antiretroviral therapy ( cART ) [1–3] . Intensive studies have postulated three central tenants that led to his functional cure . First , the conditioning regimen that was administered prior to transplantation with allogeneic hematopoietic stem and progenitor cells ( HSPCs ) helped to clear the primary hematological malignancy , and also facilitated engraftment of donor HSPCs [4 , 5] . Although conditioning also likely ablated a proportion of latently infected cells , we have demonstrated that the corresponding loss of virus-specific immunity offsets this benefit [6] . In addition , the ability of conditioning regimens such as myeloablative total body irradiation ( TBI ) to target viral reservoirs in tissues is limited [7] . Hence , the conditioning regimen plays an important role in gene therapy-mediated cure of HIV infection , but is most likely insufficient to induce cART-independent virological remission . The second tenant of remission/cure in the Berlin patient was the infusion of allogeneic donor cells , and the resultant “graft versus HIV” effect . Despite close HLA matching between host and donor products , donor cells still frequently recognize host cells as foreign , and destroy these cells through well-characterized immunological mechanisms [5 , 8] . Graft-vs . -tumor effects are an essential component of effective allogeneic stem cell transplantation strategies for various leukemias [9 , 10] , contributing to reduction of tumor burden and engraftment of donor stem cells . Therefore , pathologies associated with graft-versus-host disease must be closely regulated in transplant patients to balance safety and efficacy [11 , 12] . In the setting of latent HIV infection , allogeneic donor cells are likely to target reservoir cells for destruction , although the frequency of targeting of infected versus uninfected host cells has not been characterized . Two HIV+ Boston Patients received allogeneic HSPC products and experienced substantial periods of virus-free remission , but did eventually rebound [13–15] . Collectively , these clinical cases suggest that the graft-versus-HIV effect contributed to the Berlin Patient’s cure , but was likely insufficient for virus eradication . The third and arguably most important tenant of remission/cure in the Berlin patient was gene-specific HIV resistance , conferred by the homozygous CCR5Δ32 mutant allogeneic donor cells ( not present in the Boston patients’ donor cells ) . CCR5Δ32 is well characterized in regard to HIV resistance [16 , 17] , and in other pathologies [18–20] , yet is not associated with significantly impaired quality of life . Notably , the Berlin patient ended cART concurrent with his first HSPC transplant with no subsequent evidence of virus recrudescence [3] . In contrast , our recent findings suggest that the early days and weeks following conditioning and transplantation provide an ideal environment for viral replication , especially in the absence of effective cART [6 , 21] . Together , these data strongly suggest that virus-protected donor cells played a crucial and immediate role in the Berlin Patient’s functional cure , and should be considered an essential facet of any cure strategy . CCR5Δ32 donor cells are rare , compounded by the difficulties in identifying an HLA-matched CCR5Δ32 donor . Furthermore , the toxicities of allogeneic transplantation and myeloablative conditioning prevent broad applicability to otherwise healthy , well-suppressed HIV+ patients . In contrast , autologous transplantation is safer and applicable to more patients [22] . We have previously demonstrated that autologous transplantation with CCR5 gene-edited HSPCs is safe and feasible , and results in long-term engraftment of CCR5-edited HSPC progeny [23] . Here , we conducted transplants with ΔCCR5 HSPCs in a robust nonhuman primate ( NHP ) model of suppressed HIV infection . Our goals were i ) to evaluate the feasibility of biallelic ΔCCR5 gene therapy in animals infected with simian/human immunodeficiency virus ( SHIV ) relative to our previous data in uninfected animals , ii ) compare the kinetics of ΔCCR5 cell engraftment in the presence or absence of unsuppressed viral replication , and iii ) to evaluate the ability of our in vivo model of HIV latency to recapitulate key features of viral reservoirs in patients .
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health ( ”The Guide” ) , and was approved by the Institutional Animal Care and Use Committees of the Fred Hutchinson Cancer Research Center and University of Washington , Protocol # 3235–03 . All animals were housed at and included in standard monitoring procedures prescribed by the Washington National Primate Research Center ( WaNPRC ) . This included at least twice-daily observation by animal technicians for basic husbandry parameters ( e . g . , food intake , activity , stool consistency , overall appearance ) as well as daily observation by a veterinary technician and/or veterinarian . Animals were housed in cages approved by “The Guide” and in accordance with Animal Welfare Act regulations . Animals were fed twice daily , and were fasted for up to 14 hours prior to sedation . Environmental enrichment included grouping in compound , large activity , or run-through connected cages , perches , toys , food treats , and foraging activities . If a clinical abnormality was noted , standard WaNPRC procedures were followed to notify the veterinary staff for evaluation and determination for admission as a clinical case . Animals were sedated by administration of Ketamine HCl and/or Telazol and supportive agents prior to all procedures . Following sedation , animals were monitored according to WaNPRC standard protocols . WaNPRC surgical support staff are trained and experienced in the administration of anesthetics and have monitoring equipment available to assist: heart rate , respiration , and blood oxygenation monitoring , audible alarms and LCD readouts , monitoring of blood pressure , temperature , etc . For minor procedures , the presence or absence of deep pain was tested by the toe-pinch reflex . The absence of response ( leg flexion ) to this test indicates adequate anesthesia for this procedure . Similar parameters were used in cases of general anesthesia , including the loss of palpebral reflexes ( eye blink ) . Analgesics were provided as prescribed by the Clinical Veterinary staff for at least 48 hours after the procedures , and could be extended at the discretion of the clinical veterinarian , based on clinical signs . Decisions to euthanize animals were made in close consultation with veterinary staff , and were performed in accordance with guidelines as established by the American Veterinary Medical Association Panel on Euthanasia ( 2013 ) . Prior to euthanasia , animals were first rendered unconscious by administration of ketamine HCl . Detailed study schematics are shown in Fig 1 and S1 Table . Delivery of CCR5 ZFN mRNA to nonhuman primate HSPCs , and pre-SHIV data for ΔCCR5 Transplant-SHIV animals ( Group A ) have been previously described [23] . These animals were infected with SHIV-1157ipd3N4 ( “SHIV-C” ) [24] approximately 200 days following transplantation . SHIV-cART-ΔCCR5 Transplant ( Groups B-C ) animals were infected identically , without prior transplantation , and suppressed by cART ( Tenofovir and FTC kindly provided by Gilead Sciences; Raltegravir kindly provided by Merck ) 6 months after infection . Following approximately 6 months of cART , these animals underwent nearly identical transplants relative to the ΔCCR5 Transplant-SHIV cohort . One minor difference between gene editing procedures for Group A and Groups B-C was that CCR5 ZFN mRNA for Groups B-C was resuspended in electroporation buffer rather than water , which may have contributed to slightly increased efficiencies of biallelic editing ( Fig 2B ) . Animals in Groups D-E and Group F served as untransplanted and unedited controls for Groups B-C , respectively , and have been previously described [6] . Groups B-F were further used as controls for Group A animals , using data collected prior to initiation of cART ( Fig 1 ) . All transplanted animals were mobilized with Granulocyte Colony-Stimulating Factor and Stem Cell Factor for 4 days prior to enrichment of bone marrow-derived CD34+ HSPCs , received a conditioning regimen consisting of 1020 cGy total body irradiation ( TBI ) , and were subsequently infused with gene-edited , autologous HSPCs as previously described [6 , 23] . All SHIV-infected animals were maintained on cART throughout the transplantation procedure . Due to the intensive nature of these experiments , animals were studied in a staggered format , rather than contemporaneously . Total genomic DNA was isolated from peripheral blood and tissues at longitudinal and necropsy time points as described previously [6 , 21] . The percentage of CCR5-edited alleles in each sample was quantified using the Illumina MiSeq platform [23] . Plasma viral load was measured as described previously [25 , 26] . Single tissue pieces <10 mm3 were collected and stored overnight in nucleic acid preservative at 4°C , blotted dry , and stored at -80°C prior to homogenization with a Precellys 24 homogenizer and CK28-R hard tissue homogenizing beads ( Bertin Corp , Washington , DC ) . Extraction of total genomic DNA and RNA and quantitation of SHIV copies per genome equivalent and normalized RNA copies were performed as described previously [6 , 21] . Briefly , SHIV DNA was normalized to a genomic DNA standard , macaque RNase P p30 subunit ( MRPP30 ) , and SHIV RNA was normalized to the cycle threshold ( Ct ) of MRPP30 RNA . The 95% limit of detection for the assay is 6 copies/reaction; for tissue specimens , the absolute limit of detection varies with the number of input cells . At the indicated longitudinal time points , lymph nodes were collected from peripheral sites ( axillary , inguinal ) and flash frozen . Total genomic DNA and RNA were prepared from these tissue samples as described above . Gastrointestinal biopsies from upper GI ( duodenum/jejunum ) and lower GI ( colon ) were collected as described [24] , and dissociated in RPMI media containing 0 . 5 mg/mL collagenase and 1 U/mL DNase I . Viability of single cell suspensions was measured using a Guava Cytometer ( Merck Millipore , Billerica , MA ) , and a small aliquot was stained with antibodies including CD3-Ax700 clone SP34-2 , CD4-PerCP-Cy5 . 5 clone L200 , CD8-APC-Cy7 clone SK1 , CD28-PE-Cy5 clone CD28 . 2 , CD45RA-FITC clone 5H9 , CD95-APC clone DX2 , CCR7-PE-Cy7 clone 3D12 , and CCR5-PE clone 3A9 , all from Becton Dickinson ( Franklin Lakes , NJ ) . Total genomic DNA was extracted from the remainder of the sample for SHIV RNA and/or DNA analyses . When nucleic acid yields from these samples were insufficient , flash-frozen GI biopsy pinches collected at the same points were utilized . Peripheral blood sorts from the indicated subsets were performed using magnetic bead kits from Miltenyi Biotec ( Bergisch Gladbach , Germany ) or via antibody labeling and a FACS ARIA cell sorter ( Becton Dickinson ) . In Group A animals , large-volume peripheral blood draws were collected immediately prior to SHIV infection , and approximately 100 days after SHIV infection . In Group B , draws were collected from infected , suppressed , transplanted animals immediately prior to cART withdrawal , and approximately 100 days after viral rebound following withdrawal of cART . Total genomic DNA was isolated from each bead-sorted sample , as well as from hemolysed total peripheral and bone marrow white blood cells ( WBC , BM-WBC ) , Ficoll-sorted PBMC , and the granulocyte-enriched Ficoll pellet fraction ( “GRANS” ) . Purity of bead-enriched fractions was confirmed by flow cytometry [23] . The percentage of CCR5-edited alleles in each sample was measured using Illumina MiSeq . To calculate SHIV-dependent enrichment in each subset , values during productive SHIV infection ( primary infection or post-cART withdrawal viral rebound ) were divided by values from pre-infection or cART-suppressed infection time points , respectively . Viral RNA ( RNAscope ) and DNA ( DNAscope ) detection and quantitative image analysis was performed as previously described [27] . QVOA assays were performed essentially as described previously [6] . Statistical analyses were performed using unpaired t-tests or nonparametric Mann-Whitney tests and GraphPad Prism 7 software , without assumption of consistent standard deviations between data sets . Throughout the text , error bars represent standard error of the mean ( SEM ) , and p-values are expressed as exact values to 3 decimal places .
Thirty-one pigtail macaques in 6 groups ( A-F ) of 4–7 animals each were analyzed in this study ( Fig 1 ) . Prior to SHIV challenge , Group A “ΔCCR5 Transplant-SHIV” animals received CCR5-edited HSPCs that were produced using our previously described CCR5 Zinc Finger Nuclease ( ZFN ) platform ( Fig 1A ) ; pre-SHIV data from these animals has been previously described [23] . Groups B and C “SHIV-cART-ΔCCR5 Transplant” received CCR5-edited cells after SHIV-1157ipd3N4 infection and stable suppression by cART , and were necropsied either following cART withdrawal ( Group B ) or while stably suppressed ( Group C ) ( Fig 1B ) . Three control groups of infected and suppressed animals were utilized . Group D-E animals did not undergo ΔCCR5 transplantation , and were necropsied either following cART withdrawal ( Group D ) or while stably suppressed ( Group E ) . Group F animals were transplanted with unedited ( “wt CCR5” ) HSPCs ( Fig 1C ) [6] . Because animals in Groups B-E did not undergo an experimental intervention until they were stably suppressed , data from primary infection served as controls for Group A ( animals transplanted prior to infection ) . A complete list of the animals used in this study can be found in S1 Table . We have previously shown that CCR5-edited macaque HSPCs engraft in uninfected animals [23] . To model the impact of our approach in cART-suppressed HIV+ patients , we measured the engraftment of CCR5-edited HSPCs in cART-suppressed SHIV+ animals . We found that the efficiency of CCR5 editing was almost identical in CD34+ HSPCs isolated from SHIV- and SHIV+ animals ( Fig 2A ) . Colony-forming assays showed that HSPCs from SHIV+ animals had slightly higher rates of biallelic disruption of CCR5 , likely due to incremental improvements in the handling of edited cells ex vivo ( Fig 2B ) . Following infusion into autologous hosts that had received myeloablative TBI , we observed similar kinetics of engraftment of edited cells in suppressed SHIV+ animals , relative to SHIV- controls ( Fig 2C and 2D ) . This included a high level of gene editing proportional to that in the respective animals’ HSPC infusion products ( Fig 2A ) at early time points post-transplant , and stable engraftment of edited cells at 3–4% of total peripheral blood at further time points , up to 13 months post-transplant . This data strongly suggests that CCR5 gene editing is equally feasible in infected and uninfected animals , and that edited cells persist comparably in infected and uninfected recipients , providing support for the feasibility of this approach in HIV-infected individuals . We next evaluated the kinetics of ΔCCR5 cell engraftment in tissues , particularly those implicated in HIV latency , including lymph nodes and gastrointestinal ( GI ) tract . Notably , tissue measurements did not exclude cells of non-HSPC origin , and therefore likely underestimated the true extent of HSPC-derived ΔCCR5 cell engraftment . In Group A animals that were transplanted first and then SHIV-infected , ΔCCR5 cells made up ~1% ( Lower GI ) and 1–6% ( Upper GI and Lymph Nodes ) of the total cells assayed from each tissue ( Fig 3A ) . In Group B-C animals that were infected and suppressed prior to transplantation , we observed similar levels of engraftment , with the exception of lymph nodes , in which ΔCCR5 engraftment was greater than 10% in several animals ( Fig 3B ) . We next performed necropsies , collected total genomic DNA from 25 tissue sites , and again measured the percentage of CCR5-edited alleles by deep sequencing . In Group A animals that were transplanted with ΔCCR5 cells and then infected with SHIV , we saw the highest level of ΔCCR5 engraftment in lymph nodes , tonsil , and thymus ( Fig 3C ) . We observed similar trends in animals that were infected , suppressed , and transplanted with ΔCCR5 HSPCs , necropsied either before ( Group B ) ( Fig 3D ) or after ( Group C ) ( Fig 3E ) cART withdrawal and viral rebound . These findings support the notion that ΔCCR5 cells traffic to and persist at lymphoid tissue sites of virus persistence . Our previous findings suggest that a threshold level of infection-resistant HSPCs is capable of improving virus-specific immune responses following SHIV infection [28 , 29] . To quantify the impact of ~4% ΔCCR5 cells on suppressed and/or unsuppressed SHIV viremia , we measured viral RNA and DNA in peripheral blood of our animals . First , we compared SHIV plasma viral loads in Group A animals that were transplanted prior to infection to primary infection data from Group B-F animals that were not transplanted prior to infection . We observed no difference between these groups ( Fig 4A ) . Plasma viral loads for Groups C and E are shown in S1 and S2 Figs , respectively , while plasma viral loads for Groups D and F have been described previously [6] . Next , we compared the magnitude and kinetics of viral rebound following cART withdrawal in three groups of infected , suppressed animals: Group B ( transplanted with ΔCCR5 cells ) , Group D ( untransplanted ) , and Group F ( transplanted with non-modified ( “wt CCR5” ) cells ) ( Fig 4B ) . We have previously observed that the magnitude of plasma viral rebound in Group F animals was higher than in Group D [6] . Interestingly , the magnitude of viral rebound in Group B ΔCCR5-transplanted animals was comparable to Group D untransplanted animals ( Fig 4D ) . Similarly , the net change in average set point viral load during rebound viremia , relative to primary infection was negative across untransplanted ( Group D ) and ΔCCR5 transplanted animals ( Group B ) , but positive across wt CCR5 controls ( Group F ) ( Fig 4D ) . Finally , the time to viral rebound trended later in ΔCCR5 transplanted animals , relative to untransplanted and wt CCR5 transplanted controls . Notably , ΔCCR5 animal IDs R10159 and Z12420 did not establish consistent set point viral load following cART withdrawal; plasma viremia in ID Z12420 was not observed for almost 2 months post-cART withdrawal ( Fig 4B–4E ) . Although these results did not reach statistical significance , the observed trends suggest that ΔCCR5 transplantation may have a beneficial impact on the time to viral rebound following cART treatment interruption , even at low levels of ΔCCR5 cells . In Group A animals that were transplanted with ΔCCR5 cells prior to SHIV infection , longitudinal gastrointestinal ( GI ) biopsy collections showed comparable levels of SHIV DNA and SHIV RNA relative to untransplanted controls , up to 28 weeks post-infection; similar trends were observed in peripheral lymph node samples ( S3 Fig ) . CD4+ T-cell percentages from Group A biopsy samples were at or below those of untransplanted controls before and after SHIV infection , likely due to residual immune suppression from the myeloablative conditioning regimen [21] ( S4 Fig ) . In Group B-C animals that were ΔCCR5 transplanted following infection and stable suppression , we observed a significant decrease in lymph node-associated SHIV DNA after transplantation and prior to cART withdrawal ( 36–50 weeks post-cART initiation ) relative to a time point immediately prior to transplantation ( 17–22 weeks post-cART initiation ) ( p = 0 . 0004 ) ( S5 Fig ) . However , control samples were not available to contextualize these results as transplantation- vs . ΔCCR5-dependent . Intriguingly , despite an unexplained decrease in gut-associated CD4+ central memory cell percentages ( CD4+ TCM ) prior to ΔCCR5 transplantation , we observed a significant increase in this subset following ΔCCR5 transplantation , relative to wt CCR5 transplant ( Group F ) ( p = 0 . 015 in upper GI ) and untransplanted controls ( Groups D-E ) ( p = 0 . 025 in upper GI , and p = 0 . 026 in lower GI ) ( Fig 5 ) . These findings are consistent with a model in which HSPC-derived ΔCCR5 CD4+ T-cells preferentially refill the virus- and TBI-depleted niche in the gut of SHIV+ suppressed animals . We observed an increased percentage of ΔCCR5 cells in tissues known to contribute to HIV/SHIV persistence ( Fig 3C–3E ) , and found that gut-associated CD4+ TCM recovered more rapidly in SHIV+ , cART-suppressed ΔCCR5 relative to controls ( Fig 5C and 5D ) . These data suggest that ΔCCR5 cells might undergo virus-dependent positive selection at these sites . To directly measure preferential expansion of ΔCCR5 cells , we isolated CD4+ T-cells from whole blood before and after productive SHIV replication . Our metric for virus-dependent selection was calculated by dividing the proportion of ΔCCR5 CD4+ T-cells in each animal during productive infection by the same value measured prior to productive infection . In animals that were transplanted prior to infection ( Group A ) , values measured ~100 days post-SHIV challenge were divided by values measured pre-SHIV challenge ( Fig 6A inset ) . In animals that were transplanted following infection and stable suppression ( Group B ) , values measured ~100 days post-cART withdrawal and viral rebound were divided by values measured pre-cART withdrawal ( Fig 6B inset ) . In both groups , we observed a ratio <2 ( indicating <2-fold virus-dependent selection ) in all subsets , with the exception of CD4+ T-cells . In CD4+ T-cell subsets sorted on the basis of CD45RA and CCR7 expression , a marked increase in virus-dependent positive selection was observed as CD4+ T-cells transitioned from naïve to central memory , effector memory , and terminally differentiated phenotypes . In animals that were transplanted prior to infection , enrichments of up to 5- , 15- , and 56-fold were observed in central memory , effector memory , and terminally differentiated subsets , respectively ( Fig 6A ) . In animals that were transplanted following infection and stable suppression , enrichments of up to 3- , 9- , and 31-fold were observed in central memory , effector memory , and terminally differentiated subsets , respectively ( Fig 6B ) . These data indicate that HSPC-derived ΔCCR5 CD4+ T-cells persist in vivo , and display trends consistent with resistance to infection with CCR5-tropic SHIV , and virus-dependent positive selection . We have previously developed a SHIV-adapted Quantitative Viral Outgrowth Assay ( QVOA ) to measure the size of the peripheral viral reservoir before and after transplantation in wt CCR5 transplant animals [6] . We found that autologous transplantation with wt CCR5 HSPCs did not significantly impact the size of the peripheral SHIV reservoir , although reservoir size decreased to undetectable levels in 2 out of 4 animals tested . Here , we asked whether ΔCCR5 transplantation impacted peripheral reservoir size . We observed a similar binary trend in ΔCCR5 transplant animals as we previously observed in wt CCR5 transplant animals . Out of 7 SHIV-infected , cART-suppressed animals that were transplanted with ΔCCR5 HSPCs , the measurable inducible reservoir size decreased to undetectable levels in 4 animals , decreased by only 1 . 5 logs in one animal , and was unchanged in two others ( Fig 7 ) . As such , the proportion of long-term persisting ΔCCR5 progeny that we were able to achieve in this study were insufficient to significantly impact the size of the latent peripheral SHIV reservoir . Despite our finding that ~4% ΔCCR5 peripheral blood cells are insufficient for HIV cure , this intervention may have significantly impacted the size of the latent SHIV reservoir in secondary lymphoid tissues , which likely act as key sites of virus persistence . To test this possibility , we first assessed the size of tissue reservoirs in stably suppressed animals . Using PCR-based assays with quantified numbers of cell inputs ( S2 Table ) , we measured levels of SHIV DNA and RNA in tissues collected at necropsy in a subset of Group C ΔCCR5-transplanted animals that were necropsied while stably suppressed on cART ( Fig 1B ) , comparing them to a cohort of 4 untransplanted controls in Group E that were infected and necropsied following stable suppression by cART ( Fig 1C ) . ΔCCR5 animals exhibited a significant reduction in the levels of SHIV DNA in lymphoid tissues ( multiple lymph nodes , spleen ) , as well as in colon , liver , and kidney , relative to suppressed , untransplanted controls ( Fig 8A ) . SHIV DNA in some tissue sites , for example basal ganglia , was driven to undetectable levels following ΔCCR5 transplant . In Group E controls , SHIV RNA was most readily detected in lymphoid tissues including lymph nodes , spleen , and tonsil , but was more variable than SHIV DNA , consistent with past findings in patients and NHP models [30–32] ( Fig 8B ) . ΔCCR5 animals showed significant/near significant reductions in SHIV RNA in inguinal and submandibular nodes , tonsil , and rectum , while an increase in SHIV RNA expression was observed in other tissues , such as duodenum . Next , we supplemented PCR-based tissue reservoir assays with DNAscope- and RNAscope-based in situ assays . Although Group E controls were not available for histological analysis , we nevertheless characterized SHIV DNA+ and RNA+ cells in animals that received ΔCCR5 HSPCs before SHIV infection ( Group A ) , or following infection and stable suppression with ( Group B ) or without ( Group C ) subsequent cART withdrawal . Productively infected ( SHIV RNA+ ) cells were detected in all tissues in Groups A-C ( S6 Fig ) . Again consistent with past reports [31–33] , these cells were found preferentially in B-cell follicles ( BCFs ) and lymphoid aggregates within secondary lymphoid tissues including lymph nodes , spleen , tonsil , thymus , and gut ( S6A–S6B Fig ) . In infected animals that were transplanted and necropsied while stably suppressed ( Group C ) , ongoing viral RNA expression ( vRNA+ cells ) was most readily detected in lymph nodes and GI tract tissue; however , vRNA+ cells were found in all tissue compartments including the male genital tract and CNS S6B Fig ) . Consistent with the possibility of ongoing viral replication and virion production during suppressive cART , we consistently found virions trapped on follicular dendritic cells ( FDCs ) within BCFs of animals that were transplanted before infection ( Group A ) and after infection and suppression ( Group C ) ( S6C Fig ) . In contrast to SHIV RNA levels , which differed substantially between groups , DNAscope measurements revealed relatively comparable levels of SHIV DNA+ cells across most tissues ( S7 Fig ) . In light of the significant decreases we observed in tissue SHIV DNA and RNA levels , we conclude that the impact of ΔCCR5 transplantation in infected , suppressed animals is primarily manifest in tissues .
Hematopoietic stem cell transplantation has led to the most dramatic HIV reservoir reductions observed in patients [1 , 14 , 34] , yet such interventions are currently also the least practical . Here , we describe a nonhuman primate model of suppressed HIV infection that facilitates the translation of gene and cell therapy-based cure approaches to the clinic . We show that autologous , CCR5-edited HSPCs engraft in infected and uninfected animals , undergo virus-dependent positive selection , and impact viral reservoirs primarily in tissues . We have previously utilized our model to identify immunological correlates of viral rebound following autologous transplantation with unmodified HSPCs [6] . Here , we added CCR5 gene editing , and evaluated impacts on latently infected cells . ΔCCR5 cells engraft with similar efficiency and kinetics in uninfected and infected animals , demonstrating that latent infection does not impact the ability of these cells to persist in peripheral blood and in tissues . However , our data suggest that the percentage of long-term persisting , CCR5-edited cells that are achievable with our current methods ( ~4% of total white blood cells ) do not meet the minimum critical threshold necessary to induce viral remission in the absence of suppressive therapy . We are currently developing optimized culture conditions and maximizing the efficiency of our gene editing techniques , in order to increase the persistence of long-term engrafting , CCR5-edited HSPCs and their progeny . Importantly , even at present levels of engraftment , active SHIV replication seems to drive enrichment of ΔCCR5 CD4+ T-cells as they differentiate into effector memory ( up to 15-fold enrichment ) and terminally differentiated phenotypes ( up to 56-fold enrichment ) . In animals that were transplanted after infection and stable suppression , rebound SHIV viremia drove up to 9- and 31-fold enrichment in these respective subsets . While we were not able to assign statistical significance to SHIV-dependent positive selection in CD4+ subsets , these data suggest that even at low levels , ΔCCR5 HSPCs may give rise to infection-resistant ΔCCR5 CD4+ T-cells that refill the SHIV-depleted CD4+ T-cell niche . Approaches involving the direct infusion of ΔCCR5 T-cells , have shown promise in clinical trials [35] . Importantly , a purely “defensive” strategy , such as CCR5 gene editing , may be necessary but insufficient for HIV cure . Future iterations of gene therapy-mediated cure approaches should focus on modification strategies that protect cells and augment virus-specific immunity in order to actively target reservoir sites during ongoing suppressive therapy . Chimeric antigen receptors , DARTs , and broadly neutralizing antibodies are among many approaches that could be combined with , or integrated into gene therapy-mediated HIV cure strategies [36–40] . Recent findings from multiple groups suggest that tissue reservoirs may be distinct from peripheral reservoirs due to limitations in penetration of cART compounds [41 , 42] , trafficking of infected cells [41 , 43 , 44] , and other anatomical barriers [45] . We extensively examined tissue viral reservoirs by measuring tissue-associated levels of SHIV RNA and DNA using multiple assays , and compared these findings to QVOA-based measurements of the peripheral reservoir . Because myeloablative conditioning regimens such as total body irradiation ( TBI ) deplete peripheral CD4+ T-cells more efficiently than tissue-associated cells [7] , we predicted that autologous transplantation would have a greater impact on the peripheral reservoir relative to tissue reservoirs . On the contrary , we demonstrate that autologous transplantation primarily impacts latently infected cells in tissue reservoirs rather than peripheral blood reservoirs . Tissue-associated SHIV DNA and RNA levels in suppressed , transplanted animals were significantly lower than those in suppressed , untransplanted controls , especially in tissues that are known to harbor replication competent virus during suppressive therapy . In contrast , the size of the peripheral reservoir , measured by QVOA , was not significantly different in transplanted vs . untransplanted animals . We conclude that transplantation primarily impacts tissue reservoirs , whereas effects in the peripheral reservoir are secondary . Our study was unable to directly address whether reductions in tissue-associated SHIV reservoirs were due to the transplantation regimen itself ( i . e . myeloablative TBI ) vs . low levels of ΔCCR5 cells . Consistent with past reports , we observed ongoing tissue-associated SHIV RNA expression in suppressed animals [30–32] , as well as in suppressed , transplanted animals . PCR-based assays showed that viral RNA expression in suppressed , ΔCCR5-transplanted animals was significantly lower than untransplanted controls in multiple lymphoid tissues including lymph nodes and tonsil . However , samples from suppressed , wtCCR5-transplanted control animals , which would distinguish whether this reduction was ΔCCR5-dependent , were unavailable . Nevertheless , our viral rebound data ( Fig 4C–4E ) are consistent with a model in which increased viral replication due to myeloablative TBI [6] was offset by even low levels of ΔCCR5 HSPCs and their progeny . These results are highly promising for future approaches that combine increased levels of CCR5 editing with more active means of reservoir targeting . Two animals in our study highlight the potential of our approach . Animals Z12420 and R10159 demonstrated a peripheral reservoir size of 0 . 600 IUPM and 0 . 064 IUPM , respectively , as measured by QVOA . Following transplantation , each was reduced to undetectable levels . Although SHIV rebound was observed in both animals following cART withdrawal , neither established a consistent plasma viral load set point . The kinetics of rebound viremia in these animals are reminiscent of “predator-prey” relationships that have been characterized between virus-specific CD8+ T-cells and viral escape mutants [46 , 47] . This oscillatory pattern has also been correlated with T cell activation in a cohort of cART-treated patients with multi-drug resistant HIV [48] , which is consistent with our observations in SHIV+ animals during post-transplant immune recovery [6] . The inability of a subset of ΔCCR5 animals to reestablish a consistent rebound viral set point reinforces the notion that increased efficiency gene editing approaches , combined with targeting persistently infected cells for destruction ( e . g . augmenting the endogenous virus-specific immune response ) represents an achievable path to HIV cure . In conclusion , we demonstrate that ΔCCR5 HSPC gene therapy is safe and feasible in a nonhuman primate model of suppressed HIV infection . ΔCCR5 HSPCs persist long term , and HSPC-derived ΔCCR5 CD4+ T-cells expand during active SHIV replication . We observe a primary and significant impact of this therapy on tissue reservoirs . Increased efficiency CCR5-editing strategies could further decrease the number of latently infected cells in these compartments , and would be significantly augmented by strategies designed to actively target latently infected cells and/or enhance the host response to recrudescent virus . Our model is ideally suited both to characterize key sites of HIV persistence , and target them with combination therapies . | Over the past decade , multiple strategies have been investigated for HIV Cure . Especially notable are cell-based approaches , inspired by the cure of the Berlin Patient , who was transplanted with hematopoietic stem cells from a donor that carried a mutation at the CCR5 locus . This mutation renders cells resistant to infection with most strains of HIV . Our goal in this study was to apply a safer version of this curative approach to more patients , using gene editing to generate a similar CCR5 mutation in a patient’s own cells . In a nonhuman primate model , we show that hematopoietic stem cells from infected , antiretroviral therapy-suppressed animals can be isolated , gene edited , and transplanted back into the infected host . Following transplantation , gene edited cells give rise to progeny , namely T-cells , that are protected against infection and hence gain a selective advantage . Most importantly , we show that these cells traffic to and reduce the size of "viral reservoirs" in secondary tissue sites that contribute to the persistence of HIV , for example in patients on antiretroviral therapy . | [
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| 2018 | Differential impact of transplantation on peripheral and tissue-associated viral reservoirs: Implications for HIV gene therapy |
To avoid crashing onto the floor , a free falling fly needs to trigger its wingbeats quickly and control the orientation of its thrust accurately and swiftly to stabilize its pitch and hence its speed . Behavioural data have suggested that the vertical optic flow produced by the fall and crossing the visual field plays a key role in this anti-crash response . Free fall behavior analyses have also suggested that flying insect may not rely on graviception to stabilize their flight . Based on these two assumptions , we have developed a model which accounts for hoverflies´ position and pitch orientation recorded in 3D with a fast stereo camera during experimental free falls . Our dynamic model shows that optic flow-based control combined with closed-loop control of the pitch suffice to stabilize the flight properly . In addition , our model sheds a new light on the visual-based feedback control of fly´s pitch , lift and thrust . Since graviceptive cues are possibly not used by flying insects , the use of a vertical reference to control the pitch is discussed , based on the results obtained on a complete dynamic model of a virtual fly falling in a textured corridor . This model would provide a useful tool for understanding more clearly how insects may or not estimate their absolute attitude .
Flying insects are subjected to a broad range of disturbances , for which fast , robust sensorimotor reflexes compensate . The flight stabilization performance of flies are even more impressive in view of the intrinsic aerodynamic instability of their flapping flight [1–4] . Compensating for this passive instability requires an active inner control of the wing kinematic in addition to an outer-loop system which responds to specific sensory cues ( looming objects , odours and navigational cues ) . Discovering insects’ abilities to sense movement via optic flow crossing the compound eye or inertially via the halteres ( for dipteran ) is still of great interest . However , very few studies have focused so far on how flying insect sense their absolute body orientation in the three-dimensional space ( attitude ) with respect to a vertical reference . Oppositely , several studies have suggested that flies may lack the ability to perceive the vertical ( via graviceptive cues ) in order to stabilize their flight [5–7] . To address this point , we used an already designed free-fall procedure [7] with which insects can be briefly exposed to near-weightless conditions in a box lined with horizontal black and white stripes . The present study focused on the following questions: The control of flight speed based on optic flow cues have been confirmed by ethological studies [8–10] . In the same time , in flying insects , as in helicopter , lift vector and body orientation are fixed in time and consequently flight speed and pitch orientation [1 , 3 , 11] , and the idea that insects’ attitude may be stabilized on the basis of the optic flow has been tested successfully on a 2 degree-of-freedom flying robot [12] . A pitch rate control process has also been proposed previously to model the drosophila’s forward velocity during flight [13 , 14] . Based on the existence of coupling between pitch control and optic flow regulation , we challenged the suitability of such closed-loop control compared with hoverflies subjected to an unsteady free fall situation . It has been previously established that the fly’s auto-stabilizer involves several sensory modalities , which interact during flight . First , insect vision is based on two physical structures , compound eyes and ocelli . The fly’s photoreceptors feature a high temporal resolution giving them a great ability to detect fast motion based on contrast changes [6] . Optic flow measurement have shown that motion vision is involved in many visually guided tasks such as flight speed and altitude control , wall following , odometry and optomotor response [9] . Most of the optic flow processing is performed by compound eyes , and local contrast motion measurements are fused by lobula plate tangential cells ( LPTC ) responsible for detecting large field motion [15 , 16] . In addition , it has been established that several groups of interneurons , including VSTCs ( Vertical Sensitive Tangential Cells ) [17] and HSTC ( Horizontal Sensitive Tangential Cells ) [18] , process the various components of visual motion and in particular that they distinguish between the rotational and translational components of the optic flow with respect to the fly’s reference frame [19] . In addition , the ocelli , which are usually composed of three simple unfocused eyes forming a triangle at the top of the head [20] , may be involved in the visuo-motor stabilization reflexes that maintain postural equilibrium by detecting the head’s rotational speed [21–25] . Dipteran also possess two minute dumbbell-shaped organs called halteres , which have evolved from hind-wings and beat simultaneously in anti-phase with wings . This active beating along with the campaniform sensilla provide flies with sensitivity to Coriolis forces and consequently to their own body’s angular speed [26–28] . The halteres enable the fly’s autopilot to respond to extremely abrupt changes in attitude with a latency as short as 5ms [29 , 30] . In addition , insect’s hairs and antennae are sensitive to airflow during flight . Airflow sensing by the Johnston’s organs present in the antennae is known to be involved in flight speed regulation complementary to optic flow regulation [31 , 32] . All in all , these sensorimotor units are mainly characterized in flies by their high temporal resolution and their low latency response [29] . Flies’ sensors are indeed highly tuned to detecting and quickly counteracting any change in their environment [6] . The combination of various sensory modalities with different bandwidth allows them to cover a wide range of dynamic perturbations . In this study , an insect flight control model was developed , based simply on the closed-loop control of the pitch rate and the regulation of the horizontal component of the optic flow . In a first step , our model was devoid of any kind of absolute reference . The results obtained with this model simulating the fly’s response in unsteady free fall situations are compared with experimental data obtained on plummeting hoverflies in a box lined with horizontal black & white stripes . The model simulated data matched what occurred during the first few milliseconds of the insects flight , but the pitch and speed responses became highly unstable after around 0 . 4s . In the second step , the accuracy of the model’s predictions was greatly improved by including two additional feedback loops: one controlling the pitch rate on the basis of the absolute estimation of the pitch orientation and one controlling the lift and thrust forces on the basis of the vertical optic flow . The ability of the fly to measure its pitch orientation with respect to an absolute reference value is discussed in term of the existence of visually mediated responses such as the dorsal light response ( DLR ) .
In a previous study on flight stabilization in plummeting hoverflies [7] , we established that the flies’ crash avoidance performance depended more on visuo-motor reflexes than on gravity perception . In order to understand those reflexes more deeply , we modeled a fly’s pitch rate control system based on optic flow cues ( see Figs 1 and 2 ) and compared the results obtained during model simulations with experimental free falling hoverflies . First , we focused on the pitch because we observed that during the period elapsing between the onset of the fall and wingbeat initiation , flies pitched down smoothly , probably because of the pin glued onto their thorax . Therefore , pitch was taken to be the main state to be controlled by the fly’s stabilizer to avoid crash . Secondly , since gravity cues do not seem to be involved in insect flight control [6 , 7 , 33] , we assumed that fly’s flight control does not rely on any absolute vertical reference of the environment but that it is based rather on visual and inertial motion perception and compensation . We therefore based our model on previous studies on insects’ flight behaviour providing clear-cut evidence that optic flow-based control are involved during several tasks ( for a review see [9] ) . We considered here that the forward speed was controlled by pitching-down from the nose the body and then orienting the force vector produced by flapping wings [34] , as occurred in the case of the helicopter analogy [11] . The pitch rate is set so as to keep the forward optic flow constant , as found to occur in bees traveling in a textured corridor [10] . In order to parametrize the gains in the PD controller in charge of the visual optic flow process in the model ( see Fig 2C ) , we conducted a series of experiments with hoverflies . The parameters of the visual Proportional-Derivative controller ( PDV ) , Kp and Kd , were estimated directly from experimental data . We first selected only the trials in which flies triggered their wingbeats in less than 150ms after the onset of the fall and were able to compensate for the fall by reaching a positive vertical speed ( i . e . , a lift force superior to their weight ) , amounting 44 experimental trials . A simulated falls was then achieved and compared with each of the selected falls as described above with several combinations of Kp , ranging from 0 to 20 , and Kd , ranging from 0 to 2 . A likelihood estimation ( MLE ) map was obtained for each fall , giving 44 maps in all , from which we extracted the average map shown in S2A Fig .
As shown in Fig 4A ( top view ) , in the box lined up with stripes only on the lateral walls ( X = −20/20 ) , the hoverflies did not seem to express any kind of preference for a specific wall . As expected from our previous study , it can be seen from Fig 4B that no crash occured in presence of visual cues ( horizontal periodic stripes ) and that most of the trajectories ended with a rising flight , which confirm the ability of hoverflies to control their flight in the free fall tests . The initiation times of the wingbeats , around 100ms in average ( see Fig 4C ) , are also coherent with our previous findings [7] . In this study , we selected only trials featuring a time to wingbeat triggering inferior to 150ms to keep a sufficient margin from the 200ms time limit , after which it is impossible for the fly to stop its fall and avoid crash onto the ground [7] . Fig 5A ( dark lines ) shows the time course of the mean pitch orientation around the onset of the flies’ wingbeats . Hoverflies pitched down ( i . e . head downward ) when falling freely , but soon after initiating their wingbeats , they were able to compensate for the misalignment of their body tilt with respect to the horizontal within 150ms . Despite the existence of significant differences in pitch orientation at flight initiation , no difference were observed in terms of the final pitch orientation or correction times between late initiation ( 125–150ms ) , medium initiation ( 100–125ms ) or early initiation ( 75–100ms ) , which shows the robustness of the reflex response involved . In Fig 5B ( dark lines ) , the mean theoretical optic flow was calculated versus time during free fall and flight recovery phases . The vertical component of ω , ωzRfly , increased to around 0 . 04rad . s−1 ( for the latest initiation group ) during the actual free fall and decreased quickly to zero after initiation of the wingbeats , whereas the horizontal component ωxRFly decreased before the wingbeats was triggered and continue to decreased slightly after the initiation of the wingbeats and reached a mean steady state value of about −0 . 04rad . s−1 regardless of the initial conditions . This result supports the idea that hoverflies may control the optic flow in closed-loop so as to keep it constant during flight . The results of the parameters identification , from which the parameters used during the simulations were selected , are presented in supporting information ( S2 Fig ) . Fig 5 shows the results of 150 simulated free falls into the virtual 40cm width corridor and the parameters used . The initial values used in simulation were determined by randomly setting a wingbeat triggering time ranging between 75 and 150ms to fit the data range ( Fig 4C ) . The initial state of the system ( i . e . the wingbeat triggering state ) , θPi , θ ˙ P i , Zi and VZi , was obtained by simulating a free fall without any friction and adding a passive rotation of the body to the pitch dynamics before the onset of wingbeat triggering which was modeled by a third order transfer function ( see supplementary materials , S2 Fig ) . The model accounted successfully for the dynamics of pitch orientation and optic flow observed experimentally during the 0 . 2s after the wingbeats initiation ( see Fig 5 ) . Fig 6A shows the time course of the mean acceleration produced by the hoverflies , estimated from experimental data after subtracting gravity acceleration . After wingbeat initiation , the acceleration increased immediately to a value around 10m . s−2 , which is equal to gravity acceleration absolute , during about 0 . 1s . After this initial phase , the acceleration increased within approximately 0 . 1s to a value of 25–30m . s−2 , representing 2 . 5-3 times the absolute value of gravity acceleration , followed by a slight descent phase to around 20m . s−2 at 0 . 4s . The force produced by flapping wings in the model was adjusted to these dynamics as shown in Fig 6A ( green line ) . However , the acceleration estimated from 3-D trajectory data is really noisy and could result in some discrepancies between simulated and experimental data . As it can be seen from Fig 5B , the average Z position observed during the simulations shows that the model was able to counteract the fall but the values obtained did not completely match the experimental data on the hoverflies . Nor did the heave and surge speeds match experimental data: they rather showed the occurrence of instability after around 200ms ( Fig 5C and 5D ) .
In this study , control theory was used to model the pitch stabilization process at work in hoverflies placed in free fall situation , using simple rules based on optic flow measurements previously described in navigational tasks context [9] . We proposed a model ( Fig 2 ) based on a virtual fly falling within a textured corridor accounting for the fly’s pitch and speed during about 200ms after the onset of the insect’s wingbeats . As in previous studies [9 , 10 , 13 , 14] , we assumed that the pitch control and hence the lift and thrust force control processes rely on the closed-loop control of the pitch rate via the halteres combined with an OF-based feedback loop . In line with [35] and [13 , 14] , we implemented in our model a pitch rate feedback-loop mimicking the halteres via a proportional-integrator ( PI ) controller . Recent results have suggested that sensory cues delivered by the eyes , halteres and antennae may interact via specific actions and coupling arrangements [37–39] . The present model involves then two nested feedback-loops , one featuring fast dynamics thanks to the halteres and one featuring much slower dynamics due to the presence of a double integrator between the pitch rate and the speed of the fly ( see Fig 2 ) . The OF was defined as the ratio between the fly’s speed and its distance to the wall which was constant during the fall and did not vary conspicuously during the 0 . 2s analyzed during insects’ flights . The simulated OF measurements are therefore very similar to the airspeeds apart from a different scaling due to dwall . As shown in Fig 1 and described by equation of T → , the forward OF can be controlled directly by adjusting the fly’s speed VRI and thus by controlling the pitch . As shown in Fig 5 , a non-null forward OF component was observed during the fall due to a passive pitching of the fly . We simplified the model of hoverflies´ flight dynamics by neglecting any coupling between the pitch control and the other two rotational axes ( roll and yaw ) . There were two main reasons for focusing only on the hoverflies´ pitch attitude control: The main characteristic of the model ( see Fig 2 ) presented here is the total absence of any kind of vertical reference for controlling the pitch in the closed-loop system . This idea was based on previous data showing the absence of graviception in dipteran’s flight control [7] . This model accounts for the fly’s transient response during a period of up to approximately 0 . 2s from the onset of the flapping flight . However , as shown in Fig 4B , most of the stabilizing manoeuvres in response to the free fall situation occurred within 0 . 2s . The ability of the optic flow model to counteract the fall without requiring any information about the insect’s absolute orientation confirms that optic flow regulation , in addition to navigation processes , may play a stabilizing role [12] . Indeed , a slight tilting of the body and hence of the lift quickly led to a involuntary translation in the environment that results in generating optic flow . Thus , actuating the wingbeats motor system to cancel the generated OF would lead to correcting the attitude . In particular , during an instable flight , the gravity acceleration would induces a permanent increase in the speed toward the ground and a simple strategy such as maintaining a constant forward optic flow will therefore intrinsically induces the pitch to decrease with respect to the horizontal and therefore a restabilization . Indeed , as shown in , the drag force experienced during free fall can be neglected , at least in the range of our experimental paradigm , reinforcing the detection of any heave acceleration by the mean of optic flow variation . In addition , previous studies [8–10] on speed regulation based on optic flow strategy validate the implementation of such feedback loops in flight control system . Future experiments would allow to better describe these sensorimotor regulation by using moving gratings on the walls of the box or a virtual reality setup [40 , 41] for example . However , some instability in the model´s responses can be clearly seen to have occured after 300-400ms in Fig 6C . This means that a closed-loop pitch control system based only on the optic flow regulation does not suffice to maintain stable flight . Despite the presence of a fast closed-loop control of the pitch rate based on the halteres , a simple PD controller cannot be fast enough to stabilize a system featuring three integrators between the measured forward optic flow and the required pitch ( see Fig 2 and eqs 6 and 7 ) . Instead of increasing the complexity of the controller COF ( s ) , we decided to improve the model shown in Fig 2 by adding two biologically plausible feedback loops . First , we added a feedback loop controlling the lift based on the vertical flow ω z R I through a proportional-integrator controller . The integrator cancels the vertical optic flow while keeping a non-null steady lift force , thus simulating the altitude control process observed in dipteran [42 , 43] . In addition , based on the existence of sensory mechanisms involved in the estimating pitch orientation such as the Dorsal Light Response and that based on an integration of the halteres’ and/or compound eyes’ signal , we added another pitch rate control loop including a Proportional-Derivative controller based on the absolute pitch orientation ( Fig 7 ) . Both additional PI and PD controllers has been set manually , gains are given in Fig 7 and all model parameters are summarized in S1 Table ( supporting information ) . These two optic flow and pitch feedback loops combined made it possible to stabilize the simulated pitch and height in steady state ( Fig 8 ) . The ability of the improved model to stabilize the hoverflies´ attitude thanks to the addition of a closed-loop control of the pitch orientation suggests therefore a complementary control strategy involving pitch rate , pitch and optic flow measurements . With the model parameters presented here , a single pitch feedback loop would be too slow to stabilize the fly within 200ms as required by the 40cm-high box . Although , the ability of flying insects to estimate their absolute orientation ( on the pitch and roll axis ) still gives rise to some controversy , the model developed here would certainly provide a basis for studying these sensorimotor reflexes and the coupling that may exists between the sensory modalities involved . It is worth noting that [12] have established that the pitch of an aerial robot can be stabilized without any need for absolute reference value by regulating the dorsal and ventral OF of a 2 degree-of-freedom flying robot . Still our simulation gives controversial results in an unsteady situation such as free-fall recovering . Probably because in their study the rotor forces are adjusted by another control based on ventral optic flow in regard to experimental observation in bees [44] . In addition to visual motion and attitude perception , we can also assume that the fly could probably relies on others sensorimotor reflexes such as those based , for example , on the expansion of the OF [45 , 46] that we did not challenge in this paper . The hoverflies rely probably on specific sensory channels to estimate its absolute attitude with respect to its environment ( i . e . a vertical reference ) and to control their attitude as shown by the comparison made here between the two versions of the present model . Although our setup did not include any salient cues such an artificial horizon , the light from above may stimulate the DLR [22 , 47 , 48] which could help insects to estimate their attitude . However , a significant improvement in the hoverflies’ ability to stop falling before crashing have been previously observed when the insects were placed in a striped box rather than uniform white environment [7] . The reflex controlling the lift force orientation may not therefore relies solely on a pitch feedback based on the position of the brightest part of the visual field . A combination between optic flow based and DLR-based control may possibly be involved . However , the exact role of the DLR and its contribution to the visual-driven stabilization of insects’ flight is still an open question . It is proposed in the future to investigate more closely how a pitch orientation could be estimated by hoverflies’ sensory system . An argument supporting the idea that pitch estimation is involved in the hoverflies’ response to free fall situations is the relative independence of the responses in regard to the initial conditions . In contrary , our model was found to be over-shooting in short initiation times ( 75-100ms ) and under-shooting in long initiation times ( 125-150ms ) . To study the processes that can underlie the insects’ pitch orientation estimation , we can start with some hypotheses: The accuracy of these four hypotheses still remains to be determined , along with the question as to whether any vertical information is really carried by one or more of these processes combined . The comparison with the optic flow strategy made here should help to determine how these various channels combined may serve to maintain a stable attitude during flight . | On the basis of vision-based feedback control of optic flow occurring during insects’ flight , we developed a dynamic model that accounts for the pitch orientation and speed in plummeting flies . We compared the hoverflies’ responses with our model and showed that an optic-flow based control strategy can be used to correct the initial pitch misorientation caused by the free fall situation . To complete the model , we combined the closed-loop control of the vertical optic flow with an additional feedback control loop based on the value of the absolute pitch orientation . The need for this measurement to stabilize the pitch orientation raises the question as whether this is also the case in dipterans . After ruling out the possibility that insects may use gravity acceleration cues to control their flight , for which no experimental evidence has been found so far , we discussed the three main sensory processes possibly involved in in their ability to control their attitude . Our model provides a useful tool for studying the various sensory processes possibly involved in dipterans’ flight stabilization abilities as well as the interactions between these processes . | [
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| 2018 | Modeling visual-based pitch, lift and speed control strategies in hoverflies |
Geographically limited dispersal can shape genetic population structure and result in a correlation between genetic and geographic distance , commonly called isolation-by-distance . Despite the prevalence of isolation-by-distance in nature , to date few studies have empirically demonstrated the processes that generate this pattern , largely because few populations have direct measures of individual dispersal and pedigree information . Intensive , long-term demographic studies and exhaustive genomic surveys in the Florida Scrub-Jay ( Aphelocoma coerulescens ) provide an excellent opportunity to investigate the influence of dispersal on genetic structure . Here , we used a panel of genome-wide SNPs and extensive pedigree information to explore the role of limited dispersal in shaping patterns of isolation-by-distance in both sexes , and at an exceedingly fine spatial scale ( within ~10 km ) . Isolation-by-distance patterns were stronger in male-male and male-female comparisons than in female-female comparisons , consistent with observed differences in dispersal propensity between the sexes . Using the pedigree , we demonstrated how various genealogical relationships contribute to fine-scale isolation-by-distance . Simulations using field-observed distributions of male and female natal dispersal distances showed good agreement with the distribution of geographic distances between breeding individuals of different pedigree relationship classes . Furthermore , we built coalescent simulations parameterized by the observed dispersal curve , population density , and immigration rate , and showed how incorporating these extensions to Malécot’s theory of isolation-by-distance allows us to accurately reconstruct observed sex-specific isolation-by-distance patterns in autosomal and Z-linked SNPs . Therefore , patterns of fine-scale isolation-by-distance in the Florida Scrub-Jay can be well understood as a result of limited dispersal over contemporary timescales .
The movement of individuals over the landscape ( dispersal ) influences biological processes and diversity at many levels [1] , ranging from interactions between individuals to the persistence of populations or species over time [2–4] . Limited dispersal is also central to generating and maintaining spatial genetic structure within species . In particular , geographically-limited dispersal can result in isolation-by-distance , a pattern of increased genetic differentiation [5 , 6] or , conversely , decreased genetic relatedness [7–9] between individuals as geographic distance increases . This pattern results because genetic drift can act to differentiate allele frequencies faster than dispersal can homogenize them among geographically distant populations . The theory of isolation-by-distance was first developed by Wright [5 , 6] and Malécot [7] , and many additional models show the relationship between genetic differentiation and geographic distance [10–13] . This correlation is observed in many empirical systems , consistent with isolation-by-distance being an important process in structuring genetic diversity [14 , 15] . Despite the fact that correlations between genetic differentiation and geographic distance are common across many types of organisms , to date , there are few existing empirical demonstrations of how contemporary patterns of dispersal generate spatial patterns of genetic variation and contribute to observed patterns of isolation-by-distance . This is , in part , because dispersal is hard to estimate empirically , as it requires monitoring many individuals over long periods of time across the full range of potential dispersal distances [16] . In addition , the effective population density of reproducing individuals must be known in order to parameterize genetic drift in models of isolation-by-distance . Therefore , in practice it is hard to know whether the observation of isolation-by-distance is truly consistent with contemporary patterns of dispersal . Indeed , many studies use genetic isolation-by-distance patterns to infer dispersal distances , as a substitute for the more difficult exercise of measuring dispersal directly in the field [17–21] . A second issue is that in many studied systems , populations are compared over larger spatial scales , so the pattern of isolation-by-distance reflects the dynamics of genetic drift and dispersal over tens of thousands of generations . These empirical patterns may often be affected by large-scale population movements ( e . g . , expansions from glacial refugia [22 , 23] ) that may not reflect the equilibrium outcome of individual dispersal and genetic drift . While studies have reported fine-scale population structure [24–30] , detailed pedigree information is usually not available in these studies , making it difficult to demonstrate what mechanisms create these patterns . Patterns of isolation-by-distance can reflect underlying biological processes . Since the early development of isolation-by-distance theory , differences in mating systems and dispersal propensity have both been known to generate differences in isolation-by-distance patterns [5] . In many organisms , dispersal often differs between the sexes: males tend to disperse farther in mammals ( male-biased dispersal ) , but females tend to disperse farther in birds ( female-biased dispersal ) [31 , 32] . When dispersal patterns differ between the sexes , the less dispersive sex tends to have stronger overall genetic structure than the more dispersive sex [26 , 33 , 34] . Similarly , sex-biased dispersal is expected to result in different levels of genetic structure in markers with different inheritance patterns . For example , in a system where females are both the heterogametic sex ( e . g . , in birds , females are ZW and males are ZZ ) and more dispersive , autosomes may exhibit higher genetic differentiation than maternally inherited markers ( e . g . , mitochondrial DNA ) , but lower genetic differentiation than the Z chromosome [20 , 35 , 36] . Here we examine the causes of fine-scale isolation-by-distance in a non-migratory bird , the Florida Scrub-Jay ( Aphelocoma coerulescens ) , based on a long-term population study that has yielded high-quality genetic and pedigree information for many individuals , as well as particularly detailed information on individual dispersal distances . Florida Scrub-Jays have limited , female-biased natal dispersal , and individuals essentially never move once established as a breeding adult [37 , 38] . A population of Florida Scrub-Jays at Archbold Biological Station in central Florida has been the focus of intense monitoring since 1969 , resulting in observed natal dispersal distances for hundreds of individuals and an extensive pedigree [37 , 39 , 40] . Moreover , nearly all nestlings and breeders present in the population during the past two decades were genotyped/assayed at genome-wide single nucleotide polymorphisms ( SNPs ) in a recent study [41] . These long-term dispersal , pedigree , and genomic data make the Florida Scrub-Jay an unusually tractable study system in which to explore how dispersal influences patterns of isolation-by-distance . Previous work on Florida Scrub-Jays using microsatellite markers has shown isolation-by-distance across multiple populations [3] . Here , we present evidence for fine-scale isolation-by-distance within a single contiguous population of Florida Scrub-Jays , and combine genomic , pedigree , and dispersal information to reveal how patterns of isolation-by-distance are created in nature . We find more isolation-by-distance in males than in females , corresponding to predicted differences resulting from female-biased dispersal patterns . We break down our data into pedigree relationships to demonstrate that isolation-by-distance is a consequence of close relatives living geographically close together . We perform simulations that successfully reconstruct the empirical distances between individuals of different kinship classes using only the dispersal curves . Finally , we use extensive coalescent simulations parameterized by the dispersal curve , population density , and immigration rate to yield an excellent fit to observed isolation-by-distance patterns for autosomal and Z-linked markers .
We documented natal dispersal distances for 382 male and 290 female Florida Scrub-Jays that were born and established as breeders within the population at Archbold Biological Station between 1990–2013 . Dispersal curves for both males and females were strongly leptokurtic , consistent with previous studies ( Fig 1A; [3 , 39] ) . Here we considered only dispersal within the Archbold population; therefore , our dispersal curves do not capture any long-distance dispersal events , which occur rarely [3] . Females disperse significantly farther than males , with a median ± SE distance of 1 , 149 ± 108 m and 488 ± 43 m , respectively ( Wilcoxon rank sum test , p < 2 . 2 x 10−16 ) . Florida Scrub-Jays disperse extremely short distances compared with other bird species [39 , 42] . The shorter dispersal distances in males compared with females may be due in part to differences in territory acquisition between the sexes . Florida Scrub-Jay males are able to acquire breeding territories through budding from the parental territory or inheritance of the parental territory [37] , while territory budding and inheritance is extremely rare in females [39] . To explore the genetic implications of this limited , sex-biased dispersal , we genotyped all breeding adults in the Archbold population in 2003 , 2008 , and 2013 ( n = 513 ) at 7 , 843 autosomal SNPs and 277 Z-linked SNPs [41] . We conducted principal component analysis ( PCA ) separately for all breeding adults , male breeders , and female breeders to visually summarize patterns of autosomal genetic variation within the population . We see genetic differentiation along the north/south axis of Archbold in the first two PC axes when we map breeders to their breeding territories ( Fig 1B , S1 Fig ) . Indeed , the top two principal components ( PC1 and PC2 , 14 . 6% and 13 . 1% of the variation , respectively ) are significantly correlated with north-south position under the Universal Transverse Mercator coordinate system ( henceforth “UTM northing”; S1 Table ) . We found significant correlations with UTM northing for both PC1 and PC2 in males , but only PC1 is significantly correlated with UTM northing in females ( S2 Fig , S1 Table ) . Correlation coefficients for PC1 with UTM northing are higher in males than in females ( S1 Table ) . This fine-scale spatial structure is likely a direct result of the unusually limited natal dispersal and female-biased dispersal of these birds ( Fig 1A; overall median ± SE = 647 ± 57 m ) . To test for isolation-by-distance , we quantified autosomal genetic relatedness between all possible pairs of individuals in the dataset as the estimated proportion of the genome shared identical-by-descent . Under a model of isolation-by-distance , the proportion of the genome shared identical-by-descent should decrease as the distance between individuals in a pair increases . Plotting genetic relatedness against geographic distance for all unique pairs across all years , we found a clear pattern of isolation-by-distance ( Fig 1C ) at a fine spatial scale ( Archbold is ~10 km from north to south; Fig 1B ) . We used Mantel correlograms to compare pairwise geographic and genetic distances ( identity-by-descent ) within distinct distance class bins across all pairwise comparisons , all male-male pairs , and all female-female pairs . Mantel correlograms are useful for testing spatial genetic structure when the relationship between geographic and genetic distance is exponential-like rather than linear [43 , 44] . We found significant correlations at more distance classes in all breeders and male-male pairs than in female-female pairs ( S2 Table ) , which indicates stronger patterns of isolation-by-distance in males than in females , consistent with the observed female-biased dispersal . To measure the strength of isolation-by-distance in different subsets of the data , we fitted loess curves and used them to estimate the distance ( δ ) where the proportion of the genome shared identical-by-descent drops halfway to the mean from its maximum value . A lower δ indicates a more rapid decay of genetic relatedness by geographic distance , i . e . , more isolation-by-distance . We bootstrapped pairs of individuals to obtain 95% confidence intervals ( CI ) to assess significance and found stronger isolation-by-distance patterns in male-male ( δ = 620 m , 95% CI = [604 , 631] ) and male-female comparisons ( δ = 645 m , [622 , 665] ) than in female-female comparisons ( δ = 903 m , [741 , 1261]; Fig 1C ) , which is consistent with the strongly female-biased dispersal observed in this system . Because of the detailed pedigree information available for the Florida Scrub-Jay population within Archbold , we have a rare opportunity to decompose the isolation-by-distance patterns found in this population by familial relationship . The Florida Scrub-Jay pedigree from our study population consists of 12 , 738 unique individuals over 14 generations and is largely complete ( see S3 Table for a summary of the pedigree ) ; here we identify relationships up to fourth cousins . For each pair of individuals in our dataset , we extracted their closest genealogical relationship from the pedigree ( e . g . , 1 , 532 of 130 , 618 pairs have a relationship closer than first cousins; S3 Table ) and calculated the pedigree-based coefficient of relationship ( r ) . We plotted identity-by-descent for pairs of individuals against the geographic distance between those individuals , coloring points by their pedigree relationship ( Fig 2 ) . These plots clearly illustrate how isolation-by-distance results , in part , from closely related individuals , such as parent-offspring and full-siblings , remaining physically close together as breeders within neighborhoods of contiguous territories ( Fig 2 ) . The stronger signal of isolation-by-distance in male-male comparisons ( Fig 2A ) seems to be driven by the particularly short geographic distances between individuals in the highest pedigree relatedness classes ( e . g . , parent-offspring , full-siblings , grandparent-grandchild , half-siblings , and aunt/uncle-nibling [“nibling” is a gender-neutral term for niece and nephew] ) . Another way of visualizing how dispersal generates the observed pattern of isolation-by-distance is to plot the distribution of geographic distances separating pairs of individuals with different pedigree relationships ( Fig 3A ) . Close relatives tend to be located closer geographically: for example , the distance between full-siblings is significantly less than the distance between pairs with r = 0 . 25 ( half-siblings , grandparent-grandchildren , and aunt/uncle-niblings; Wilcoxon rank sum test , p = 0 . 01 ) . More generally , if we compare a given pedigree relationship class ( r ) with the pedigree relationship class that is half as related ( 0 . 5r ) , we find shorter distances in the more related pairs for all sequential comparisons out to third cousins ( comparing pairs with r to 0 . 5r , Wilcoxon rank sum test , p < 0 . 003 for all except for the comparison between r = 0 . 0625 and r = 0 . 03125; S4 Table ) . Geographic distances between two males with a close , known pedigree relationship are shorter than in either female-female or male-female comparisons ( Fig 3A , S5 Table ) , and this pattern holds generally in comparisons up to second cousins . We can further assess the contribution of various relationship types by sequentially removing pedigree relationship classes and observing the resulting isolation-by-distance curves ( Fig 3B ) . As expected , the relationship between identity-by-descent and geographic distance flattens and the strength of isolation-by-distance ( measured by δ ) decreases as closely related pairs are removed ( Fig 3B , S6 Table ) . For example , removing pairs with r ≥ 0 . 5 ( parent-offspring and full-siblings ) and r ≥ 0 . 25 ( parent-offspring , full-siblings , half-siblings , grandparents , and aunt/uncle-nibling ) caused significant increases in δ ( Fig 3B , S6 Table ) . However , even after removing all pairs with r ≥ 0 . 0625 , we still see a significant pattern of isolation-by-distance ( S6 Table ) . Therefore , isolation-by-distance is not driven only by highly related individuals . Instead , it appears that highly related individuals ( r ≥ 0 . 25 ) play a primary role in determining the strength of the observed isolation-by-distance patterns ( measured by δ ) , but isolation-by-distance still exists even when these individuals are removed from the dataset . The pattern of isolation-by-distance in more distantly related pairs suggests that isolation-by-distance is generated from dispersal events over many generations even at this small spatial scale , and is not simply a result of dispersal events over only one or two generations . Patterns of genetic diversity on the Z chromosome are expected to differ from those on the autosomes because of the difference in inheritance patterns and sex-specific dispersal behavior [45] . In birds , males are the homogametic sex ( ZZ ) , while females are heterogametic ( ZW ) . Thus , the Z chromosome spends two-thirds of its evolutionary history in males . In addition , the Z chromosome has a smaller effective population size compared with the autosomes [46] . These facts lead to two predictions: ( 1 ) Owing to the reduced effective population size of the Z chromosome , we expect to see higher identity-by-descent on the Z compared to the autosomes . ( 2 ) Because females disperse much farther than males in this system , we expect to find more isolation-by-distance in Z-linked SNPs than in autosomal SNPs [36 , 45] . We separately assessed patterns of isolation-by-distance in 277 Z-linked SNPs . PCA results for Z-linked markers are similar to those observed in autosomes . We found significant correlations for PC1 and PC2 with UTM northing , though correlations between PC2 and UTM northing were significant only for all breeders and male only comparisons ( S3 Fig , S1 Table ) . To fairly compare autosomes and Z chromosomes , which differ in the number of SNPs present , we used unbiased estimates of identity-by-descent for Z-linked and autosomal SNP comparisons . These unbiased estimates do not undergo the final transformation step involved in the estimates of identity-by-descent used previously , and therefore are not bounded by 0 and 1 ( see S1 Text for more details ) . Though these unbiased , unbounded estimates can take negative values , they make comparisons between the autosome and Z datasets more straightforward . Bounding identity-by-descent estimates by 0 and 1 for the Z chromosome would generate upwardly biased estimates . Note that the autosomal identity-by-descent estimates are based on a larger set of SNPs and so values are similar between the bounded estimates and unbiased estimates . Therefore the bias is minimal and not a problem for the previous autosomal analyses . Similar to autosomal SNPs , isolation-by-distance patterns in Z-linked SNPs are stronger in male-male comparisons ( δ = 615 m , [592 , 639] ) than in either female-female ( δ = 979 m , [673 , 2048] ) or male-female comparisons ( δ = 637 m , [601 , 674]; S4 Fig ) . Mean identity-by-descent is higher for the Z chromosome ( 0 . 014 , [0 . 013 , 0 . 015] ) compared with the autosomes ( 0 . 0027 , [0 . 0024 , 0 . 0030]; Fig 4 ) . This pattern is due to both the smaller effective population size of the Z chromosome ( S5A Fig ) as well as higher identity-by-descent for the Z chromosome among immigrants ( S5B and S6 Figs , S2 Text ) . Our simulations , discussed below , show that this latter factor ( higher identity-by-descent among immigrants on the Z ) is the major determinant of the differences between the Z and autosomes ( S6 Fig , S2 Text ) . If we momentarily ignore the influence of immigrant identity-by-descent on differences between the Z and autosomes , we show that the observed female-biased dispersal in our system should indeed lead to a larger drop in identity-by-descent with geographic distance for Z-linked markers compared to autosomal markers ( S7 Fig , S2 Text ) . However , in our empirical data , we do not find evidence for more isolation-by-distance on the Z chromosome: δ for Z-linked SNPs ( 647 m , [620 , 677] ) is not significantly different from δ for autosomal SNPs ( 621 m , [608 , 633]; Fig 4 ) . It is possible that we lack the power to estimate identity-by-descent on the Z chromosome accurately , given the small number of Z-linked SNPs available ( 277 ) , which leads to more noise and uncertainty in the estimates of identity-by-descent on the Z chromosome and therefore a high variance in δ . This is consistent with the larger standard errors for the Z ( Fig 4 ) and the larger confidence interval for δ . Future work will increase marker density on the Z to increase resolution and will incorporate maternally-inherited markers like the W and mitochondria to provide additional insights into the consequences of sex-biased dispersal on markers with different inheritance modes . To test our understanding of the population mechanisms leading to fine-scale isolation-by-distance , we used simulations to explore whether observed patterns could be predicted strictly by dispersal curves and other population parameters . We first conducted simulations of local dispersal in a contiguous population to determine how well the observed distribution of geographic distances between individuals of known pedigree relationships was predicted by the observed natal dispersal curves . Assuming that the dispersal curves are constant and that dispersal distance has negligible heritability , we simulated the distance between individuals of a known , close pedigree relationship using random draws from the sex-specific dispersal curves . For example , for two female first cousins , we first simulated the dispersal distances of the parental siblings from the grandparental nest ( randomly picking their sexes ) . We then simulated dispersal distances of the two female cousins from their respective parental nests and calculated the distance ( d ) between them ( Fig 5A ) . We repeated this procedure 10 , 000 times to obtain a distribution of d . We found that the dispersal simulations generally nicely reconstruct the observed distribution of geographic distances between related individuals up to second cousins ( Fig 6 , S7 Table; Kolmogorov-Smirnov Test with Bonferroni correction , p > 0 . 004 for most pairs ) . For more distantly related pairs , some of the simulations are significantly different from the observed distances ( Fig 6 , S7 Table; Kolmogorov-Smirnov Test , p < 0 . 004 for male-female first cousins and female-female second cousins ) . Notably , the observed distributions in male-male comparisons of closely related uncle-nephew pairs are significantly different from the simulated distributions—we see more short distances between individuals in the observed data than expected from the simulations ( Fig 6 , S7 Table ) . The distance simulations described above randomized the sexes for all ancestral individuals and therefore averaged across all possible lineages for a given pedigree relationship . However , given the strongly sex-biased dispersal in the Florida Scrub-Jay , we expect the geographic distance between a given pair of individuals to also depend on the sexes of the ancestors . For example , two females can be cousins because their mothers are siblings ( four female dispersal events ) , their mother and father are siblings ( three female and one male dispersal events ) , or because their fathers are siblings ( two female and two male dispersal events ) . To assess the relationship between the sex of the ancestors and geographic distance between a pair of individuals of a given pedigree relationship , we conducted additional simulations of first cousins in which we fixed the sexes for the two common ancestors ( aunts or uncles ) in addition to the focal individuals ( the cousins ) . As predicted , we found that the median geographic distance between two cousins strongly correlates with the number of female dispersal events in the lineage ( Spearman rank correlation: ρ = 0 . 8208 , p = 0 . 0067 ) . For example , the median distance between two cousins depends on the number of female dispersal events in their lineage , such that male cousins related through their fathers ( median ± SE = 1 , 715 ± 130 m ) are geographically closer than male cousins related through their mothers ( 2 , 474 ± 235 m ) . Similar to our more general dispersal simulations ( i . e . , those with randomized ancestral sexes ) , we found that the simulated distributions closely fitted the empirical patterns ( S8 Fig , S8 Table ) . The observed distributions only differed from the simulated distributions in simulations with a male-female cousin pair related by their fathers ( S8 Table; Kolmogorov-Smirnov Test , p = 0 . 0005 ) . In nature , we know that dispersal movements are largely restricted to the bounded area that is the study population . Because our natal dispersal curves include only within-population dispersal events , we do not think a violation of this assumption is problematic for simulations of closely related pairs , which involve just a few dispersal events . To accurately simulate distances between more distantly related pairs , we would need to consider the spatial extent of the population and not allow dispersal movements outside of population boundaries . Malécot envisioned identity-by-descent as being due to the chain of ancestry running from present day individuals back to their shared ancestors ( “les chaînes de parenté gamétique”; Fig 5B; [9 , 47] ) . These ideas are the forerunner of modern coalescent theory [48 , 49] . Malécot’s interpretation of the relationship between identity-by-descent and geographic distance reflects the fact that geographically close pairs of individuals are more likely to be closely related , i . e . , trace back to a more recent common ancestor ( coalesce ) , than geographically distant individuals [9] . To empirically demonstrate the underlying mechanisms behind Malécot’s model , we calculated the expected identity-by-descent values as a function of geographic distance for male-male , male-female , and female-female pairs using a spatially-explicit coalescent model . We parameterized these simulations using the observed pedigree , dispersal curves , immigration rate , and basic demographic information about the study system . Our simulations extended Malécot’s framework to include immigration from other populations because previous work has demonstrated a non-negligible rate of immigration into our study population [41] . For a given pair of individuals , we traced the ancestry of their two alleles at each autosomal locus backwards in time until the two lineages found a common ancestor or at least one of the lineages was a descendant of an immigrant into the population ( Fig 5C ) . The probability that a lineage in a given generation was brought into the population by an immigrant ( M ) is given by the proportion of individuals who are immigrants . If one or both of our lineages traced back to an immigrant , we assigned the pair of individuals the observed level of identity-by-descent between immigrants . We kept track of the geographic location of each non-immigrant ancestor by sampling dispersal events from the natal dispersal curve . If our lineages are a distance dk apart in generation k , the probability of our lineages finding a shared ancestor in the next generation back ( k+1 ) is given by the proportion of pairs that are dk apart who are full-siblings , half-siblings , or parent-offspring pairs ( see S9 Fig ) . If the two lineages traced to one of these relationships , we assigned them the expected level of identity-by-descent for that relationship . We simulated expected identity-by-descent values for many pairs of individuals at a given distance bin . We ran five different simulations to investigate how increasing the complexity of the model improved our fit to the observed isolation-by-distance patterns in male-male , male-female , and female-female pairs ( S3 Text ) . We began with a model that used sex-averaged values for all parameters . This model ( M0 ) explained a large proportion of the variance in mean identity-by-descent across geographic distance for male-female pairs ( coefficient of determination R2 = 0 . 90 ) , but not for male-male and female-female comparisons ( R2 = 0 . 61 and -0 . 10 , respectively; Fig 7 , S10 Fig , Table 1 ) . We then tried to improve the fit of our model by incorporating sex-specific parameters . First , we simulated dispersal back in time in a sex-specific manner by sampling from the male or female dispersal curve . Because of the strongly female-biased dispersal in Florida Scrub-Jays , the per-generation coalescent probability for females is greater at larger distance bins , and immigrants are more likely to be female [39 , 41] . By allowing sex-specific dispersal ( model M1 ) , sex-specific coalescent parameters ( model M2 ) , and also sex-specific immigration parameters ( model M3 ) , our models more closely reconstructed the observed relationship between identity-by-descent and geographic distance ( R2 = 0 . 88–0 . 90 for model M3; S10 Fig , Table 1 ) . The fully sex-specific model overestimated identity-by-descent at longer distances for male-male pairs , which we hypothesized was a result of observed isolation-by-distance in the immigrants . By extending our model to account for variation in relatedness among immigrants with distance , our final pedigree-based simulations ( model M4 ) recovered the observed pattern of isolation-by-distance for both autosomal and Z-linked loci , with slightly lower performance for female-female comparisons and for the Z chromosome ( S11 Fig , Table 1 ) . The fact that our simulations , which only span 10 generations , recovered the observed decrease in genomic relatedness within 10 km suggests that limited dispersal can generate isolation-by-distance over short timescales in this population . A number of studies have compared direct estimates of dispersal obtained from field observations to indirect estimates of dispersal obtained by regressing pairwise genetic differentiation on geographic distance [50–53] . Several of these studies found that direct and indirect estimates of dispersal are fairly concordant ( off by no more than a factor of two; [19 , 52–54] ) , yet others find discrepancies between the two estimates [55 , 56] . Additional empirical evaluations of dispersal inference methods are necessary to better assess sensitivity to violations of the model assumptions [17] . Although we have direct estimates of dispersal and observed patterns of isolation-by-distance , the relatively high and temporally-variable rate of immigration into our study population [41] make the regression method proposed by Rousset [50 , 51] inappropriate , as it assumes equilibrium conditions and is sensitive to long-distance dispersal events [17 , 52] . Instead , we take advantage of detailed pedigree information to explicitly demonstrate the mechanism by which limited dispersal can generate fine-scale isolation-by-distance over contemporary timescales . Here we have used single-marker estimates of genome-wide identity-by-descent to study relatedness . Additional power to infer recent demography and dispersal history can be gained by studying shared identity-by-descent blocks—linked segments of the genome that are shared identical-by-descent between pairs of individuals [57–59] . A number of methods exist for inferring identity-by-descent blocks from dense genotyping or sequencing data [60] . By tracing the spatial distribution of identity-by-descent blocks of varying lengths , we can uncover how recent dispersal shapes the transmission of genomic segments across the landscape . Furthermore , we will assess how dispersal shapes patterns of genetic variation over larger spatial scales by extending this approach to multiple populations spanning the entire range of this species . This question has vital conservation implications , as for example , decreasing rates of immigration are driving increased inbreeding depression within the population at Archbold Biological Station [41] . Isolation-by-distance is a commonly observed pattern in nature . Despite its ubiquity and the frequent use of isolation-by-distance patterns to indirectly estimate dispersal in diverse organisms , few studies to date have deconstructed the causes of isolation-by-distance . Here , we have shown how limited dispersal can result in isolation-by-distance in the Florida Scrub-Jay . The extremely short dispersal distances of this species allow us to detect a signal of isolation-by-distance within a single , small contiguous population over just a few generations . In systems with longer dispersal distances , patterns of isolation-by-distance will likely only be observed over larger spatial scales , and reflect relatedness over potentially much longer timescales . The extensive dispersal , pedigree , and genomic data in this well-studied system provided a rare opportunity to empirically unpack Malécot’s isolation-by-distance model [9]: we have shown how limited dispersal leads to closely related individuals being located closer together geographically , which results in a pattern of decreased genetic relatedness with increased geographic distance .
The Florida Scrub-Jay is a cooperatively breeding bird endemic to Florida oak scrub habitat [37 , 38] . Individuals live in groups consisting of a breeding pair and non-breeding helpers ( often previous young of the breeding pair ) within territories that are defended year-round . A population of Florida Scrub-Jays at Archbold Biological Station ( Venus , Florida , USA ) has been intensely monitored by two groups for decades: the northern half by Woolfenden , Fitzpatrick , Bowman , and colleagues since 1969 [37 , 39] and the southern half by Mumme , Schoech , and colleagues since 1989 [40 , 61] . Standard population monitoring protocols in both studies include individual banding of all adults and nestlings , mapping of territory size and location , and surveys to determine group composition , breeding status/success , and individual territory affiliation [37 , 39] . Immigration into our study population is easily assessed because every individual is uniquely banded ( so any unbanded individual is an immigrant ) . Blood samples for DNA have been routinely obtained from all adults and day 11 nestlings through brachial venipuncture since 1999 . This intense monitoring has generated a pedigree of 14 generations over 46 years . All activities followed protocols approved by the Cornell University Institutional Animal Care and Use Committee ( IACUC , 2010–0015 ) , the University of Memphis IACUC ( 0667 ) , and the Archbold Biological Station IACUC ( AUP-006-R ) . All work was permitted by the U . S . Geological Survey ( banding permits 07732 , 23098 ) , the U . S . Fish and Wildlife Service ( TE824723-8 , TE-117769 ) , and the Florida Fish and Wildlife Conservation Commission ( LSSC-10-00205 ) . Here , we measured dispersal distances of individuals banded as nestlings within Archbold and that subsequently bred within Archbold between 1990 and 2013 ( 382 males and 290 females ) . We began our sampling in 1990 because the study site was expanded to its current size by 1990; hence , dispersal measures before this year are systematically shorter ( i . e . , lack the longer distances ) . Thus , we have a comprehensive measure of dispersal tendencies of individuals within Archbold over a 24-year period . We measured natal dispersal distance as the distance from the center of the natal territory to the center of the first breeding territory in meters using ArcGIS Desktop v10 . 4 [62] , independent of the age of first breeding ( definition from [63] ) . As part of a previous study , 3 , 984 individuals have been genotyped at 15 , 416 genome-wide SNPs using Illumina iSelect Beadchips [41] . Details of SNP discovery , identification of Z-linked SNPs , genotyping , and quality control can be found in [41] . Here , we focused on breeding adults in Archbold during the years 2003 , 2008 , and 2013 ( n = 513 ) , when almost all individuals present have been genotyped . Autosomal SNPs were pruned for linkage disequilibrium using PLINK v1 . 07 [64] . We conducted analyses on both the entire set of SNPs and the dataset pruned for linkage disequilibrium . We found qualitatively similar results , so we present only the results from the pruned dataset here . Our final dataset included 7 , 843 autosomal and 277 non-pseudoautosomal Z-linked SNPs . All of the presented analyses were conducted on the combined dataset across all three years . For any individuals present in multiple years , we randomly selected presence in a single year for inclusion in this combined analysis . To determine genetic relatedness , we estimated the proportion of the genome shared identical-by-descent relative to the population frequency for all individual pairwise comparisons within and across years using the ‘genome’ option in PLINK v1 . 07 [64] for autosomal SNPs . As PLINK does not calculate identity-by-descent for sex-linked markers , we used a custom R script to estimate the proportion of the genome shared identical-by-descent for Z-linked SNPs ( S1 File ) . Identity-by-descent for Z-linked SNPs was calculated using a method-of-moments approach using observed allele counts similar to that in [64] . Identity-by-descent values reported by PLINK are constrained to biologically plausible values between 0 and 1 in a final transformation step . To avoid introducing biases when comparing identity-by-descent estimates obtained from very different numbers of SNPs ( on the Z chromosome versus the autosomes ) , we used untransformed autosomal and Z-linked identity-by-descent values for comparisons between the autosomes and Z . All identity-by-descent calculations used allele frequencies from the sample of all individuals in the population through time . See S1 Text for further details and S1 File for the R code . Additionally , we estimated relatedness of all individual pairwise comparisons using the pedigree . We calculated the coefficient of relationship by using the ‘kinship’ function within the package kinship2 [65] in R v3 . 2 . 2 [66] and multiplied the values by two ( to convert them from kinship coefficients ) . The pedigree-based coefficient of relationship was calculated separately for expectations under autosomal and Z-linked scenarios using the ‘chrtype’ option within the ‘kinship’ function . Because kinship2 assumes an XY system , we swapped the sex labels of our individuals and swapped mothers and fathers in the pedigree to calculate the coefficient of relationship for a ZW system . The autosomal coefficient of relationship r and proportion of the genome shared identical-by-descent are highly correlated ( S12 Fig; Pearson’s product moment correlation: t = 688 . 85 , p < 0 . 0001 ) . Because genomic estimators of relatedness are more precise than pedigree-based estimators [67] , we only report results for genomic measures of relatedness in the text ( but see S13 and S14 Figs and S2 Table for analyses using pedigree-based measures of relatedness ) . We used three approaches to test for isolation-by-distance patterns in our data . First , we conducted principal component analysis on the autosomal and Z-linked genomic data using custom Perl and R scripts . We conducted separate analyses on males only , females only , and all individuals . We then compared the first two PC axes from each analysis with the UTM northing values of the territory centroids for each individual using Spearman rank correlations . To ensure these patterns were not driven by differences in genetic diversity within the study site , we estimated observed heterozygosity and inbreeding coefficients ( FIII from [68] ) from the autosomal SNPs in PLINK . We compared individual heterozygosity and inbreeding coefficients with UTM northing and found no relationship ( Pearson’s product-moment correlation , t = 1 . 493 , p = 0 . 136 for heterozygosity , t = -1 . 559 , p = 0 . 120 for inbreeding coefficient ) . Second , we conducted Mantel correlogram tests using the ‘mantel . correlog’ function in the vegan package [69] in R v3 . 2 . 2 [66] . Mantel tests compare two distance matrices and test for significance through permutation of the matrix elements [70 , 71] . While Mantel tests are useful for assessing linear relationships , they will not accurately represent the spatial structure found in systems with exponential-like decreases in structure ( i . e . , strong spatial structure in the short distance classes that decreases and stabilizes at larger distances ) . Mantel correlograms are able to assess these more complex spatial structures by utilizing the traditional Mantel test within distinct distance bins [43 , 44] . Here , we use Mantel correlograms to compare a matrix of individual pairwise comparisons of geographic distances to a matrix of pairwise comparisons of relatedness between individuals ( either estimated from the genomic data or from the pedigree , and for autosomes or the Z chromosome ) . We conducted separate analyses for comparisons between males only , females only , and all individuals . Note that we cannot conduct Mantel correlograms on male-female comparisons alone , as we cannot use unbalanced matrices in this type of analysis . We limited our analyses to the following distance class bins to ensure that enough comparisons fell within each: 250–750 m , 750–1250 m , 1250–1750 m , 1750–2250 m , 2250–2750 m , 2750–3250 m , 3250–3750 m , 3750–4250 m , 4250–4750 m , 4750–5250 m . We did not include comparisons between breeders in the same territory or self-self comparisons ( distance < 250 m ) . We performed 10 , 000 permutations to obtain corrected p-values . Finally , we fitted a loess curve to the scatterplot of identity-by-descent and geographic distance between pairs of individuals . We tested for isolation-by-distance by determining whether identity-by-descent at the smallest distance interval was larger than the overall mean . To measure the strength of isolation-by-distance , we estimated the distance where identity-by-descent drops halfway to the mean from its maximum value , which we define as δ . To assess uncertainty in these estimates , we used a bootstrapping method in which we randomly resampled pairs with replacement , fitted a loess curve , and estimated identity-by-descent at distance bin 0 , mean identity-by-descent , and δ . We repeated this procedure 1 , 000 times to obtain 95% bias-corrected and accelerated bootstrap confidence intervals . We used simulations to determine whether we could generate the observed distribution of geographic distances between related pairs using only the natal dispersal curve . For each of several focal pairwise relationships ( full-siblings , aunt/uncle-nibling , first cousins , and second cousins ) , we simulated dispersal events starting at their common ancestral nest and then recorded the resulting distance between the two focal individuals using a custom script in R ( Fig 5A , S2 File ) . We located the shared ancestral nest of the birds at ( 0 , 0 ) in an unbounded two-dimensional habitat . The number of dispersal events for a given focal pair ranged from two ( full-siblings ) to six ( second cousins ) . For each dispersal event , we randomly sampled a dispersal angle ( 0–360° ) and a dispersal distance from the sex-specific dispersal distribution ( Fig 1A ) . The sexes of the final individuals in the focal pair were fixed ( either male-male , male-female , or female-female ) . In most cases , the sexes of ancestral individuals up to the common ancestor were chosen randomly . To further assess the impact of sex-specific dispersal on the distribution of geographic distances between pairs , we performed simulations for first cousins with fixed sexes for the two focal individuals ( the cousins ) and the two common ancestors ( aunts or uncles ) . This resulted in nine possible simulations ( with sex combinations of male-male , male-female , and female-female for both the focal pair and the common ancestors ) . We performed these simulations 10 , 000 times for each focal pairwise relationship , calculating the resulting distance between the two focal individuals each time . We determined the empirical distances between individuals of different pedigree relationships and compared the observed distributions to the simulated distributions using Kolmogorov-Smirnov tests and the medians using Wilcoxon rank sum tests , both with Bonferroni corrections . Code for the dispersal simulations is included in S2 File . We generated the expected isolation-by-distance pattern for the autosomes and the Z chromosome given the observed dispersal curves and immigration rate using spatially-explicit pedigree-based simulations that are extensions of Malécot’s model of isolation-by-distance [9] . For each pair of individuals , we simulated their lineages backwards in time until we reached a common ancestor or one or more of the lineages was a descendent of an immigrant into the population ( Fig 5C ) . In each generation g , we first sampled a dispersal distance from the empirical sex-specific dispersal curve ( Fig 1A ) and a dispersal angle ( 0–360° ) uniformly at random and calculated the geographic distance dg between the two individuals . Here , we assumed that there is no genetic variation for dispersal distance , and sampled a dispersal distance for all individuals of a given sex from the same distribution . After the first dispersal event , we randomly assigned sexes for all ancestors . We then calculated the probability that the two lineages located at distances ( d1 , … , dg ) did not coalesce ( share a common ancestor ) or have an immigrant ancestor in the previous g–1 generations and the probability that they either coalesce or have an immigrant ancestor in generation g . Given the relatively small population size and high immigration rate , we found that nearly all pairs either shared a common ancestor or had an immigrant ancestor within 10 generations , and so we used g ≤10 ( increasing this limit had no effect on our results ) . Here we define the probability that two individuals share a common ancestor in the preceding generation as the probability the pair is closely related ( parent-offspring , full-siblings , or half-siblings ) . For a pair of individuals at distance d , we estimated the probability they are parent-offspring ( Pp ( d ) ) , full-siblings ( Pf ( d ) ) , or half-siblings ( Ph ( d ) ) from the observed pedigree and distances between these relative classes ( S9 Fig ) . We calculated the sex-specific probability an individual is an immigrant as the proportion of breeding male or female individuals in a given year who were not born in Archbold ( M = 0 . 197 for males and 0 . 345 for females ) . Using mean identity-by-descent values for immigrant-immigrant and immigrant-resident pairs obtained from our data , we estimated the expected proportion of the genome shared identical-by-descent for a given pair of individuals as follows: Z^=∑g=110[∏k=1g−1 ( 1−M ) 2[1−Pp ( dk ) −Pf ( dk ) −Ph ( dk ) ]]×[Pp ( dg ) E ( Zp ) +Pf ( dg ) E ( Zf ) +Ph ( dg ) E ( Zh ) +2M ( 1−M ) E ( Zr ) +M2E ( Zm ) ] Where E ( Zp ) , E ( Zf ) , and E ( Zh ) are the expected identity-by-descent values for parent-offspring , full-sibling , and half-sibling pairs , respectively ( S9 Table ) . E ( Zm ) , and E ( Zr ) are the sex-specific empirical mean identity-by-descent values for immigrant-immigrant and immigrant-resident pairs , respectively . Because we found a pattern of isolation-by-distance in immigrant-immigrant pairs , we used expected identity-by-descent values for immigrant-immigrant and immigrant-resident pairs conditional on distance . We binned distances into 15 quantiles and ran 1 , 000 simulations for each distance bin . To evaluate the fit of our model , we calculated the coefficient of determination R2 for each type of comparison as follows: R2=1−∑i ( yi−Z^i ) 2∑i ( yi−y¯i ) 2 Where yi is the mean observed identity-by-descent value in distance bin i and Z^i is the mean simulated identity-by-descent value in distance bin i . Note that it is possible to obtain negative values of R2 when the model performs so poorly that the mean of the data provides a better fit than our model . We ran simulations using parameters estimated from the full dataset , and then performed two-fold cross-validation to check for over-fitting . As results from both sets of models were similar , we discuss results from the full dataset in the text . See S3 Text for the full derivation of our model , S2 Text for additional details on the differences between the autosomal and Z-linked models , and S3 File for the R code . | Dispersal is a fundamental component of the life history of most organisms and therefore influences many biological processes . Dispersal is particularly important in creating genetic structure on the landscape . We often observe a pattern of decreased genetic relatedness between individuals as geographic distances increases , or isolation-by-distance . This pattern is particularly pronounced in organisms with extremely short dispersal distances . Despite the ubiquity of isolation-by-distance patterns in nature , there are few examples that explicitly demonstrate how limited dispersal influences spatial genetic structure . Here we investigate the processes that result in spatial genetic structure using the Florida Scrub-Jay , a bird with extremely limited dispersal behavior and extensive genome-wide data . We take advantage of the long-term monitoring of a contiguous population of Florida Scrub-Jays , which has resulted in a detailed pedigree and measurements of dispersal for hundreds of individuals . We show how limited dispersal results in close genealogical relatives living closer together geographically , which generates a strong pattern of isolation-by-distance at an extremely small spatial scale ( <10 km ) in just a few generations . Given the detailed dispersal , pedigree , and genomic data , we can achieve a fairly complete understanding of how dispersal shapes patterns of genetic diversity over short spatial scales . | [
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| 2017 | Deconstructing isolation-by-distance: The genomic consequences of limited dispersal |
Australia is currently canine rabies free . However , communities located on the northern coastline–such as the Northern Peninsula Area ( NPA ) , Queensland–are at risk of an incursion due to their large populations of susceptible free-roaming dogs and proximity to rabies-infected Indonesian islands . A rabies-spread model was used to simulate potential outbreaks and evaluate various disease control strategies . A heterogeneous contact structure previously described in the population of interest–explorer dogs , roamer dogs and stay-at-home dogs–was incorporated into the model using six spatial kernels describing contacts between dog roaming categories . Twenty-seven vaccination strategies were investigated based on a complete block design of 50% , 70% and 90% coverage for each of the three roaming categories to simulate various targeted vaccination strategies . The 27 strategies were implemented in four population structures in which the proportion of dogs in each category varied–explorer dominant , roamer dominant , stay-at-home dominant and a field population ( based on field estimates of population structure ) . The overall vaccination coverage varied depending on the subpopulation targeted for vaccination and the population structure modelled . A total of 108 scenarios were simulated 2000 times and the model outputs ( outbreak size and duration ) were compared to Strategy 14 ( a standard recommended overall 70% vaccination coverage ) . In general , targeting explorer dogs–and to a lesser extent roamer dogs–produced similar outbreaks to Strategy 14 but with a lower overall vaccination coverage . Similarly , strategies that targeted stay-at-home dogs required a higher vaccination coverage to produce significantly smaller and shorter outbreaks . This study provides some theoretical evidence that targeting subpopulations of dogs for vaccination based on their roaming behaviours ( and therefore risk of rabies transmission ) could be more efficient than blanket 70% vaccination campaigns . Such information can be used in preparedness planning to help improve control of a potential rabies incursion in Australia .
Rabies is a viral encephalitis , estimated to cause approximately 59 , 000 human deaths worldwide; of these deaths , 99% are caused by dog bites [1] . Currently , the rabies-free communities of the Northern Peninsula Area ( NPA ) , Queensland , Australia are at risk of a rabies incursion due to proximity to rabies-infected islands of Indonesia [2] . These communities also have large populations of free-roaming domestic dogs that are capable of transmitting and maintaining the rabies virus [3–4] . Simulation models can be used to assess different control strategies and inform decision makers on best practice if an outbreak was to occur , to improve preparedness and reduce the risk of disease spread . Models that simulate direct-contact disease transmission benefit from incorporating movement and contact patterns of the at-risk population . In the NPA , three roaming patterns in the domestic dog population have been recently described–explorer dogs who frequently roam away from their owner’s residence , roamer dogs who mainly remain around their owner’s residence but roam away sometimes , and stay-at-home dogs who spend most ( if not all ) of their time around their owner’s residence , roaming to only the nearest neighbours [5] . These roaming patterns have been shown to create heterogeneous contact probabilities between individuals in the population and have been described by six spatial contact kernels based on all possible combinations of categories between a pair of dogs [6]; two explorer dogs ( EE kernel ) , an explorer dog and a roamer dog ( ER kernel ) , an explorer dog and a stay-at-home dog ( ES kernel ) , two roamer dogs ( RR kernel ) , a stay-at-home dog and a roamer dog ( SR kernel ) and two stay-at-home dogs ( SS kernel ) . Previously , these kernels were individually incorporated into a rabies-spread model for the NPA developed by Dürr and Ward [3] . This was to study in isolation the differences in the contact probabilities produced by the spatial kernels and to quantify the effects of the different contact probabilities on predicted disease spread [3 , 6] . However , heterogeneous contacts within the population can greatly affect epidemic spread and subsequent model predictions [7] . Therefore , to model the spread of rabies through the NPA dog population more accurately , all six kernels need to be used in the NPA rabies-spread model to describe the heterogeneous contact structure in this population . The representation of a heterogeneous contact structure in the NPA rabies-spread model not only leads to more probable outbreak scenarios and predictions , it also allows exploration of targeted vaccination strategies for specific dog sub-populations in this area . Vaccination is the most effective control strategy to limit the spread of rabies in the reservoir population and exposure to humans , and 70% vaccination coverage of dogs has been recommended to eliminate rabies [8–9] . Several countries–including Tanzania [10] , Indonesia [11] , Chad [12–13] and several in Latin America [14]–have experienced a reduction in rabies transmission and human cases by reaching a 70% vaccination coverage in the dog population . Modelling has also shown that annual dog vaccination that reaches 70% coverage is sufficient for rabies elimination [15–17] . With limited resources , vaccination programs should be tailored to suit the region’s individual characteristics–including human-dog interaction and dog population ecology–to efficiently and cost-effectively reduce rabies spread . For example , Kaare et al . [18] found that centre point vaccination ( a common approach in rabies endemic regions in which vaccination stations are set up and owners bring dogs to the station ) alone reached a 70% coverage and was cost-effective in agro-pastoralist communities in Tanzania . However , centre point vaccination alone was insufficient to reach 70% coverage and more expensive in remote pastoralist communities of Tanzania [18] . The three roaming categories of dogs in the NPA described by Hudson et al . [6] , provides an opportunity to use the NPA rabies model developed by Dürr and Ward [3] to evaluate targeted vaccination strategies . Targeting explorer and roamer dogs for vaccination in preference to stay-at-home dogs could produce the same or better results as the recommended overall 70% vaccination coverage potentially with fewer resources such as vaccine numbers . This is because explorer and roamer dogs travel longer distances and subsequently come into contact with more dogs , producing a higher probability of transmitting rabies compared to stay-at-home dogs [6] . Previous rabies control modelling studies have explored targeted vaccination strategies in which high-risk populations are prioritised over lower risk populations . For example , a previous study modelled canine rabies spread in the dog populations of the Serengeti District , Tanzania as a metapopulation model with various sub-populations [19] . The study evaluated vaccination strategies that either targeted sub-populations that would result in the greatest decrease in global risk ( disease occurrence ) , or targeted sub-populations based only on their relative size , and assessed their performance based on their reduction of mean rabies occurrence compared to an unvaccinated population . The former strategy was found to be up to 62% more effective at reducing mean rabies occurrence compared to the latter strategy . Another study that used a network model to investigate rabies spread in Chad found vaccination strategies that either targeted dogs with higher degree centrality , betweenness centrality , or dogs that roam larger areas , reduced outbreak size and probability more than a random vaccination strategy [20] . The above modelling studies provide theoretical evidence that targeting high-risk populations for vaccination is effective . The objectives of this study were to incorporate the six spatial contact kernels for each of the roaming categories and their combinations created by Hudson et al . [6] into the rabies-spread model for the NPA developed by Dürr and Ward [3] , to more accurately represent the heterogeneous contact structure of the dog population . With this updated model , we then explore reactive vaccination strategies that target the individual dog categories within the NPA population at different coverage levels , and compare model predictions to the recommended 70% population vaccination coverage . The results could then be used by decision makers to further investigate and develop reactive vaccination programs in the event of a rabies outbreak in the NPA .
The NPA is a local government area located at the tip of Cape York Peninsula , Queensland , Australia . It consists of five Aboriginal and Torres Strait Islander communities–Seisia , New Mapoon , Bamaga , Umagico and Injinoo . Most of the human population ( 99%; N = 2773 ) live within these communities [21] . The total dog population in the five NPA communities is estimated to be 813 ( range = 770–8680 ) and most of these dogs are free-roaming [4] . It is believed that most if not all dogs in these communities are owned [4] . The rabies-spread simulation model used was previously developed by Dürr and Ward [3] and modified by Hudson et al . [6] . Model parameters are shown in S1 Table . Briefly , the modifications included updating the dog population size ( 813 dogs ) and spatial arrangement of dog-owning houses in the communities–based on recent demographic studies of the NPA dog population [4]–and the inclusion of two parameters to describe the probability of a clinically infected dog developing furious rabies and a subsequent increased bite probability of a furious rabid dog . In addition , the model was modified in the current study to include a birth and death rate to simulate a dynamic steady-state population . Based on data collected by Hudson et al . [4] , 12 . 6% of the adult dog population ( 1 year and older ) die each year . The model runs on a daily time step and so the death rate was converted to a daily probability of death and a Poisson distribution was used to determine how many dogs are randomly selected to die each day from cause other than rabies . Any live dog in the model can be randomly selected to die , regardless of rabies infection status . The birth rate used in the model was chosen to be equivalent to the death rate to simulate a steady-state population . Again , a Poisson distribution was used to determine how many new dogs are added to the population at the beginning of each day . Dog-owning houses were selected at random to receive the new dogs . The random selection of houses was with replacement , so that one house can receive multiple new dogs . The six spatial kernels developed by Hudson et al . [6] describe the daily probability of contact between pairs of dogs dependent on the distance between their residences and the roaming behaviour of the dogs ( explorer , roamer or stay-at-home type of dog ) . All dogs in the population are assumed to be susceptible and are in contact with other dogs , regardless of roaming category . These kernels were used simultaneously in the model . For example , if the infected dog is an explorer ( E ) dog and the contacted dog is a roamer ( R ) dog , the ER kernel is used to estimate their daily probability of contact . If both dogs are a stay-at-home ( S ) dog then the SS kernel is used to estimate the daily probability of contact . The current proportions of dogs in each roaming category are unknown in the NPA . Therefore , three population structures were modelled and compared–Explorer Dominant ( ED ) in which 60% , 20% and 20% of the population are explorer dogs , roamer dogs and stay-at-home dogs , respectively , Roamer Dominant ( RD ) in which 20% , 60% and 20% of the population are explorer dogs , roamer dogs and stay-at-home dogs , respectively , and Stay-at-home Dominant ( SD ) in which 20% , 20% and 60% of the population are explorer dogs , roamer dogs and stay-at-home dogs , respectively . A fourth population structure based on roaming category proportions of dogs collected from field data ( 9 stay-at-home dogs and 6 roamer and explorer dogs each ) in Hudson et al . [5] was also modelled ( Field Population; FP ) in which 29% , 29% and 42% of the population are explorer dogs , roamer dogs and stay-at-home dogs , respectively . All dogs in the model were randomly assigned a roaming category at the beginning of each simulation based on the respective population structure ratios , and the chosen roaming category for each dog was retained until the end of the simulation . New dogs added to the population from the birth rate function were also randomly assigned a roaming category based on these ratios . For example , the probability a new dog will be an explorer dog in the ED population is 60% but only 20% in the RD and SD populations and 29% in the FP population . Full details of the rabies simulation model and how reactive vaccination strategies are implemented are described in Dürr and Ward [3] . Briefly , a vaccination campaign is triggered after detection of the first case with clinical signs and the delay ( 7 days ) to acquire vaccines and organise the campaign , which is simulated as a door-to-door campaign . All dogs in the population that are not yet clinically rabid or vaccinated are available for vaccination . The number of dogs selected as vaccination candidates is dependent on the vaccination capacity ( default 50 dogs/day based on semi-structured interviews with people with local knowledge and knowledge of Australia’s biosecurity responses ) and the overall target population vaccination coverage . For example , if the desired overall vaccination coverage is 70% and the vaccination capacity is 50 dogs/day , the 72 dogs with the closest residences to all detected cases are selected as vaccination candidates that day . From these candidates , 70% ( which is the vaccination capacity of 50 dogs ) are randomly chosen to be vaccinated , subsequently producing 70% vaccination coverage . The vaccination campaign is continued each day until the aimed coverage ( e . g . 70% ) of the entire dog population was reached . Vaccination coverages of 90% , 70% and 50% were trialled for each roaming category in a complete block design to produce 27 vaccination strategies for each population structure ( Table 1 ) . The overall population-level vaccination coverages varied dependent on the population structured used . The selected vaccination candidates were grouped into their roaming categories and the individual category vaccination coverages were applied to randomly select dogs for vaccination within their respective category . For example , if the vaccination coverage for the stay-at-home , roamer and explorer categories were 70% , 90% and 50% respectively , then 70% , 90% and 50% of the stay-at-home , roamer and explorer dogs chosen as vaccination candidates were finally vaccinated , respectively . The 27 vaccination strategies were modelled for the four population structures to produce 108 scenarios . Each scenario was simulated 2000 times , which is sufficient for convergence of summary statistics of outbreak outputs for this simulation model [6] . To better compare the effect of vaccination on the outbreak size ( number of rabid dogs ) and outbreak duration ( days ) , simulations within each vaccination strategy in which vaccination was not triggered ( either because of no propagation of rabies or disease fade-out before vaccination was triggered ) were removed after the original 2000 simulations were complete . Model simulations were conducted on The University of Sydney’s High Performance Computer using the R statistical program [22] . Within each population structure , a Kruskal-Wallis test was performed to determine overall significance of differences in outbreak size and duration between the 27 vaccination strategies . A Dunn’s Test with Bonferroni correction was used as a post-hoc test to compare the vaccination strategies to Strategy 14 ( which represented 70% vaccination coverage in all three roaming categories ) in a pairwise fashion at a 0 . 01 significance level . Statistical analyses were performed in R [22] .
The results for these population structures are presented together because their results were similar . Outbreaks tended to be smaller with shorter durations and less variation compared to the RD and ED population structures ( Fig 1 and Fig 2 ) . In the SD population structure , Strategy 16 ( 70–90–50 ) , Strategy 22 ( 90–70–50 ) and Strategy 2 ( 50–50–70 ) had the same overall vaccination coverage ( 62% ) , lower than the overall 70% vaccination coverage in all dog categories ( non-targeted vaccination ) of Strategy 14 ( 70–70–70 ) . However , only Strategies 16 and 22 , which focussed on vaccinating explorer and roamer dogs , produced similar outbreaks to Strategy 14 . Strategy 2 focussed on vaccinating stay-at-home dogs and produced significantly larger and longer outbreaks than Strategy 14 . Also , Strategy 25 ( 90–90–50 ) which had an overall vaccination coverage of 66% produced similar outbreaks to Strategy 14 , unlike Strategy 5 ( 50–70–70 ) and Strategy 11 ( 70–50–70 ) , which has the same overall vaccination coverage of 66% but produced significantly different outbreaks than Strategy 14 . Furthermore , Strategy 6 ( 50–70–90 ) , Strategy 12 ( 70–50–90 ) and Strategy 26 ( 90–90–70 ) had an overall population coverage of 78% ( higher than Strategy 14 ) , but only Strategy 26 , which focussed on vaccinating explorer and roamer dogs , performed significantly better than Strategy 14 . Similar to the SD population , Strategy 16 ( 70–90–50 ) and Strategy 22 ( 90–70–50 ) –both with overall vaccination coverage of 67%–produced similar outbreaks to Strategy 14 ( 70–70–70 ) for the FP population structure . Strategy 3 ( 50–50–90 ) –which has the same overall vaccination coverage ( 67% ) as Strategies 16 and 22 –focussed on vaccinating stay-at-home dogs and produced significantly larger and longer outbreaks than Strategy 14 . Strategy 6 ( 50–70–90 ) , Strategy 12 ( 70–50–90 ) and Strategy 25 ( 90–90–50 ) all have an overall vaccination coverage of 73% . However , only Strategy 25 –which targets explorer and roamer dogs over stay-at-home dogs–produced significantly smaller and shorter outbreaks than Strategy 14 . Similarly , Strategy 9 ( 50–90–90 ) –which focussed on vaccinating roamer and stay-at-home dogs over explorer dogs–produced similar sized outbreaks compared to Strategy 14 , even with a higher overall vaccination coverage of 78% . The ED population tended to produce the largest and longest outbreaks compared to the other population structures , especially when overall vaccination coverage was low ( Fig 1 and Fig 2 ) . Strategy 11 ( 70–50–70 ) and Strategy 13 ( 70–70–50 ) produced similar outbreaks to Strategy 14 ( 70–70–70 ) with a lower overall vaccination coverage of 66% . Conversely , Strategy 9 ( 50–90–90 ) –which also has an overall vaccination coverage of 66%–produced significantly larger and longer outbreaks to Strategy 14 . Furthermore , of Strategies 15 ( 70–70–90 ) , 17 ( 70–90–70 ) and 19 ( 90–50–50 ) with an overall vaccination coverage of 74% each , only Strategy 19 –which focused on vaccinating explorer dogs over the other categories–performed better than Strategy 14 . The RD population generally produced larger and longer outbreaks than the FP and SD populations but smaller and shorter outbreaks than the ED population ( Fig 1 and Fig 2 ) . Strategy 5 ( 50–70–70 ) , Strategy 13 ( 70–70–50 ) and Strategy 21 ( 90–50–90; overall vaccination coverage of 66% each ) produced similar sized outbreaks to Strategy 14 ( 70–70–70 ) . Strategies 5 and 13 also produced similar outbreak durations , whereas Strategy 21 produced significantly longer durations . Strategy 7 ( 50–90–50 ) , Strategy 15 ( 70–70–90 ) and Strategy 23 ( 90–70–70 ) had a higher overall vaccination coverage ( 74% ) than Strategy 14 . However , only Strategy 7 performed significantly better than Strategy 14 for both outbreak size and duration . Strategy 23 produced significantly shorter outbreaks than Strategy 14 , but no significant difference in outbreak size was found .
The most effective vaccination strategy ( smallest and shortest outbreaks ) assessed in this study for all population structures was Strategy 27 ( 90–90–90% of all subpopulations ) . However , to achieve this coverage during an outbreak would require a considerable amount of resources and labour , likely limiting in a remote area such as the NPA . Also , 70% population coverage has been shown to effectively limit rabies spread and potentially lead to elimination [8–9] . Therefore , a 90% population coverage may not be necessary . Although the 70% coverage recommendation for halting rabies transmission in Coleman and Dye [8] is based on epidemic outbreak data from the United States [23] , Malaysia [24] , Indonesia [25] and Mexico [26] , it has been mainly used and assessed in rabies endemic regions . This level of coverage has also been successful in limiting spread following the 2008 Bali rabies outbreak [11] . Given the adoption of 70% vaccination coverage as a recommended standard for rabies control , it was used in the present study to compare the effectiveness of alternative strategies . This study provides theoretical evidence that targeting subpopulations of dogs at higher risk–explorer or roamer dogs–instead of stay-at-home dogs could be more resource efficient than the recommended 70% coverage ( i . e . no targeting of subpopulations; Strategy 14 ) . The efficiency of a vaccination strategy to perform similarly to Strategy 14 ( 70–70–70 ) with fewer resources depended on whether it was explorer dogs , roamer dogs , or both categories that were targeted for higher vaccination coverage rather than stay-at-home dogs . For example , in the FP and SD population structures , targeting both explorer and roamer dogs at either 70% or 90%–regardless of the stay-at-home coverage–gave similar outbreaks to Strategy 14 with a lower overall vaccination coverage . In the ED population explorer dogs had to be targeted at 70% coverage and either roamer or stay-at-home dogs also at 70% coverage for the strategy to be similar to Strategy 14 with fewer resources . Finally , in the RD population , roamer dogs had to be targeted with 70% coverage and either explorer or stay-at-home dogs also with 70% coverage for the strategy to be similar to Strategy 14 . These findings are similar to those of Laager et al . [20] in which targeted vaccination strategies for high-risk dogs ( high betweenness centrality , large area roamed , and high degree centrality ) were more effective at reducing outbreak probability and outbreak size compared to a random vaccination strategy when modelled in a network simulation of rabies spread in Chad [20] . The success of targeting explorer and roamer dogs versus stay-at-home dogs in the SD and FP population structures suggests that it is more efficient to target high-risk dogs rather than the most abundant type of dogs . This is consistent with output from another rabies model developed for Tanzania , which compared vaccination strategies that either prioritised the largest subpopulations for vaccination or subpopulations that would cause the largest reduction in global risk [19] . For the high-risk vaccination strategies , sub-populations had risk scores defined by multiple factors , including probability of rabies occurrence in each sub-population , spatial arrangement of sub-populations , distance to other possible sources of infection and population size . Conversely , only population size was a factor for population prioritisation of vaccination . In this study , it was found that the high-risk prioritised vaccination strategies reduced mean rabies occurrence by 33 . 4% from an unvaccinated population compared to the population size prioritised strategy , which reduced mean rabies occurrence by 16 . 9% . In the risk-prioritised strategy , the largest subpopulation received no vaccine because it would cause the smallest reduction of global risk compared to other high-risk , smaller subpopulations [19] . The results in the current study , in conjunction with previous modelling studies , provide theoretical evidence that targeting high-risk dogs ( explorer and roamer dogs ) is likely to be a more resource efficient approach in the event of a rabies outbreak in the NPA . The results can therefore be used by decision makers to develop a reactive vaccination policy by providing justification for further investigations into operational logistics to practically implement these findings , or direct investigations on larger dog populations in other geographical regions to test if these findings can be generalised . Previous studies have suggested that far-roaming dogs ( explorer dogs in our study ) are important for disease spread , including rabies [27–29] . Hudson et al . [6] demonstrated that rabies infection in explorer dogs can result in fast developing and large outbreaks , but suggested that “mid-roaming” ( roamer ) dogs are also important for rabies spread . Explorer dogs roam away from their owner’s residence frequently whereas roamer dogs mostly remain around their owner’s residence , only roaming away occasionally . Further to Hudson et al . [6] , this study also demonstrates the importance of roamer dogs . For example , in the RD population , Strategy 7 ( 50–90–50 ) –which has an overall vaccination coverage of 74%–performs significantly better than Strategy 14 but Strategy 23 ( 90–70–70 ) –which also has an overall vaccination coverage of 74%–only reduces outbreak duration but not size . This demonstrates targeting roamer dogs for vaccination was more important than targeting explorer dogs when comparing strategies with similar vaccination coverages . This differs from the other population structures modelled in which the level of explorer dog , and sometimes roamer dog , vaccination was important when comparing strategies with similar overall population vaccination coverage . Also , not only do roamer dogs influence spread within the dog population , they are likely to have more human contact than explorer dogs due to the relative longer time spent at home [5] , and could be an important source of human infection . Therefore , targeted vaccination of explorer and roamer dogs could be important to reduce the risk of transmission in both the dog and human population during an outbreak . In the current response plan for a rabies outbreak in Australia ( AUSVETPLAN ) , there are no specific strategies for implementing a vaccination campaign , rather the goal should be to vaccinate as many dogs as possible [30] . This could make Australia vulnerable to an increased risk of delayed rabies control . For example , centre point vaccination stations and blanket house-to-house vaccination strategies are commonly used in rabies infected areas [10 , 11 , 18 , 31] . If these general strategies were implemented as a response to a rabies outbreak in the NPA ( our study area ) , stay-at-home dogs would more likely be presented at vaccination points or vaccinated at their residence because they are more accessible and available ( compared to explorer and roamer dogs ) [5] . According to the results in this study , inadvertently targeting stay-at-home dogs versus explorer and roamer dogs–e . g . Strategy 6 ( 70–50–90 ) and Strategy 12 ( 70–50–90 ) –would require a higher overall vaccination coverage than 70% , and subsequently more resources , to be as effective as Strategy 14 ( 70–70–70 ) . For example , Strategy 12 ( 70–50–90 ) in the SD population had a higher overall vaccination coverage than Strategy 14 ( 78% and 70% , respectively ) , but produced similar outbreaks to Strategy 14 , and therefore was not more effective . Furthermore , a lack of acceptable means of identifying vaccinated animals could exacerbate the problem [30] . Without identification of vaccinated dogs , resources could be wasted revaccinating or culling vaccinated dogs [11] . Common identification methods include collars or microchips . However , microchipping would be laborious and time consuming and collars are rarely used and are known to be impractical in the NPA ( a high percentage of collar loss has been reported when these were used for GPS studies [5] ) . Therefore , preparedness against rabies in the NPA can be improved by considering targeted vaccination strategies in emergency planning as well as by investigating effective and practical vaccination identification methods . The population structure in terms of roaming category proportions in the NPA is unknown . Due to the general success of strategies that target explorer and roamer dogs versus stay-at-home dogs across the various population structures modelled in this study , the evidence suggests that in the case of an unknown population structure , targeting explorer and roamer dogs would increase the success of a vaccination campaign to control the spread of rabies with limited resources . However , it is difficult to determine the roaming category of dogs without GPS data collection and analysis . There are a few studies that investigated the influences and predictors ( such as sex and reproductive status ) of roaming for free-roaming domestic dogs [28 , 29 , 32] . This research needs to be expanded to include the predictors of roaming without reliance on extensive GPS studies so that vaccination campaigns can be better targeted . However , the practical recommendations from this study is to vaccinate dogs that are currently roaming in the NPA , which would , by definition , target the roamer and explorer dogs instead of stay-at-home dogs . Therefore , detailed knowledge of individual dogs’ roaming category might not be required for effective vaccination targeting . A potential vaccination method could be vaccination baits in strategic areas which would indirectly target roaming dogs ( explorer and roamer dogs ) instead of stay-at-home dogs , without prior knowledge of roaming category , which has also been suggested by Laager et al . [20] . Some studies have shown oral vaccinations to be an effective complementary method to parenteral vaccinations in dog populations that are free-roaming [33–36] , and could be considered when developing vaccination campaigns for the free-roaming dog populations of the NPA . Cost efficiency was not considered in this analysis because the main aim was to explore resource efficient vaccination strategies in an area which is resource poor and provide some information on resource allocation that still effectively reduces rabies spread . The theoretical results in this study–targeting explorer or roamer dogs versus stay-at-home dogs–can be used to justify and direct future studies such as a cost-benefit analysis to help develop implementation of cost-efficient targeted reactive vaccination campaigns . For example , a recent study in India found that oral bait hand-outs vaccinated 35 dogs/person/day and was more cost-efficient when compared to a capture-vaccinate-release method ( 9 dog/person/day ) for vaccinating roaming dogs that were difficult to handle or not at the owner’s residence [37] . The dog roaming categories were randomly assigned in the model allowing for multiple roaming categories per house . Roaming category is likely clustered by house–there is likely a higher probability of dogs in the same house to have the same roaming behaviours . There is some evidence that spatial clustering within a population could influence a rabies outbreak and vaccination campaigns [19 , 38] . However , the dog populations in these studies are modelled as metapopulations with spatially separate sub-populations , rather than dogs relatively spatially homogenous but clustered by individual traits like roaming behaviour . The current model is spatially explicit for individual dogs . Therefore , further studies should investigate the effects of clusters of dogs with the same roaming category on the predicted spread of rabies . Alternatively , a network model might be more suitable to capture the complexities of the contact structure and roaming category distribution because it can account for individualised contacts and pack roaming , unlike the current model . A social network model has been developed for three islands in the Torres Strait [39] . However , the information and resources needed to create a social network model in the NPA would be vast due to the large dog population ( approximately 813 dogs ) [4] compared to 40–45 dogs on these small islands [40] . The results in this study apply to a steady-state dynamic population . The birth and death rate used in this current study were modest ( median number of dogs born and naturally died in all outbreaks = 40 ) . However , in dog populations with high turnover or high growth populations herd immunity wanes because of influxes of susceptible dogs which can hinder elimination campaigns [15 , 17 , 41] . Therefore , in a growing or high turnover population , the outbreaks will likely be larger and longer ( only 4% of all outbreaks lasted longer than a year in this study ) and the vaccination strategies might perform differently–overall vaccination coverage might not be achieved with targeted strategies , or targeting explorer and roamer dogs could be even more critical to stop rabies spread . Some studies have modelled vaccination campaigns in high turnover dog populations and have found that annual vaccination with 70% coverage is sufficient to maintain herd immunity above a critical level [42 , 43] . However , these studies were performed on rabies endemic populations . Therefore , further research into the effects of targeted vaccination campaigns on a growing or high turnover population during an epidemic is needed . This study provides promising theoretical evidence that targeting explorer and roamer subpopulations of dogs for vaccination campaigns instead of stay-at-home dogs could be more efficient than a random 70% overall vaccination coverage to control a rabies outbreak in the NPA . Practically , this means targeting dogs roaming during an outbreak ( explorer and roamer dogs ) for vaccination instead of more accessible or potentially more abundant non-roaming dogs ( stay-at-home dogs ) that stay around their owner’s residence . This information can be used to inform decision makers on potential resource efficient control measures in the event of a rabies incursion in the NPA , which can be used further to develop reactive vaccination strategies to improve Australia’s preparedness plans . How to efficiently target roaming dog subpopulations for vaccination in the field remains a challenge . There is a need for more research into novel vaccine delivery systems–for example , oral baits–that might allow roaming domestic dogs to be targeted to control rabies . This study and other modelling studies [19–20] can be used to direct further investigations into the usefulness of targeted vaccination in other regions to test the generalisation of the current findings . | Australia is currently free of canine rabies . However , northern communities are at risk of an incursion . The free-roaming dogs in the at-risk communities of the Northern Peninsula Area , Queensland , Australia , display various roaming behaviours which affect how they interact with each other and subsequently affects how rabies could be spread within the population . The presence of these different roaming behaviours provides an opportunity to assess targeted vaccination strategies . Using a rabies spread model , this study provides evidence that targeting roaming dogs ( that cause rapid rabies spread ) for vaccination instead of dogs that remain around their owner’s residence , can be more efficient than the recommended overall 70% vaccination coverage to control rabies . Furthermore , if dogs that stay at home most of the time are targeted versus other roaming dogs , a higher overall coverage and more resources would be required to control a potential rabies outbreak than the overall 70% vaccination coverage . Comparing various vaccination strategies to find efficient and effective response options is beneficial to contribute to emergency planning and increase preparedness to control a rabies outbreak , should it occur . | [
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| 2019 | Modelling targeted rabies vaccination strategies for a domestic dog population with heterogeneous roaming patterns |
Pregnancy and parturition are intricately regulated to ensure successful reproductive outcomes . However , the factors that control gestational length in humans and other anthropoid primates remain poorly defined . Here , we show the endogenous retroviral long terminal repeat transposon-like human element 1B ( THE1B ) selectively controls placental expression of corticotropin-releasing hormone ( CRH ) that , in turn , influences gestational length and birth timing . Placental expression of CRH and subsequently prolonged gestational length were found in two independent strains of transgenic mice carrying a 180-kb human bacterial artificial chromosome ( BAC ) DNA that contained the full length of CRH and extended flanking regions , including THE1B . Restricted deletion of THE1B silenced placental CRH expression and normalized birth timing in these transgenic lines . Furthermore , we revealed an interaction at the 5′ insertion site of THE1B with distal-less homeobox 3 ( DLX3 ) , a transcription factor expressed in placenta . Together , these findings suggest that retroviral insertion of THE1B into the anthropoid primate genome may have initiated expression of CRH in placental syncytiotrophoblasts via DLX3 and that this placental CRH is sufficient to alter the timing of birth .
The complex process of completing gestation and initiating parturition must be tightly controlled to prevent the dangerous consequences of preterm or postterm birth to the mother and offspring . In humans , corticotropin-releasing hormone ( CRH ) production by the placenta increases exponentially with gestational age , predicting the onset of parturition [1] . This exponential increase in placental CRH production has been observed earlier in the pregnancy when that pregnancy was destined to end in preterm birth and later in the pregnancy for postterm birth , indicating that placental CRH may play a role in the timing and onset of labor [1–4] . The peptide hormone CRH has a well-characterized role in the hypothalamic-pituitary-adrenal axis that is highly conserved in vertebrates [5] . However , its expression in the placenta is observed only in anthropoid primate species , consistent with evolutionary changes in placental gene regulation [6–14] . One major driver of evolutionary diversity in gene regulation is the class of mobile DNA sequences known as transposable elements [15 , 16] . Recent advances in genome sequencing , high-throughput screening for active chromatin , and computational resources facilitating comparative genomic analysis have made it possible to identify lineage-specific DNA sequences with signs of regulatory activity , many of which are derived from transposable elements [17–21] . In a recent study , species-specific enhancers active in early placental development were found to be highly enriched for long terminal repeats ( LTRs ) , a subset of transposable elements derived from retroviruses [22] . LTR regions of retroviruses utilize available host factors to recruit transcription machinery and produce virus , and isolated LTRs have been reported to function as promoters or enhancers of the host’s genes [23–28] . To understand whether similar co-option of LTRs into the host’s function has occurred in placental regulation of CRH expression , we examined the vicinity of the CRH gene for elements that are conserved in anthropoid primates but not present in species without placental CRH expression . We identified a retroviral LTR element of the transposon-like human element 1B ( THE1B ) family , which invaded the anthropoid primate genome approximately 50 million years ago [29] . We hypothesized that the introduction of this retroviral LTR element into the genome of anthropoid primate common ancestors initiated expression of CRH in placental tissue . Here , we tested this hypothesis by introducing the human CRH locus into transgenic mouse lines and selectively editing regions of a nearby THE1B LTR element to control the expression of human CRH in mouse placental tissue . We show that this LTR element interacts with distal-less homeobox 3 ( DLX3 ) , a transcription factor ( TF ) required for placental development [30] . Transgenic mice expressing human CRH in their placentas exhibited significant changes in their length of gestation , suggesting that placental CRH expression is a potential mechanism of controlling the timing of birth that is unique to anthropoid primates .
Because CRH is expressed in placenta of anthropoid primate species only , we hypothesized that any potential regulatory DNA sequence would be conserved only in the anthropoid primate lineage . We compared the genomes of 12 anthropoid primates , 3 prosimians , and 3 nonprimate mammals in the region of CRH and identified an endogenous retroviral LTR of the THE1B type that correlated with placental CRH expression ( Fig 1A ) . THE1B LTR elements have been shown to activate transcription under certain conditions [31 , 32] . To determine activity of this THE1B element in anthropoid primate placenta , we utilized a PCR screening system to amplify transcripts containing both THE1B and the coding exon of CRH from placental tissue . Both human and rhesus macaque placentas expressed a detectable fusion transcript with an identical splice junction connecting sequence downstream of THE1B to noncoding exon 1 of CRH ( Fig 1B ) . This novel splice junction is conserved in the primate species included in Fig 1A , suggesting a possible role for THE1B as a regulatory element of CRH in the placenta ( S1 Table ) . The THE1B-CRH fusion transcript was detectable by PCR screening but not detectable by RNA sequencing ( RNA-seq ) from term human placenta , indicating low abundance relative to total CRH expression ( Fig 1B ) . To better detect potential THE1B-CRH fusion transcripts , we utilized a capture sequencing approach in which we first enriched for cDNAs containing THE1B elements using a library of biotinylated complementary RNA probes that could be pulled down with streptavidin beads prior to deep sequencing . Despite obtaining greater than 1 , 000-fold enrichment of THE1B-containing transcripts , we remained unable to detect any chimeric reads linking THE1B and CRH . In contrast , we found abundant reads transcribed in the antisense direction through the THE1B element ( Fig 1B ) . Because these reads are unidirectional and within 2 kb of the CRH promoter region , it is likely that these reads were initiated from a bidirectional CRH promoter . In sum , the comparative genomics and RNA-seq data led us to hypothesize that the anthropoid-specific THE1B element serves as an enhancer , not an alternative promoter , of the CRH gene . Previous studies have implicated endogenous retroviral LTR elements as promoters and enhancers that drive placenta-specific gene expression [22 , 33–38] . To examine the effect of THE1B LTRs on placental expression of genes , including CRH , we first defined a set of genes that had a THE1B element within 10 kb of the transcription start site . This set of THE1B-associated genes contained 2 , 311 gene–THE1B pairings , encompassing about 10% of THE1B elements in the human genome . We then compared the expression pattern of THE1B-associated genes across different tissues using a relative coexpression analysis as described by Pavlicev and colleagues [33] . Briefly , the analysis was run on all expressed genes and tested whether the THE1B-associated genes were equally correlated in their expression between a focal tissue and each of 17 other tissues than expected for a random size-matched subset of THE1B-unassociated genes . Consistently lower correlations in all tests involving a tissue of interest imply that some feature of THE1B-associated genes confers the particular expression status in that particular tissue , and in fact , the relative coexpression of THE1B-associated genes in tests involving placenta was significantly lower than for a random gene set ( Fig 1C ) . An association between a THE1D element and placental expression of a single gene has been reported [39]; however , Pavlicev and colleagues found no change in relative coexpression of genes associated with other THE1 family members in placenta [33] . In a second analysis , we quantified gene expression from RNA-seq of human and mouse tissues to identify genes with human-specific , placenta-enriched gene expression . We then examined whether these differentially expressed genes were enriched for THE1B elements within 20 kb of their promoters relative to a control set of genes . Indeed , we found a significant association of THE1B elements with human placenta–enriched genes ( Fig 1D ) . Taken together , these data show that genomic presence of THE1B is associated with differential expression of nearby genes in anthropoid primate placenta . Enrichment of histone H3 lysine 27 acetylation ( H3K27ac ) or histone H3 lysine 4 monomethylation ( H3K4me1 ) , histone modifications associated with classical enhancers , was not detected at the THE1B upstream of CRH in chromatin immunoprecipitation sequencing ( ChIP-seq ) analyses performed on human placental tissue; however , other genomic THE1B elements were associated with these modifications ( S1 Fig ) . In order to study the regulation of CRH in placenta , we created a novel mouse model by incorporating a bacterial artificial chromosome ( BAC ) with the human CRH gene and approximately 180 kb of flanking sequence , including the THE1B LTR , into the genome of FVB/N mice ( Fig 2A ) . Random integration of this BAC resulted in two founder animals , denoted Tg ( BAC1 ) and Tg ( BAC2 ) , which were bred independently to C57BL/6 mice and maintained as separate lines . We then tested for the presence of human CRH in adult tissues and determined that expression of CRH in Tg ( BAC1 ) /+ and Tg ( BAC2 ) /+ animals is remarkably specific to the hypothalamus and placenta at embryonic day 18 . 5 ( E18 . 5 ) ( Fig 2B ) . No human CRH was detected in any tissues of nontransgenic littermates . As expression of CRH in human placenta is known to correlate with timing of parturition [1] , we examined the timing of birth in Tg ( BAC1 ) and Tg ( BAC2 ) mouse lines . When compared with strain-matched control litters , Tg ( BAC1 ) /Tg ( BAC1 ) litters were born an average of 14 . 9 hours later ( one-way ANOVA with Tukey post hoc , P < 0 . 0001; Fig 2C ) , and Tg ( BAC2 ) /Tg ( BAC2 ) litters were born an average of 9 . 3 hours later ( one-way ANOVA with Tukey post hoc , P = 0 . 0006; Fig 2C ) , suggesting that placental expression of human CRH is sufficient to alter gestation length in mice . This effect of CRH appears to be independent of progesterone withdrawal , as no differences were detected in maternal serum progesterone or in uterine expression of the contractile-associated proteins oxytocin receptor ( Oxtr ) , connexin-43 ( Gja1 ) , caveolin-1 ( Cav1 ) , cyclooxygenase 1 ( COX-1; Ptgs1 ) , or cyclooxygenase 2 ( COX-2; Ptgs2 ) at E18 . 5 ( S2 Fig ) . A significant decrease in prostaglandin F2α was noted in uterine tissue of Tg ( BAC1 ) /Tg ( BAC1 ) litters at E18 . 5 ( S2 Fig ) . To eliminate the possibility that this alteration of gestation length was a result of hypothalamic human CRH expression , we repeated these experiments with C57BL/6 ( nontransgenic ) mothers . Wild-type mice delivering Tg ( BAC1 ) /+ litters completed gestation an average of 11 . 0 hours later than strain-matched control litters ( unpaired two-tailed t-test , P = 0 . 0003; Fig 2D ) , associating the delayed parturition phenotype with the genotype of the fetal-derived placenta . To determine if retroviral LTR THE1B is necessary for placental CRH expression , we used the clustered regularly interspaced short palindromic repeat/CRISPR-associated 9 ( CRISPR/Cas9 ) system to specifically delete THE1B from the genome of Tg ( BAC1 ) mice . We utilized two guide RNAs targeting the 5′ end of THE1B and the 3′ end immediately flanking the 366-bp THE1B sequence such that the THE1B element would be deleted by nonhomologous end joining of the surrounding sequence ( Fig 2E ) . Microinjection of guide RNAs and Cas9 mRNA into the cytoplasm of the zygotes generated by Tg ( BAC1 ) /+ crosses resulted in two founder animals with deletions greater than 300 bp . Sanger sequencing of the deletions revealed that the larger deletion , termed Tg ( CR1 ) , completely lacked the THE1B element ( Fig 2F ) . The other deletion , termed Tg ( CR2 ) , contained 12 bp at the 5′ end of THE1B that were deleted in Tg ( CR1 ) . This 12-bp sequence is comprised of 2 bp of THE1B sequence and 10 bp immediately upstream of the THE1B element ( Fig 2F ) . Next , we measured the mRNA expression of human CRH in placenta of Tg ( CR1 ) /+ and Tg ( CR2 ) /+ animals at E18 . 5 . Complete deletion of THE1B in the Tg ( CR1 ) line abolished placental expression of human CRH ( one-way ANOVA with Tukey post hoc , P = 0 . 0006 compared to Tg[BAC1]; Fig 2G ) . The Tg ( CR2 ) line retains 2 bp of the 5′ end of THE1B and subsequently expressed about 20% human CRH relative to the Tg ( BAC1 ) parent line ( one-way ANOVA with Tukey post hoc , P = 0 . 0035; Fig 2G ) . We next examined the effect of THE1B deletion on the other primary site of CRH expression , the hypothalamus . As expected , deletion of THE1B did not abolish hypothalamic expression of human CRH in either transgenic line . Complete deletion of THE1B in the Tg ( CR1 ) line appeared to increase expression of human CRH in the hypothalamus ( one-way ANOVA with Tukey post hoc , P = 0 . 0033 compared to Tg[BAC1] , P = 0 . 0013 compared to Tg[CR2]; Fig 2H ) . We detected compensatory down-regulation of mouse Crh in hypothalamus , resulting in no difference in serum corticosterone relative to nontransgenic mice ( S3 Fig ) . We also examined the effect of THE1B deletion on the other protein-coding gene contained on the BAC , TRIM55 , which is a ubiquitin E3-ligase expressed in striated muscle [40 , 41] . TRIM55 expression was detectable by RNA-seq in Tg ( BAC1 ) placenta but not in Tg ( CR1 ) placenta at E18 . 5 , demonstrating that THE1B elements may control expression of multiple genes in a region ( S4 Fig ) . To further interrogate the role of placental CRH in delayed onset of parturition , we examined the gestation length of Tg ( CR1 ) and Tg ( CR2 ) animals . The gestation length of litters homozygous for either Tg ( CR1 ) or Tg ( CR2 ) was not significantly different from strain-matched control litters ( one-way ANOVA , P = 0 . 0568; Fig 2I ) , demonstrating that complete deletion of THE1B and subsequent lack of placental CRH rescued the extended-gestation phenotype of the Tg ( BAC1 ) parent line . Tg ( CR2 ) /Tg ( CR2 ) litters had unusual variability in their gestation length , which could be due to the low level of expression of CRH from the residual 5′ insertion site of THE1B ( Fig 2G ) . Despite this variability , Tg ( CR2 ) /+ litters showed no differences in birth timing relative to control litters ( S5 Fig ) . Transposable elements , especially those arising from retroviral LTRs , can activate gene expression by recruiting binding of available TFs [18 , 22] . To determine the pool of TFs present in anthropoid primate placenta when CRH is known to be highly expressed , we analyzed placental transcriptomes generated by RNA-seq from two human and two rhesus macaque placentas near term . We identified a set of 90 TFs expressed at 10 transcripts per million ( TPM ) or greater in all samples , which we defined as moderately expressed TFs , and proceeded to examine potential binding of these TFs to the THE1B sequence with the prediction tool CisBP [42] . Two families of TFs known to contribute to placental trophoblast differentiation , GATA [43] and DLX [44] , were predicted by CisBP to interact with the 5′ end of the THE1B sequence that was retained in Tg ( CR2 ) but deleted in Tg ( CR1 ) . When tested by gel shift assay , DLX3 , but not GATA2 , was able to bind the 5′ end of THE1B ( Fig 3A ) . This DLX3 binding site is located at the junction of the THE1B element and the surrounding DNA ( Fig 3B ) . We then performed ChIP with anti-DLX3 antibody in human term placental tissue and quantified by real-time PCR to determine the occupancy of this DLX3 binding site in vivo . DLX3 was significantly associated with the 5′ end of THE1B and with a previously described placental regulatory element [45] , when compared to a negative control region ( one-way ANOVA with Dunnett’s post hoc , P < 0 . 0001 for THE1B and P = 0 . 0184 for human glycoprotein hormone α-subunit junctional regulatory element [JRE] relative to JRE distal; Fig 3C ) . In human placental tissue , the syncytium is the site of CRH expression and secretion into maternal and fetal circulations [48 , 49] . We demonstrated localization of human CRH to the labyrinth of the Tg ( BAC1 ) /+ mouse placenta , which contains mouse syncytiotrophoblasts ( Fig 3D ) . We also confirmed by quantitative PCR ( qPCR ) that expression of human CRH was absent in the junctional zone ( Fig 3E ) . Despite differences in placental morphology between human and mouse , DLX3 is produced in both human [50] and mouse [47] syncytiotrophoblasts . In Tg ( BAC1 ) /+ placenta , 80%–99% of total Dlx3 expression was localized to the labyrinth tissue ( n = 5 Tg[BAC1]/+ , P = 0 . 0003 by paired two-tailed t-test; Fig 3E ) .
Here , we present a case of anthropoid primate–specific placental gene expression induced by retroviral LTR insertion into the genome . Our study provides evidence that an LTR element found in a separate evolutionary lineage is capable of operating as a novel placental enhancer in another species . Placental tissue is globally hypomethylated and remarkably hospitable to endogenous retroviral elements [51 , 52] . Endogenous retroviral LTRs have been co-opted for placenta-specific expression of hormones [35] , endothelial factors [37 , 53] , and immune receptors [39] . Several of the THE1B-associated genes in our study that are expressed at significantly higher levels in human relative to mouse placenta have been previously implicated in human placental function , including the regulation of birth timing ( CGA [54] , CRH [1] ) and adverse pregnancy outcomes like preeclampsia ( ADAM12 , PGF [55] , ZFAT [56] , TUSC3 [57] ) and recurrent miscarriage ( ADM [58] ) . These findings suggest that THE1B elements likely influence the expression of a network of genes in human placenta and that this network of THE1B-associated genes may contribute to proper placental function . Despite several lines of evidence implicating THE1B as an enhancer for CRH , we did not detect classical enhancer chromatin marks ( H3K27Ac , H3K4me1 ) on this element in human term placental samples . It is possible that the THE1B near CRH enhances transcription using a different mechanism than the majority of vertebrate enhancers or , alternatively , that the positive enhancer signal is being diluted by the cells within the placenta that do not express CRH . Previous studies of the species specificity of placental CRH expression have predominantly focused on the proximal promoter sequence of CRH [59 , 60] . Scatena and Adler reported no expression of a luciferase reporter construct containing the 5-kb upstream region including THE1B in rat choriocarcinoma cells; this and other experiments led them to conclude that the species specificity of placental CRH expression was caused by trans-acting factors [59] . Our BAC transgenic mouse model incorporates both the human CRH and the mouse Crh promoter sequences and flanking regions; thus , differences in expression in this model are due to sequence differences rather than trans-acting factor availability . We observed differences in CRH expression when the THE1B sequence was intact ( Tg[BAC1] and Tg[BAC2] ) , partially intact at the 5′ end ( Tg[CR2] ) , and absent ( Tg[CR1] ) , suggesting that the 5′ end of the THE1B LTR and its upstream insertion site are disproportionally active in the regulation of CRH in placenta . The partially intact region of THE1B in Tg ( CR2 ) mice contains an active binding site for the TF DLX3 . DLX3 is present in mouse placenta and required for trophoblast differentiation; mice lacking Dlx3 are unable to develop past embryonic day 10 due to placental failure [30] . The coexpression of Dlx3 and CRH in Tg ( BAC1 ) mouse placenta further associates DLX3 availability and THE1B-dependent expression of CRH . Notably , the DLX3 site occurs where the THE1B element joins the surrounding sequence , suggesting a possible mechanism for activation of this particular LTR upon retroviral insertion into the anthropoid primate common ancestral genome . Our study is the first , to our knowledge , to provide evidence that placental expression of CRH in a nonprimate model alters gestation length . Surprisingly , human CRH expression in the placentas of our transgenic mice resulted in postterm rather than preterm birth . Although uterine prostaglandin F2α was significantly lower in Tg ( BAC1 ) animals , progesterone withdrawal and expression of contractile-associated proteins were unaffected , indicating that CRH may alter birth timing without impeding luteolysis . Previous studies have shown that CRH plays a role in myometrial quiescence , inhibiting myometrial contractility at low CRH concentration [61–63] . This inhibition of uterine contractility is consistent with the postterm birth effect seen in our mouse model . These data suggest that CRH may be a factor contributing to the extended gestational period of anthropoid primates and that increased production of CRH in pregnancies ending prematurely may be an attempt to block the progression toward preterm labor and delivery . The extraordinary diversity found in eutherian placental gene expression is thought to result from conflict between mother and fetus in determining optimal conditions for fetal development and maternal investment [64–66] . Placental tolerance to LTRs may play a role in this ongoing evolutionary conflict by providing a mechanism for rapid changes in gene expression . Our study demonstrates that retroviral insertion of an LTR such as THE1B can alter gene expression at the level of an individual gene and potentially across the entire placental tissue . Conservation of both THE1B and placental CRH expression in anthropoid primate species and the correlation of maternal serum CRH concentrations with gestation length support our assertion that the THE1B-CRH regulatory system is critical for birth timing .
Human ( GEO accession: GSE87726 ) and rhesus macaque ( Macaca mulatta; GEO accession: GSE118284 ) placental transcriptomes were generated as previously described [67 , 68] . Briefly , two human placentas were collected by cesarean section at 39 weeks 1 day and 39 weeks 2 days gestational age ( IRB protocol: CCHMC IRB 2013–2243 ) . Two macaque placentas were collected by cesarean section at 128 days and 131 days gestational age ( 80% completed gestation ) . RNA was extracted from placental biopsies with the TRIzol reagent ( ThermoFisher Scientific ) according to the manufacturer’s instructions . After passing initial quality control metrics , RNA-seq of the four samples was performed on an Illumina HiSeq machine using a paired-end approach with 50-bp reads , generating approximately 30 million paired reads per sample ( human ) and 15 million paired reads per sample ( macaque ) . The reads were aligned and pseudocounted using kallisto [69] . To make the two transcriptomes comparable , we used the set of genes that are orthologous between the two species and recalculated the levels of expression of single genes in each species based only on this set . The levels are expressed on a TPM scale [70] . Human and rhesus placental RNA was converted to cDNA using the QuantiTect Reverse Transcriptase Kit ( Qiagen ) according to manufacturer’s instructions . PCR screening was performed with primers specific to THE1B and CRH exon 2 ( forward: 5′-CCTCCCCTACCATGTGAAAA-3′ , reverse: 5′-GGAAGAAATCCAAGGGCT-3′ ) as well as with internal primers downstream of THE1B and in CRH exon 1 ( forward: 5′- TGCTGTGCATAGCTTCTCCTC-3′ , reverse: 5′-TGCCTCTGCTCCTGCATAAA-3′ ) . PCR products were examined by agarose gel electrophoresis and confirmed by Sanger sequencing . The statistical test for the effect of the presence of THE1B in the vicinity of the gene on the tissue-specific expression is described in detail by Pavlicev and colleagues [33] . Briefly , we compared the correlation between tissues based on the expression ( expressed as the square root of the TPM value ) of THE1B-associated genes to the correlation between tissues based on THE1B-absent genes . We repeated this procedure for all pair-wise tissue combinations . If the THE1B elements confer no particular status to the genes , this ratio corrTHE1B+/corrTHE1B− should be close to 1 for every pair-wise tissue comparison . The significance of the effect was assessed based on the null distribution of the statistics for each pair of tissues compared , generated by randomly resampling 5 , 000 times the set of genes size matched to the number of THE1B-associated genes and calculating the statistics . Total RNA was purified from term placentas using a combination of TRIzol and Qiagen columns . RNA-seq libraries were prepared using the SureSelect Strand-Specific RNA Library Prep for Illumina Multiplexed Sequencing kit ( Agilent ) . For capture sequencing , preamplified RNA-seq libraries were enriched for THE1B containing transcripts using a custom panel of 120 nt THE1B RNA oligos and the SureSelectXT RNA Target Enrichment kit ( Agilent ) . Libraries were sequenced as 75-bp paired-end reads on an Illumina HiSeq machine . Reads were aligned to the human genome ( hg19 ) using Tophat2 ( -I 100000—max-segment-intron 100000 -r 300 ) [71] . Data are available at GEO accession GSE118289 . We compiled a collection of RNA-seq data for different human and mouse tissues from different resources , including ( 1 ) newly generated ( GEO accession: GSE118289 ) and public ( GEO accession: GSE43520 ) data for human placenta , ( 2 ) data for mouse placenta ( GEO accession: GSE43520 ) , and ( 3 ) data for different human tissues ( brain , heart , kidney , liver , lung , ovary , skeletal muscle , and testis ) from BodyMap 2 . 0 ( GEO accession: GSE30611 ) . Reference genome and gene annotation files for human ( GRCh38 ) and mouse ( GRCm38 ) were downloaded from the ENSEMBL database . Human placenta–enriched genes were identified by a procedure comparing human placenta against other human tissues and against mouse placenta . In brief , we first quantified gene expression levels for different samples as TPM using RSEM [72] . Then , we focused the analysis on the 13 , 068 1-to-1 orthologous genes between human and mouse according to ENSEMBL orthologue annotation . After the TPM values were normalized among different samples using the trimmed mean of M-values ( TMM ) method [73] , differential expression analyses between human placenta and other analyzed human tissues and mouse placenta were performed using Rank Product method [74] , with an FDR cutoff of 0 . 15 . Finally , human placenta–enriched genes were annotated as genes that show significantly higher expression in human placenta against other human tissues and mouse placenta . We obtained repeat annotations for human THE1B subfamily from the RepeatMasker website ( http://www . repeatmasker . org/ ) on May 27 , 2016 . To determine if the THE1B subfamily is associated with human placenta–enriched genes , we first determined the overlapping between THE1B repeat elements and promoter ( defined as 20 kb upstream the longest transcript for each gene ) for each protein-coding gene using the windowBed function of bedtools [75] . Then , the occurrences of THE1B in all these genes and in human placenta–enriched genes were counted . Finally , Binomial Test was performed to test if the THE1B subfamily is significantly associated with human placenta–enriched genes . The Cincinnati Children’s Hospital Medical Center Institutional Animal Care and Use Committee approved all animal experiments for this study under IACUC protocol number 2017–0051 . Animal procedures were designed and executed according to the guidelines of the National Institutes of Health . Human BAC RP11-366K18 was obtained from the CHORI BACPAC repository . DNA was amplified and purified from bacteria using NucleoBond BAC kit ( Clontech ) and dissolved in polyamine buffer for microinjection . To generate lines Tg ( BAC1 ) and Tg ( BAC2 ) , FVB/N zygotes were used for pronuclear injection of purified BAC DNA at 0 . 5 ng/ul and transferred into pseudopregnant CD-1 mice . The resulting pups were genotyped for presence of the human BAC with primers specific to human CRH ( forward: 5′-TTTCTAATGTGAAAACTGCGTGAT-3′ , reverse: 5′-ACACGTGGGAATTATGGGGG-3′ ) . Transgenic lines were maintained as hemizygotes by crossing to C57BL/6 mice for 3–4 generations . To delete the THE1B in the BAC transgenic mice , we selected 6 guide RNAs targeting the THE1B flanking region ( 3 per end ) , based on the web design tool ( http://www . genome-engineering . org ) . Selected guide RNAs were cloned into pX458 vector ( addgene #48138 ) and validated in HEK293 cells by T7E1 assay as described by Ran and colleagues [76] . Two validated guide RNAs ( target sites: 5′-ATACATTTTGGATAATGATA-3′ and 5′-AGACAAATACAGATAAATTG-3′ ) , along with Cas9 mRNA , were introduced by cytoplasmic injection into zygotes obtained from Tg ( BAC1 ) mice [77] . Injected embryos were transferred into pseudopregnant CD-1 mice . The resulting pups were screened for deletions to the THE1B region with primers flanking THE1B ( forward: 5′-CAGTTTGTGTGCCTCTGCTG-3′ , reverse: 5′-CTCCCATTGGGCTATCTGGG-3′ ) by PCR and Sanger sequencing . Male mice were housed with females overnight to determine time of conception . Pregnant female mice were killed on the designated day of gestation , and placentas were harvested , dissected into labyrinth and junctional zone where indicated , and flash frozen in liquid nitrogen . Tissue from the tail of corresponding embryos was used to determine placental genotype with primers specific to human CRH , as described earlier . Hypothalamus , kidney , heart , liver , lung , and leg quadriceps muscle tissue were harvested from adult transgenic animals and littermate controls of both sexes , and tissues were immediately frozen in liquid nitrogen . Tissues were homogenized using stainless steel beads in a TissueLyser II apparatus ( Qiagen ) , and RNA was purified using the RNeasy Mini Kit ( Qiagen ) . Heart and skeletal muscle RNA was purified using the RNeasy Fibrous Tissue Mini Kit ( Qiagen ) . Labyrinth and junctional zone RNA was purified using the TRIzol reagent ( ThermoFisher Scientific ) according to manufacturer’s instructions . Mouse tissue RNA was converted to cDNA using the QuantiTect Reverse Transcriptase Kit ( Qiagen ) according to manufacturer’s instructions . Human CRH was quantified using the TaqMan system with TaqMan Gene Expression Master Mix ( ThermoFisher Scientific ) and specific probes for human CRH ( Hs01921237_s1 , ThermoFisher Scientific ) and for mouse Gapdh as endogenous control ( Mm99999915_g1 , ThermoFisher Scientific ) . Labyrinth and junctional zone expression was quantified with specific probes for human CRH ( Hs00174941_m1 , ThermoFisher Scientific ) , mouse Dlx3 ( Mm00438428_m1 , ThermoFisher Scientific ) , and eukaryotic 18S as endogenous control ( catalog 4310893E , ThermoFisher Scientific ) . A 50-ng cDNA template was used per well , and samples were run in triplicate . qPCR reactions were run on an Applied Biosystems StepOnePlus Real-Time PCR instrument . Placentas of E18 . 5 pups were fixed in 4% paraformaldehyde in PBS overnight at 4 °C , washed in PBS , and dehydrated in 70% ethanol before embedding in paraffin . Paraffin sections were cut using a microtome . Sections were deparaffinized , incubated in citrate buffer pH 6 . 0 for antigen retrieval , and incubated in methanol with hydrogen peroxide for peroxidase activity removal . Following washes , slides were incubated with 5% goat serum and 4% fish skin gelatin for 60 minutes . Slides were incubated with CRH primary antibody ( rabbit , Peninsula Lab T-4036 , 1:1 , 000 ) overnight at 4 °C . Following washes , slides were incubated with secondary antibody ( biotinylated goat anti-rabbit , Vector Lab BA-1000 , 1:250 ) for 60 minutes . Tyramide amplification was performed with TSA cyanine 5 reagent kit ( Perkin Elmer ) according to manufacturer protocol . Experimental animals and their respective control animals were generated by crossing hemizygous transgenic mice and subsequently crossing their homozygous offspring such that all study animals were the offspring of littermates . Male mice were housed with nulliparous female mice from approximately 1900 to 2200 hours once per week to precisely control time of conception . Pregnant females were monitored by observation from E17 . 5 until delivery . Pups were counted and weighed individually on the day of birth . Females were excluded from the study if there were fewer than four pups in the litter , if the female was older than 240 days of age on the date of conception , if the female was in labor for longer than 12 hours , or if the entire litter could not be delivered because of dystocia . Human and rhesus macaque placental transcriptome data were analyzed as previously stated . TFs expressed at 10 or greater TPM were populated into the predictive program CisBP [42] . Binding sites were predicted using the position-weight matrix ( PWM ) –LogOdds setting ( threshold 8 ) . Immortalized extravillous trophoblast cell line HTR8/SVneo ( obtained from the American Type Culture Collection , item number CRL-3271 ) was transfected with Lipofectamine3000 ( ThermoFisher Scientific ) and overexpression vector ( DLX3 or GATA2 Myc-DDK vectors , Origene ) for 72 hours according to manufacturer protocol . Cytoplasmic protein fraction was isolated by suspending cell pellet in cytoplasmic lysis buffer ( 10 mM HEPES , 10 mM KCl , 1 . 1 mM EDTA in dH2O ) and adding 10% Nonidet-P40 . Nuclear protein fraction was subsequently isolated by suspending nuclear pellet in nuclear lysis buffer ( 20 mM HEPES , 0 . 4 M KCl , 1 mM EDTA in dH2O ) . DNA probes were generated with IRDye700 at the 5′ end ( GATA2 TCR probe: 5′-CACTTGATAACAGAAAGTGATAACTCT-3′ [78] , DLX3 JRE probe: 5′-GGGGGGTAATTACAGGCCC-3′ [79] , THE1B 5′ probe: 5′-GGATAATGATATGGTTAGA-3′ ) . Binding reactions were performed using the Odyssey EMSA Buffer Kit ( LICOR Biosciences ) according to manufacturer protocol with primary antibody ( monoclonal anti-FLAG M2 , Sigma-Aldrich F3165 ) or isotype control ( mouse IgG , Abcam ab37355 ) and run on 6% TBE gels with 0 . 5× TBE buffer ( ThermoFisher Scientific ) . Gels were imaged on the Odyssey CLx Imaging System ( LICOR Biosciences ) . Human placental tissue was homogenized in aliquots of 100 mg tissue per mL PBS with protease inhibitor ( Abcam ab65621 ) . Homogenate was transferred to a new tube with 18-gauge needle and syringe to ensure single cell suspension . Crosslinking was performed by adding 1 . 5% formaldehyde to suspension and incubating at room temperature for 10 minutes . Glycine was added to a final concentration of 125 mM to stop crosslinking reaction . Cells were washed twice with PBS and resuspended in SDS Lysis Buffer ( Millipore ) . Cell lysate was sonicated to an average of 500-bp fragments with a Covaris S220 instrument at 175 peak power for 7 minutes . Immunoprecipitation was performed with the EZ-ChIP kit ( Millipore ) according to manufacturer protocol . Mouse anti-RNA pol II ( Millipore 05-623B ) and Control Mouse IgG antibodies ( Millipore 12-371B ) were used as a positive and negative control according to EZ-ChIP protocol . For DLX3 ChIP , rabbit anti-DLX3 ( Abcam ab66390 ) and rabbit IgG isotype control ( Abcam ab171870 ) were used at 5 μg per immunoprecipitation . qPCR reactions were run on an Applied Biosystems StepOnePlus Real-Time PCR instrument with primers specific to GAPDH ( forward: 5′-TACTAGCGGTTTTACGGGCG-3′ , reverse: 5′- TCGAACAGGAGGAGCAGAGAGCGA-3′ ) , JRE ( forward: 5′- TGACCTAAGGGTTGAAACAAGATAAG-3′ , reverse: 5′- GGAAATTCCATCCAATGATTGA-3′ ) [45] , distal region of JRE ( forward: 5′- AGTTTCTTTGTGGATGAAGAGATAGACG-3′ , reverse: 5′- TTTTCCGAACTTCAAAGGCCCTG-3′ ) [45] , and the 5′ end of the THE1B near CRH ( forward: 5′- TTTCTAATGTGAAAACTGCGTGA-3′ , reverse: 5′- ACACGTGGGAATTATGGGGG-3′ ) . Statistical analysis was performed with GraphPad Prism ( GraphPad Software ) and R Statistical Programming Language . Specific statistical tests are described above . | The proper timing of delivery is critical during pregnancy; if too early or too late , the baby will be at risk of serious health problems and even death . Corticotropin-releasing hormone ( CRH ) is a protein that can be detected in maternal blood , and its concentration correlates with the timing of birth . In humans and other anthropoid primates , CRH is made by the placenta , whereas in other mammals , it is produced in a specialized region of the brain . To understand the regulation and evolution of this key protein , we inserted the human CRH gene and nearby regions into the mouse genome , which resulted in human CRH expression in the mouse placenta . Mouse litters that make CRH in their placentas are born later than control mice , showing that CRH can directly affect birth timing . Using our mouse model , we then selectively deleted a remnant of an ancient retrovirus that is normally found in the DNA of anthropoid primates and demonstrated that this specific region controls expression of CRH in the placenta . Deletion of this region also restored normal birth timing in the mice by eliminating CRH production from the placenta . We propose that retroviral regulation of CRH in the placenta may be a mechanism of controlling birth timing in humans and other anthropoid primates . | [
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| 2018 | Anthropoid primate–specific retroviral element THE1B controls expression of CRH in placenta and alters gestation length |
Cyclic-di-GMP ( c-di-GMP ) is a ubiquitous bacterial signaling molecule that regulates a variety of complex processes through a diverse set of c-di-GMP receptor proteins . We have utilized a systematic approach to identify c-di-GMP receptors from the pathogen Vibrio cholerae using the Differential Radial Capillary Action of Ligand Assay ( DRaCALA ) . The DRaCALA screen identified a majority of known c-di-GMP binding proteins in V . cholerae and revealed a novel c-di-GMP binding protein , MshE ( VC0405 ) , an ATPase associated with the mannose sensitive hemagglutinin ( MSHA ) type IV pilus . The known c-di-GMP binding proteins identified by DRaCALA include diguanylate cyclases , phosphodiesterases , PilZ domain proteins and transcription factors VpsT and VpsR , indicating that the DRaCALA-based screen of open reading frame libraries is a feasible approach to uncover novel receptors of small molecule ligands . Since MshE lacks the canonical c-di-GMP-binding motifs , a truncation analysis was utilized to locate the c-di-GMP binding activity to the N-terminal T2SSE_N domain . Alignment of MshE homologs revealed candidate conserved residues responsible for c-di-GMP binding . Site-directed mutagenesis of these candidate residues revealed that the Arg9 residue is required for c-di-GMP binding . The ability of c-di-GMP binding to MshE to regulate MSHA dependent processes was evaluated . The R9A allele , in contrast to the wild type MshE , was unable to complement the ΔmshE mutant for the production of extracellular MshA to the cell surface , reduction in flagella swimming motility , attachment to surfaces and formation of biofilms . Testing homologs of MshE for binding to c-di-GMP identified the type II secretion ATPase of Pseudomonas aeruginosa ( PA14_29490 ) as a c-di-GMP receptor , indicating that type II secretion and type IV pili are both regulated by c-di-GMP .
Cyclic diguanosine monophosphate ( c-di-GMP ) is a ubiquitous bacterial nucleotide secondary signaling molecule that regulates cellular processes in response to environmental and cellular stimuli . The elements of this canonical pathway of signal production , signal transduction , altered activity and signal removal were elegantly described by the Benziman lab over twenty-five years ago [1 , 2] . C-di-GMP is synthesized by diguanylate cyclases ( DGCs ) via a catalytic GGDEF domain [1 , 3 , 4] . Once made in the cell , c-di-GMP binds to macromolecule receptors to allosterically alter their activities . C-di-GMP signaling is terminated through hydrolysis by phosphodiesterases ( PDEs ) that contain catalytic EAL or HD-GYP domains [5–8] . In the characterization of bacterial cellulose synthase in Komagataeibacter xylinus , the Benziman lab demonstrated the importance of c-di-GMP in the allosteric activation of the cellulose synthase complex [1 , 9 , 10] . Recent structure elucidation of the BcsA-BcsB-c-di-GMP complex validated these early finding and provided a molecular mechanism for c-di-GMP activation of cellulose biosynthesis [11] . Since this initial description of c-di-GMP regulation of cellulose biosynthesis , genome sequencing has revealed genes for DGCs in diverse bacteria indicating that c-di-GMP is a ubiquitous and important signaling molecule in prokaryotes that regulates a variety of phenotypes [12] . The identification of receptor proteins for c-di-GMP is needed for understanding the regulation by this ubiquitous signaling molecule . However , the process of identifying c-di-GMP binding proteins has been challenging for several reasons . First , c-di-GMP simultaneously regulates complex traits including promoting biofilm formation , inhibiting motility and additional pathways [13–15] indicating that there are likely many c-di-GMP receptors in the cell . Second , although there are several defined protein domains that bind c-di-GMP ( see below ) , these domains do not accurately predict c-di-GMP binding proteins . For example , the PilZ domain binds c-di-GMP [16] , but the PilZ protein in P . aeruginosa , for which the domain is named , does not bind c-di-GMP [17] . In V . cholerae , there are five PilZ domain proteins , but these five proteins do not fully explain all of the observed c-di-GMP regulated effects [18] . Third , c-di-GMP binds a number of proteins that do not have predicted binding motifs or were predicted to bind a different ligand . Examples of novel c-di-GMP binding proteins include VpsT [19] , VpsR [20] and FlrA [21] in V . cholerae as well as FleQ in P . aeruginosa [22] . In addition , the Clp protein in Xanthomoas species , a homolog of the cAMP receptor protein ( CRP ) of E . coli , binds c-di-GMP rather than cAMP [23 , 24] . Another example of a c-di-GMP receptor that was not predicted is the BldD of Streptomyces coelicolor binds two dimers of c-di-GMP to repress transcription [25] . Together , these studies reveal the diversity of cellular targets of c-di-GMP that mediate complex regulation and the highlight the challenges in identifying the c-di-GMP-binding receptors that are responsible for c-di-GMP regulation . Many approaches have been utilized to identify c-di-GMP binding proteins . The first proteins shown to bind c-di-GMP included the enzymes that make and degrade c-di-GMP . The I-site of DGCs binds c-di-GMP to provide product feedback inhibition of DGC activity [4 , 26] . Enzymatically active or inactive PDEs are also capable of c-di-GMP binding [5 , 27–30] . Bioinformatic studies revealed that the PilZ domain is a c-di-GMP binding domain [16] . In addition , targeted and unbiased approaches have been employed to identify c-di-GMP receptor proteins . In the targeted approach , genes of c-di-GMP regulated processes were tested for c-di-GMP binding [19–25 , 31 , 32] . These studies led to the discoveries of the c-di-GMP receptor proteins that have not been predicted by bioinformatics and have motivated identification of novel c-di-GMP receptor proteins using systematic approaches . Affinity pull-down assays using c-di-GMP conjugated sepharose resin , biotin , or a tripartite c-di-GMP capture compound enriched c-di-GMP binding proteins from whole cell lysates , which were subsequently identified by mass spectrometry [33–35] . This approach has also been employed to identify binding proteins of another prokaryotic cyclic dinucleotide , cyclic-di-AMP ( c-di-AMP ) [36–38] . An alternative unbiased approach utilizes the Differential Radial Capillary Action of Ligand Assay ( DRaCALA ) to systematically screen protein expression libraries for ligand binding activity . DRaCALA relies on the differential spreading of bound and unbound radiolabeled ligand when mixed with protein and spotted on a nitrocellulose membrane [39] . In addition , DRaCALA allowed direct detection of c-di-GMP receptors expressed in E . coli whole cell lysates thus enabling the screening of individual genes from a target genome [39] . This approach has been used on Staphylococcus aureus and Escherichia coli open reading frame ( ORF ) libraries to identify c-di-AMP and c-di-GMP binding proteins , respectively [36 , 40] . Vibrio cholerae was chosen as an organism for a DRaCALA based screen of c-di-GMP binding proteins since an open reading frame library was available [41] and it is an organisms that extensively utilize c-di-GMP signaling system to regulate motility , biofilm formation , pathogenesis , and survival upon dissemination to environmental reservoirs [42–46] . The V . cholerae O1 El Tor N16961 genome encodes 62 proteins with domains for c-di-GMP metabolism including 31 GGDEF , 13 EAL , 9 GGDEF+EAL , and 8 HD-GYP domain proteins [47–49] . However , only 5 c-di-GMP receptor proteins have been identified , including 2 PilZ domain proteins PlzC and PlzD [18] , and 3 transcription factors VpsT , VpsR , and FlrA [19–21] . From the DRaCALA screen of the V . cholerae open reading library , a number of predicted c-di-GMP binding proteins were identified . In addition , MshE , an ATPase in the mannose sensitive hemagglutinin ( MSHA ) type IV pilus operon , was also revealed as a c-di-GMP receptor . Purified MshE specifically binds c-di-GMP with a high affinity ( Kd approximately 2 μM ) . Screening of related type II secretion and type IV pili ATPases identified a gene in P . aeruginosa , PA14_29490 , as a c-di-GMP receptor . Through fragmentation of MshE and site-directed mutagenesis of the conserved residues , the arginine at position 9 was identified as a residue required for c-di-GMP binding . Complementation of ΔmshE mutants with wild type mshE restored c-di-GMP regulation of motility , pilus production , and biofilm formation . In contrast , complementation with mshE R9A failed to restore c-di-GMP regulation of MshA pilus function . These results define a novel set of c-di-GMP-binding ATPases associated with type IV pili and type II secretion systems and demonstrate the utility of a DRaCALA screen for identification of c-di-GMP receptor proteins .
We sought to systematically identify protein receptors of c-di-GMP using DRaCALA by individually testing Vibrio cholerae ORFs expressed in E . coli whole cell lysates for 32P-c-di-GMP binding activity . The 3 , 812 unique ORFs from the V . cholerae O1 El Tor N16961 ORFeome pDONR plasmids were recombined into gateway-compatible histidine ( His-ORF ) or His-maltose binding protein ( His-MBP-ORF ) expression vectors in a single Gateway reaction [50] and selected on agar plates containing either carbenicillin or gentamicin , respectively . For each ORF , multiple transformants were inoculated into a single well of a 96-well microtiter plate to create His-ORF and His-MBP-ORF libraries from which whole cell lysates were generated . Protein expression in whole cell lysates was tested for 348 ORFs by PAGE separation and revealed by staining with Coomassie . A band corresponding to the predicted molecular weight was visualized for 49% of His-ORF and 76% of His-MBP-ORF fusions for a combined coverage of 81% of the V . cholerae ORFeome . These results indicate that most V . cholerae proteins are overexpressed in the His-ORF and His-MBP-ORF libraries , thus enabling a systematic genome-wide DRaCALA screen for c-di-GMP binding proteins . Whole cell lysates from His-ORF and His-MBP-ORF libraries were tested for c-di-GMP binding by DRaCALA using a 96 well pin tool . The fraction bound of 32P-c-di-GMP was measured in duplicate for each whole cell lysate and positive V . cholerae ORFs were defined as those having c-di-GMP fraction bound three standard deviations above the mean for both measurements ( Fig 1 , S1 Table ) ( see Material and Methods ) . The positive control expressing PelD , a known c-di-GMP-binding protein , was above the cutoff in each 96-well plate . This primary screen identified 55 His-ORF and 47 His-MBP-ORF proteins that significantly increased the fraction bound of 32P-c-di-GMP . A secondary screen was performed to validate these ORFs . In total , 23 His-ORFs and 22 His-MBP-ORFs ( 28 unique ORFs total ) were validated as positive for c-di-GMP binding ( Fig 1 , Table 1 ) . The specificity for c-di-GMP binding of cell lysates expressing positive ORFs was determined by competition experiments using unlabeled guanosine nucleotides ( Table 1 ) . Unlabeled c-di-GMP significantly reduced 32P-c-di-GMP binding for ORFs listed in Table 1 . In contrast , unlabeled GTP and cGMP did not reduce 32P-c-di-GMP binding , suggesting that the measured binding activity was specific for c-di-GMP . Together these results illustrate how sequential high-throughput DRaCALA screens can identify genetic elements that encode proteins with specific ligand binding activity . This screen of the V . cholerae ORFeome for c-di-GMP binding proteins identified known and candidate c-di-GMP receptors . The ORFs identified in our DRaCALA-based ORFeome screen included several proteins with either predicted or demonstrated c-di-GMP binding activity . The list of 28 positive ORFs include three with GGDEF domains , four with EAL , four with both GGDEF and EAL , six with HD-GYP , three with PilZ , two encoding known c-di-GMP binding transcription factors , and six proteins lacking a defined c-di-GMP binding domain ( Table 1 ) . Of the proteins containing only the GGDEF domain , three ORFs identified contain the RxxD motif required for c-di-GMP binding ( VC1370 , VC2370 , VCA0965 ( CdgF ) ) , which is present in only 11 of the 37 ORFs with GGDEF domains ( S1 Fig ) . Four ORFs ( VC0072 , VC0658 , VC0703 ( MbaA ) , VC1934 ) were identified with both GGDEF and EAL domains , but lacking the RxxD motif , suggesting that c-di-GMP binding occurs at the EAL domain . Both EAL and HD-GYP domains can bind c-di-GMP as a substrate for hydrolysis . The screen identified four ORFs containing an EAL domain ( VC1641 , VC1652 ( VieA ) , VC1710 , VCA1083 ) and six ORFs containing a HD-GYP domain ( VC1295 , VC1348 , VCA0210 , VCA0681 , VCA0895 , VCA0931 ) . The V . cholerae genome contains 5 ORFs that encode PilZ domains . While four of these ( PlzA , -C , -D , and -E ) retain RxxxR and DxSxxG motifs required for c-di-GMP binding , only PlzC ( VC2344 ) and PlzD ( VCA0042 ) have been demonstrated to bind c-di-GMP biochemically [18 , 51] . From the DRaCALA-based ORFeome screen , PlzC and PlzD were identified in both His-ORF and His-MBP-ORF libraries , while PlzE ( VCA0735 ) was identified only in the His-MBP-ORF library . Previous work demonstrated c-di-GMP binding for His-fusions of PlzC and PlzD but not PlzE , suggesting that a MBP fusion to PlzE may be required for proper folding of the c-di-GMP binding site during heterologous expression of PlzE [52 , 53] . Finally , two of three c-di-GMP binding transcription factors , VpsT ( VCA0952 ) [19] and VpsR ( VC0665 ) [20] were identified , but not FlrA ( VC2137 ) [21] . In total , DRaCALA identified 22 of 46 ( 48% ) proteins predicted to bind c-di-GMP and 6 of 11 ( 55% ) proteins previously shown to bind c-di-GMP ( Table 2 ) . These results demonstrate that DRaCALA-based screen can identify all known categories of c-di-GMP binding proteins and represents an unbiased approach to discovering receptor proteins of signaling molecules . Six ORFs were identified which encode proteins that are not known or predicted to bind c-di-GMP , namely VC0405 ( MshE ) , VC1308 ( TyrR ) , VC2066 ( FliA ) , VC2529 ( RpoN ) , VCA0071 ( PstC ) , and VCA0593 ( Table 1 ) . These ORFs represent potentially novel types of c-di-GMP binding proteins . To determine if these proteins bind c-di-GMP directly , His-MBP-ORF fusions were purified and assayed for c-di-GMP binding activity by DRaCALA . C-di-GMP binding was detected for purified MshE , but not for RpoN , FliA , TyrR , or VCA0593 ( S2 Fig ) . We were unable to purify PstC . These results suggest that heterologous expression of RpoN , FliA , TyrR , or VCA0593 can induce the expression of c-di-GMP binding proteins encoded within the E . coli genome . In the remainder of the manuscript , we characterize the c-di-GMP binding properties of MshE . To determine the affinity and specificity of c-di-GMP-binding to MshE , purified His-MBP-MshE was assayed for binding to 32P-c-di-GMP by DRaCALA . The affinity of c-di-GMP-binding was determined by quantifying the fraction bound of 32P-c-di-GMP in serial dilutions of His-MBP MshE ( Fig 2A ) . Non-linear regression analysis of c-di-GMP-binding vs . protein concentration using a one site-binding model estimated the dissociation constant ( Kd ) for c-di-GMP to be 1 . 9 ± 0 . 4 μM . To determine the specificity of c-di-GMP-binding to MshE , we measured the fraction bound of 32P-c-di-GMP in the presence of unlabeled nucleotide competitors . 32P-c-di-GMP-binding to His-MBP-MshE was significantly decreased by unlabeled c-di-GMP , but not by GTP , GDP , GMP cGMP , ATP , ADP , AMP , cAMP , CTP , or UTP ( Fig 2B ) . MshE also binds ATP specifically since only ATP and ADP compete for 32P-ATP binding ( Fig 2C ) . These results indicate that MshE specifically binds c-di-GMP with micromolar affinity and at a site that is distinct from the ATP binding site . MshE belongs to a family of ATPases associated with the biosynthesis and retraction of type IV pili and secretion by type II secretion systems . These phylogenetically related ATPases are called as T2SSE ATPases based on the type II secretion system ( T2SS ) nomenclature for General secretion protein E ( GspE ) [54 , 55] . To determine if c-di-GMP-binding is a conserved feature of T2SSE ATPases , we identified homologs of MshE and assayed them for c-di-GMP-binding . Protein-Blast search with the full-length MshE amino acid sequence against the complete protein sets of V . cholerae O1 El Tor N16961 and P . aeruginosa PA14 identified five additional ATPases in V . cholerae and nine in P . aeruginosa with an E value < 1x10-15 . These ATPases include those required for type IV pili function: PilB ( VC2424 and PA14_58750 , ) PilT ( PA14_05180 and PA14_59340 ) , PilU ( PA14_05190 ) , and TcpT ( VC0835 ) ; and type II secretion: GspE ( VC2732 ) , XcpR ( PA14_23990 ) , HxcR ( PA14_55440 ) , and HxrA ( PA14_29490 ) [56–63] . We constructed His-ORF fusions for each V . cholerae and P . aeruginosa T2SSE ATPase and assayed c-di-GMP-binding by DRaCALA in E . coli whole cell lysate ( Fig 3A ) . Expression of V . cholerae MshE and P . aeruginosa PA14_29490 , but not other T2SSE ATPases , significantly increased the fraction bound of 32P-c-di-GMP . These data identified MshE and PA14_29490 as c-di-GMP-binding receptor proteins , the only ones among the T2SSE family in V . cholerae and P . aeruginosa . PA14_29490 is the reciprocal best BLAST hit for MshE [55] . Based on genomic context , MshE functions within the MSHA operon [64 , 65] , while PA14_29490 is encoded within a putative T2SS operon [66 , 67] . Sequence comparison of T2SSE-type ATPases from V . cholerae and P . aeruginosa showed that they differ substantially in protein length . MshE and PA14_29490 , both 575 aa long , were ~200 aa longer than PilT ATPases VC0462 , VC0463 , PA14_05180 , PA14_05190 , PA14_58760 and PA14_59340 . Other T2SSE-type ATPases , annotated as GspE- and PilB-type , ranged in length from 469 to 562 aa , and only PA14_68820 was the same length as MshE . All these enzymes had very similar C-terminal ATPase domains and differed primarily in their N-terminal parts , with longer proteins containing an additional domain , referred to as T2SSE_N ( PF05157 ) domain in the Pfam database [48] . A phylogenetic tree of the N-terminal fragments of T2SSE_N-containing ATPases showed that MshE is located in the branch related to PilB ATPases responsible for type IV pili , whereas PA14_29490 is located in the branch related to GspE ATPases , which participate in type II secretion ( Fig 3B ) . The canonical PilB and GspE ATPases that were tested ( VC2424 , VC2732 and their counterparts in P . aeruginosa ) belong to branches of the tree that are distinct from those including MshE and PA14_29490 . Expansion of the branches containing MshE ( Fig 3C ) and PA14_29490 ( Fig 3D ) reveal many homologous proteins that may also be receptors of c-di-GMP . Together , these results suggest that a subset of T2SSE ATPases represented by MshE and PA14_29490 are c-di-GMP-binding proteins . Both MshE and PA14_29490 lack known c-di-GMP binding protein sequence motifs . To locate the binding site ( s ) on these proteins , truncation analysis was performed on both proteins . The N-terminal T2SSE_N domain and C-terminal T2SSE ATPase domain of PA14_29490 and MshE were separated at three points that were predicted to be at the ends of secondary structural elements ( Fig 4A and 4E ) . These fragments were expressed in E . coli and the whole cell lysates were tested for c-di-GMP binding . The N-terminal fragments ( F1-F3 ) of both PA14_29490 and MshE bound 32P-c-di-GMP , while the C-terminal fragments ( F4-F6 ) did not ( Fig 4B and 4F ) . Purified fragment 1 of PA14_29490 binds c-di-GMP with a Kd of 480 ± 60 nM ( Fig 4C ) and this binding was competed away only with c-di-GMP ( Fig 4D ) , indicating that binding is specific . Each of the fragments of MshE was purified and tested for binding to 32P-c-di-GMP . Only the purified N-terminal fragments ( F1-F3 ) of MshE bound 32P-c-di-GMP , while the C-terminal fragments ( F4-F6 ) did not ( Fig 4G ) . These results indicate that the binding site for c-di-GMP is located in the N-terminal domain of the protein and is distinct from the ATPase domain in the C-terminus . We hypothesized that c-di-GMP regulation of MSHA is an evolutionarily conserved property . To test this idea , we identified MshE homologs from genomes containing MSHA-like operons as defined by having the mshN gene upstream of mshE and the mshG gene downstream of mshE ( S3A Fig ) . ClustalW alignment revealed residues within the first 151 amino acids that are 100% identical among these proteins , including 4 conserved motifs: RLGDLLV , ARRxRAL , SDPADL , and DxxYRRT ( S3B Fig ) . Since charged residues , in particular arginines , have been shown to participate in c-di-GMP binding in a variety of receptor proteins , mutant variants with alanine replacements within these 4 motifs were generated by site-directed mutagenesis including R9A/D12A , R88A/R89A , D108A/D111A , and R146A/R147A ( Fig 5A ) . As a control , the conserved E191 and D192 residues , which are located outside of the first 151 amino acid fragment , were also changed to alanine . Purified R9A/D12A , R88A/R89A , and D108A/D111A variants were reduced for binding to c-di-GMP , whereas R146A/R147A and E191A/D192A did not affect binding to c-di-GMP ( Fig 5A ) . These results indicate that motifs 1 , 2 and 3 contribute to c-di-GMP binding , while motif 4 is dispensable . The E191A/D192A variant binds c-di-GMP similarly to the wild-type protein ( Fig 5A ) , in agreement with the results from fragment analysis ( Fig 4E ) . To determine the contribution of each amino acid residue within the first 3 motifs and the other conserved , charged amino acids , we generated and purified MshE variants with single alanine substitution in positions R9 , D12 , Q32 , E51 , R88 , R89 , D108 , D111 and D142 ( S4A Fig ) . The R9A variant had an 83% reduction in c-di-GMP binding , while Q32A and R88A variants had 61% and 50% reduction , respectively ( Fig 5C ) . Interestingly , the R9/D12 residues represent an RxxD motif described in DGC I-site [26] , PelD [31] and GIL domain of BcsE [40] . In those proteins , both residues are critical for c-di-GMP binding . In contrast , the MshE D12A variant actually binds c-di-GMP better than wild-type MshE ( Fig 5C ) , indicating that MshE does not contain an I-site-like binding sequence . Each of these MshE variants was also tested for ATP binding . The R88A variant showed an 88% reduction in ATP binding and was the only variant that had a reduction by more than 50% ( S4B Fig ) . Thus , the defect associated with c-di-GMP binding in R88A variant may be due to a general folding problem for this specific protein . Together , these results indicate MshE contains a novel c-di-GMP binding site that requires R9 residue with contribution from the Q32 residue . The effect of c-di-GMP on the ATPase activity of MshE was assessed by testing the WT MshE and the R9A proteins in the presence and absence of c-di-GMP . WT MshE produced 68 μM of phosphate from ATP without c-di-GMP and increased to 75 and 76 μM with the addition of 10 and 33 μM of c-di-GMP , respectively ( S5 Fig ) . In contrast , R9A protein produced 60 , 64 , and 58 μM of phosphate with addition of 0 , 10 , and 33 μM of c-di-GMP . These results indicate that the ATPase activity is increased for WT MshE in response to c-di-GMP at concentrations above the dissociation constant , while c-di-GMP had no effect on the R9A protein . However , the magnitude of the enhanced ATPase activity is only about 10% suggesting that c-di-GMP may have additional effects on MshE interaction with the MshA substrate or other components of the MSHA export machinery . MSHA is a responsible for initial attachment of V . cholerae to surfaces and subsequent biofilm formation [68] . Recently , MSHA was demonstrated to reduce flagella mediated swimming motility [69] . We sought to determine whether MshE is the c-di-GMP receptor regulating MSHA activity by assessing the amount of MshA pili on the bacterial surface , the effect on swimming motility , and biofilm formation . The effect of MshE on the export of MshA to the surface of V . cholerae was assessed by surface ELISA using antibodies specific to MshA ( Fig 6A , WT and ΔmshA ) . The ΔmshE mutant is defective for MshA export ( Fig 6A ) . This defect was complemented by wild type mshE and the D12A allele , but not the R9A allele ( Fig 6A ) . Complementation with the R88A/R89A allele was able to restore the export of ~10% of MshA observed in wild type cells . A recent study revealed that V . cholerae swimming motility is reduced by interaction of MSHA with surfaces [69] . V . cholerae was assessed for motility through soft agar assay . Wild type V . cholerae had reduced swimming motility and the ΔmshE mutant has increased motility ( Fig 6B , WT and ΔmshE vector ) recapitulating previous observations [69] . The ability of either wild-type mshE or mshE variant under the tac promoter in trans on a plasmid to complement ΔmshE phenotypes were evaluated . Induction of either allele that binds c-di-GMP , wild-type mshE or the D12A variant , reduced motility to wild type levels ( Fig 6B ) . Induction of the variant defective for c-di-GMP binding ( R9A ) failed to reduce motility ( Fig 6B ) . The R88A/R89A allele , that had reduced binding to c-di-GMP and ATP , also failed to reduce motility . Additionally , the requirement for MshE to bind c-di-GMP on biofilm formation was assessed using a flow cell system . At 4 hours post-inoculation , wild-type was able to attach to the surface , whereas fewer ΔmshE mutants attached to the surface ( Fig 6C ) . Complementation of ΔmshE mutants with wild type mshE and the D12A allele restored attachment , whereas complementation with either R9A or R88A/R89A allele did not ( Fig 6C ) . At 24 hours post-inoculation , the initial differences in attachment were more pronounced . Wild type and ΔmshE mutant complemented with either mshE or D12A formed mature biofilm ( Fig 6D ) . Complementation with the R88A/R89A allele also restored biofilm formation indicating a small amount of surface MshA can restore MSHA activity . In contrast , the ΔmshE mutant complemented with R9A showed small patches of biofilms similar to ΔmshE with the vector control ( Fig 6D ) . Quantification of the biofilms revealed that both biomass ( Fig 6E ) and surface coverage ( Fig 6F ) are reduced for ΔmshE complemented with R9A . Together , these results indicate that the ability of MshE to bind c-di-GMP via the R9 residue is required for MshA export to the cell surface and MSHA-mediated phenotypes .
DRaCALA screens are a relatively new method for identifying ligand binding proteins that combines arrayed protein libraries with a high-throughput biochemical assay of protein-ligand interactions . Two recent publications have successfully used DRaCALA screening to identify novel cyclic-dinucleotide protein receptors . A screen for c-di-AMP binding proteins in the Staphylococcus aureus strain COL ORFeome identified PstA and KdpD [36] . The screen also identified KtrA , which was the only protein identified by affinity pull-down using c-di-AMP magnetic beads in the same study [36] . The crystal structures of PstA-c-di-AMP [70–72] and KtrA-c-di-AMP complexes [73] have been solved . KdpD binding motif has been recently identified [74] . Since the DRaCALA screen , one other additional c-di-AMP receptor has been identified in Gram-positive bacteria , pyruvate carboxylase of Listeria monocytogenes [38] . The S . aureus pyruvate carboxylase has different residues within the binding motif than the L . monocytogenes and does not bind c-di-AMP [38] . Together these studies demonstrate that DRaCALA was able to identify three novel bona-fide c-di-AMP receptors and correctly not detect a protein that does not bind c-di-AMP . Additionally , a DRaCALA screen for c-di-GMP binding proteins in the E . coli K12 ASKA overexpression gene library revealed three clones overexpressing putative c-di-GMP binding proteins: BcsE , IlvH and RimO [40] . Of these , BscE was further characterized and shown to contain a novel c-di-GMP binding domain , which the authors named GIL [40] . In conjunction with the results from these screens , our identification of MshE as a c-di-GMP receptor important for MSHA pilus function in V . cholerae demonstrates that DRaCALA is a powerful approach for finding new small-molecule receptors across different bacterial species . The results of these three DRaCALA screening experiments allow us to further assess the limitations of the screen . Our screen of the V . cholerae ORFeome identified many , but not all , of the expected binding proteins . These false negatives could be due to several factors . First , the enzymatic activity of these proteins may prevent detection . Phosphodiesterases active in the assay conditions could degrade the 32P-c-di-GMP probe prior to application on nitrocellulose , while active diguanylate cyclases could produce excess c-di-GMP to compete for binding with the subsequently added 32P-c-di-GMP probe . The failure to detect binding in many EAL and GGDEF domain proteins in our screen , as well as the lack of binding detected for all I-site-containing GGDEF proteins assayed in the E . coli DRaCALA screen [40] , could be due to these factors . Second , poor expression of the ORF can result in a false negative because DRaCALA relies on the expression of proteins above the Kd . In our assay of protein expression , we found that only a subset of the ORFs tested had a protein band of the correct size on SDS-PAGE . Similarly , some false negatives of c-di-GMP binding proteins in the E . coli screen were due to poor protein expression as detected by protein band intensity on SDS-PAGE [40] . Third , the DRaCALA screen interrogates binding of individually expressed ORFs within a heterologous system . Thus proteins that require endogenous binding partners or activating factors to interact with ligand may not be active . Our screen also yielded false positives , which could be due to two factors . First , the expression of proteins that can activate the production of c-di-GMP binding proteins encoded in the E . coli genome can result in a positive signal . Second , the statistical method utilized to identify “positive” fraction bound may falsely identify proteins whose fraction bounds are near the cutoff , as was likely the case for Adk , a false positive result in the S . aurus c-di-AMP screen [36] . Both these categories of false positives can be detected after re-assaying binding with purified protein . We suggest several methods to increase the fidelity of DRaCALA-based screens: 1 . Testing the ORFs with multiple fusion proteins can increase the likelihood of overexpressing recombinant proteins that retain ligand binding activity , 2 . Express the ORFeome in a strain genetically modified to remove endogenous c-di-GMP signaling components and thus reduce the likelihood of false positives [75 , 76] , and 3 . Alter the buffer used to resuspend lysates—in the case of EAL domain PDE-As , resuspension in a buffer containing Ca2+ rather than Mg2+ can inhibit the activity of PDE-As [2] and increase the likelihood of detecting proteins that degrade c-di-GMP . Although DRaCALA did not identify all c-di-GMP proteins in the V . cholerae genome , DRaCALA is an unbiased approach that allows discovery of novel receptor protein such as MshE . We believe DRaCALA-based approach can be a powerful tool for the discovery of novel receptor proteins of other small nucleotide signaling molecules . The MshE is a bona fide c-di-GMP binding protein was demonstrated by 1 . High affinity binding with the Kd of 1 . 9 μM , 2 . High specificity of binding based on competition assays , 3 . A defined binding site located in the N-terminal T2SSE_N domain and 4 . The requirement of the conserved R9 residue for c-di-GMP binding in vitro and for MSHA function in vivo . MshE represents a new category of c-di-GMP binding proteins since it lacks any of the previously defined c-di-GMP binding domains ( DGC I-sites , EAL , HD-GYP , PilZ or GIL ) and is the first T2SSE ATPase demonstrated to bind c-di-GMP . The conserved R9 and D12 residues are reminiscent of the RxxD c-di-GMP binding motif present in the I-site of DGCs or the RxGD binding motif present in GIL domains . For both DGC I-sites and GIL domains , the R and D residues are required for c-di-GMP binding . In contrast , MshE requires only the R9 residue for binding to c-di-GMP , while the D12 residue of MshE is dispensable . In addition to MshE , only one other type II secretion/type IV pili ATPase , PA14_29490 from P . aeruginosa , bound c-di-GMP . Other members of this subfamily likely will also have the ability to bind c-di-GMP , including XpsE from X . campestris ( Fig 3B , blue line ) . In contrast , related ATPases including PilT and PilU are unlikely to be regulated by c-di-GMP since they are shorter and lack the T2SSE_N domain . Although MshE and PA14_29490 both bind c-di-GMP in the N-terminal T2SSE_N domain , their sequence conservation within this domain is quite low . Nonetheless , proteins containing the T2SSE_N domain should be investigated for their ability to interact with c-di-GMP ( Fig 3B , 3C and 3D ) . Two T2SSE_N domain of T2SSE ATPases have been structurally characterized including N-terminal fragment of V . cholerae GspE ( Protein Data Bank ( PDB ) entry 2BH1_X ) [77] and a related domain from Xanthomonas campestris ( PDB: 2D27 , 2D28 ) [78] . Sequence alignment the N-terminal 151-aa fragment of MshE to PA14_29490 , V . cholerae GspE and X . campestris GspE by the Conserved Domain Database [79] revealed that the V . cholerae GspE lacks the R9 and Q32 residues . However , these residues were conserved in the GpsE/XpsEN domain from X . campestris ( S6A Fig ) . Remarkably , the X . campestris XpsEN was crystallized in two different forms that reflect two distinct conformational states that differ in the position of two N-terminal helices [78] , which include the R9 and Q32 residues ( S6B Fig ) . In one of the structures ( PDB: 2D27 ) , R9 and Q32 are positioned within a reasonable distance from each other and , upon the rotation of first helix , could form a potential c-di-GMP-binding site ( S6B Fig ) . In the other structure ( PDB: 2D28 ) , the region around Q32 proved so flexible that the exact position of Q32 could not be resolved [78] , which is consistent with the ability of this residue to move around and participate in ligand binding . Such flexibility has also been observed in the binding sites of PilZ-containing c-di-GMP receptors [18 , 51] . The conformational change of the two N-terminal helices of MshE upon binding c-di-GMP has the potential to alter its activity . Thus , type II secretion/type IV pili ATPases with the conserved arginine and glutamine residues corresponding to R9 and Q32 of MshE represent candidate c-di-GMP receptors ( S6B Fig ) . V . cholerae cycles between environmental reservoirs and human infections in part using two type IV pili , MSHA and Tcp . MSHA contributes to the V . cholerae infection cycle by maintaining an environmental reservoir through attachment to chitin surfaces [80 , 81] and tolerance to environmental hypotonic stress [82] through biofilm formation [83] . Upon entry into the next host , the preformed biofilms protect V . cholerae from lower pH [84] and bile [85] . Subsequently , V . cholerae express toxin co-regulated pili ( Tcp ) to promote colonization [86 , 87] while concomitantly turning off MSHA [88] . The repression of MSHA within the host is critical since a strain with the msha promoter replaced with the constitutive Plac promoter was outcompeted by the wild type strain in an infant mouse infection model [88] . This defect can be attributed to binding of secretory IgA directly to MSHA since these strains do not show a competitive difference in mice lacking IgA [88] indicating that repression of MSHA during infection is a necessary for immune evasion . Based on recombination-based in vivo expression technology ( RIVET ) studies [46] , tcpA is transcribed while the msh operon is repressed early in the infection; in contrast , the expression pattern is reversed at the late stage of infection . The decrease in msh transcription early in the infection is likely regulated in part by the reduced levels of c-di-GMP since several DGCs are not expressed until late in the infection [46] . However , the precise mechanism for regulation of the c-di-GMP signal is subject to strain specificity [49 , 89] and changes in the host microenvironment . Another mechanism to reciprocally regulate Tcp and MSHA is from the TcpJ pre-pilin peptidase . TcpJ has the unique property of processing both TcpA [90] and MshA [91] , but cleavage of MshA by TcpJ leads to rapid degradation [91] . Our finding that MshE binding to c-di-GMP is required for MSHA production and function adds another mechanism to enhance the switch between Tcp and MSHA pili . Upon entering the host , the c-di-GMP levels are reduced , thus reducing both transcription of the msh operon and the function of existing export machinery to export MshA . In combination with the degradation of newly synthesized MshA by TcpJ , V . cholerae can quickly change from the immunogenic MSHA to the adhesive Tcp in the host . Late in the infection , this regulation is reversed allowing the bacteria exiting the host to express MSHA instead of Tcp to prepare for the environment . Binding of MshE to c-di-GMP activates MSHA dependent phenotypes including 1 . Production of MshA pili to the cell surface , 2 . MSHA reduction of flagella motility , 3 . Adherence to surfaces and biofilm formation . These studies show that all of these effects are lost in the mshE R9A mutant that is defective in binding to c-di-GMP . The implication of our results is that c-di-GMP binding to MshE is required for its activity in polymerizing MshA . This finding is intriguing since several type IV pili are regulated by c-di-GMP through different mechanisms . In both Xanthomonas and Pseudomonas spp . , type IV pili are regulated by PilZ and FimX proteins [92–95] . However , the precise mechanisms of regulation appear to be different . In X . axonopodis citri and X . campestris , PilZ binds FimX and the PilB ATPase , a homolog of MshE , to form a tripartite regulatory complex , but these interactions are not conserved in P . aeruginosa [94 , 96 , 97] . In addition to regulating the PilB ATPase , c-di-GMP interacts with a second PilZ-domain protein XC_2249 to regulate interactions the PilT and PilU retraction ATPases in X . campestris [98] . These studies and our MshE results indicate that c-di-GMP regulates type IV pili ATPases through a number of different mechanisms . The ability of PA14_29490 to bind c-di-GMP indicates that there may be also complex regulation of type II secretion systems by c-di-GMP . C-di-GMP regulates major lifestyle changes in response to altered environmental cues . These changes incur a high cost in expenditure of cellular resources as well as an opportunity cost of committing to a sessile lifestyle . There is an emerging pattern in which c-di-GMP regulates the same phenotype at the transcriptional and post-translational levels . Examples of this include Pel polysaccharide synthesis in P . aeruginosa [31 , 99] , and now MSHA in V . cholerae [100] . The idea of regulation by c-di-GMP first to activate gene expression and later to activate protein function can be thought of as “sustained sensing” . Sustained sensing enables bacteria to repeatedly assess environmental and cellular conditions through multi-tiered regulation , and has been previously described for responses to iron availability , oxidative stress , and other signals [101 , 102] . The concept of sustained signaling applied to c-di-GMP enables prediction of additional c-di-GMP receptors . In addition to known examples , there are additional c-di-GMP-regulated processes that could be regulated by sustained sensing . These include two classes: 1 . Operons that are transcriptionally regulated by c-di-GMP , but lack known c-di-GMP receptor proteins and 2 . Operons that encode c-di-GMP receptor proteins , but lack known c-di-GMP transcriptional regulation . Examples of the first class include a number of biosynthetic operons of extracellular polysaccharides , such Vps and Eps in V . cholerae [19 , 100 , 103] , Psl in P . aeruginosa [22] , xagABC in X . campestris [32 , 104] , and Bcam1330-Bcam1341 in Burkholderia cenocepacia [105] . These operons also likely encode a c-di-GMP binding protein to regulate polysaccharide biosynthesis at the post-translational level . Examples of the second class include cellulose synthesis in E . coli [40] , the alginate biosynthesis operon in P . aeruginosa [17] and the large adhesin protein ( Lap ) of P . fluorescens [28] . Furthermore , c-di-GMP can also bind to riboswitches [106 , 107] to provide another regulatory tier in sustained sensing . Therefore , based on the emerging theme of sustained sensing of c-di-GMP through multi-tiered regulation , we suggest that newly discovered processes regulated by c-di-GMP either transcriptionally or post-translationally be investigated for additional levels of c-di-GMP control .
pVL791 Cb GW and pVL847 Gn GW were constructed as destination vectors in LR reactions ( Life Technologies ) for cloning VC ORFs . pVL791 Cb and pVL847 Gn are pET-19 derivatives that are carbenecillin or gentamycin resistant and produce N-terminal His- and His-MBP fusions respectively . The gateway destination cassette was amplified from pRFA and cloned in frame with the N-terminal fusions to produce the gateway adapted vectors . The V . cholerae N16961 ORF library was obtained from BEI . LR Clonase reactions were performed per NEB protocol using miniprepped pDONR vectors from the V . cholerea ORFeome in combination with pVL791 Cb GW and pVL847 Gn GW destination vectors . Gateway reactions were transformed into an E . coli T7IQ strain ( NEB ) and recombinants were selected on LB agar plates containing either carbenecillin or gentamycin . Multiple colonies from individual transformations were inoculated in LB M9 rich media in 96-well plate format and grown overnight with shaking at 30°C . Overnight cultures were subcultured 1:50 into fresh media and grown for 4 hours at 30°C with shaking . Protein expression was induced by addition of 1 mM IPTG and cultures were grown for an additional 4 hours after induction . 1 . 5 mL of induced culture was centrifuged and cells were resuspended in 150 μL of c-di-GMP-binding buffer supplemented with 10 μg / mL DNAse , 250 μg / mL Lysozyme and 1 μM PMSF . 20 μL aliquots were transferred to 96-well microtiter plates and stored at -80°C . Whole cell lysates for DRaCALA screening were prepared by freeze-thawing resuspended cells in microtiter plates a total of 3 times . After the final thaw , 20 μL of c-di-GMP-binding buffer supplemented with 16 pM 32P-c-di-GMP and 500 mM unlabeled GTP was added to whole cell lysate plates or purified proteins . 2 μL of this mixture was then spotted in duplicate on nitrocellulose using a 96-well pin tool . DRaCALA of purified proteins was performed with concentrations of protein and unlabeled competitor as indicated . Spots were allowed to dry completely ( about 20 minutes ) before exposing a phosphorimager screen and capturing with a Fujifilm FLA-7000 . Photostimulated luminescence ( PSL ) from the inner spot and total PSL of the spot were quantitated with Fuji Image Gauge software . The fraction bound was calculated using measurements of the total area ( Aouter ) , the area of the inner circle ( Ainner ) , the total PSL intensity ( Itotal ) , and the inner intensity ( Iinner ) as described [39] . DRaCALA was also used to determine the ability of purified MshE and variants to bind c-di-GMP . Dissociation constants were estimated assuming a one-site binding model by a nonlinear regression of protein concentration and fraction bound where , Fraction bound = ( Maximum possible Fraction Bound ) * [protein concentration] / ( Kd + [protein concentration] ) . In the primary screen of His-ORF and His-MBP-ORF libraries , mixtures of whole cell lysate and radiolabeled 32P-c-di-GMP were spotted twice . To identify DRaCALA spots with significantly increased fraction bound 32P-c-di-GMP , a positive cutoff three standard deviations above the mean fraction bound was created for each 96-well plate of whole cell lysates . Positive spots were iteratively removed from calculations of mean and standard deviation for individual plates , thereby decreasing the positive cutoff until no additional positives were identified . His-ORFs and His-MBP-ORFs for which both DRaCALA spots had positive binding were defined as positive and subjected to a secondary screen . Additionally , 8 His-ORF and 8 His-MBP-ORFs with only 1 DRaCALA spot with positive binding were also included in the secondary screen , but none of these ORFs were positive for c-di-GMP binding in the secondary screen . For each primary positive His-ORF and His-MBP-ORF , 8 whole cell lysates were generated from individual clones that were isolated from the pooled transformants used to create the His-ORF and His-MBP-ORF libraries . Each His-ORF and His-MBP-ORF lysate was spotted twice by DRaCALA and individual lysates were compared to a set of 8 lysates generated from vector controls . Positive fraction bound 32P-c-di-GMP for DRaCALA spots was defined as those with at least 2 fold increase above the average fraction bound for the set of plate-matched vector controls . Additionally , each lysate was assayed by PCR to verify the size of the inserted ORF . Positive ORFs from this secondary screen displayed positive fraction bound 32P-c-di-GMP for both DRaCALA spots created from PCR positive lysates . Protein sequences corresponding to COG2840 were obtained from the EggNOG 4 . 1 database . Each sequence aligned to Pfam Family PF05157 ( Type II Secretion System , protein E , N-terminal domain ) using the version 10 HMM with HMMer 3 . 1 and the subsequences corresponding to the T2SSE N-terminal domain were extracted . The remaining 1437 domain sequences were aligned using the MAFFT 7 . 157b E-INS-i algorithm and trimmed using TrimAl 1 . 4 to eliminate columns with more than 90% gaps . An unrooted phylogenetic tree was constructed using FastTree 2 . 1 . 8 . Strains used are listed in S2 Table . Plasmids are listed in S3 Table . Fragments of MshE and PA14_29490 were generated using the primers indicated in S4 Table . Site-directed alanine substitutions of MshE were generated by PCR amplification with the indicated primers , DpnI digest and transformation into E . coli DH5α . pVL791-MshER88A , pVL791-MshER89A , pVL791-MshED111A were generated using the primers indicated in S3 Table and the NEB Q5 Site-Directed Mutagenesis Kit . All constructs were verified by DNA sequencing . V . cholerae MshE and variants were purified as previously described [39] . Briefly , E . coli T7Iq strains or E . coli BL21 ( DE3 ) containing expression plasmids were grown overnight , subcultured in fresh media and grown to OD600~1 . 0 when expression was induced with 1 mM IPTG . Induced bacteria were pelleted and resuspended in 10 mM Tris pH 8 , 100 mM NaCl , 25 mM imidazole and frozen at -80°C until purification . Proteins were purified over a Ni-NTA column and eluted with 10 mM Tris pH 8 , 100 mM NaCl , 250 mM imidazole . Purified proteins were exchanged into 10 mM Tris pH 8 , 100 mM NaCl using Sephadex G25 . Proteins were aliquoted , and frozen at -80°C until use . Motility plates consist of LB containing 0 . 3% agar supplemented with 20μg/mL ampicillin and 25 or 100μM IPTG where appropriate . Plates were poured and allowed to dry at room temperature for 4 h prior to inoculation . Colonies from overnight LB agar plates grown at 30°C were transferred to motility plates and incubated for 16 h at 30°C . Motility diameter was measured and normalized to the average of WT on each plate . Experiments were performed with three biological replicates in triplicate and data were analyzed with a Oneway ANOVA followed by Dunnett’s multiple comparison test . Inoculation of flow cells was done by diluting overnight-grown cultures to an OD600 of 0 . 04 and injecting into a μ-Slide VI0 . 4 ( Ibidi , Martinsried , Germany ) . To inoculate the flow cell surface , bacteria were allowed to adhere at room temperature for 1 h . Flow of 2% v/v LB ( 0 . 02% tryptone , 0 . 01% yeast extract , 1% NaCl; pH 7 . 5 ) containing 20μg/mL ampicillin and 100μM IPTG was initiated at a rate of 7 . 5 ml/h and continued for 24 h . Confocal images were obtained on a Zeiss LSM 5 PASCAL Laser Scanning Confocal microscope . Images were obtained with a 40X dry objective and were processed using Imaris ( Bitplane , Zurich , Switzerland ) . Quantitative analyses were performed using the COMSTAT software package [108] . Statistical significance was determined using Oneway ANOVA with Dunnett’s Multiple Comparison test . Two biological replicates were performed in triplicate . Images presented are from one representative experiment . Surface pili composed of MshA were quantified using an ELISA based on a previously published protocol [92] . Briefly , overnight culture was diluted 1:100 in fresh LB medium and grown to OD600 0 . 5 at 30°C . Cells ( 125μL ) were added to a 96-well plate ( Greiner Bio-One , Monroe , NC ) and incubated at 30°C for one hour . Cells were fixed with 100μL of methanol for 10 minutes at room temperature , then washed twice with PBS . Samples were blocked in 5% nonfat dry milk and immunoblotted with polyclonal rabbit anti-MshA ( 1:1000 dilution , gift of J . Zhu ) and horseradish peroxidase ( HRP ) -conjugated secondary antibody ( Santa Cruz Biotechnology , Santa Cruz , CA ) . After three washes in PBS , 100μL of TMB ( eBioscience , San Diego , CA ) was added and incubated for 30 minutes at room temperature followed by the addition of 100μL of 2N H2SO4 . Absorbance was recorded at 490nm and the samples were normalized to the change in WT . Two biological replicates were assayed in duplicate and statistical significance was determined with a Oneway ANOVA followed by a Dunnett’s Multiple Comparison test . E . coli BL21 harboring plasmids for gene expression were grown to an OD600 of 0 . 4 at 30°C in LB containing 100μg/mL ampicillin . Cultures were shifted to 18°C and IPTG was added to a final concentration of 100μM . 16h post induction , cells were harvested by centrifugation at 10 , 000 x g for 15 minutes and stored at -80°C . Cell pellets were resuspended in GST Lysis Buffer ( 25mM Tris pH 8 . 0 , 0 . 5M NaCl containing PI cocktail tablets ( Roche Life Science , Indianapolis , IN ) . Cells were lysed by sonication and cell lysate was cleared via centrifugation . Cleared lysate was loaded onto GS4B resin and washed with five column volumes of lysis buffer . Protein was eluted from the resin in 5mL elution buffer ( 25mM Tris pH8 . 0 , 0 . 25M NaCl , 10mM glutathione ) . Samples were dialyzed against buffer ( 25mM Tris-HCl , 150mM NaCl , 250μM DTT , pH 7 . 5 ) overnight using 12 kDa cutoff dialysis tubing ( Fisherbrand , Pittsburgh , PA ) and concentrated to approximately 1mL using an Amicon 10KDa cutoff spin filter ( EMD Millipore , Darmstadt , Germany ) . An aliquot of dialyzed protein was diluted in 6M guanidinium HCl and concentration determined via A280 . ATPase activity of purified proteins was determined by measuring the production of inorganic phosphate from ATP using the Enzchek Phosphate Assay Kit ( Invitrogen ) . The standard reaction mixture was prepared with the addition of 2mM MgCl2 , 10mM KCl , and 1mM DTT . Purified protein in buffer ( 25mM TrisHCl pH 7 . 5 , 100mM NaCl ) was added to the standard reaction mixture to a final concentration of 1μM with the indicated concentration of c-di-GMP . After a 10 minute incubation at room temperature , ATP was added to a final concentration of 10mM and reactions were incubated at 22°C for 30 minutes . Production of inorganic phosphate was monitored by reading OD360 and compared to a standard curve of solutions of KH2PO4 . The experiment was performed in triplicate . Significance was determined via ANOVA and Bonferroni test . | Cyclic-di-GMP ( c-di-GMP ) is a ubiquitous bacterial signaling molecule that regulates important bacterial functions , including virulence , antibiotic resistance , biofilm formation and cell division . The list of known c-di-GMP receptors is clearly incomplete . Here we utilized a systematic and unbiased biochemical approach to identify c-di-GMP receptors from the 3 , 812 genes of the Vibrio cholerae genome . Results from this analysis identified most known c-di-GMP receptors as well as MshE , a protein not known to interact with c-di-GMP . The c-di-GMP binding site was identified at the N-terminus of MshE and requires a conserved arginine residue in the 9th position . MshE is the ATPase that powers the secretion of the MshA pili onto the surface of the bacteria . We show that c-di-GMP binding to MshE is required for MshA export and the function of the pili in attachment and biofilm formation . ATPases responsible for related processes such as type IV pili and type II secretion were also tested for c-di-GMP binding , which identified the P . aeruginosa ATPase PA14_29490 as another c-di-GMP binding protein . These findings reveal a new class of c-di-GMP receptor and raise the possibility that c-di-GMP regulate membrane complexes through direct interaction with related type II secretion and type IV pili ATPases . | [
"Abstract",
"Introduction",
"Results",
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"Methods"
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| []
| 2015 | Systematic Identification of Cyclic-di-GMP Binding Proteins in Vibrio cholerae Reveals a Novel Class of Cyclic-di-GMP-Binding ATPases Associated with Type II Secretion Systems |
New promising avenues for the pharmacological treatment of skeletal and heart muscle diseases rely on direct sarcomeric modulators , which are molecules that can directly bind to sarcomeric proteins and either inhibit or enhance their activity . A recent breakthrough has been the discovery of the myosin activator omecamtiv mecarbil ( OM ) , which has been shown to increase the power output of the cardiac muscle and is currently in clinical trials for the treatment of heart failure . While the overall effect of OM on the mechano-chemical cycle of myosin is to increase the fraction of myosin molecules in the sarcomere that are strongly bound to actin , the molecular basis of its action is still not completely clear . We present here a Molecular Dynamics study of the motor domain of human cardiac myosin bound to OM , where the effects of the drug on the dynamical properties of the protein are investigated for the first time with atomistic resolution . We found that OM has a double effect on myosin dynamics , inducing a ) an increased coupling of the motions of the converter and lever arm subdomains to the rest of the protein and b ) a rewiring of the network of dynamic correlations , which produces preferential communication pathways between the OM binding site and distant functional regions . The location of the residues responsible for these effects suggests possible strategies for the future development of improved drugs and the targeting of specific cardiomyopathy-related mutations .
Sarcomeric modulators are small molecules that can modify the function of proteins in the sarcomere , the fundamental repeating unit of skeletal and cardiac muscle cells . New promising avenues for the pharmacological treatment of different muscle and heart diseases rely on direct sarcomeric modulators , which are molecules that can directly bind to sarcomeric proteins and either inhibit or enhance their activity [1] . Part of the current research is focusing on modulators of myosin II , the motor protein responsible for muscle contraction , with different drugs either in preclinical development or in clinical trials [1–3] . The possibility to modulate myosin function by either up or down-regulating it is particularly appealing for the treatment of inherited cardiac diseases . Indeed , myosin mutations are associated with cardiomyopathies with different phenotypes , including hypertrophic ( HCM ) and dilated cardiomyopathy ( DCM ) , and myosin modulators could be potentially used to counteract their damaging effect , with specific drugs tailored for specific mutations [1 , 4 , 5] . The action of myosin modulators is closely related to the allosteric nature of the protein and in particular of its motor domain ( Fig 1A ) , which is the domain responsible for the hydrolysis of ATP and the conversion of the resulting chemical energy into mechanical work . The motor domain is composed of four main subdomains , namely the N-terminal ( N-term , green ) , the upper 50-K ( U50K , red ) , the lower 50-K ( L50K , grey ) and the converter ( blue ) subdomains , connected by linkers ( cyan and yellow ) . The motor domain is then connected to the rest of the myosin molecule through the lever arm helix ( blue ) , which is strongly coupled to the converter . According to most of the current models of the molecular mechanisms at the basis of myosin function , the relative orientation of the subdomains is controlled by the conformation of the linkers , which is in turn regulated by the biochemical state of myosin . In particular , Switch 2 ( SW2 ) , the relay helix and the SH1 helix adopt different conformations in the different stages of the acto-myosin cycle , where myosin switches from actin-bound to actin-unbound states according to the nature of the nucleotide bound to it ( Fig 1B ) . The conformational changes occurring in the linkers are propagated and amplified by the reorientation of the subdomains connected to them , so that small changes in the ATP-binding site in the U50K subdomain can ultimately lead to the powerstroke , a large swinging motion of the lever arm that is caused by the rotation of the converter subdomain and is responsible for the production of mechanical work when myosin is bound to actin . The powerstroke conformational changes are reversed upon ATP binding and actin unbinding in the recovery stroke , which is the transition from the post-rigor to the pre-powerstroke state that restores the up position of the lever arm [6 , 7] . In addition to actin and ATP binding sites , the motor domain of myosins has different pockets that can be bound by small molecules [3 , 9] . Allosteric modulators binding to these sites have been shown to affect myosin function in different ways and using different mechanisms . In particular , myosin II can be targeted by both inhibitors and activators . Most of the myosin II inhibitors discovered so far have been found to decrease the release of inorganic phosphate ( Pi ) after ATP hydrolysis , the rate limiting step of the acto-myosin cycle [3] . While many of these molecules have been used only as research tools because of their toxicity , the recently developed MYK-461 [5] and MYK-491 have passed the preclinical development stage and are currently in clinical trials [10] . In particular , MYK-461 has been tested in mouse models of HCM , where it is has been shown to slow down the progression of the disease [5] . Structural information on the binding site is available only for some of these inhibitors . Blebbistatin [11] binds to the 50-K cleft between the U50K and L50K subdomains , while the smooth muscle myosin inhibitor CK-571 has been recently shown to bind to a pocket between the relay and SH1 helices [12] . Interestingly , CK-571 uses a unique inhibition mechanism where the drug stabilises a previously unknown intermediate step along the recovery stroke and prevents myosin to reach the pre-powerstroke state , thus hindering the ATP hydrolysis [12] . Myosin activators , namely compounds that enhance myosin activity instead of inhibiting it , have been relatively less studied [3] . A recent breakthrough in the pharmacological treatment of cardiac disease has been the discovery of the myosin activator omecamtiv mecarbil ( OM ) [13] . The overall effect of OM on the acto-myosin cycle is to increase the duty ratio , which is the fraction of myosin molecules in the sarcomere that are strongly bound to actin . This effect is considered to result from an increase in the rate of transition from weakly to strongly bound states that leads to the faster release of Pi measured in the presence of actin[13] . The larger duty ratio causes an increase in the force produced by the sarcomere ( ensemble force ) [4] , so that the overall effect of OM binding is an increased heart contractility [13] . At the same time , OM has been shown to have an inhibitory effect on the velocity of actin filaments in in vitro motility assays [14 , 15] and on the powerstroke rate in time resolved FRET experiments [16] , while contrasting results have been found when studying its effect on the actin-activated ATP hydrolysis rate [13 , 15] . The increased force generation induced by OM makes it suitable for the treatment of heart conditions that are characterised by a decreased cardiac contractility . Indeed , OM is currently due to start phase III clinical trials for the treatment of heart failure [17 , 18] , after previous investigations showed that it was well tolerated and it had beneficial pharmacological effects such as an increased systolic function , a reduced left ventricular wall stress and a beneficial left ventricular remodelling [19–24] . The trials did not show any evidence of the adverse effects observed with the administration of the drugs currently used to treat heart failure , such as increased heart rate , arrhythmias or hypotension , while the presence of other dose-dependent side effects ( increased troponin levels and reduced diastolic filling times ) will be further investigated in larger patient cohorts [18] . Understanding the molecular basis of OM action is essential to design strategies for the development of new modulators and their tailoring to specific diseases . The opposing effects of OM on the kinetics of the acto-myosin cycle suggest that its binding has a complex effect on myosin at the atomistic level . Recent structural studies [8] showed that OM binds to a deeply buried pocket between the L50K and N-terminal subdomains , close to the SH1 helix and the converter ( Fig 1A ) . Since these regions are critical for the allosteric communication between the lever arm and the nucleotide binding site , it was suggested that OM can affect the coupling between the lever arm motion and the nucleotide state [8] . Moreover , small differences were found between Apo and OM-bound structures of the central β-sheet . Modifications in this part of the protein were suggested to be related to the increase of strongly actin-bound states induced by OM since the central β-sheet is part of the transducer , the element of the motor domain that mediates the communication between the nucleotide and actin binding sites [25] . The lack of atomistic information on the OM-mediated modulation of myosin dynamics prompted us to perform Molecular Dynamics simulations on the cardiac motor domain bound to OM , where the effects of the drug on the dynamical properties of the protein are investigated for the first time with atomistic resolution . We find that OM has a double effect on myosin dynamics , inducing a ) an increased coupling of the motions of the converter and lever arm subdomains to the rest of the protein , which produces a strong reduction in the amplitude of their motions and b ) a rewiring of the network of dynamic correlations , which produces preferential communication pathways between the OM binding site and functional regions in the U50K subdomain . The residues and interactions mostly responsible for these effects are also identified and we discuss the possible use of these findings for the future development of improved drugs and the targeting of specific pathogenic mutations .
The overall dynamics of cMotorD during the Apo trajectories was first analysed in terms of the Root Mean Square Fluctuation ( RMSF ) of its Cα atoms . A high flexibility was found for the long loops in the U50K ( loop1 and cardiomyopathy loop ) and L50K ( loop2 ) subdomains ( Fig 2 ) , as expected on the basis of their length and of the disordered nature of loops 1 and 2 [26] . Interestingly , large RMSF values were observed for the CLD and , to a less extent , the nearby SH3 domain and relay helix . These large amplitude motions of the CLD domain were found in all the Apo trajectories , albeit with a reduced extent for ApoB2 , indicating that they are independent from either the overall initial conformation of the domain ( chain A or B ) or the loop model used ( 1 or 2 ) . The cMotorD dynamics was further investigated by identifying the collective motions with a Principal Component Analysis ( PCA ) . PCA was performed on the Cα atoms only and by excluding the modelled loops to reduce the noise from these disordered regions ( Methods ) . The first two principal components ( PC1 and PC2 ) of each trajectory , describing the most important collective motions observed during the simulation , accounted together for a significant portion of the overall variance ( S2 Table ) , ranging from 33% ( ApoB2 ) to 65% ( ApoB1 ) . Two recurring types of motions were found , involving mainly quasi-rigid rotations of the CLD around two different hinge axes , with hinge regions located in the SH1 helix and the relay helix/loop ( Fig 3A and 3B ) . These CLD motions were the main contributions to the PCs in almost all the cases , even if in different proportions , so that each type of rotation was found either in PC1 or in PC2 according to the specific simulation ( S1 Fig ) . The exceptions were PC2 from ApoA2 and PC1 from ApoB2 , where rotational motions of the upper 50K domain were found to be of comparable or larger amplitude than the CLD ones . It is interesting to note that the CLD rotation described by PC1 ( ApoA1 ) has similarities with the conformational changes occurring in the same region during the acto-myosin cycle . In particular , the transition from the post-rigor to pre-powerstroke state ( ‘recovery stroke’ ) as described by X-ray structures involves a rotation of the CLD around an axis with similar direction as PC1 and similar hinge regions ( Fig 3B ) , but with an amplitude much larger than the one observed here ( ~70° instead of ~ 30° in ApoA1 ) . The CLD rotation in the recovery stroke is also known to be associated with the formation of a kink in the relay helix [6] . Remarkably , a significant bending of the relay helix was found during the ApoA1 and B1 simulations ( Fig 4 ) . In particular , at the end of the ApoB1 simulation the helix formed a kink similar in amplitude and position to that found in the X-ray structures of the pre-powerstroke state ( Fig 4 , left inset ) . The bent part of the helix was found to adopt different orientations in addition to those observed in the experimental structures . Overall , these data suggest that the motor domain can partially sample motions similar to those involved in the recovery stroke even in the absence of ATP . To summarise this section , we found that the intrinsic dynamics of cMotorD in the Apo state is dominated by the CLD , which is relatively free to move as a quasi-rigid domain . These types of CLD motions , namely rigid rotations around hinges located in the SH1 and relay helices , are consistent with the changes associated with the actomyosin cycle , even if of smaller amplitude . Moreover , in two of the simulations the CLD motions were associated with a significant bending of the relay helix . As mentioned above , OM binds to a critical region of cMotorD where it can interact with multiple subdomains at the same time ( Fig 5A ) . Indeed , the X-ray structures show that in both chain A and chain B conformations OM is in contact with residues of the N-terminal domain ( A91 , M92 , L96 , S118 , G119 , F121 ) , the relay helix ( M493 , E497 ) , the SH1 helix in the L50K subdomain ( V698 , G701 , I702 , C705 ) and the CLD ( P710 , N711 , R712 , L770 ) [8] . OM was stably bound to the protein during the simulations ( S2A Fig ) . Its interactions with M90 , A91 , M92 , L96 , S118 , M493 , I702 , C705 , P710 and R712 were found to be particularly stable , since the OM-residue distance was below 4 Å for at least 50% of the frames in all the OM-bound trajectories ( Fig 5B and S3 Table ) . Chain A and chain B simulations produced similar contact fingerprints . Residues A91 , S118 , L120 , C705 , N711 , R712 and K762 formed transient hydrogen bonding interactions with OM , which were dependent on the specific conformation adopted by OM ( S4 Table and S3 Fig ) , while strong hydrophobic interactions with M90 , A91 , M92 , L96 , M493 and I702 were observed in all the simulations ( side chains of non-polar residues within 4 Å from OM for at least 50% frames , S3 Table ) . The molecule was relatively flexible ( S2B Fig ) , as expected from the fact that it adopts different conformations in the X-ray structures of chain A and chain B ( blue and light blue sticks in S2C Fig ) . A cluster analysis ( S1 Text ) performed on the concatenated trajectories ( S5 Table and S2C Fig ) showed that the most populated cluster ( 82% ) was sampled almost equally in all the simulations ( except for OMA1 , where it contributes with a smaller proportion ) , with the representative structure having an RMSD from the X-ray structures of 2 . 2 ( chain A ) and 2 . 1 ( chain B ) Å ( red sticks in S2C Fig , left panel ) . A significantly different conformation ( cluster 4 , S5 Table ) was sampled for a short amount of time ( 2% ) at the end of the OMA1 simulation , where the methyl-pyridinyl ring was shifted upwards toward the β1-β2 loop ( S2C Fig , right panel ) . This conformation might be related to the restructuring of the OM binding pocket recently observed in the pre-powerstroke state , where the pocket is shifted upwards as a result of the lever arm motion during the recovery stroke [27] . The behaviour of the OM-binding site was further analysed by monitoring the inter-residue contacts in proximity of OM and comparing their stability with that found in the Apo trajectories ( Fig 6 and S4 Fig ) . OM-bound simulations in general presented a larger number of stable inter-residue contacts ( inter-residue distance < 4 Å for at least 70% of the simulation ) . OM-stabilised contacts were found between the N-terminal domain and the converter ( T94-N711 for OMA simulations and T94-G771 for OMB ) , and the relay helix and the converter ( Y501-R712 for OMA and E497-R712 and E500-K762 for OMB ) . Moreover , OMA trajectories showed additional hydrophobic contacts between the central β-sheet and the SH1 helix ( F121-V698 and F121-G697 ) and the β-sheet and the relay helix ( L120-F489 ) , while stronger intra-CLD contacts ( F709-R712 and N711-G768 ) were found in OMB trajectories . The reduced number of CLD OM-stabilised contacts found in chain A simulations is probably due to the small re-organisation of CLD observed in these trajectories . Indeed , chain A and B initial structures have small differences in the orientation of the CLD , with the chain B CLD slightly rotated in the direction of the recovery stroke ( S5 Fig ) . During the OM-bound simulations , chain A relaxed towards the chain B conformation , while the chain B conformation was stable throughout the whole trajectory ( S6 Fig ) . The overall effect of OM on cMotorD interactions was then to enhance the contacts between the different subdomains that compose its binding site , both by directly interacting with them and by stabilising the inter-residue contacts around it . This had a dramatic effect on the overall flexibility of the protein , as shown by the RMSF profiles ( lower panels in Fig 2 and S7 Fig ) . Indeed , a significant reduction in mobility was found for the CLD , together with the neighbouring SH3 domain and the SH1 and relay helices . Correspondingly , the first two collective motions ( PC1 and 2 ) , while showing a higher diversity across the OM-bound replicas compared to the Apo simulations , were consistently found to have higher contributions from the other subdomains ( the upper domain for chain A simulations and either the N-terminal domain or the lower domain for chain B ) rather than the CLD ( Fig 3A and S8 Fig ) . Moreover , when the CLD contributed significantly to the PCs , its motion was correlated with the neighbouring subdomains rather than anti-correlated ( insets in Fig 3A ) . The overall amplitude of the global motions was also significantly reduced compared to the Apo simulations ( S9 Fig ) . The results obtained comparing the single simulations were confirmed by a PCA performed on the combined Apo and OM-bound trajectories ( S10 Fig ) . The directions in the conformational space that best discriminate between the two binding states ( 43% of the overall variance ) are represented by the two types of CLD hinged rotations observed in the single Apo simulations ( S10A Fig ) . A projection onto PC1 and 2 shows a clear separation of the Apo and OM simulations , since the Apo trajectories span a much larger range of values along both components while the OM trajectories are located in the same region of the space ( S10B Fig ) . Finally , no significant bending was found for the relay helix in the presence of OM ( blue hues and right inset in Fig 4 ) . A comparison of representative structures from Apo and OM-bound trajectories is presented in S11 Fig , where it is possible to see the different arrangement adopted by the CLD and the relay helix at the end of the simulations . The previous analysis indicates that the CLD motions found in the Apo simulations are significantly decreased in the presence of OM and that CLD moves in a concerted way with other subdomains rather than freely rotating around the SH1 hinge as was instead observed in the unbound state . In agreement with this , the comparison of the dynamical cross-correlation matrices ( DCCM ) showed that OM binding induces in all the simulations an increase of the correlations between the CLD and the rest of the protein ( red lines in Fig 7 and red dots in S12 Fig ) , in particular the N-terminal domain , while at the same time decreasing the CLD intra-domain correlations ( green lines in Fig 7 and green dots in S12 Fig ) . The OM-induced changes in the dynamical correlation within cMotorD were further analysed by reconstructing the network of local correlated motions using the M32K25 Structural Alphabet ( SA ) ( Methods ) . This type of analysis highlights correlations between changes in the conformational state of 4-residue fragments of the protein backbone during an MD simulation . While the PCs and the DCCM networks presented in the previous sections are usually dominated by hinge motions of quasi-rigid dynamical domains , local correlations represent more subtle effects involving correlated changes in the local shape of the protein backbone . Local correlation networks were calculated for each simulation of the Apo and OM-bound state . A consensus network was then generated for each binding state ( S1 Text ) , resulting in two networks to be compared ( Apo and OM ) . The networks were first analysed by calculating a preferential connection score ζ between each 4-residue fragment in the OM binding site and the rest of the cMotorD domain ( Methods ) . Fragments with negative ζ values have a network distance from OM-binding fragments that is smaller than the average , so that they can be considered as preferentially connected to the OM-binding site . The scores obtained from the OM-bound simulations were then compared with the Apo ones and Δζ differences were calculated by subtracting the Apo from the OM-bound profiles ( Fig 8 ) . In the following , we will focus on the fragment starting with V698 ( or fragment V698 ) , but consistent results were obtained for the other OM-binding site fragments ( S13 Fig ) . The ζ values in most of the functional regions show either no change or a decrease upon OM binding ( Δζ < 0 ) , while increased values ( Δζ > 0 ) were usually observed outside these regions ( Fig 8A ) . This indicates that the OM-binding site tends to have a stronger preferential connection to the functional regions in OM-bound simulations compared to the Apo ones . Indeed , the Apo simulations showed either a reduced ( smaller |ζ| values compared to the OM ones ) or no preferential connection ( ζ = 0 ) to these regions ( S14 Fig ) . The most pronounced increases in preferential connection were observed for the G helix , the β5 strand and Switch 2 in the ATP binding site ( Fig 8B ) , which consistently showed negative Δζ values for all the OM-binding site fragments ( S13 Fig ) . Increased preferential connections were also observed for the β3 and β4 strands and part of Loop 1 in most of the cases . Interestingly , mapping the mutations known to be associated with dilated cardiomyopathy ( DCM ) [28] onto the Δζ profiles ( yellow points in Fig 8 ) , shows that some of them are in regions preferentially connected to OM . In particular , I201T ( Loop 1 ) and A223T ( G helix ) are found in functional regions with consistently negative Δζ values . This would suggest that their effect could potentially be counteracted by OM binding . The ζ score changes induced by OM in regions related to myosin function suggest the presence of significant differences in the local correlation network of OM-bound and Apo states . To investigate this further , the shortest paths were determined between fragment V698 and the preferentially connected functional regions identified above ( Methods ) . The comparison of the OM-bound and Apo results ( Fig 9A ) shows that the endpoints are more directly connected in the OM-bound network ( bottom panels ) than the Apo one ( top panels ) . As expected , the paths connecting the OM-binding site ( V698 , magenta ) on one side and the functional regions ( coloured cartoon ) on the other involve a smaller number of edges ( purple lines ) and are thus shorter in terms of distance in the network ( Fig 9B ) in the OM-bound simulations . The network representation also shows that the paths in the Apo networks involve more nodes that are distant in space from the end points compared to the OM-bound one ( Fig 9A ) . Moreover , both networks contain a hub node with a large number of connections ( fragment T177 for the OM-bound network and fragment C705 for the Apo one ) , but while T177 is directly connected to most of the functional regions , C705 needs to go through other nodes to reach the endpoints . In order to identify the residues and interactions mostly involved in this reorganisation of the local correlation network , we analysed the modifications in inter-residue contacts induced by OM binding ( i . e . contacts stabilised or destabilised by OM ) in the region between the OM-binding site and the G-helix ( Fig 10 ) . Contact matrices were first determined for each Apo and OM-bound simulation by calculating the fraction of the trajectory for which each residue pair was found in contact . A consensus contact matrix was then derived for each binding state and the OM-Apo difference was used to obtain a matrix representing a network of contact changes ( S1 Text ) . Calculating the paths connecting V698 ( magenta sphere ) and the G-helix residues ( red cartoon ) in the network ( yellow edges ) shows chains of contact changes going through the two sides of the central β sheet . The side closer to Switch 2 contains the nodes with the largest number of paths going through them ( larger spheres ) , suggesting that the corresponding residues ( namely F121 , N696 , L693 , T177 , G178 , I462 , K246 , Y266 , L277 and A223 ) are important in mediating the effects on the local correlation network induced by OM binding . Interestingly , residues T177 and G178 , which are part of the hub fragment T177 in the OM-bound local correlation network ( Fig 9A ) , are involved in most of the paths ( 83% of the paths contain either T177 or G178 , S6 Table ) and they participate in contacts that have among the largest changes in frequency upon OM binding ( S7 Table ) .
In this work , we used MD simulations to investigate the effect of the sarcomeric modulator omecamtiv mecarbil ( OM ) on cardiac myosin dynamics with atomistic resolution . Simulations were performed to reconstruct the sub-microsecond dynamics of the motor domain of cardiac myosin ( cMotorD ) in the absence and presence of OM , starting from the recently solved structures of the Apo and OM-bound cMotorD in the near-rigor state[8] . The light-chain containing regulatory domain ( RD ) was not considered here since no experimental structure is currently available for the cardiac isoform , however the simulated system included a small fragment of the lever arm helix . The regulatory role of the RD in cardiac myosin is mediated by the phosphorylation of a disordered portion of the regulatory light chain ( RLC ) , which is thought to be involved in the regulation of the transition from an inactive ( off ) to an active ( on ) form of the two-headed myosin molecule in the thick filament [29 , 30] . OM has been shown to leave the RLC phosphorylation levels unchanged [31] , suggesting that the molecular mechanisms mediated by OM are independent from those mediated by the RD . To the best of our knowledge , the present simulations represent the first fully atomistic simulations of cardiac myosin bound to a small molecule modulator . Previous computational works have focused so far on the effect of nucleotide [32] or actin binding [33] on myosin dynamics , the interactions between actin and myosin [34] , the release of Pi [35] , the modelling of the recovery stroke [36–39] or in general of the coupling between the actin binding site , the nucleotide binding site and the converter [40–43] . A significant part of these studies used enhanced sampling techniques to accelerate the transitions between the different states in the actomyosin cycle [35 , 36 , 38–41 , 43 , 44] , while unbiased simulations with length > = 50 ns have been performed only recently [32 , 33 , 37 , 45] thanks to the increase of the available computational power . The simulations presented here show that , in the absence of OM , the dynamics of cMotorD is dominated by the hinge motions of the converter+lever arm helix subdomain ( CLD ) . These have some resemblance to the CLD rotation observed during the transition from the experimental near-rigor to pre-powerstroke structure ( recovery stroke ) in that a ) they involve similar hinge regions and b ) they are associated with the bending of the relay helix . However , the CLD motions in the simulations seem to be more heterogeneous since , in addition to recovery stroke-like rotations ( Fig 3B ) , the domain can perform rotations in other directions ( e . g . Apo PC2 in Fig 3A ) . Moreover , the amplitude of the rotations is much smaller than in the recovery stroke , which is expected on the basis of the length of the simulation and the absence of ATP . The motions observed here , while not representing an actual transition to the pre-powerstroke state and while they might have been enhanced by the absence of the Regulatory Domain , suggest a conformational selection scenario for the recovery stroke , where the type of motions involved in the stroke are already partially sampled by the CLD before the binding of ATP . This finding is in agreement with the emerging model that CLD rotation is stabilised by the closure of Switch 2 upon ATP binding rather than being induced by it , so that it can occur at least partially before the changes in the nucleotide binding site take place [12 , 43 , 46] . This would also explain the recent observation that myosin can be trapped by inhibitors in intermediate states of the recovery stroke without closure of Switch 2 [12] . Moreover , a decoupling between Switch 2 and lever arm motions is consistent with the recent observation of a new structural state of Myosin VI where a large conformational change of Switch 2 does not produce a corresponding change in the lever arm position [47] . OM is shown by the simulations to have a double effect on myosin dynamics . The first is to dramatically reduce the CLD motions observed in the Apo state and couple them to the rest of cMotorD , as indicated by the large decrease in CLD motions and the increase of positive correlation between the CLD and the other regions , in particular the SH3 subdomain in the N-terminal region . A possible role of this subdomain in myosin activation is also suggested by the recent observation that one of the few other myosin activators currently known might bind to SH3[48] . Our results indicate that OM acts as a “glue” between the different subdomains that compose its binding site , both by directly interacting with them and by stabilising pre-existing inter-domain interactions . This effect was observed albeit with different extent in all the simulations , indicating that it is independent from the specific starting conformation . The second OM-induced effect emerging from the simulations is a reorganisation of the network of correlated local motions , which results in a more efficient and direct connection between the OM-binding site and functional regions compared to the Apo state . In particular , the networks show the formation of preferential pathways between the OM binding site and distant U50K regions close to ATP binding site , namely the G helix and Switch 2 . The communication between sites is mediated by a chain of OM-induced contact changes involving residues in the central β-sheet . Preferential connections in the dynamic correlation networks have been previously observed to be involved in allosteric communication [49] . Our results thus indicate that OM can modulate the cMotorD dynamics through at least two different molecular mechanisms , which would explain its complex and apparently contradictory effects on the kinetic parameters of the actomyosin cycle[16] . In particular , assuming that OM can induce similar effects on the pre-powerstroke state , the reduction of CLD rotational motions upon OM binding might explain the strong reduction in the powerstroke rotation rate measured with FRET [16] and the overall decrease of the actin sliding velocity [14 , 15] . This inhibitory effect on the lever arm motions is considered to be consistent with the overall increase in muscle contractility produced by OM binding , since it increases the fraction of time spent by myosin in the force generating state where it is strongly bound to actin [14 , 16] . On the other hand , the enhanced correlation with the G helix and Switch 2 might be related to the change in the number of myosin molecules strongly bound to actin . Indeed , the G helix has been shown to move concertedly with Switch 1 during the opening of the actin binding site when myosin dissociates from actin [40] . Changes in the conformation and/or dynamics of the G helix could affect the energetics of the reverse process , where strongly bound myosin states are produced upon closure of the actin binding site . Moreover , a Pi release mechanism involving mainly Switch 2 rather than Switch 1 motions has been recently suggested on the basis of a newly found structural state of Myosin VI [47] . During the revision process of this paper , a new OM-bound structure has become available , where OM is bound to the pre-power stroke state [27] . Simulations using the same protocol will confirm whether OM has similar effects on the dynamics of this state , which is further down the actomyosin cycle . Moreover , simulating the transition between the near-rigor state studied in this work and the pre-power stroke state will allow the quantification of possible effects on the energy landscape of the recovery stroke , in particular on the relative stability of the two states and the energy barrier between them [50] . Our findings suggest that the future development of OM-based drugs could act on two different sets of molecular features to produce novel compounds with improved action . In particular , changes in the interactions with the central β-sheet could modify the strength of the preferential connection to regions close to the ATP binding site and ultimately the transition to strongly bound states . On the other hand , modifications in the interactions with the N-terminal domain or the CLD could change the degree of coupling between the CLD and the rest of cMotorD and thus its rate of rotation during the powerstroke . Obtaining an optimal balance between these two effects could be essential to produce drugs with reduced side effects or specific therapeutic properties . The present results can also be used to identify specific pathogenic mutations to be targeted by OM . Indeed , small molecule activators of myosin have been previously proposed as possible drugs to counteract the effect of dilated cardiomyopathy ( DCM ) mutations , since these are usually associated with a decrease of the power output of the cardiac muscle [4] . Comparing the distribution of the currently known DCM mutations [28] with the regions preferentially connected to the OM-binding site can give information on the mutants that are most likely to be directly affected by OM binding ( yellow dots in Fig 8 ) . Mutations I201T [51] ( Loop1 ) and A223T [52] ( G helix ) are particularly interesting since a ) they are located in regions with high preferential connection and b ) they are far from the OM binding site , so they are less likely to directly interfere with the drug binding . Further investigations on OM-bound mutants will be able to highlight if OM has a rescuing effect on them , namely if it can reverse any effect induced by these mutations on the structural and dynamical properties of myosin .
The initial structure of the motor domain of the human β-cardiac myosin ( residues 1 to 783 of UniProt sequence P12883 ) was extracted from the Protein Data Bank ( PDB ) for the OM-bound ( PDB ID: 4PA0 ) and Apo ( PDB ID: 4P7H ) state[8] . Both structures are nucleotide-free . Two chains ( A and B ) were found in both PDB files , which show structural differences in the converter , lever arm and central β-sheet . Simulations were started from both chains and labelled accordingly ( S1 Table ) . Homology modelling was used to model unsolved parts of the protein in the X-ray structure , which included loops localised either in the actin-binding region or in the converter ( S8 Table ) . MODELLER 9 . 15 [53] was used to model the missing loops by keeping the rest of the structure unchanged , using as templates structures from chicken skeletal ( PDB ID: 1M8Q ) and smooth ( PDB ID: 1BR1 ) muscle myosin . Since these loops were expected to be highly flexible , to make sure that the results obtained from the simulations were not dependent on their specific initial structure , two different initial loop conformations were generated . These were then used as starting points to run two different replicas for each system ( x = 1 or 2 in S1 Table ) , resulting in a total number of eight different MD simulations ( further details on loop modelling can be found in S1 Text ) . The protein was described using the Amber99SB*-ILDN force field , which has been extensively tested for its ability to reproduce the correct relative stability of secondary structure elements and side-chain conformations in proteins [54] . For compatibility with the force field used for the protein , the OM ligand was described using parameters from the General Amber Force Field ( GAFF ) [55] . PyMOL ( version 1 . 8 . 2 . 0 ) [56] was used to add the hydrogen atoms to the OM structure and generate the connectivity table . The Antechamber and tLeap tools from the AmberTools 15 [57] suite were used to generate the files with the OM topology and force field parameters , together with the initial coordinates . These were then converted to GROMACS format using Acpype[58] . The atomic point charges were generated using the AM1-BCC[59] method using either the chain-A or the chain-B conformation of OM . As expected , only small differences were found in the two sets of charges , with an overall RMSD of 0 . 003 a . u . The atom types and charges used for OM are reported in S9 Table , while OM structural formula is reported in S15 Fig together with the atom numbering . Additional details on OM parametrisation are described in S1 Text and S10 Table . All MD simulations were performed using GROMACS 4 . 6 . 7 [60] . Each system was solvated using a truncated octahedral box of TIP3P water molecules . Periodic boundary conditions were applied , using the Particle Mesh Ewald ( PME ) method for electrostatic interactions , a 9-Å cutoff for the direct space sums and for van der Waals interactions , and long-range corrections to the dispersion energy . Energy minimisation of each system was followed by equilibration first in NVT ( T = 300 K ) and then in NPT conditions ( T = 300 K , p = 1 bar ) for a total 6 . 5 ns . Production NPT runs were then performed for 300 ns , saving the coordinates every 1 ps . The stability of the simulations was checked by monitoring the Root Mean Square Deviation ( RMSD ) from the initial structure ( S16 Fig ) and the time evolution of the DSSP secondary structure annotation ( S17 Fig ) . The detailed MD protocol with full references can be found in S1 Text . The Principal Component Analysis ( PCA ) [61] was performed with GROMACS using Cα atoms coordinates of snapshots extracted from the production trajectory every 100 ps . The trajectories were then projected onto the PCs associated with the two largest eigenvalues ( PC1 and PC2 ) . PCs were analysed using the DynDom [62] software , which can be used to identify dynamic domains in the protein and their relative motion . DynDom also determines hinge regions at the interface between the domains and rotational axes . A PCA was performed also on a pseudo-trajectory obtained by combining the last 100 ns of all the Apo and OM-bound trajectories . The correlation networks were generated by calculating the dynamical cross-correlation ( DCC ) matrices with Wordom [63] using Cα atoms coordinates of snapshots extracted from the production trajectory every 100 ps . Multiple matrices were first generated by calculating time averages over 5 ns . Averaging over these matrices produced the final DCC matrix [64] . The calculation and analysis of local correlation networks was performed as described in [49] . In brief , local conformational changes and their correlation were described using the M32K25 [65 , 66] Structural Alphabet ( SA ) , namely a collection of fragments of 4 consecutive Cα atoms representing prototypical backbone conformations . The correlation of conformational changes in a pair of protein fragments is calculated as normalized Mutual Information ( MI ) between the two sequences of SA letters representing the different conformations explored by the fragments during the simulation . As previously shown , MI networks can be used to describe allosteric transmission pathways in proteins [49 , 67] . In particular , transmission pathways between two regions can be identified by calculating the set of shortest paths connecting them in the MI network . If a source site is selected ( e . g . an allosteric site ) , it is possible to detect the regions in the protein that have a preferential connection with it by identifying the fragments that are closer to the source site than the average [49] . The SA analysis was performed on Cα atoms coordinates extracted every 1 ps . The statistical significance of the MI values was determined by generating a random background distribution of 1000 samples as described in [49] . Fragments are labelled in the text using the first residue of the fragment . The matrix of OM-Apo contact changes was derived by first calculating the frequency of inter-residue contacts for each Apo and OM-bound simulation . Two residues were considered to be in contact if the minimum distance calculated over all the pairs of non-hydrogen atoms was < 4 Å . A consensus contact matrix was then calculated from the four simulations of each binding state ( S1 Text ) . The final matrix of OM-Apo contact changes was derived by subtracting the Apo consensus matrix from the OM one and calculating the absolute value , so that elements different from 0 indicate contacts that are either stabilised or destabilised upon OM binding . In order to remove the noise from the modelled loops , most of which showed a high flexibility ( S7 Fig ) , the PCs and DDC , MI and contact change matrices were calculated considering only the residues solved in the starting X-ray structures ( see S8 Table for a list of the excluded residues ) . All the residues in the protein were used for the RMSD , RMSF and secondary structure analyses . The analyses were performed using GROMACS and GSATools [68] , together with in-house R scripts using the Bio3D [69] library . Networks were visualised with the xPyder [70] plugin for PyMOL . A more detailed description of the analyses performed for this work can be found in S1 Text . | Cardiac myosin is a motor protein responsible for the contraction of the heart muscle . New strategies for the cure of heart diseases are currently being developed by using myosin modulators , which are small molecules that can interact with myosin and modify its activity . The advantage of this approach over traditional drugs is that by directly targeting cardiac myosin it is possible to have drugs with reduced side effects . Moreover , the availability of a spectrum of molecules to finely tune myosin to a desired level of activity opens the possibility to develop more precise and personalised drug therapies . In this work , we study a recently discovered activator of cardiac myosin , omecamtiv mecarbil , in order to understand its mechanism of action . In particular , we use Molecular Dynamics simulations to unravel the effects of the drug on myosin motions , which are closely related to its function . We find that omecamtiv has a strong effect on myosin dynamics and it changes the way regions of the protein that are critical for its function interact with each other . We use these data to identify genetic mutations associated with heart diseases that could be targeted by the drug and to suggest a possible route to design drugs with different therapeutic properties . | [
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| 2017 | Allosteric modulation of cardiac myosin dynamics by omecamtiv mecarbil |
We resequenced and phased 27 kb of DNA within 580 kb of the MHC class II region in 158 population chromosomes , most of which were conserved extended haplotypes ( CEHs ) of European descent or contained their centromeric fragments . We determined the single nucleotide polymorphism and deletion-insertion polymorphism alleles of the dominant sequences from HLA-DQA2 to DAXX for these CEHs . Nine of 13 CEHs remained sufficiently intact to possess a dominant sequence extending at least to DAXX , 230 kb centromeric to HLA-DPB1 . We identified the regions centromeric to HLA-DQB1 within which single instances of eight “common” European MHC haplotypes previously sequenced by the MHC Haplotype Project ( MHP ) were representative of those dominant CEH sequences . Only two MHP haplotypes had a dominant CEH sequence throughout the centromeric and extended class II region and one MHP haplotype did not represent a known European CEH anywhere in the region . We identified the centromeric recombination transition points of other MHP sequences from CEH representation to non-representation . Several CEH pairs or groups shared sequence identity in small blocks but had significantly different ( although still conserved for each separate CEH ) sequences in surrounding regions . These patterns partly explain strong calculated linkage disequilibrium over only short ( tens to hundreds of kilobases ) distances in the context of a finite number of observed megabase-length CEHs comprising half a population's haplotypes . Our results provide a clearer picture of European CEH class II allelic structure and population haplotype architecture , improved regional CEH markers , and raise questions concerning regional recombination hotspots .
The human major histocompatibility complex ( MHC ) is a highly polymorphic genomic region of over 3 Mb on chromosome 6p21 . MHC polymorphisms include critical determinants for tissue transplantation success and show strong correlation both with many genetic diseases and with ethnic origin . Haplotype analysis of DNA sequence containing specific allele combinations of two or more nearby genetic loci was first established in the MHC . Many individuals within a human population share a small number of specific MHC haplotypes . These 1 to 3 Mb stretches of nearly identical MHC DNA sequence with high population frequency are called conserved extended haplotypes ( CEHs ) [1]–[4] or ancestral haplotypes [5] , [6] . Virtually all MHC allele-disease associations involve marker alleles of CEHs [2]–[6] . Early work defined CEHs by their alleles at HLA-B , HLA-DR loci and at intermediate MHC genes ( i . e . , ‘complotypes’ [7] ) . Later CEH reports extended the core region from HLA-C to HLA-DQB1 . With technological refinements , it became clear individual CEHs carry only one allele ( or , rarely , a limited number of variants ) at any given locus in this region [2]–[6]; [8]–[16] without apparent recombination . Intervening DNA sequence is therefore essentially conserved ( i . e . , identical ) among the population haplotypes comprising each CEH , and CEHs are essentially identical by descent common population haplotypes . CEH sequence conservation has been verified whenever investigated , whether determined by microsatellite , restriction fragment length polymorphism , dense single nucleotide polymorphism ( SNP ) or partial resequencing analyses of multiple haplotypes from unrelated individuals [9]–[13] , [15] . We have previously referred to the existence of such “fixed” CEH alleles [1]–[4] , [8] , [14] and the intervening sequence conservation of CEHs as “genetic fixity” [3] , [4] , [8] , [14]–[16] . We define genetic , haplotype or sequence “fixity” to be sequence identity and conservation of a large stretch of genomic sequence , shared by a relatively large number of apparently unrelated individuals , without apparent recombination from an ancestral sequence . By “identity” we mean “essential identity , ” thus allowing for minor private mutation or microvariation within individual haplotype sequences comprising a particular CEH ( or CEH fragment or block ) . Several studies have described the extension of genetic fixity in some CEHs telomerically in the class I region to HLA-A and centromerically in the class II region to HLA-DPB1 [1]–[6] , [8] , [14]–[16] . However , little is known about CEH alleles and conserved dominant sequences between HLA-DQB1 and HLA-DPB1 or those centromeric to HLA-DPB1 . We sought both to confirm the existence of sequence conservation in multiple examples of a wide variety of CEHs and to identify the dominant sequences centromeric to HLA-DQB1 for the core MHC CEHs most similar to the previously sequenced “common” European haplotypes [17]–[20] . Using consanguineous cell lines , the Wellcome Trust Sanger Institute ( http://www . sanger . ac . uk ) undertook the MHC Haplotype Project ( MHP ) [17]–[20] ( http://www . ucl . ac . uk/cancer/medical-genomics/mhc ) in which eight “common” European MHC haplotypes were fully or nearly fully sequenced over a genomic distance of up to 4 . 75 Mb . However , no systematic analysis has determined whether these sequences accurately represent the previously established CEHs [1]–[6] . As we argued earlier [4] , the extent to which the MHP sequences could be exploited for deciphering genotype-phenotype relationships would require resequencing multiple independent population haplotypes to determine consensus sequence , microvariation , and population representation for each CEH . Here , we sought to answer two main questions: 1 . Are the centromeric portions of the MHP sequences representative of European CEHs ? 2 . What is the extent of and sequence of a retained dominant sequence centromeric to HLA-DQB1 for each CEH “represented” by a MHP sequence ? We compare the classical HLA markers of the eight MHP sequences with the analogous markers of previously reported European CEHs [3] , [4] , [6] , [21] . Then , we describe partial resequencing of the region centromeric to HLA-DQB1 of multiple population haplotypes for each CEH these eight sequences putatively represent . The MHC class II region centromeric to HLA-DQB1 is important both because of its strong association with many complex diseases and its known or suspected recombination hotspots [22] , [23] . We document the extent of CEH dominant sequence conservation from HLA-DQA2 to DAXX , and we identify the SNP and deletion-insertion polymorphism ( DIP ) alleles of the dominant sequence for each CEH . Finally , we identify where MHP sequences accurately represent those dominant sequences , and we discuss several structural and conceptual issues related to local recombination hotspots and linkage disequilibrium ( LD ) .
Five MHP haplotypes contain the markers of a previously reported CEH [3] , [4] , [6] , [21] from HLA-C to HLA-DQB1 ( what we define as the “core” or “classical” ( for CEH purposes ) MHC region ) . HLA specificities are used in Table 1 to designate each CEH name for historical reasons . The five apparent MHP CEHs are: PGF , the human reference sequence for the MHC , containing the core MHC markers of the CEH “B7 , DR15”; COX , representing the CEH “B8 , DR3”; QBL , representing the CEH “B18 , DR3”; MANN , with the core MHC markers of two “B44 , DR7” CEHs ( exhibiting both HLA-C*04/C*16 variation and , independently , C4A*0/C4A*3 microvariation ) ; and , DBB , representing the CEH “B57 , DR7 . ” The other three MHP cell lines do not contain a previously described MHC CEH , and it is therefore uncertain how “common” these haplotypes are in any European population . SSTO contains HLA-DQB1*03:05 , a DQ8 specificity allele , but is not a reported CEH . Several reported CEHs contain a DR4 , DQ8 specificity block ( like SSTO ) MHC haplotype , although they all have HLA-DQB1*03:02 alleles . The MCF haplotype exists in our database only once and is not a known CEH [21] . The CEH “B44 , DR4 , DQ7” ( in which there is C4B*Q0/C4B*1 microvariation ) is the reported CEH most similar to MCF in the class II region . ( HLA-DQB1*03:01 is a DQ7 specificity allele . ) Finally , the APD MHC haplotype ( HLA-C*06:02 , B*40:01 , DRB1*13:01 , DQB1*06:03 ) does not exist among our 2675 normal Boston haplotypes [21] and has not been reported to be a CEH . In the core MHC region , the CEH [HLA-C*03:04 , B*40:01 , SC02 , DRB1*13:02 , DQB1*06:04] ( “B60 , DR13” ) is the reported CEH [3] , [4] , [6] , [21] most similar to the APD haplotype . ( HLA-B60 is the specificity of the HLA-B*40:01 allele in this CEH . ) After aligning MHP sequences [18]–[20] , we chose centromeric class II amplicons that would maximize the resultant SNP-DIP haplotypic variation if the MHP sequences were representative of common population haplotypes . We supplemented our initial choices with amplicons to localize transition points where MHP sequences ceased to represent common long-range haplotypes . We designed 56 primer pairs to sequence amplicons in five regions from HLA-DQA2 to DAXX ( Figure 1 ) . We resequenced about 27 kb of this 580 kb region . HLA-DQA2 and DAXX are located approximately 80 kb and 660 kb centromeric to HLA-DQB1 , respectively . The sequenced amplicons and their polymorphisms we report along with their human genome ( assembly 37 . p10 ) locations and rs number designations are given in Table S1 and Table S2 . Where known polymorphisms are missing in Table S2 from genomic locations within the boundaries of amplicons described in Table S1 , all of the haplotypes we report here matched the human reference sequence . We determined the phased sequences of 158 population haplotypes primarily through segregation analysis in pedigrees . For each pedigree , we determined between two and four founder haplotypes , varying by the number of unrelated haplotypes identifiable in the subjects available for each pedigree . All haplotypes followed Mendelian inheritance patterns except for rare null alleles and intra-family crossovers . For the latter , as with all pedigrees , we only report the unrelated non-crossover founder haplotypes . We achieved 91% to 100% sequence completion in the amplicons from HLA-DQA2 through HLA-DMA and from RING1 through DAXX , 85% sequence completion for the 158 haplotypes in BRD2 and about 45% to 65% completion from HLA-DOA through HLA-DPB1 . Text S1 provides details about the population haplotypes chosen for each CEH group , and Table S3 provides details of the resequencing coverage ( i . e . , completion ) for each haplotype group and region . Table S5 provides the complete phased SNP-DIP sequence data for all 158 haplotypes and the eight MHP haplotypes for a central portion of the covered region from HLA-DOB to BRD2 . Table S4 provides the MHP and dominant CEH sequences from HLA-DQA2 to DAXX , including the annotated genomic locations of the SNPs and DIPs . The dominant sequences were taken from Table S5 and analogous data from other regions . The CEH groups are organized in numerical order by their HLA-DR/DQ specificities . Some current MHC haplotype scaffolds contain sequence gaps for and/or do not extend centromerically to several of the amplicons we sequenced . We sequenced the QBL ( Figure S1 ) , MANN ( Figure S2 ) and DBB ( Figure S3 ) haplotypes within those amplicons . The SNP and DIP alleles within those amplicons for the three MHP haplotypes are highlighted both within the sequences shown in Figures S1–S3 and surrounded by yellow borders in the summary Table S4 and in Table S5 . These sequences have assigned GenBank accession numbers as described in the Materials and Methods and the Figures . We compared the sequences of the five MHP haplotypes ( PGF , COX , QBL , MANN and DBB ) containing markers of specific CEHs in the core MHC with population haplotypes bearing the same CEH markers ( Table 1 ) . We also compared both the PGF and MANN haplotypes each with a second CEH with which they shared HLA-DRB1 and HLA-DQB1 alleles . We compared SSTO and MCF with CEHs that shared at least some class II specificity , allele or sequence identity . Because 90% of the 50 DR4 , DQ8 haplotypes we sequenced shared identity to SSTO in the HLA-DQA2 and HLA-DQB2 regions , we compared SSTO with the sequences of all six resequenced DR4 , DQ8 CEHs ( Table 1 ) . We compared the MCF sequence with the only known CEH with which it shares HLA-DR/DQ alleles ( please see above ) . Finally , we compared the APD sequence with the only DR13 population haplotype ( out of 13 total ) we sequenced having an identical HLA-DQA2 to HLA-DOB sequence . We therefore did not compare the APD sequence with any known CEH because no known CEH shared sequence identity with APD . The break point ( or lack thereof ) at which a MHP sequence no longer represented any CEH is shown in Figure 2 . The APD cell line is not shown in Figure 2 because it does not represent a reported CEH . Figure 2 displays were based on analyses of sequences shown in Table S4 , and those analyses were based on phased data ( Table S5 and analogous data in other regions ) . PGF had the B7 , DR15 CEH dominant sequence and QBL had the B18 , DR3 CEH dominant sequence throughout the HLA-DQA2 to DAXX region ( Figure 2 ) . PGF also represented the B18 , DR15 CEH up to a point within the 1 kb region from intron 8 to intron 6 of TAP2 ( Table S4 and Table S5 ) , where this CEH's dominant sequence began to differ from that of the B7 , DR15 CEH . The other five MHP sequences represented at least one CEH in class II for variable distances . We determined precisely the centromeric break point for the COX sequence . Twenty-five of the 30 B8 , DR3-like haplotypes showed sequence identity to COX from HLA-DQA2 through the DOB9 amplicon within intron 6 of TAP2 . However , within the same intron , at rs60045856 ( SNP ID #147 , Table S2 ) , less than 600 bp centromeric to the DOB9 amplicon , the COX sequence differed from all of the previously identical 25 haplotypes ( Table S4 and Table S5 ) . The G allele at rs60045856 appeared to be a regional tag marker of the B8 , DR3 CEH in that all 25 haplotypes identical to the COX sequence up to the DOB9 amplicon possessed this allele whereas all of the other 133 haplotypes reported here ( as well as COX and the other seven MHP sequences ) had the T allele ( Table S5 ) . COX never shared significant regional sequence with the dominant B8 , DR3 sequence centromeric to TAP2 . MANN represented two related B44 , DR7 CEHs through at least a region 2 kb centromeric to HLA-DOB ( amplicon DOB5 ) . Approximately 5 . 5 kb telomeric to TAP2 , where the two B44 , DR7 CEH dominant sequences diverged , MANN continued to represent the C4 , B44 , DR7 CEH ( despite MANN carrying the HLA-C*16:01-defining allele of the C16 , B44 , DR7 CEH ) . Within intron 8 of TAP2 , the C4 , B44 , DR7 dominant sequence split into major and minor variants , and MANN ceased to represent either B44 , DR7 CEH dominant sequence , although it continued to be identical to the B49 , DR4 , DQ8 CEH throughout all of the TAP2 amplicons we sequenced ( Table S4 and Table S5 ) . DBB possibly represented the B57 , DR7 CEH throughout the class II region in which the CEH maintained a dominant sequence . We could not pinpoint the end of representation by DBB or MCF due to incomplete sequencing ( Figure 2; Text S1 ) . However , MCF stopped representing its CEH within the 26 . 9 kb region at or centromeric to BRD2 ( Table S4 ) , telomeric to lost CEH fixity . Of the seven MHP sequences in Figure 2 , SSTO represented its nearest CEH group for the shortest distance . The centromeric break point for SSTO representation of the CEH HLA-B62 , SC33 , DR4 , DQ8 was within a 13 . 5 kb region between HLA-DQB2 and HLA-DOB . The SSTO centromeric break point for any DR4 , DQ8 CEH was narrowed to a different 11 . 2 kb region between HLA-DQB2 and HLA-DOB ( Table S4 ) . However , we can predict the latter break point more precisely using MHP sequence data . Telomeric to rs9276712 ( SNP ID #89 , amplicon DC13 , Table S2 ) , SSTO was identical to the CEHs HLA-B62 , SB42 , DR4 , DQ8 , HLA-B60 , SC31 , DR4 , DQ8 and HLA-B38 , SC21 , DR4 , DQ8 and was highly similar to the PGF sequence but differed significantly from the APD sequence . At and centromeric to rs9276712 ( for 141 kb , until amplicon DMP1 ) , the SSTO sequence was highly similar to APD but significantly different from PGF ( which remained highly similar to the three CEHs ) . The recombination event that caused this SSTO switch from similarity to PGF to similarity to APD was likely between rs9276712 and rs1158783 , the last SNP telomeric to rs9276712 at which the telomeric APD-PGF-SSTO pattern was clear . The distance between the two SNPs is 286 bp in the human reference sequence . In an attempt to identify SNP/DIP markers near TAP2 differentiating the relatively similar sequences of the B18 , DR3 CEH ( represented by QBL ) and the B44 , DR7 CEHs ( represented by MANN ) , we resequenced B44 , DR7 haplotypes ( including MANN ) at the DOB7 . 5 amplicon ( Table 2 ) . Among the eight MHP sequences , only MANN had a T allele at SNP DOB7 . 5-2 ( rs2857100; ID #2 , Table 2 ) . We confirmed this by sequencing MANN at amplicon DOB7 . 5 . However , all nine of the 10 B44 , DR7 haplotypes we sequenced ( including all five essentially identical to MANN telomeric to the DOB7 . 5 amplicon ) had the C allele at rs2857100 . We concluded the MANN haplotype had a private mutation at rs2857100 . If the T allele exists in other B44 , DR7 haplotypes , the frequency is likely to be extremely low . This was the only private SNP allele we found in any MHP sequence within a region in which it otherwise had the dominant sequence of a CEH it represented . From HLA-DQA2 to HLA-DQB2 , all CEHs retained a dominant sequence ( i . e . , maintained “genetic fixity” ) shared by 50 to 100% of the population haplotypes in each group ( Figure 3A-C ) . Most CEHs also retained a dominant sequence by DAXX ( 660 kb centromeric to HLA-DQB1 ) , although there was usually a gradual reduction in the number of population haplotypes sharing that sequence . CEH dominant sequence results are organized based on the MHP cell line sequences in the order in which they were published . Complete detail of fixity loss for each CEH is provided in Text S1 . No results are presented for the DR13 , DQ6 CEH because APD did not represent that CEH in the regions we resequenced . Loss of sequence fixity ( Figure 3A–C ) is in terms of the gene and/or amplicon at which individual haplotypes stopped sharing SNP/DIP alleles of their CEH dominant sequence . Minor variations , almost always apparently unlinked and isolated private SNPs or DIPs and isolated dominant sequence microvariation , were not counted as loss of sequence fixity ( e . g . , Table S5 ) . CEH fixity loss is therefore due to past recombination of the dominant sequence with other haplotype sequences . This conclusion is strengthened by our observation that sequences centromeric to the dominant sequence break point are rarely unique and are often found in other CEH groups ( e . g . , Table S5 ) . We therefore make the explicit assumption that intervening unsequenced regions of population haplotypes sharing a CEH dominant sequence ( i . e . , telomeric to the break point for any given population haplotype ) have similarly limited microvariation and are essentially identical sequences . Although recombination is the explanation for population haplotype crossover from a CEH dominant sequence ( previously identical by descent ) , it is not possible to calculate CEH recombination rates . The number of meioses experienced by the CEH prior to ( or since ) its recombination to form any particular population haplotype is unknown . To quantify and display observed population haplotype recombinants responsible for the breakdown of each CEH dominant sequence , we developed a new metric , normalized crossover frequency ( NCF , see Materials and Methods ) . Our sequencing amplicons and data were not distributed evenly across the region analyzed ( Figure 1 , Table S1 , Table S2 , Table S4 ) . We therefore display NCF values , calculated for 11 separate sub-regions ( see Materials and Methods and Table S2 ) , on genomic maps drawn to scale ( Figure 3D–O ) . The areas and locations of the bars in those figures quantify and localize the effect of recombination on the loss of CEH fixity displayed in Figure 3A–C . Sequence recombination is difficult to localize with precision ( please see below ) . Furthermore , the breakdown of a CEH dominant sequence likely varies in different population cohorts . Nevertheless , a few general observations are evident from the results shown in Figure 3 . First , the breakdown locations and frequencies vary significantly between different CEHs . Although certain sub-regional crossover sites are more common ( e . g . , between HLA-DQB1 and HLA-DQA2 ( sub-region 1 ) , between HLA-DQB2 and HLA-DOB ( sub-region 3 ) , between TAP2 and HLA-DMA ( sub-region 6 ) , between BRD2 and HLA-DPB1 ( sub-regions 8 and 9 ) , and between HLA-DPB1 and VPS52 ( sub-region 10 ) ) , none is common to the majority of all analyzed CEHs . Also , some dominant sequences break down gradually in many locations whereas others seem to break down in a more focused fashion . These differences may be due to different relative timelines of CEH expansion and recombination events . Finally , while specific CEHs show a range from high to no sequence conservation through DAXX , most CEHs show approximately 50% dominant sequence retention around BRD2 ( in sub-region 7 , between HLA-DMB and HLA-DOA ) . Below , we highlight the results for specific CEHs . The precise location ( and , consequently , quantitation ) of historic recombination leading to the breakdown of a dominant sequence is often not definable . Perhaps the clearest example of this is the apparent strong crossover frequency for the B7 , DR15 CEH in TAP2 ( sub-region 5 , Figure 3D ) . Sequence fixity was maintained for 19 of 23 ( 83% ) B7 , DR15 haplotypes from HLA-DQA2 through intron 8 of TAP2 ( Figure 3A ) . Beginning in intron 6 of TAP2 , fixity of the B7 , DR15 CEH declined to 14 haplotypes ( 61% ) , and was maintained through TAP2 . The detected crossover of five B7 , DR15 haplotypes was to a sequence that defined the B18 , DR15 CEH dominant sequence . Thus , the crossovers detected within TAP2 in these five haplotypes could have occurred anywhere within the region shared by the two CEHs . That region extends telomerically past HLA-DRB1 . The B18 , DR15 CEH dominant sequence from HLA-DQA2 to BRD2 ( apparently identical to the B7 , DR15 dominant sequence through intron 8 of TAP2 ) was found in 80% of the population haplotypes we resequenced ( Table S4 and Table S5 ) . B7 , DR15 haplotype resequencing centromeric to TAP2 showed a gradual loss in CEH fixity through DAXX ( Figure 3A ) and declined below 50% near HLA-DMB . The nine B7 , DR15 haplotypes identical at HLA-DPB1 ( 39% of the original 23 ) either had the unique exon 2 sequence of or were classically typed as HLA-DPB1*04:01 . The dominant B7 , DR15 CEH sequence from HLA-DQA2 through DAXX was found in 26% of all resequenced B7 , DR15 haplotypes . All 30 B8 , DR3 haplotypes were identical at HLA-DQA2 . Sequence conservation decreased to 29 haplotypes ( 97% ) at HLA-DQB2 , to 87% at 16 . 4 kb centromeric to HLA-DQB2 ( amplicon DC10 ) and to 25 haplotypes ( 83% ) from HLA-DOB through TAP2 ( Figure 3A ) . B8 , DR3 sequence fixity decreased below 50% in the 20 kb region between amplicons DMP10 and DMP11 and declined to 40% at HLA-DOA ( sub-region 8 , Figure 3E ) . Seven of the 30 haplotypes ( 23% ) had the dominant sequence and were essentially identical through DAXX . Six of those contained HLA-DPB1*01:01 ( the seventh had HLA-DPB1*03:01 ) . ( If the haplotype with HLA-DPB1*03:01 is different from the other six ( in spite of having essentially identical SNP-DIP alleles from HLA-DQA2 to DAXX ) , the dominant sequence at DAXX would be represented by only six of 30 ( 20% ) of the studied haplotypes rather than 23% . ) Only 12 of the 18 ( 67% ) B18 , DR3 haplotypes showed sequence identity at HLA-DQA2 ( Figure 3A ) . In contrast to this relatively high crossover frequency between HLA-DQB1 and HLA-DQA2 ( sub-region 1; Figure 3F ) , 61% of all 18 haplotypes remained essentially identical to one another and QBL from HLA-DQB2 through BRD2 . The only microvariation found among these 11 haplotypes was at the second BRD2 DIP ( ID #204; Table S2 ) . By DAXX , nine of the 18 ( 50% ) sequences were still essentially identical . All 10 B44 , DR7 haplotypes shared sequence identity from HLA-DQA2 through 21 kb telomeric to HLA-DOB ( amplicon DC13 ) , and 90% of the haplotypes remained essentially identical up to 6 . 5 kb centromeric to HLA-DOB ( Figure 3B ) . As outlined in Text S1 , we studied six C4 , B44 , DR7 ( Figure 3G ) and four C16 , B44 , DR7 ( Figure 3H ) haplotypes . The dominant sequences of these two CEHs became different near and within TAP2 ( Table S4 and Table S5 ) . The DOB6 DIP at 5 . 5 kb telomeric to TAP2 and the DOB7-2 SNP 2 . 8 kb telomeric to TAP2 defined this split . The four C16 , B44 , DR7 examples we sequenced maintained sequence identity to one another for the remaining amplicons in TAP2 and three of these ( 75% ) remained identical through the BRD2 region . The five essentially identical C4 , B44 , DR7 haplotypes split into two groups within intron 8 of TAP2 ( at SNP DOB8 . 4 ) . The dominant sequence , in three of the haplotypes ( 50% ) appeared to be shared through at least 2 kb telomeric to BRD2 . We did not sequence the haplotypes comprising the dominant sequence of either CEH sufficiently to determine the extent ( or lack ) of sequence identity centromeric to BRD2 . Three of the four ( 75% ) B57 , DR7-like haplotypes shared a common sequence from HLA-DQA2 through approximately 16 . 5 kb centromeric to HLA-DQB2 . Sequence fixity declined to two of the four ( 50% ) haplotypes between HLA-DQB2 and HLA-DOB ( Figure 3B; sub-region 3 , Figure 3I ) and continued through BRD2 . We did not sequence the two identical haplotypes from HLA-DOA ( amplicon DMP11 ) through HLA-DPB1 ( amplicon DMP17 ) , but we began sequencing again just centromeric to RING1 ( at amplicon CTB8 ) . At amplicon CTB8 , those two haplotypes remained identical to one another . However , 4 . 5 kb centromeric , at amplicon CTB9 , the two haplotypes also differed from one another . Thus , we could only localize the B57 , DR7 CEH lost fixity to a 244 kb region between amplicons DMP10 and CTB9 ( Figure 2 and Figure 3B ) . Although there was a significant loss of CEH sequence fixity between HLA-DQB1 and HLA-DQA2 , four of the seven ( 57% ) B44 , DR4 , DQ7 haplotypes retained identical sequence from within intron 1 of HLA-DQA2 through at least HLA-DOA ( Figure 3B and Figure 3J ) . Within the first intron of HLA-DPA1 ( amplicon DMP15 ) , the number of identical CEH sequences decreased to three haplotypes ( 43% ) . The haplotype that became different was not sequenced at amplicon DMP14 . The three identical haplotypes retained sequence identity to one another through DAXX . The sequence presented in Table S4 contains HLA-DPB1*04:01 and represents the dominant B44 , DR4 , DQ7 CEH sequence . Although APD did not represent any known CEH in the resequenced region , APD shared sequence identity with its single represented population haplotype from at least HLA-DQA2 through TAP2 , a distance of almost 100 kb . The previously identical sequence differed from the APD sequence at every SNP 2 . 1 kb telomeric to HLA-DMB but was otherwise identical at amplicons DMP2 through DMP6 . Within and near HLA-DMB and HLA-DMA ( amplicons DMP1-6 ) , no other haplotype we sequenced had the APD sequence , and only two other haplotypes ( both non-standard haplotypes from other groups ) had the same sequence as the DR13 , DQ6 haplotype we report in Table S4 . The APD sequence in amplicons DMP7 through DMP13 has not been reported , but its sequence near and in the HLA-DP genes differed from the other DR13 , DQ6 haplotype we report .
Identifying the genetic elements responsible for complex genetic diseases requires knowing the genomic haplotype architecture of the population ( s ) in which the diseases exist . Toward that goal , the MHP made a major advance in the early to mid-2000's by determining the sequences of eight European Caucasian MHC haplotypes [17]–[20] . However , although the MHP sequences were described as “common” European Caucasian MHC haplotypes , that remains an open question [4] . Other than a comparison of the eight sequences with 180 European Caucasian population haplotypes at 54 SNPs covering 214 kb of the MHC class II region ( from HLA-DRB9 to 20 kb centromeric to HLA-DQB1 ( Figure 1 ) ) [20] , no systematic study has determined the representative nature of each MHP haplotype's complete sequence . MHC CEHs are common population haplotypes [1]–[6] , [8]–[16] . Outside of their core conserved region from HLA-C to HLA-DQB1 , CEHs contain dominant alleles on the telomeric class I side at HLA-E [14] and HLA-A [1]–[6] , [8]–[11] , which is consistent with the region between HLA-A and HLA-C being one of notably low recombination [22] . The class II region between HLA-DQB1 and DAXX is thought to be less conserved than that analogous distal class I region , and class II contains several reported recombination hotspots [22] , [23] and regional LD breaks [24] ( which are thought to be related ) . Furthermore , the best-characterized CEH , [HLA-B8 , SC01 , DR3] , previously showed significant variability centromeric to HLA-DQB1 [9] , [10] , [25] . By contrast , previous reports also documented dominant HLA-DPB1 alleles for a number of European Caucasian CEHs [3] , [4] , [15] , including the B8 , DR3 ( in shortened nomenclature ) CEH . We used an amplicon resequencing approach [10] to determine the dominant class II sequences centromeric to HLA-DQB1 and to delineate the breakdown of sequence conservation among multiple examples of previously identified CEHs sharing telomeric class II alleles or specificities with the eight MHP sequences . The dominant class II sequences were unique for each CEH , and we confirmed or discovered over 300 SNP and DIP markers . The phased polymorphisms of each CEH dominant sequence are shown in Table S4 . Most CEHs showed significant sequence conservation ( “fixity” ) centromeric to HLA-DQB1 , crossing multiple reported recombination hotspots [22] , [23] . Although a few CEHs lost a dominant sequence by DAXX ( 660 kb centromeric to HLA-DQB1 ) , most CEHs retained a dominant sequence throughout the region . Seven of the eight MHP sequences represented the dominant class II sequence of at least one CEH for varying distances . Several general observations derive from our data . First , microvariation was low within a CEH's dominant class II sequence , even at DIPs , similar to findings within the core MHC for the two CEHs previously reported [9]–[11] . We detected only a single private mutation among the MHP sequences within regions where they otherwise represented the dominant sequences . These findings suggest CEH sequences are recent enough not to have sustained significant mutation during their expansion . Second , CEH dominant sequence conservation appears to be lost primarily due to recombination events with other relatively high frequency haplotypes because non-consensus sequences centromeric to the point of differentiation typically are identical to other common regional sequences . These are often the local fragments of other CEH dominant sequences . Third , except in a few cases where CEHs split apart from a common sequence shared by related CEHs , the dominant sequences did not usually transition to multiple examples of a conserved minor sequence past the recombination point . Although this last observation remains to be confirmed in studies of larger numbers of the same CEH , it also suggests the minor variant recombinants are relatively recent compared to the ages of the original CEHs . A fourth conclusion is somewhat complex . Although the location of dominant sequence breakdown varied between CEHs ( Figure 3D–O ) and did not appear to be primarily at previously reported recombination hotspots or LD breaks [22]–[24] , many CEHs showed a steady loss of fixity throughout the region . The major reported recombination hotspots in the region are [22]–[24]: a ) between HLA-DQB1 and HLA-DQA2 ( near MTCO3P1 ) [22]; b ) within intron 2 of TAP2 ( just centromeric to the most centromeric amplicon of TAP2 we sequenced ) [22]; c ) just telomeric to HLA-DMB , near the next amplicon we sequenced centromeric to TAP2 [23]; d ) between BRD2 and HLA-DOA [22] , [23]; e ) between HLA-DOA and HLA-DPA1 [22] , [24]; and , f ) between HLA-DPB1 and RING1 [24] . However , losses of sequence conservation occurred in the region between HLA-DQA2 and intron 2 of TAP2 , a region not previously reported to contain a recombination hotspot . Our directly observed haplotype results reveal complexity missed by a casual analysis of LD maps . The results we present regarding CEH structure renew questions we previously raised regarding both LD and recombination hotspots [3] . However , our study was not designed to identify nor to challenge the existence of recombination hotspots in the extended class II region , and further study of this region is warranted . An interesting feature of several CEH pairs and groups is a pattern of shared sequence identity surrounded both telomerically and centromerically by regions in which the CEHs differ significantly ( Figure 4 ) . This “block” nature of CEHs and haplotype groups sharing regional alleles has been noted previously [3]–[8] , [13]–[16] , [19] , [22] , [25] . The MHP reported [19] a 158 kb “SNP desert” from HLA-DRB1 and MTCO3P1 between the two DR3 CEHs ( Figure 4A ) . Our study expands upon that concept and provides a richer picture of these relationships . For example , the B7 , DR15 and B18 , DR15 CEHs were previously known to share alleles within the HLA-DR/DQ block [1] , [3]–[6] , [8] , but it was unknown whether they had identical or distinct sequences centromeric to HLA-DQB1 . Our results show these two CEHs share sequence identity throughout the 88 kb stretch from HLA-DQA2 through intron 8 of TAP2 , centromeric to which they maintain fixed but distinctly different sequences ( Figure 4B ) . Although the two CEHs theoretically could have different sequences between HLA-DQB1 and HLA-DQA2 and in the domains we skipped within the 88 kb region mentioned above , previously published results suggest such variation would be minimal . The MHP showed , using a set of dense SNP typings , that a set of ( HLA-DRB1*15:01 , -DQB1*06:02 ) population haplotypes were identical to one another centromeric to HLA-DQB1 until they split into primarily two subtypes in a region near or within TAP2 [19] . The sudden TAP2 transition they reported was likely both the centromeric break point of the shared sequence for the two DR15 CEHs and the continuation of the two separate but conserved CEH sequences we report here . Similarly , the two B44 , DR7 CEHs [16] may share essential sequence identity for the region from HLA-B to HLA-DOB but have separate conserved sequences on either side of that larger than 1 . 5 Mb region . The two CEHs may have recombined in the early history of a common ancestral haplotype and expanded separately . We observed a more complex structural pattern among the DR4 , DQ8 CEHs than among the DR15 CEHs: two separate regions of shared sequence separated by a variable region of sequence divergence . Specifically , four DR4 , DQ8 CEHs telomerically identical at HLA-DQB1 , HLA-DQA2 and HLA-DQB2 and centromerically identical from HLA-DMB through BRD2 , each had different sequences for varying distances within the 170 kb span between the two sub-regions ( Figure 4C ) . This pattern may be analogous to the pattern within the core MHC region exhibited by the related CEHs [HLA-B62 , SB42 , DR4 , DQ8] and [HLA-B62 , SC33 , DR4 , DQ8] ( which , interestingly , are the most divergent of the four DR4 , DQ8 CEHs within the 170 kb mentioned above ) . These patterns of alternating blocks of shared and divergent sequence/alleles may be a type of CEH supergroup microvariation created by early differentiation from a common ancestral sequence due to recombination or , perhaps more likely , localized hypermutation followed by expansion of separate but related CEHs . Although our dense sequencing results raise questions specific to the class II region , the main issue is essentially the same question we and others have asked about CEHs generally: How can long-range conserved sequences comprise up to half a population's haplotypes crossing numerous putative recombination hotspots or regions of LD breakdown ? For example , one of the strongest reported MHC recombination hotspots is located in the TNF-LTA region [22] , yet that region is located within the core MHC , the only human genomic region well-documented to contain CEHs . We conclude CEHs are recent expansions of separate ancestral progenitors . Thus , multiple population examples of each CEH are essentially identical by descent but have spread through the population into pedigrees that are not now highly related . The few mutations within a stretch of conserved sequence can be used to calculate the age of the long-range haplotype [10] , [26] . However , plausible values for the variables in such calculations are often difficult to verify . We also conclude LD values are not particularly useful indicators of population haplotype architecture [3] , [4] . LD variation is likely useful to demarcate localized changes in the relationships between individual haplotypes , but LD is all too often simplistically and incorrectly interpreted to suggest the population haplotype architectual dominance of short blocks of conserved sequence separated by narrow regions of relatively frequent randomized sorting . It is likely not coincidental that the MHC is both the region most often studied by segregation analysis in pedigrees and the only well-documented region to contain megabase-length CEHs . Haplotype sequence and population haplotype architecture accuracy requires both direct observation and the consideration of long-range sequence fixity . Whole genome sequencing will soon allow direct determination of full haplotype sequences if analyzed appropriately [27] . This requires either sequencing individual chromosomes after physical isolation [28] or sequencing moderate to large pedigrees to phase pedigree data directly [29] , [30] . The latter allows both sequence integrity crosschecking and directly observed recombination . Samples homozygous for a particular long-range haplotype are useful for identifying putative CEH alleles [10] , [17]–[20] , [31] , [32] , but such cell lines are rare . Direct haplotype determination and counting [2] is the only method capable of revealing the details of haplotype structure and population haplotype architecture essential for disease gene localization [4] . Computational phasing to “impute” haplotype structure in unrelated subjects has been advocated for monetary or feasibility reasons , but this does not usually provide accurate haplotype structure [33] . Reports over 30 years show that MHC CEHs are high population frequency ( “common” ) megabase-length conserved sequences [1] , [3]–[6] , [8]–[16] , [23] , [24] . The evidence for CEH sequence conservation ( with minor microvariation ) increased whenever loci were defined at higher resolution or at intervening locations . We update and improve the definition of the centromeric points up to which the published reference MHC sequences essentially represent CEH dominant sequences . The dominant class II CEH sequences we provide ( far from a complete list ) should be useful for future European Caucasian haplotype comparisons . More complete resequencing of larger numbers of pedigree-determined haplotypes is required to determine population haplotype architecture both within the MHC and throughout the genome . Furthermore , non-European CEHs [34] must be studied in a similar manner . Finally , an appreciation of long-range haplotype sequence conservation throughout the genome is required to localize efficiently the genomic structural elements responsible for complex genetic traits ( including disease susceptibility ) .
All participants gave informed consent in accordance with Institutional Review Board ( IRB ) -approved protocols . All work was conducted under IRB protocols approved by the Immune Disease Institute ( or its predecessors ) and/or Boston Children's Hospital IRBs . Blood samples were provided by 180 individuals in 43 unrelated families and by 10 unrelated subjects ( the latter homozygous for portions of the MHC ) , mostly from the Boston metropolitan area . We obtained extensive demographic and personal health information ( including family histories ) from all subjects . The relatively diverse European Caucasian population in Boston and our recruitment methods make it highly unlikely any of the pedigrees or unrelated subjects are directly related to one another . We also obtained B-lymphocytic cell lines of 15 individuals in four families from the Human Biological Data Interchange ( HBDI; Philadelphia , PA ) . International Histocompatibility Workshop ( IHW ) homozygous cell lines ( n = 12 ) , including three of the MHP ( DBB , MANN and QBL ) , were used for a limited number of haplotypes . All samples had been typed at classical markers within the MHC prior to selection , although typing was conducted at various resolutions ( from serological to high resolution DNA typing ) . Pedigrees were chosen to obtain multiple examples of a wide variety of MHC CEHs or at least the HLA-DR/DQ fragments of CEHs putatively represented by MHP haplotypes . HLA-DPB1 typing was not considered during subject and haplotype selection so that the degree of fixity in the centromeric class II region was random . DNA was extracted from EDTA-treated blood , peripheral blood mononuclear cells or B-lymphocytic cell lines . Genomic DNA was isolated using the QIAmp DNA mini kit ( Qiagen , Valencia , CA ) . Molecular MHC allele typing was performed by PCR and sequence-specific oligonucleotide probes ( in-house or Lifecodes ) or by sequence-specific primer kits ( Invitrogen ) at low to high resolution . Some HLA types were identified serologically [35] . CFB ( previously known as BF ) and C4 allele typing was by agarose gel electrophoresis and immunofixation with specific antisera; C2 alleles were determined by isoelectric focusing of serum in polyacrylamide gels and a C2-sensitive hemolytic overlay [36] . MHC complement gene haplotypes or complotypes are designated by their CFB , C2 , C4A , and C4B alleles , in that arbitrary order [7] . Null or Q0 alleles are simply designated 0 . Thus , FC31 indicates the complotype CFB*F , C2*C , C4A*3 , C4B*1 . Complotypes for some IHW cell lines were described previously [30] , [31] . IHW cell typing was known ( http://www . ebi . ac . uk/ipd/imgt/hla/cell_query . html ) and/or verified as described above . We analyzed eight different MHC class II and extended class II sequences determined by the Sanger Institute [17]–[20] for distinguishing SNPs and deletion/insertion polymorphisms ( DIPs ) . Currently available MHC sequence data for these cell lines may be found via: http://www . ucl . ac . uk/cancer/medical-genomics/mhc or http://www . ensembl . org/index . html or at the URL listed under “MHC Typing . ” MHP haplotypes represent the human reference sequence ( PGF ) as well as the following alternative sequences for the human MHC: ALT_REF_LOCI_1 ( APD ) , ALT_REF_LOCI_2 ( COX ) , ALT_REF_LOCI_3 ( DBB ) , ALT_REF_LOCI_4 ( MANN ) , ALT_REF_LOCI_5 ( MCF ) , ALT_REF_LOCI_6 ( QBL ) , and ALT_REF_LOCI_7 ( SSTO ) . We used an amplicon-based resequencing approach [10] to distinguish the dominant sequences of CEHs in the class II region . CLC Combined Workbench software program ( CLCBio LLC , Cambridge , MA ) was used to align these sequences for the region from MTCO3P1 to DAXX ( Figure 1 ) . After aligning all MHP sequence data available for the eight haplotypes , we analyzed the entire region from MTCO3P1 to DAXX to find an optimal distribution of amplicons that balanced the needs for relatively even coverage and for maximizing differences between the sequences . After preliminary resequencing and localization of regions in which some of the MHP haplotypes appeared to cease representing many population haplotypes or in which we had poor sequencing results , we added or substituted amplicons . Finally , in some cases , we skipped relatively large regions having known low polymorphism . We designed primers , using a version of Primer 3 software ( http://frodo . wi . mit . edu ) , at monomorphic ( or near-monomorphic ) positions in regions near or within genes that would likely offer maximal differentiation of the various MHP haplotypes . The primer sequences we used and the amplicons we resequenced are shown in Table S1 . We sequenced a total of approximately 27 kb using 56 sets of primers covering five separate regions spanning a total distance of approximately 580 kb of genomic DNA . In some cases where sequence phase could be determined without some members of a particular pedigree , the DNA of those members was not sequenced , but we often sequenced all members of a pedigree to confirm results for a particular haplotype in multiple carriers of that haplotype . We also sequenced portions of three MHP cell lines in data gaps of current scaffolds , and we report and have provided that information to GenBank . PCR products were excised from agarose gels and purified using the QIAEX II gel extraction kit ( Qiagen ) or were drawn out of recovery wells directly ( Lonza , Inc . ) and sequenced by dideoxy sequencing using Big Dye Terminator V3 . 0 chemistry ( Genewiz , Inc . , South Plainfield , NJ and/or Davis Sequencing , Inc . , Davis , CA ) . All sequences were analyzed and compared using both alignment software and direct visual inspection of chromatograms . DIP sizes in heterozygotes were usually decipherable and deducible in both directions based on the known sequence surrounding the DIP . At least two individuals inspected visually and agreed upon the sequence of each chromatogram used to determine sequence . Excluding private mutations , we identified 274 SNPs and DIPs in the 342 kb region from HLA-DQA2 to HLA-DPB1 and 34 SNPs and DIPs in the 103 kb region from centromeric to RING1 to DAXX ( Table S2 ) . We defined the centromeric point where a particular MHP sequence no longer represented a haplotype group as the location at which the dominant sequence shared by those haplotypes began to contain SNP and DIP alleles not in the MHP sequence . The vast majority of haplotypes ( n = 132; 83 . 5% ) were phased by segregation analysis in pedigrees , showed Mendelian inheritance patterns ( except in rare cases of null alleles or detected crossovers ) and were assigned unique identifiers as unrelated founder chromosomes . Six unrelated subjects or IHW cell lines each known not to be consanguineous were homozygous for specific haplotypes throughout the region analyzed and provided 12 additional unrelated chromosomes ( 7 . 6% ) . Six IHW cell lines either known to be consanguineous or of unknown status provided six additional unrelated chromosomes ( 3 . 8% ) . Finally , four unrelated subjects known not to be consanguineous who were heterozygous for at least some portion of the region studied provided the final eight unrelated chromosomes ( 5 . 1% ) . Haplotype phasing in the classical CEH region ( between HLA-C and HLA-DQB1 ) was established for 95% of all haplotypes or was inferred from known CEH allele combinations . Over 96% of SNP and DIP alleles were unambiguously phased: a ) by segregation analysis in pedigrees [1] , [2]; or , b ) using IHW or locally-identified MHC homozygous samples . Such cell lines were assumed to be of consanguineous origin unless known not to be and received only one haplotype assignment . The remaining alleles ( <4% overall and <4% in all regions except for HLA-DPB1 , where the percentage of inferred phasing was 10 . 8% ) were assigned to haplotypes by inference as follows . In a family in which all subjects were heterozygous identical at a locus or in a heterozygous individual without relatives , one of the alleles was arbitrarily assigned to one of the haplotypes to be consistent with its surrounding ( unambiguous ) markers , defined by the unambiguous haplotypes in the group to which it belonged or , if the haplotype was no longer representative , by all unambiguous haplotypes . Phasing of the remaining pedigree haplotype ( s ) was/were thus established . We report here on 158 haplotypes ( and an additional seven at HLA-DQA2 ) that fell into one of the eight MHP groups . Physical distances between MHC genes , locations and amplicons were found at the NCBI website ( http://www . ncbi . nlm . nih . gov/projects/genome/guide/human/index . shtml ) . We used the human reference sequence NC_000006 . 11 from Genome Reference Consortium assembly GRCh37 . p10 and reference sequence ( rs ) numbers are from dbSNP build 138 . All novel SNP ( n = 1 ) and DIP ( n = 7 ) variations ( shown in Table S2 ) were submitted to dbSNP ( http://www . ncbi . nlm . nih . gov/snp/ ) using the handle CAALPER . All novel DNA sequences for the three MHP cell lines have GenBank accession numbers ( http://www . ncbi . nlm . nih . gov/genbank/ ) KF880997-KF880999 ( for QBL ) , KF881000-KF881006 ( for MANN ) and KF881007-KF881009 ( for DBB ) ( Figures S1–S3 ) . To determine sequence fixity , we assumed sequence identity within intervening regions we did not resequence among the population haplotypes bearing the genotypic markers and/or dominant sequence of a CEH ( except for rare private mutations and infrequent microvariations ) . To quantify and represent crossover events leading to the breakdown of CEH dominant sequences , we define a new metric: normalized crossover frequency ( NCF ) . NCF is the fraction of remaining dominant sequences of a single CEH that begin to differ from the dominant sequence due to apparent recombination within a defined region , normalized over a unit ( 1 Mb ) distance . Our data were not distributed evenly across the region we studied , and we therefore calculated our data over sub-regions of varying size ( Table S2 ) . Thus , we required normalization to a unit distance to compare the sequence breakdown by separate crossovers . We displayed these data in a bar graph format in which the abscissa is drawn to genomic scale . Therefore , the areas ( not the heights ) of the bars representing NCFs are compared to determine the relative contribution of regional recombinants to the breakdown of CEH sequence conservation . NCF was calculated using the equation: NCF = ( crossovers/total remaining haplotypes ) × ( 1 Mb/distance covered ) where: a ) “crossovers” are the number of haplotypes that lost the CEH dominant sequence centromeric to the prior ( telomeric ) analyzed region due to recombination events ( as opposed to minor microvariation in an otherwise identical sequence ) , and include both any crossovers directly observed in the currently analyzed region and any deduced to have occurred between the currently and prior analyzed regions . b ) the “total remaining haplotypes” are the number of remaining population haplotypes having the dominant sequence of a given CEH throughout the region immediately telomeric to the analyzed region . For the first region ( HLA-DQA2 ) , it was assumed all population haplotypes of a given CEH had the dominant sequence at the centromeric end of HLA-DQB1 . c ) the “distance covered” is measured by subtracting the genomic position of the most centromeric point of the prior region ( the region immediately telomeric to the currently analyzed region ) from the genomic position of the most centromeric point of the currently analyzed region . As an example , if 16 population haplotypes of a single CEH had the dominant sequence through HLA-DQA2 and 3 of these crossed over to a non-dominant sequence by the centromeric end of the HLA-DQB2 region ( in which the distance from the most centromeric polymorphism analyzed in the HLA-DQA2 region through the most centromeric polymorphism analyzed in the HLA-DQB2 region is 21 , 096 bases ) , the NCF would be: ( 3/16 ) × ( 1 , 000 , 000/21 , 096 ) = 8 . 9 and the “total remaining haplotypes” for the next region ( between HLA-DQB2 and HLA-DOB , covering 25 , 927 bases ) would be 13 ( i . e . , 16–3 ) . | The human major histocompatibility complex ( MHC ) is a gene-dense region highly enriched in immune response genes . MHC genetic variation is among the highest in the human genome and is associated with both tissue transplant compatibility and many genetic disorders . Long-range ( 1–3 Mb ) MHC haplotypes of essentially identical DNA sequence at relatively high ( ≥0 . 5% ) population frequency ( “genetic fixity” ) , called conserved extended haplotypes ( CEHs ) , comprise roughly half of all European population haplotypes . We sequenced an aggregate of 27 kb over 580 kb in the MHC class II region from HLA-DQA2 to DAXX in 158 European haplotypes to quantify the breakdown of this genetic fixity in the centromeric portion of the MHC and to determine the representative nature within that region of eight previously fully or nearly fully sequenced “common” European haplotypes . We identified the dominant sequences of 13 European CEHs and determined where the “common” sequences did ( or did not ) represent related CEHs . We found patterns of shared sequence identity among different CEHs surrounded by fixed ( for each CEH ) but differing sequence . Our direct observational results for population haplotypes explain the mutual occurrence of CEHs and short ( 5–200 kb ) blocks of fixed sequence detected by the statistical measure of linkage disequilibrium . | [
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| 2014 | Dominant Sequences of Human Major Histocompatibility Complex Conserved Extended Haplotypes from HLA-DQA2 to DAXX |
Maize leafbladeless1 ( lbl1 ) encodes a key component in the trans-acting short-interfering RNA ( ta-siRNA ) biogenesis pathway . Correlated with a great diversity in ta-siRNAs and the targets they regulate , the phenotypes conditioned by mutants perturbing this small RNA pathway vary extensively across species . Mutations in lbl1 result in severe developmental defects , giving rise to plants with radial , abaxialized leaves . To investigate the basis for this phenotype , we compared the small RNA content between wild-type and lbl1 seedling apices . We show that LBL1 affects the accumulation of small RNAs in all major classes , and reveal unexpected crosstalk between ta-siRNA biogenesis and other small RNA pathways regulating transposons . Interestingly , in contrast to data from other plant species , we found no evidence for the existence of phased siRNAs generated via the one-hit model . Our analysis identified nine TAS loci , all belonging to the conserved TAS3 family . Information from RNA deep sequencing and PARE analyses identified the tasiR-ARFs as the major functional ta-siRNAs in the maize vegetative apex where they regulate expression of AUXIN RESPONSE FACTOR3 ( ARF3 ) homologs . Plants expressing a tasiR-ARF insensitive arf3a transgene recapitulate the phenotype of lbl1 , providing direct evidence that deregulation of ARF3 transcription factors underlies the developmental defects of maize ta-siRNA biogenesis mutants . The phenotypes of Arabidopsis and Medicago ta-siRNA mutants , while strikingly different , likewise result from misexpression of the tasiR-ARF target ARF3 . Our data indicate that diversity in TAS pathways and their targets cannot fully account for the phenotypic differences conditioned by ta-siRNA biogenesis mutants across plant species . Instead , we propose that divergence in the gene networks downstream of the ARF3 transcription factors or the spatiotemporal pattern during leaf development in which these proteins act constitute key factors underlying the distinct contributions of the ta-siRNA pathway to development in maize , Arabidopsis , and possibly other plant species as well .
Small RNAs are important regulators of development , particularly in plants , where many of the abundant and conserved microRNAs ( miRNAs ) target transcription factors that direct or reinforce cell fate decisions [1] . Consequently , mutations in genes required for miRNA processing or function condition defined developmental defects . Likewise , plants defective for the biogenesis of trans-acting short interfering RNAs ( ta-siRNAs ) show distinctive patterning defects due to the deregulation of key developmental targets [1] . ta-siRNAs are generated in response to miRNA activity via one of two possible mechanisms , referred to as the “one-hit” and “two-hit” pathways . In both pathways , a single miRNA-guided cleavage event triggers the conversion of target transcripts into long double stranded RNAs by RNA-DEPENDENT RNA POLYMERASE6 ( RDR6 ) and SUPPRESSOR OF GENE SILENCING3 ( SGS3 ) , and sets the register for the subsequent production of phased 21-nt siRNAs by DICER-LIKE4 ( DCL4 ) [2]-[4] . In the one-hit pathway , transcripts targeted by a single , typically 22-nt , miRNA will generate ta-siRNAs downstream of the miRNA cleavage site , whereas transcripts producing ta-siRNAs via the two-hit pathway harbor two binding sites for 21-nt miRNAs and the ta-siRNAs are processed upstream of the cleaved 3′ miRNA target site [5]–7 . Analogous to miRNAs , a subset of the phased ta-siRNAs act at the post-transcriptional level to repress the expression of genes involved in development or other cellular processes . The phenotypes conditioned by mutations affecting ta-siRNA biogenesis vary greatly across species . In Arabidopsis , such mutants exhibit a relatively subtle phenotype , developing downward curled leaves that are weakly abaxialized and undergo an accelerated transition from the juvenile to the adult phase [2]–[3] . These defects result from misregulation of the AUXIN RESPONSE FACTOR ARF3 , which is targeted by TAS3-derived ta-siRNAs , termed tasiR-ARFs [8]–[10] . Biogenesis of the TAS3 ta-siRNAs follows the two-hit model and involves a subspecialized pathway , which requires the unique association of miR390 with its effector AGO7 to trigger siRNA production [11] . Localized expression of TAS3 and AGO7 confines tasiR-ARF biogenesis to the adaxial/upper most cell layers of developing leaves , which then limits accumulation of ARF3 to the abaxial/lower side [12] . The developmental defects of sgs3 , rdr6 , and dcl4 are phenocopied by mutations in AGO7 and TAS3A , as well as by expression of tasiR-ARF- insensitive ARF3 transgenes [2] , [8]–[9] , [13] , indicating that the contribution of ta-siRNAs to Arabidopsis development is primarily mediated by tasiR-ARFs . In contrast to Arabidopsis , loss of AGO7 activity in Medicago results in the formation of highly lobed leaves [14] , and mutants defective for ta-siRNA biogenesis components in rice and tomato exhibit severe defects in meristem maintenance , mediolateral blade expansion , and adaxial-abaxial leaf polarity [15] , [16] . Likewise , mutations in maize lbl1 and ragged seedling2 ( rgd2 ) , which encode the orthologs of SGS3 and AGO7 , respectively , have severe effects on meristem function and leaf development [17]–[18] . lbl1 mutants , in particular , develop radial , fully abaxialized leaves ( Fig . 1A ) . Importantly , while the TAS3 ta-siRNA pathway is evolutionarily conserved , the number and nature of phased siRNA loci vary greatly between plant species [19] . In Arabidopsis , three TAS families have been described in addition to TAS3 . TAS1- , TAS2- , and TAS4-derived ta-siRNAs are generated via the one-hit model following miR173- or miR828-directed cleavage , and function in the regulation of members in the MYB transcription factor and PPR families [4] , [20]–[21] . Each of these pathways has been identified in other plant species , but their evolutionary origin appears to lie within the eudicots [19] . Depending on the species , variation is seen in the genes targeted by the ta-siRNAs derived from these TAS loci , as well as in the miRNA that triggers their biogenesis [22] , [23] . In addition , apparent species-specific TAS pathways may exist , as novel TAS loci with unique targets have been identified in tomato and the moss Physcomitrella patents [24] , [25] . Moreover , genome-wide small RNA analyses in a number of plant species have uncovered clusters of phased siRNAs , generated primarily via the one-hit model that , unlike the ta-siRNAs , are proposed to act in cis . These are generally referred to as phasiRNAs and are processed from non-coding transcripts , such as in the panicles of the grasses [26]–[28] , or from protein-coding genes , including members of the NB-LRR , MYB , and PPR gene families [19] . The set of genes regulated by phased siRNAs thus varies widely across plant species . The basis for the phenotypes of maize ta-siRNA biogenesis mutants remains unclear . In light of the tremendous diversity in phased siRNAs seen across plant species , it is conceivable that TAS loci , other than the four known TAS3 genes [17] , exist in maize . Moreover , phased siRNAs other than the tasiR-ARFs may target genes with roles in development , and contribute to the defects seen in lbl1 mutants . To assess these possibilities and to obtain a comprehensive view of LBL1-dependent siRNAs active in the maize vegetative apex , where the mutant phenotype manifests itself , we compared the small RNA content between wild-type and lbl1 shoot apices . This revealed unexpected contributions of LBL1 to the regulation of transposons , particularly the gyma class of LTR-retrotransposons . Interestingly , in contrast to other plant species , we found no evidence for the existence of phased siRNAs generated via the one-hit model . Our analyses identified nine TAS loci all belonging to the TAS3 family . Data from RNA deep sequencing and PARE analysis present the ARF3 genes as the only LBL1-dependent small RNA targets with a role in development . Consistent with this finding , plants expressing a tasiR-ARF insensitive arf3a transgene recapitulate the phenotype of lbl1 mutants . These findings underscore the importance of the tasiR-ARF - ARF3 regulatory module to maize development , and indicate that diversity in TAS pathways and their targets cannot fully account for the phenotypic differences conditioned by ta-siRNA biogenesis mutants across plant species . Instead , divergence in the gene networks downstream of the ARF3 transcription factors or the spatiotemporal pattern in which these tasiR-ARF targets act emerge as a testable hypotheses to explain the diverse contributions of the ta-siRNA pathway to development in maize , Arabidopsis , and possibly other plant species as well .
To understand the basis for the lbl1 phenotype , we compared the small RNA content between vegetative apices , comprising the shoot apical meristem ( SAM ) and up to five leaf primordia , of two-week old B73 and lbl1-rgd1 seedlings . Three independent biological replicates were analyzed for each genotype . Approximately 92% of the 18- to 26-nt reads in both sets of libraries mapped to the unmasked B73 reference genome ( S1 Table ) . Of the mapped reads , 43–48% , corresponded to unique small RNAs suggesting that , despite the highly repetitive nature of the maize genome , close to half of the small RNAs expressed in the vegetative apex are distinct . The small RNA size distribution profiles in both wild-type and lbl1 resemble those described previously for maize [29]–[30] , suggesting that LBL1 , in contrast to components of the heterochromatic siRNA pathway [29] , has a relatively subtle effect on the overall small RNA population ( S1 Figure ) . However , consistent with a role for SGS3 proteins in the biogenesis of 21-nt secondary siRNAs [2] , [4] , the 21-nt small RNA population is slightly reduced in lbl1 compared to wild-type . In addition , an unexpected modest reduction in the 22- and 24-nt small RNA fractions is seen in lbl1 ( S1 Figure ) . To more precisely define the effects of LBL1 on small RNA biogenesis , we identified genomic loci that differentially accumulate 21- , 22- , or 24-nt small RNAs in wild-type versus lbl1 apices ( S2 Figure ) . Consistent with a relatively subtle effect of lbl1 on the overall small RNA population , this identified 79 , 172 , and 209 loci that generate 21- , 22- , or 24-nt small RNAs , respectively , that are significantly changed ( q-value<0 . 05 ) at least 2-fold between wild-type and lbl1 ( Fig . 1B; S1 Dataset ) . A small subset of these ( 11/79 ) , correspond to low copy genic regions that generate significantly fewer 21-nt small RNAs in lbl1 , properties predicted for phasiRNA and ta-siRNA loci ( Fig . 1C; S1 Dataset ) . Indeed , the four previously described TAS3 genes , tas3a-d [17] , are among these loci . Five additional genes appear non-coding , whereas the remaining two correspond to arf3a and arf3d ( Fig . 1C; S1 Dataset ) . With the exception of arf3a , each of these loci also generate 22-nt LBL1-dependent siRNAs and five differentially accumulate 24-nt small RNAs , albeit generally to substantially lower levels than seen for the corresponding 21-nt small RNAs ( Fig . 1C ) . One additional gene ( GRMZM2G093276 ) , encoding a zinc/iron transporter , also accumulates 22-nt small RNAs that are significantly reduced in lbl1 . Finally , six predicted protein-coding genes of unknown function generate low levels of 24-nt small RNAs that are lost or significantly reduced upon mutation of lbl1 . Thus , a total of 18 distinct low copy genic regions generate small RNAs in an apparent LBL1-dependent manner . These include the four known maize TAS loci , tas3a-d , and the remaining present candidate novel phasiRNA or TAS loci that are active in the vegetative apex and may contribute to the developmental defects resulting from mutation of lbl1 . To further discern whether additional phased siRNA loci are active in the vegetative maize apex , we developed a pipeline that scans the genome for clusters of siRNAs showing a regular phasing of 21 , 22 , or 24 nucleotides ( S2 Figure ) . This pipeline includes a phasing score calculation ( P-score ) that identifies clusters in which the majority of small RNAs produced are phased . With a P-score threshold ( P≥25 ) that has been shown to identify 7 of the 8 TAS loci in Arabidopsis [21] , [31] , we identified 16 phased 21-nt siRNA clusters , 102 phased 22-nt siRNA clusters , and 8 phased 24-nt siRNA clusters ( S2 Dataset ) . However , combining this analysis with the differential small RNA accumulation data described above ( S1 Dataset ) , showed that small RNA levels in most of the clusters are not changed significantly between wild-type and lbl1 . In fact , small RNA accumulation at just 8 of the phased siRNA clusters is changed in the lbl1 mutant ( S3A Figure ) . A closer inspection of the remaining clusters indicates that these correspond primarily to repetitive regions in the genome . Moreover , these siRNA clusters are typically embedded within large windows , frequently spanning over 5 kb , that generate an uncharacteristically high number of small RNA reads , which inflates the P-score ( see Materials and Methods ) . As such , it is unlikely that these clusters correspond to new TAS loci or other miRNA-triggered phased siRNA loci . Instead these loci , particularly the LBL1-independent 22-nt siRNAs , appear to be processed from long hairpin RNAs or overlapping antisense transcripts ( S3B-C Figure ) . In Arabidopsis , natural antisense transcripts are processed by DCL1 into 21-nt small RNAs [32] , [33] , whereas long hairpin RNAs are targeted by multiple DCL enzymes to give rise to variably sized small RNAs [34] . The preferential processing of such double stranded RNAs into 22-nt siRNAs presents a possible basis for the uncharacteristically high overall abundance of 22-nt small RNAs in maize ( S1 Figure ) , and suggests diversification in the action of DCL family members between Arabidopsis and maize . Of the eight phased siRNA clusters whose small RNA levels are significantly changed in lbl1 , one generates 24-nt phased siRNAs ( S3A Figure; S2 Dataset ) . However , its small RNA levels are increased in lbl1 , again making it unlikely that this cluster corresponds to a novel phased secondary siRNA locus . Thus , in the vegetative apex , only seven loci generate phased siRNAs with a P-score≥25 in an LBL1-dependent manner . These phased siRNAs are 21-nt in size and are derived from low copy genic regions predicted to generate non-coding transcripts . Importantly , the four previously described maize TAS3 loci , tas3a-d [17] , are among these seven loci ( S2 Dataset ) . The three additional loci , GRMZM5G806469 , GRMZM2G082055 , and GRMZM2G512113 , present novel maize phased siRNA loci that could contribute to the developmental defects resulting from mutation of lbl1 . A closer analysis of the three novel phased siRNA loci indicates that these represent new members of the TAS3 family . Transcripts from these loci contain two miR390 binding sites and have the potential to generate small RNAs homologous to tasiR-ARFs ( Fig . 2A-D; S4A-G Figure ) . As mentioned above , two additional low copy regions in the genome ( GRMZM2G155490 and GRMZM2G588623 ) generate 21-nt LBL1-dependent small RNAs from predicted non-coding transcripts ( Fig . 1C; S1 Dataset ) . While both loci did not pass the P-score filter in the phased siRNA analysis ( S2 Dataset ) , a closer analysis indicates that both contain two miR390 binding sites ( S4D , F Figure ) . Both loci generate relatively few small RNAs in vegetative apices with many being out of phase ( S4 D , F Figure ) , presenting a likely explanation for the observed low P-score . Supporting this , a similar P-score analysis of 21-nt small RNAs in Arabidopsis failed to detect the confirmed TAS4 ta-siRNA locus due to the low abundance of its reads [21] . Data from PARE ( parallel analysis of RNA ends; [35] ) libraries generated from B73 apices , which allow the detection of small RNA-directed mRNA cleavage products , confirms that the 3′ miR390 target site in transcripts generated from all nine loci is cleaved ( Fig . 2A-B ) . As such , the new loci were named tas3e to tas3i ( Table 1 ) . The number of potential ta-siRNAs generated from these loci varies from 9 to 13 per strand ( Table 1 ) . However , not all predicted ta-siRNAs are detected in the vegetative apex; in fact , only 99 of the 194 possible ta-siRNAs are present in our datasets ( Fig . 2A-D; S4A-G Figure ) . Moreover , our analyses reveal that while not all ta-siRNAs are 21-nt in size , only the 21-nt long small RNAs are phased , and this class is more abundant than the longer , or out of phase , small RNAs ( Fig . 2C; S4A-G Figure ) . This indicates that DCL4 processing is occasionally out of phase and/or a different DCL enzyme takes over . Intriguingly , besides the generally low ta-siRNA levels coming from tas3f and tas3h , the potential tas3f-derived tasiR-ARF was not detected in our small RNA libraries , and tas3h lacks the potential to generate this small RNA altogether . Although it is conceivable that tas3f and tas3h have other biological roles , it seems that , with respect to the vegetative apex , these loci are diverging and losing their function in the tasiR-ARF pathway . A similar analysis of the remaining seven low copy regions accumulating significantly fewer 22- and 24-nt small RNAs in lbl1 ( Fig . 1C; S1 Dataset ) , make it unlikely that these represent new phased siRNA loci . Target prediction and PARE analysis indicate that none of these genes are targeted by the known maize miRNAs or ta-siRNAs ( see below ) . Interestingly , similar analyses of phased siRNA clusters in rice and Brachypodium inflorescences did detect 24-nt phased siRNA loci in addition to 21-nt phased siRNAs [26]–[28] . miR2275 , which triggers the biogenesis of the rice and Brachypodium 24-nt phased siRNAs , is not detected in the maize vegetative apex . This miRNA is , however , present in maize inflorescence tissues [26] , implying that some of the observed diversity may reflect tissue-specificity of small RNA pathways . Likewise , many of the 21-nt phased siRNAs identified in rice and Brachypodium inflorescences form a family distinct from the TAS3 loci whose biogenesis is triggered by miR2118 , which in maize accumulates specifically in the inflorescences [26] . In addition , no loci generating phased siRNAs via the “one-hit” model were identified in our analysis , despite the presence of 22-nt small RNAs shown to serve as triggers in this process [6] , [7] . The maize apex appears unique in this regard , as all similar genome-wide analyses of small RNAs in seedling tissues in other species identified both types of phased siRNA loci [21] , [31] , [36] , [37] . Perhaps such miRNAs are not loaded into maize AGO proteins or , alternatively , loading of these miRNAs fails to trigger a reprogramming of the RNA-induced silencing complex required to trigger secondary siRNA biogenesis [7] . Taken together , the above data indicate that , in the maize vegetative apex , phased secondary siRNAs are generated from nine loci , all belonging to the TAS3 family . This rules out a possible contribution of novel TAS families to the distinctive phenotype of lbl1 mutants . Considering that the lbl1 phenotype is not explained by the presence of novel TAS loci , we next asked whether TAS3-derived ta-siRNAs other than the tasiR-ARFs contribute to the developmental defects seen in lbl1 . To test this possibility , we constructed degradome libraries from B73 apices , and used these in target prediction [4] and PARE analyses to identify potential targets for all 99 TAS3-derived ta-siRNAs detected in our small RNA libraries . Allowing for a maximum score of 4 . 5 , well above the maximum score of 3 . 0 obtained for the tasiR-ARF ARF3 duplexes , and filters comparable to Zhai et al . [31] , PARE analysis confirmed 18 cleavage sites in 11 target genes ( S3 Dataset ) . Five of the TAS3 transcripts are among the verified tasiRNA targets , indicating possible feedback regulation in the tasiRNA pathway . The remaining targets include GRMZM2G018189 , which encodes an uncharacterized protein homologous to AtSLT1 that mediates salt tolerance in yeast [38] , as well as the five members of the maize ARF3 gene family ( Fig . 3A-C; Table 2 ) . The ARF3 genes are targeted by multiple closely related tasiR-ARFs , and account for 8 of the validated cleavage sites ( Figs . 2D; 3A; S3 Dataset ) . To assess a possible contribution of the ta-siRNA targets to the lbl1 seedling defects , we next determined whether their expression levels are changed in the mutant . The same tissue samples from wild-type and lbl1 vegetative apices were used to identify differentially expressed genes by RNA deep sequencing ( S2 Table; S3 Dataset ) . 1116 genes show differential expression in lbl1 compared to wild-type ( fold change ≥2 , q-value<0 . 05 ) . Of the verified ta-siRNA targets , transcript levels for arf3a and arf3c-e are significantly increased in lbl1 ( Table 2 ) . Misregulation of these abaxial determinants is consistent with the lbl1 leaf polarity defects [17] , but , unexpectedly , expression for arf3b is unchanged in the mutant . qRT-PCR analysis of RNA isolated from lbl1-rgd1 apices similarly shows that transcript levels for arf3a and arf3c-e are significantly increased , by approximately 2-fold , whereas expression of arf3b remains unchanged ( Fig . 3B ) . The latter is correlated with a relatively low number of PARE signatures at arf3b ( S3 Dataset ) , and suggests that this arf3 member may not substantially add to the adaxial-abaxial polarity defects seen in lbl1 . Likewise , transcript levels for GRMZM2G018189 are not significantly changed in the mutant , and the number of PARE signatures precisely at the predicted cleavage site is low . Along with its predicted role in salt detoxification , this makes a contribution of GRMZM2G018189 to the lbl1 phenotype unlikely . Interestingly , arf3a and arf3d generate 21-nt small RNAs , which are significantly downregulated in lbl1 ( Fig . 1C; S1 Dataset ) . Both ARF3 genes contain two tasiR-ARF target sites and the majority of the LBL1-dependent siRNAs map between these sites ( Fig . 3A; S3 Dataset ) . This suggests that tasiR-ARF-mediated regulation of arf3a and arf3d triggers the biogenesis of third tier secondary siRNAs also via the two-hit model , and implies positive feedback in the regulation of ARF3 expression . Although the significance of such feedback regulation remains to be established , considering the role of tasiR-ARFs in limiting the activity of ARF3 abaxial determinants to the lower side of leaf primordia , positive feedback could be important to reinforce adaxial cell fate , and to sharpen or maintain the boundary between adaxial and abaxial domains [10] , [17] . In contrast to dicot species [21] , [31] , [36] , [37] , the miR390-dependent TAS3 ta-siRNA pathway , thus , is the only phased secondary siRNA pathway active in the maize shoot apex . This ta-siRNA pathway , including the regulation of ARF targets , is conserved throughout land plant evolution [4] , [5] , [10]–[17] , although substantial diversity has accumulated over evolutionary time . The tasiR-ARFs show sequence divergence , and not all maize TAS3 genes generate this biologically active ta-siRNA ( Fig . 2D; S4F Figure ) . In addition , like the Physcomitrella patens AP2 targets [24] , GRMZM2G018189 presents a possible novel maize TAS3 ta-siRNA target . Despite such divergence in the TAS3 pathway , the ARF3 genes form the prime ta-siRNA targets also in maize , suggesting that the developmental phenotype of lbl1 results , at least in part , from a failure to correctly regulate their expression . A requirement for LBL1 in the production of phased siRNAs explains , however , only a subset of the small RNA level changes identified in the vegetative apex of lbl1 mutants . A further 68 , 161 , and 198 loci generating 21- , 22- , or 24-nt small RNAs , respectively , show a significant change ( q-value<0 . 05 ) in small RNA accumulation of at least 2-fold between wild-type and lbl1 apices ( S1 Dataset , Fig . 4A-B ) , and these could conceivably contribute to the developmental defects of lbl1 mutants . To assess this possibility and to gain insight into the possible function of these LBL1-dependent siRNAs , we first determined whether genes differentially accumulating small RNAs in lbl1 show a corresponding change in transcript levels . In addition to the ARF3 and TAS3 loci discussed above , 23 protein coding genes within the maize filtered and working gene sets show a significant difference in 21- , 22- , or 24-nt small RNA accumulation between wild-type and lbl1 apices ( Fig . 4A-B; S1 Dataset ) . However , only GRMZM2G093276 , which encodes a ZIP zinc/iron transport protein , shows a significant increase in transcript levels in lbl1 that is correlated with a decrease in siRNAs at the locus ( S4 Dataset ) . Plants overexpressing ZIP proteins show a reduction in plant height and axillary bud outgrowth [39] , [40] , but such plants are otherwise morphologically normal . As such , a contribution of ZIP deregulation to the severe polarity defects of lbl1 mutants seems speculative . In addition , levels of eight miRNAs are significantly changed in lbl1 ( Fig . 4B; S1 Dataset ) . However , even though LBL1 is necessary for the proper spatiotemporal pattern of miR166 accumulation [17] , its levels overall appear not significantly changed in the mutant . Unexpectedly , miR156 , which represses the juvenile to adult phase transition [41] , is upregulated in lbl1 . Arabidopsis ta-siRNA biogenesis mutants exhibit an accelerated transition from the juvenile to the adult phase [2] , [9] , whereas increased levels of miR156 in lbl1 might imply a delayed vegetative phase change . It is conceivable that rather than contributing to the phenotype , the changes in miR156 levels are a consequence of the lbl1 phenotype . Similarly , while miR169 , miR528 , and miR529 are more abundant in lbl1 , transcript levels for their targets remain unchanged in the mutant . The abaxialized leaf phenotype of lbl1 can however not account for the increased accumulation of miR390 , as this small RNA is expressed on the adaxial side of leaf primordia [42] . Instead , upregulation of miR390 in lbl1 is consistent with feedback regulation in the TAS3 ta-siRNA pathway [42] that is not expected to further impact the phenotype of lbl1 . The remaining loci differentially accumulating small RNAs in lbl1 correspond primarily to DNA and retrotransposons ( Fig . 4A-B; S1 Dataset ) . Interestingly , nearly all retrotransposons showing a significant change in 21- and 22-nt small RNA levels generate siRNAs preferentially or exclusively in the mutant ( Fig . 4A-B ) . Moreover , more than 75% of these upregulated siRNA loci belong to the gyma class of Gypsy-like LTR retroelements ( S1 Dataset ) , indicating a role for LBL1 specifically in the silencing of this class of retrotransposons . Recent studies in Arabidopsis revealed that ta-siRNA biogenesis components can act in a hierarchical manner to the transcriptional gene silencing pathway to silence repetitive regions in the genome [43]–[46] . While most repeats in the Arabidopsis genome are repressed at the transcriptional level through the action of PolIV/V , RDR2 , and DCL3-dependent 24-nt heterochromatic siRNAs [47] , in instances where this canonical silencing pathway is lacking or perturbed , 21- and 22-nt secondary siRNAs trigger the post-transcriptional repression of repetitive elements [43]–[46] . However , the observations presented here reveal an additional layer of small RNA-mediated transposon regulation . The data implies that when LBL1 activity is lost , a subset of gyma retroelements become targets for yet another small RNA pathway generating 21- and 22-nt LBL1-independent siRNAs . Unlike the repeat derived siRNAs generated by ta-siRNA pathway components in Arabidopsis [46] , [48] , the 21- and 22-nt LBL1-independent siRNAs map to the long terminal repeats ( LTR ) of the gyma elements . These seem to maintain the repression of these repeats , as gyma transcript levels are unchanged in the mutant . Differentially expressed 24-nt small RNAs are also largely derived from retrotransposons and DNA transposons ( Fig . 4A-B; S1 Dataset ) . For many of these repeats , siRNA levels are reduced in lbl1 , consistent with the hypothesis that LBL1 also functions in the biogenesis of heterochromatic siRNAs associated with transcriptional gene silencing . While a role in RNA-dependent DNA methylation has been proposed for other distantly related members in the Arabidopsis SGS3-like protein family , SGS3 itself is not considered part of this subgroup [49]-[51] . Moreover , whether the SGS3-like proteins affect the accumulation of 24-nt siRNAs remains controversial . Consistent with a role for LBL1 in the production of 24-nt heterochromatic siRNAs , reduced expression of lbl1 in extended transition stage leaves is correlated with demethylation and reactivation of MuDR transposons [52] . Importantly , the loci accumulating fewer 24-nt siRNAs in lbl1 are distinct from the gyma retrotransposons generating increased levels of 21- and 22-nt small RNAs . This implies multiple contributions for LBL1 in the repression of repetitive elements in the genome: one via production of 24-nt siRNAs , and a distinct , not yet fully understood , role in the regulation of gyma retroelements . It also supports the presence of greater complexity in small RNA-mediated transposon silencing pathways , and that such alternate pathways may act preferentially on a select subset of transposon families [43] , [45] . To assess the contribution of repeat-derived small RNAs to the lbl1 defects , we next asked whether any non-phased , differentially expressed 21- or 22-nt small RNAs act in trans to regulate developmental genes at the post-transcriptional level in a manner analogous to miRNAs or ta-siRNAs . Assuming that all siRNAs are loaded into an AGO effector complex , we performed a target prediction and PARE analysis for those 21- and 22-nt differentially expressed small RNAs with three or more reads in either the wild-type or lbl1 libraries . With a maximum target score of 4 . 5 [4] , this identified eight genes targeted by the same gyma-derived siRNA ( S3 Dataset ) . However , in contrast to the verified ta-siRNA targets ( see above ) , these genes are not or scarcely expressed ( <1 RPKM ) in the vegetative apex and retain normal expression upon mutation of lbl1 . We further considered that the epigenetic regulation of transposable elements can influence expression of adjacent genes [53] . We , therefore , tested whether any of the repetitive or intergenic regions differentially accumulating 24-nt small RNAs are positioned in close proximity to a gene within the maize filtered or working gene sets . 106 of the 185 windows differentially accumulating 24-nt siRNAs are positioned within 10 kb of an annotated protein coding gene , but only three genes show a significant difference in transcript levels between wild-type and lbl1 apices ( S5 Dataset ) . GRMZM2G027495 and GRMZM2G016435 show increased expression in lbl1 , even though 24-nt siRNAs levels at the adjacent repetitive and intergenic regions are also upregulated . This correlation is opposite to what is expected if silencing at the repeat region spreads into the adjacent gene , suggesting that expression of these genes is indirectly affected upon mutation of lbl1 . The third gene , GRMZM2G089713 encodes for SHRUNKEN1 . Its expression is significantly increased in lbl1 consistent with a downregulation in 24-nt siRNAs at the adjacent repeat region . However , as SHRUNKEN1 modulates starch levels [54] , a contribution to the developmental defects in lbl1 is not immediately obvious . Taken together , these analyses reveal unexpected crosstalk between small RNA pathways , with LBL1 making multiple unique contributions to the regulation of repeat-associated siRNAs , in addition to functioning in the biogenesis of ta-siRNAs . It is conceivable that , due to the highly repetitive nature of the maize genome , additional silencing pathways were co-opted to maintain genome integrity . The repeat-derived LBL1-dependent siRNAs are , however , unlikely to contribute substantially to the developmental defects of lbl1 mutants . Instead , the data predicts that the essential role for lbl1 in development reflects its requirement for the biogenesis of TAS3-derived tasiR-ARFs and the correct regulation of their ARF3 targets . The finding that loss of tasiR-ARF activity and the correct regulation of its ARF3 targets underlie the developmental defects of lbl1 mutants is unexpected . Mutations in rgd2 ( ago7 ) , which is likewise required for tasiR-ARF biogenesis , are reported to yield a phenotype distinct from lbl1 . Plants homozygous for rgd2-R develop narrow strap-like leaves , but these maintain adaxial-abaxial polarity [18] . Based on this difference in phenotype , LBL1 was proposed to have functions other than in tasiR-ARF biogenesis that contribute to maize leaf polarity . The described rgd2-R allele , however , results from a transposon insertion in the first large intron of the gene , presenting the possibility that it is not a complete loss-of-function allele . Moreover , the rgd2-R phenotype has been characterized primarily in the Mo17 inbred background , and natural variation present between B73 and Mo17 is known to affect the phenotype of developmental mutants . We therefore introgressed the rgd2-Ds1 allele ( Fig . 5A ) , which contains a Ds-transposon insertion in the essential PIWI domain and is predicted to completely disrupt protein activity , into the B73 background . The phenotype of rgd2-Ds1 in B73 is more severe than that described for rgd2-R , giving rise to seedlings with reduced thread-like leaves that often arrest shortly after germination ( Fig . 5B-C ) . The thread-like rgd2-Ds1 leaves lack marginal characters , including the saw tooth hairs and sclerenchyma cells , as well as a ligule , macrohairs , and bulliform cells that characterize the adaxial epidermis . In transverse sections , such leaves show a radial symmetry with photosynthetic and epidermal cells surrounding a central vascular bundle ( Fig . 5D-E ) . These defects closely resemble the phenotype of lbl1-rgd1 [17] , [55] , consistent with our finding that the developmental defects of lbl1 reflect a loss of tasiR-ARF activity . In fact , the phenotype of the rgd2-Ds1 null allele appears slightly more severe than that of lbl1-rgd1 , and this is correlated with a more pronounced effect on ARF3 expression . Transcript levels for arf3a and arf3c-e are significantly increased up to 3 . 5 fold in rgd2-Ds1 seedling apices ( Fig . 5F ) . In addition , expression of arf3b is changed significantly in rgd2-Ds1 , albeit less than two-fold . The fact that mutations in lbl1 and rgd2 condition comparable adaxial-abaxial leaf polarity defects supports the finding that LBL1 contributes to development through the biogenesis of TAS3-derived ta-siRNAs . To confirm that this requirement lies specifically in the production of tasiR-ARFs and the downstream regulation of the ARF3 abaxial determinants , we generated transgenic lines that express either a native or tasiR-ARF-resistant version of arf3a . The latter ( arf3a-m ) harbors silent mutations in each of the two tasiR-ARF target sites ( Fig . 6A ) . Most plants expressing the native arf3a cDNA from the endogenous arf3a regulatory regions ( arf3a:arf3a ) are phenotypically normal . Only occasionally do such plants develop slightly narrower leaves and these become less evident as the plant matures . In contrast , transgenic plants expressing the tasiR-ARF insensitive arf3a:arf3a-m transgene displayed pronounced vegetative and reproductive abnormalities ( Fig . 6B-I ) . arf3a:arf3a-m seedlings resemble seedlings homozygous for the weak lbl1-ref allele , and develop half leaves and thread-like abaxialized leaves , as well as leaves with ectopic blade outgrowths surrounding abaxialized sectors on the upper leaf surface [55]; ( Fig . 6B-F ) . However , as such plants matured , their phenotypes became progressively more severe and resembled the phenotypes of lbl1-rgd1 . Mature arf3a:arf3a-m plants have a dramatically reduced stature ( Fig . 6G-H ) , and exhibit developmental abnormalities in both male and female inflorescences that result in complete sterility ( Fig . 6I ) . Thus , misregulation of arf3a alone is sufficient to recapitulate phenotypic defects seen in lbl1 and rgd2 mutants [17] , [18] , [55] . The initial milder phenotypes of arf3a:arf3a-m plants are potentially explained by the fact that only arf3a expression is affected in these plants , as ARF3 has been shown to condition dose-dependent phenotypes in Arabidopsis [10] . These data demonstrate a conserved role for the ARF3 transcription factors in promoting abaxial fate , and confirm our findings from genome-wide analysis that LBL1 contributes to development through the biogenesis of TAS3-derived tasiR-ARFs and the regulation of their ARF3 targets . Moreover , the severe defects conditioned by mutations in lbl1 and rgd2 thus underscore the importance of the tasiR-ARF ARF3 regulatory module to maize development . The present work reveals substantial diversity in small RNA pathways across plant species , both in the regulation of repeat-associated siRNAs and the spectrum of phased siRNAs . Only TAS loci belonging to the TAS3 family are active in the maize vegetative apex . In other plant species for which genome-wide small RNA analyses were completed , additional phased siRNA loci belonging to either the one-hit or two-hit sub-families were identified [21] , [24] , [26]–[28] , [31] , [36] , [37] , [56] . Some of the observed diversity may reflect variation in small RNA pathways across different tissue types , but our results indicate that essential steps in the one-hit phased siRNA pathway may function distinctly in the maize seedling apex . Whether this reflects a broader difference between monocots and dicots could be resolved by a similar in depth analyses of phased siRNAs in vegetative apices of e . g . rice and Brachypodium . Our data further shows that loss of tasiR-ARF mediated regulation of ARF3 genes is responsible for the developmental phenotypes of ta-siRNA biogenesis mutants in maize . Mutants affecting ta-siRNA biogenesis display phenotypes that differ widely from species to species . The TAS3 ta-siRNA pathway , including the regulation of ARF3 targets , is conserved throughout land plant evolution , but the population of phased siRNAs and their targets otherwise vary extensively [4] , [5] , [10]–[19] . Our findings indicate that this diversity in TAS pathways cannot fully account for the phenotypic differences of ta-siRNA biogenesis mutants . As in maize , the developmental defects of Arabidopsis and Medicago ta-siRNA biogenesis mutants can be mimicked by overexpression of a tasiR-ARF insensitive allele of ARF3 [8]–[9] , [14] . Nonetheless , in contrast to the severe polarity phenotype of lbl1 leaves [17] , Arabidopsis and Medicago ta-siRNA biogenesis mutants exhibit relatively subtle defects in leaf development , giving rise to downward-curled and highly lobed leaves , respectively [2] , [12] , [14] , [57] . The fact that ARF3 proteins act as repressors of the auxin response [58] may be crucial to understanding these diverse phenotypes . Through their effect on the pattern and level of ARF3 accumulation , the ta-siRNA pathway allows the auxin response to be modulated in a precise spatiotemporal manner . While the TAS3 ta-siRNA pathway itself is highly conserved , its expression in time and space seems to vary across organisms . tasiR-ARFs act in the incipient maize leaf to polarize ARF3 expression and establish adaxial-abaxial polarity , whereas tasiR-ARF biogenesis in Arabidopsis and Medicago is delayed until later in primordium development [10] , [12] , [14] , [42] . Moreover , the nature and wiring of auxin responsive gene networks regulated by the ARF3 transcriptional repressor may vary between plants . Indeed , the polarity network in Arabidopsis and maize appears to be wired differently , as reflected in the distinct redundancies between polarity determinants in these species [10] , [59] . As such , divergence in the gene networks downstream of the ARF3 transcription factors or the spatiotemporal pattern in which these tasiR-ARF targets act emerge as a testable hypotheses to explain the diverse contributions of the ta-siRNA pathway to development in maize , Arabidopsis , and possibly other plant species as well .
Families segregating the lbl1-rgd1 allele [17] introgressed at least three times into B73 were grown in growth-chambers at 16 hour 28°C/light and 8 hour 24°C/dark cycles . Shoot apices including the meristem and up to 5 leaf primordia were dissected from 2 week-old plants in triplicate . Total RNA was prepared using the mirVana RNA isolation kit ( Life Techologies ) , and 1ug per sample used to generate small RNA libraries using the small RNA-seq kit ( Illumina ) . RNA 3′ and 5′ adapters were ligated in consecutive reactions with T4 RNA ligase . Ligated RNA fragments were primed with an adapter-specific RT primer and reverse transcribed with Superscipt II reverse transcriptase ( Life Technologies ) followed by eleven cycles of amplification with adapter specific primers . Resulting cDNA libraries were separated on a 6% TBE gel and library fragments with inserts of 18-25p excised . Recovered cDNA libraries were validated by QC on an Agilent Bioanalyzer HiSens DNA chip ( Agilent Technologies Inc . ) and were sequenced for 50 cycles on the Illumina GAIIx according to Illumina protocols with one sample per lane . The same RNA samples from two biological replicates were also used for RNA deep sequencing by Macrogen Inc , Korea . Trimmed reads 18 to 26 nt in length were aligned to the maize B73 RefGen_v2 genome ( release 5a . 57 ) using Bowtie v0 . 12 . 7 [60] . While the observations presented in this study are robust across a wide range of mapping parameters , the specific data presented uses the following standard filtering criteria [28] , [31]: only perfectly matched reads were considered and , taking into consideration the characteristics of previously described miRNA and ta-siRNA loci , a maximum of 20 alignments per read were reported . Reads matching known structural RNAs ( rRNAs , tRNAs , sn-RNAs and sno-RNAs ) from Rfam 10 . 0 [61] were removed from further analysis . As expected , considering only uniquely mapped reads eliminated several of the developmentally important ta-siRNAs and miRNAs . Whereas allowing 100 alignments per read in both the phased and LBL1-dependent siRNA analyses identified some additional and distinct repeat loci without impacting the overall conclusions . Using similar criteria as previously described [24] , [62] , the abundances of small RNA reads in each individual library were calculated using non-overlapping 500 nt windows . Any windows with fewer than 10 reads total , across all libraries were removed from further analysis . For the remaining windows , edgeR [63] was used to model the counts distribution using a negative binomial model with common dispersion estimate . Differentially expressed loci were defined as windows with at least a 2-fold difference in abundance between the wild-type and lbl1 samples , and an adjusted P-value<0 . 05 , corrected according to the method of Benjamini and Hochberg [64] . Differentially expressed 24-nt small RNAs derived from the lbl1 introgression interval were excluded from further analysis . Differential accumulation of total reads in the 18-26 nt size classes between wild-type and lbl1 were calculated using a two-tailed t-test , as the millions of reads sequenced in each size class would follow an approximately normal distribution . Mapped reads from the three wild-type libraries were normalized by the number of genome-matched reads in the library and pooled into a single database . Reads matching the forward and reverse strand were merged , adjusting for the 2-nt 3′ overhangs generated by DICER processing . Using a sliding window of 500 bp , genomic regions containing at least 5 reads of 21-nt with a phasing distance of exactly 21-nt were identified as candidate phased clusters . To identify those candidate clusters in which the majority of small RNAs are phased , we modified the phasing score calculation of De Paoli et al . [65] to the following: where n is the number of phase cycle positions occupied by at least one small RNA read , C is the number of phase cycles that fit within a 500 bp window ( 23 for 21nt small RNAs , 22 for 22-nt small RNAs , 20 for 24-nt small RNAs ) , P is the abundance of reads in each phase cycle , and U is the abundance of out of phase reads in the window . Analogous to previous studies [28] , [31] , a stringent threshold of P-score≥25 was used to identify phased siRNA clusters . Target predictions for ta-siRNAs and lbl1-dependent siRNAs were made using Target Finder , allowing a maximum score = 4 . 5 . Scoring was assigned as described previously [4] . PARE libraries were generated from B73 apex tissues as described previously [35] . PARE analysis was performed according to Zhai et al . [31] , with the following minor modification: two windows flanking each predicted target site were defined and the total abundance of PARE tags matching to ( 1 ) a small window “WS” of 5 nt ( cleavage site ±2 nt ) , and ( 2 ) a large window “WL” of 31 nt ( cleavage site ±15 nt ) calculated . Cleavage sites were filtered to retain only those for which WS/WL≥0 . 75 and WS≥4 . Reads were trimmed and aligned to the B73 RefGen_v2 genome ( release 5a . 57 ) and the annotated exon junctions ( version 5b_AGPv2 ) using TopHat v1 . 2 . 0 . The majority ( 78–80% ) of the trimmed reads were uniquely mapped and further analyzed for read counts per genes in the two replicates for each wild-type and lbl1 mutant samples . RPKM values were determined using the Cufflinks package Cuffdiff v1 . 0 . 3 with default parameters , except that the “minimum alignment count” ( -c ) was set to 50 [66] . Differentially expressed transcripts were selected as those showing a 2-fold change in expression with a Cuffdiff determined , BH-corrected P-value<0 . 05 . To analyze expression of repetitive elements , RNAseq reads were allowed to map up to 50 times to genome . Any multi-mapping reads were weighted 1/n , where n is the number of possible alignments . A counts table containing the total reads mapped to each repeat locus in each wild-type and lbl1 replicate was analyzed as above . The MultiSite Gateway System ( Invitrogen ) was used to create the arf3a:arf3a and the arf3a:m-arf3a constructs . The coding sequences of arf3a ( GRMZM2G030710_T01 ) , and the arf3a regulatory regions ( 2 . 838 kb promoter and 1 . 059 kb 3′UTR ) were amplified and cloned into pDONR entry vectors . To generate the tasiR-ARF insensitive arf3a version , mutations were introduced at the two tasiR-ARF target sites using megaprimer PCR mutagenesis . Entry clones for each of the two constructs were combined in a single MultiSite Gateway LR recombination reaction with the pTF101 Gateway-compatible maize transformation vector . Positive clones were transferred into Agrobacterium tumefaciens strain EHA101 and transformed into maize by the Iowa State University Plant Transformation Facility . The rgd2-Ds1 allele [18] was introgressed six times into B73 , and segregating families were grown in growth-chambers at 16 hour 28°C/light and 8 hour 24°C/dark cycles . Leaves from mutant and wild-type plants were fixed and embedded as described [67] . Paraplast blocks were sectioned at a thickness of 10 µM and stained with Safranin-O and Fast Green according to Johansen's method . Shoot apices were dissected from 12 day-old seedlings of lbl1-rgd1 , rgd2-Ds1 , and their respective non-mutant siblings . Total RNA was prepared using Trizol reagent ( Invitrogen ) and treated with DNase I ( Promega ) . cDNA from 500 ng of RNA per sample was synthesized using Superscript III First-Strand Synthesis System ( Invitrogen ) according to manufacturer's protocol . Gene-specific primers were designed ( sequence available upon request ) for use with iQ SYBR Green Supermix ( BioRad ) in qPCR . The specificity of all amplification products was determined using dissociation curve analyses . Relative quantification ( RQ ) values were calculated based on at least three biological and two technical replicates using the 2−ΔCt method , with the ΔCt of glyceraldehyde-3-phosphate dehydrogenase ( gapc ) as normalization control , taking into consideration the efficiencies of each primer pair as described [68] , [69] . High throughput sequencing data , both raw and processed files , has been submitted to the Gene Expression Omnibus and is available upon publication at accession number GSE50557 . | Mutations in maize leafbladeless1 ( lbl1 ) that disrupt ta-siRNA biogenesis give rise to plants with thread-like leaves that have lost top/bottom polarity . We used genomic approaches to identify lbl1-dependent small RNAs and their targets to determine the basis for these polarity defects . This revealed substantial diversity in small RNA pathways across plant species and identified unexpected roles for LBL1 in the regulation of repetitive elements within the maize genome . We further show that only ta-siRNA loci belonging to the TAS3 family function in the maize vegetative apex . The TAS3-derived tasiR-ARFs are the main ta-siRNA active in the apex , and misregulation of their ARF3 targets emerges as the basis for the lbl1 leaf polarity defects . Supporting this , we show that plants expressing arf3a transcripts insensitive to tasiR-ARF-directed cleavage recapitulate the phenotypes observed in lbl1 . The TAS3 ta-siRNA pathway , including the regulation of ARF3 genes , is conserved throughout land plant evolution , yet the phenotypes of plants defective for ta-siRNA biogenesis are strikingly different . Our data leads us to propose that divergence in the processes regulated by the ARF3 transcription factors or the spatiotemporal pattern during development in which these proteins act , underlies the diverse developmental contributions of this small RNA pathway across plants . | [
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| 2014 | Genome-Wide Analysis of leafbladeless1-Regulated and Phased Small RNAs Underscores the Importance of the TAS3 ta-siRNA Pathway to Maize Development |
Food choice and eating behavior affect health and longevity . Large-scale research efforts aim to understand the molecular and social/behavioral mechanisms of energy homeostasis , body weight , and food intake . Honey bees ( Apis mellifera ) could provide a model for these studies since individuals vary in food-related behavior and social factors can be controlled . Here , we examine a potential role of peripheral insulin receptor substrate ( IRS ) expression in honey bee foraging behavior . IRS is central to cellular nutrient sensing through transduction of insulin/insulin-like signals ( IIS ) . By reducing peripheral IRS gene expression and IRS protein amount with the use of RNA interference ( RNAi ) , we demonstrate that IRS influences foraging choice in two standard strains selected for different food-hoarding behavior . Compared with controls , IRS knockdowns bias their foraging effort toward protein ( pollen ) rather than toward carbohydrate ( nectar ) sources . Through control experiments , we establish that IRS does not influence the bees' sucrose sensory response , a modality that is generally associated with food-related behavior and specifically correlated with the foraging preference of honey bees . These results reveal a new affector pathway of honey bee social foraging , and suggest that IRS expressed in peripheral tissue can modulate an insect's foraging choice between protein and carbohydrate sources .
Multicellular animals have distinct energy demands but can modulate their growth and energy consumption in response to nutrient availability [1] , [2] . This state of metabolic homeostasis is central to health and lifespan . Metabolic homeostasis is maintained by physiological feedback mechanisms that include the behavioral system [3] . The association between metabolic biology and behavior is of much interest since human food-choice and eating behavior contribute to many public health-issues such as obesity and diabetes [4] . In mammals , food-related behavior is influenced by several factors , including age [5] , sex and reproductive physiology [6] , genotype [7] , sensory perception [8] , [9] , and environment or social setting [10] . Many of these factors interact in complex ways to affect behavior [11]–[13] , and the underlying cause-effect relationships are challenging to test . However , similar relationships are found in highly manipulable insect models where metabolic biology shows considerable homology to mammalian systems [14] . Insect food-related behavior , as exemplified by individual foraging choice between a carbohydrate source ( nectar ) and a protein source ( pollen ) , is studied in detail in honey bees ( Apis mellifera ) [15]–[17] . Honey bees are social insects organized in colonies with one reproductive queen and several thousands of largely sterile female helpers called workers [18] . Workers progress through an age-associated series of tasks that culminate in foraging activity when bees are 2–3 weeks old . As foragers , workers collect nectar , pollen , water and propolis , which are essential resources for colony growth and survival . Nectar and pollen are stored ( hoarded ) inside the nest and consumed as a function of colony needs . A worker can collect both nectar and pollen during a foraging trip , but she will often bias her collection toward one of these resources [19] . Bidirectional colony-level artificial selection for the amount of stored pollen ( pollen-hoarding ) resulted in high and low pollen-hoarding honey bees that are maintained as standard strains [17] . These strains are characterized by significantly different foraging behavior in workers: Similar to the wild type ( unselected commercial stocks ) , high and low pollen-hoarding strain bees collect nectar , pollen or both during foraging trips , but high strain workers are more likely to collect pollen [16] , [17] , [20] , [21] . Physiological , sensory and behavioral systems are tightly linked in animals [7] , [8] , [22] , including insects [23] . As a likely consequence , bidirectional selection for pollen-hoarding affected not only foraging behavior , but also behavior-associated physiology such as circulating levels of vitellogenin ( yolk protein precursor/behavioral affector molecule [15] , [24]–[26] ) and sensory systems ( sucrose responsiveness [27]–[29] ) . Studies in wild-type honey bees have confirmed correlations as well as direct relationships between these traits [24] , [29] , [30] . Moreover , genome mapping has identified highly epistatic quantitative trait loci ( QTL , pln1- pln4 ) that explain variation in honey bee foraging behavior and sucrose responsiveness [31]–[33] . The 95% confidence interval of the least gene-dense QTL , pln4 , contains four genes . One is the insulin receptor substrate ( IRS ) , which is an appealing positional candidate gene for regulation of honey bee behavioral physiology due to known interactions between the IIS pathway and food-related behavior [34] , [35] . IRS genes encode for a conserved membrane-associated adaptor protein that is central to transduction of insulin/insulin-like signals ( IIS ) ( reviewed by [36] ) . IIS pathways , including IRS proteins , are active in the central ( neural ) and peripheral ( non-neural ) tissues of eukaryotes and regulate metabolic responses to food-intake [2] , [22] , [37] . Central nervous system IIS ( central IIS ) can also coordinate eating behavior directly ( reviewed by [38] , [39] ) ; e . g . , following administration or natural secretion of insulin , elevated central IIS will change food-intake behavior [40] . In mammals , the increase in blood nutrient-levels after eating leads to enhanced synthesis and release of insulin from pancreatic β-cells , while insects release insulin-like peptides ( ILPs ) from neural cells [41] . The activity of pancreatic cells is further influenced by gastrointestinal hormones ( incretins ) and signals from the autonomic nervous system ( reviewed by [42] , [43] ) , whereas recent work in the fruit fly Drosophila melanogaster shows that humoral signals from peripheral fat body ( insect functional homolog of mammalian liver and adipose tissue ) can regulate ILP secretion in brain [44] . The Drosophila IRS homologue CHICO is crucial for IIS function in fly tissues including neural cells , and fly behavior is affected if central IIS is experimentally impaired [45] . Contrasting these and other findings about roles of central IIS in behavior , less is known on how behavior is influenced by peripheral IIS , i . e . , signaling that is endogenous to peripheral tissues . Here , we use honey bees to test the prediction that perturbation of peripheral IIS can affect food-related behavior . Experimental workers were obtained from the standard strains of high and low pollen-hoarding bees , while wild type was used to test the general validity of methods and select results . Pollen-hoarding strain bees were preferred as experimental animals because the set of well-defined phenotypic differences between them allow treatment effects and their interactions with genotype to become more readily apparent ( [15] , [24] and Discussion ) . Perturbation of peripheral IIS was achieved by RNA interference ( RNAi ) -mediated gene knockdown of IRS in fat body . The results presented here show that food-related behavior can be influenced by changes in peripheral IIS: IRS RNAi , which reduced IRS expression levels in worker fat body but not in brain , biased bees to forage for the protein source , pollen . Our detailed analyses of genotype-specific behavioral patterns and established factors connected to variation in honey bee foraging behavior ( vitellogenin gene expression , sucrose sensory sensitivity ) point to distinct roles of IRS in regulation of worker foraging choice .
Newly emerged ( 0–24 h old ) adult workers from high and low pollen-hoarding strains were injected intra-abdominally [46] , [47] with double-stranded RNA ( dsRNA ) against the only IRS-encoding gene in honey bees ( GenBank XM_391985 ) . This approach to RNAi targets honey bee fat body [30] , [46] , [48] , [49] while being ineffective in brain [47] , [50] . Knockdown was assessed relative to an established honey bee control procedure for non-specific effects of treatment or handling in RNAi experiments . This protocol requires injection of dsRNA toward a gene not found in the bee ( a green fluorescent protein ( GFP ) encoding gene in vector , GenBank AF097553 ) [30] , [48] , [49] . The design was replicated twice by introducing workers into two separate host colonies . Using the IRS RNAi procedure above , IRS knockdown and control treatment groups were established for high and low pollen-hoarding strain bees . This experiment excluded wild-type bees , because their increased heterogeneity of genotype and behavior was anticipated to mask effects of a single gene , here IRS , on a complex quantitative trait like food-related behavior ( [34] , [48] and Discussion ) . All bees were marked and allowed to mature for 10 days in two host colonies . Subsequently , for five days , marked bees were captured as they returned from foraging trips and their foraging loads of pollen and nectar were quantified ( n = 101 high vs . n = 168 low strain bees , further details in Materials and Methods ) [32] . We identified ‘nectar load weight’ and the ‘proportion of pollen collected’ as behavioral traits that were significantly affected in the experiment . These variables were influenced by the RNAi treatment scheme ( factorial ANOVA: treatment , F ( 2 , 214 ) = 5 . 0528 , p = 0 . 0071 ) and by strain ( factorial ANOVA: genotype , F ( 2 , 214 ) = 19 . 3706 , p<0 . 0001 ) . Host colony environment ( factorial ANOVA: colony , F ( 1 , 214 ) = 1 . 2328 , p = 0 . 2935 ) did not affect behavior , and no interaction between treatment and genotype was detected ( F ( 2 , 214 ) = 1 . 1618 , p = 0 . 3149 ) . Post hoc tests on the behavioral data were performed separately for nectar load and the proportion of pollen collected , as nectar load explains part of the variance in the proportional load of pollen [25] . The effect of IRS RNAi on nectar load ( Fisher's LSD: p = 0 . 0255 ) and the proportion of pollen collected ( Fisher's LSD: p = 0 . 0447 ) were independently significant ( Figure 5A and 5B ) . Further analysis showed that the behavioral response in worker nectar load weights after peripheral IRS RNAi remained suggestive also when the dataset was split by strain ( Fisher's LSD: p = 0 . 0549 , Figure 5A insert ) . These results indicated that reduced IRS expression in fat body affected the nectar loading behavior of the two strains similarly: nectar loads were reduced by peripheral IRS knockdown irrespective of genotype . Thereby , the data from both strains contributed additively to statistical power such that the significant effect of IRS RNAi on behavior was detected in the full dataset ( Figure 5A ) . For the proportional load that was pollen , a similar pattern of post hoc significance showed that both strains contributed to the significant influence of IRS down-regulation on behavior . This effect was observed as a consistent bias of the strains' mean foraging effort toward the pollen protein source ( Figure 5B ) . When the data were split by strain and each set analyzed separately , the effect remained suggestive within the high strain genotype ( Fisher's LSD: p = 0 . 0771 ) . In our experiment , IRS did not affect pollen loads per se ( factorial ANOVA , treatment , F ( 1 , 270 ) = 0 . 3699 , p = 0 . 5435 , Figure 5C ) , but the strain effect was significant ( factorial ANOVA: genotype , F ( 1 , 270 ) = 20 . 94 , p<0 . 0001; Figure 5C insert ) . In addition , high strain bees demonstrated a trend toward increased pollen load sizes in response to IRS RNAi , while the opposite was true for low strain bees ( Figure 5C insert ) . To understand the relationships between the strain-associated pattern of pollen loading , the more general ( strain-independent ) effect of IRS on nectar load sizes ( Figure 5A ) , and the workers' overall food-loading behavior , we analyzed the total load masses of the bees . In this analysis , the pollen load was counted twice toward the foraging effort of each worker [30] , [52] . This correction of total load mass to estimate individual effort is in general use [30] , [52] , and takes into account that aerodynamic power influences the pollen load and nectar load of workers differently: it is possible for a forager to carry a maximum load size of nectar that is approximately twice as heavy as the maximum load size of pollen she is capable of carrying [52] . Using the raw ( uncorrected ) weights of nectar and pollen did not influence conclusions ( Fisher's LSD test , uncorrected data , praw-values in italics , below ) . The main effects of IRS RNAi , strain genotype , and host environment did not affect the total foraging effort of the worker bees ( factorial ANOVA: treatment , F ( 1 , 271 ) = 2 . 9831 , p = 0 . 0852; genotype , F ( 1 , 271 ) = 2 . 1811 , p = 0 . 1409; colony , F ( 1 , 271 ) = 0 . 0164 , p = 0 . 8982 ) . However , when contrasting the loading relationships of the two genotypes in a planned comparison ( Fisher's LSD test ) , we found that the average total load mass of high strain IRS knockdowns and controls was identical ( Fisher's LSD: p = 0 . 7337 , praw = 0 . 5008 ) , while low strain bees responded to IRS down-regulation with a significant decrease in their total load mass average ( Fisher's LSD , p = 0 . 0130 , praw = 0 . 0154 , Figure 5D ) . These results indicated that in response to IRS downregulation , increased pollen-loading ( Figure 5C insert ) counterbalanced reduced nectar loading in high strain bees ( Figure 2A insert ) while the low strain genotype collected less nectar without increasing pollen loads , leading to reduced total food-loading . The sucrose response is a general neural property related to foraging choice behavior in wild-type honey bees [53] and selected pollen-hoarding strains [20] , [27] , [54] . Thus , after detecting significant effects of IRS on foraging bias , we wanted to resolve if IRS knockdown influenced the workers' foraging choice by modulating the sucrose response system . To test this relationship , we quantified the effect of IRS RNAi on individual sucrose responsiveness measured as the gustatory response score ( GRS ) [20] , [27] , [28] , [49] , [53] . As before , knockdowns and controls were established and introduced into two host colonies . The experimental bees were retrieved after 11 days ( n = 42−54 ) , i . e . , at a chronological age similar to the bees tested for foraging choice behavior . In the laboratory , the proboscis extension response ( PER ) was measured using a standard series of water and six increasing sucrose concentrations [27] , [28] . Individual bees were assigned a GRS based on the number of elicited PER ( 0 = lowest score , not responding to gustatory stimulation; 7 = highest score , responding to water and all six sucrose concentrations ) . As shown before [20] , [27] , [28] , we found that high strain workers were more responsive to sucrose compared with low strain bees ( factorial ANOVA: genotype , F ( 1 , 185 ) = 13 . 1205 , p = 0 . 0003; colony , F ( 1 , 185 ) = 0 . 88636 , p = 0 . 3540 ) . The sucrose response is a defining character difference between pollen-hoarding strains [16] , [26] , [54] , and in our dataset the effect of genotype was significant in IRS knockdowns ( Fisher's LSD: p = 0 . 0184 ) as well as controls ( Fisher's LSD , p = 0 . 0001 , Figure 6A ) . In contrast , IRS RNAi did not influence the bees' sucrose response ( factorial ANOVA: treatment , F ( 1 , 185 ) = 0 . 8823 , p = 0 . 3488 ) . There was also no interaction between the treatment and genotype factors ( F ( 1 , 185 ) = 0 . 7861 , p = 0 . 3764 ) . A validation test in wild type ( n = 40−41 , Figure 6B ) supported that worker sucrose responsiveness is not strongly affected by reduced peripheral IRS expression ( two-tailed Student's t-test , T ( 1 , 79 ) = 1 . 6928 , p = 0 . 0945 ) . An influence of IRS expression on foraging choice but not the sucrose response system of worker bees , could point to a function of IRS in behavioral regulation that is separate from known roles of vitellogenin: Honey bee vitellogenin encodes a multifunctional yolk protein precursor [25] . The gene is expressed in fat body and affects worker sucrose responsiveness , foraging onset , foraging choice , and lifespan [30] , [49] , [55] . RNAi-mediated knockdown of vitellogenin increases sucrose responsiveness in wild-type bees , leading to higher GRS [49] . In our experiment however , GRS remained constant despite IRS RNAi . This finding led us to predict that when IRS is knocked down , vitellogenin expression remains unchanged . To test this hypothesis , we measured the amount of vitellogenin transcript in the fat body of IRS knockdowns and controls ( selected strains , n = 18; wild type , n = 12 ) . As established previously , the level of vitellogenin mRNA was significantly different between high and low pollen-hoarding strain bees ( factorial ANOVA: strain , F ( 1 , 59 ) = 14 . 3995 , p = 0 . 0004 ) . Young ( less than 15 day-old ) high strain workers are characterized by elevated vitellogenin expression levels compared to same-aged low strain bees [15] , [48] . In our experiment , this pattern was confirmed in the data from IRS knockdowns ( Fisher's LSD , p = 0 . 0202 ) and controls ( Fisher's LSD , p = 0 . 0003 , Figure 6A ) . Moreover , and as predicted , IRS RNAi did not influence the amount of vitellogenin transcript overall ( factorial ANOVA: treatment , F ( 1 , 59 ) = 0 . 9660 , p = 0 . 3297 ) . A planned comparison in each strain ( Student's t-test ) , however , indicated that low pollen-hoarding strain bees tend to reduce vitellogenin expression after IRS RNAi ( T ( 1 , 31 ) = 1 . 8274 , p = 0 . 0386 , Figure 7A ) . This response was not paralleled in high strain workers ( T ( 1 , 32 ) = 0 . 1637 , p = 0 . 4355 ) . Wild-type ( Figure 7B ) also did not show an effect of IRS RNAi on vitellogenin ( two-tailed Student's t-test , T ( 1 , 22 ) = −0 . 1720 , p = 0 . 8650 ) .
Here , we show that IRS can affect the foraging decisions of an insect . Knockdown of peripheral IRS gene expression led 10–15 day-old worker honey bees of two standard genetic backgrounds to collect less nectar and to bias their foraging effort toward pollen . The effect of IRS on foraging behavior was subtle but significant . Modest influences are the common denominator of intrinsic behavioral affectors in worker bees , including the vitellogenin gene [30] , the TOR ( target of rapamycin ) signaling pathway , and fat body adiposity [35 , and references therein] . Worker behavioral traits , including food-related task performance , are complex quantitative genetic characters [31]–[34] that also are modulated by social environmental factors like the amount of larval brood and stored food-resources in colonies [19] . Many genes that influence honey bee behavior , therefore , may not have major effects [24] . Before obtaining behavioral data , we validated RNAi in 7 day-old bees ( Figure 1 , Figure 2 , Figure 3 , Figure 4 ) . IRS mRNA and protein levels were measured in these workers and not in the bees from our behavioral experiment , because transcript abundance can be influenced ( and thus confounded ) by the considerable laboratory handling that is required for collection and quantification of honey bee foraging loads . Yet , RNAi can last up to 25 days in honey bee workers [30] , [46] , and more than 4 months in the flour beetle Tribolium castaneum [56] . In other insect species , such as aphids [57] , [58] and termites [59] , RNAi-mediated gene-silencing is not as long-lasting , but the transient effect is sufficient to induce enduring changes in life-history . Thus , the cumulative evidence from insect functional genomics , in combination with our treatment-specific results , strongly suggests that IRS RNAi persisted beyond the 7th day validation point . We used wild-type honey bees to validate the RNAi tool , and to show that connections between IRS and sucrose response , and between IRS and vitellogenin expression , could be generalized . Yet , only the standard stocks of high and low pollen-hoarding strains were used to test whether knockdown of peripheral IRS expression could influence foraging behavior . Honey bee foraging choice is a complex quantitative trait: it is governed by many genes and some loci are highly epistatic [34] . Effects on behavior might be undetectable if one gene in such networks is perturbed within a highly heterogeneous group of animals , like wild-type honey bees . Wild-type colonies differ in levels of pollen-hoarding , and wild-type workers show variation in food-related behavior [17] , [25] . Within each of the standard pollen-hoarding strains , such variance is present but reduced , and the well-documented differences between the genotypes can be controlled for so treatment effects are more easily detected [48] . Therefore , we assumed that the effects of peripheral IRS RNAi were more likely to be revealed by using these two standard genetic backgrounds in our test of behavior . Artificial selection can result in spurious phenotypic associations , and it can be relevant to ask whether results from selected stocks can be generalized to unselected animals ( wild type ) [60] . Pollen-hoarding has affected a suite of traits in worker bees , including sucrose responsiveness ( Figure 6 ) , and vitellogenin expression ( Figure 7 ) , in addition to behavior [15] , [17] , [24] , [27] , [48] . The majority of these trait-associations are tested and verified to extend to wild type [16] , [24] , [25] , [29] . Thus , it is likely that a set of worker traits including food-related behavior are pleiotropically regulated , and that the underlying gene network responded to artificial selection on pollen-hoarding [61] . This network can be represented in the pln1-pln4 QTL , where IRS is a positional candidate gene . The pln network has been mapped in different genetic sources of honey bees , which suggests that it is generally important for worker behavior [34] . Genetic background , however , affects both gene expression ( as shown in Figure 1 , Figure 2 , and Figure 7 ) and behavior ( as exemplified in Figure 5 and Figure 6 ) , and thereby , our data on foraging behavior are not generalizable . Yet , the contributions from this study do not only draw from an ability to generalize to wild-type bees . Rather , the results serve as a first illustration of a role of peripheral IRS in behavioral control . We identify a behavioral outcome of IRS down-regulation that is independent of genotype: the increased preference for a protein source ( pollen ) . However , we also reveal that the behavioral bias toward protein can be achieved through genotype-specific behavior . The strain selected for a high level of pollen-hoarding responded to reduced peripheral IRS expression by collecting smaller nectar loads and larger pollen loads than controls , resulting in a significant increase in the proportion of pollen collected . Overall , the total food load did not change . The strain selected for a low level of pollen-hoarding , on the other hand , did not compensate for reduced nectar loading by collecting more pollen . Thereby , the total food load declined . We propose that these behavioral responses can be explained if the foraging choice behavior of the worker honey bees is jointly influenced by fat body IRS and vitellogenin expression . Our explanation builds on three insights; that the total load mass of bees has an upper limit during foraging and therefore nectar vs . pollen loading is negatively correlated [52]; that vitellogenin expression encourages pollen loading [15] , [25] , [30] , and , that vitellogenin protein may reduce IIS transduction [55] , [61] , [62] . Explicitly , workers decrease nectar loading in response to IRS down-regulation ( Figure 5A ) , and in the presence of high vitellogenin levels available loading-capacity fills up with pollen ( high strain , Figure 5 , Figure 7A ) . The general pollen bias of high strain bees is consistent with this explanation , as the higher intrinsic vitellogenin level of this genotype would reduce IIS transduction and encourage pollen loading also in unmanipulated workers . In the low strain , conversely , lower intrinsic vitellogenin levels may normally encourage IIS transduction and nectar loading . And , when IRS is artificially suppressed in conjunction with low ( and further declining ) levels of vitellogenin expression ( our experiment ) , reduced nectar loading is not counterbalanced by release of pollen foraging behavior . As a result , the total load mass declines ( low strain , Figure 5 , Figure 7A ) . Vitellogenin is a glyco-lipoprotein that may convey a general signal of fat body adiposity [63] . In Drosophila , central IIS can be regulated remotely by nutrient sensing in fat body cells , but increased nutrient availability is associated with increased IIS in the fly [44] . The inverse influence of nutrition ( or vitellogenin action ) on IIS in honey bees is under study but poorly understood [55] , [61] , [62] . Correlations in our data may add to this investigation: high strain bees have high vitellogenin transcript abundance and somewhat increased IRS mRNA levels compared with low strain bees ( Figure 1A , Figure 7A ) . These relationships could imply that vitellogenin does not influence IIS by reducing IRS expression . It remains to be tested whether the elevated amount of IRS transcript in high strain bees is a compensatory response to reduced IIS transduction . Manipulation of IIS pathways can disrupt energy homeostasis and metabolism and produce extreme hyper- and hypoglycemic states leading to changes in food-related behavior [39] , [64] . Could similar processes influence our results ? In Drosophila , circulating blood sugar levels increase if ILP secretion is suppressed [65] . However , mutations in the fly IRS gene homologue chico lead to elevated lipid levels but the amount of circulating carbohydrate is unchanged [66] . Indeed , it has been suggested that ILPs may not be primary regulators of glucose homeostasis in insects: Adipokinetic hormone ( AKH ) , an endocrine factor with functions similar to glucagons , may govern global carbohydrate levels instead [67] . Thus , if energy homeostasis is similarly controlled in honey bees and fruit fly , it is less probable that the behavioral changes we observe here result from non-physiological hyperglycemia . This conclusion is supported by general results from high and low pollen-hoarding strain bees , which do not differ in baseline blood glucose levels or in blood glucose response to diets of varying sugar concentration ( Supplementary Figure 1 in Text S1 ) . The genetic differences between the strains ( which likely influence IIS processes [34] ) , thereby , may not confer measurable differences in glucose homeostasis . Many questions remain unanswered about how nutrients , vitellogenin , and IIS modulate physiology and behavior in honey bees . In this context , the work presented here represents the first successful gene knockdown of a central and conserved IIS pathway gene , and provides the first look at consequences for behavior . The honey bee is a study system in metabolic biology , sociobiology , behavioral biology , and neuroscience [50] . Thus , in addition to revealing a role of IRS in worker foraging behavior , our results provide tools for research on how life-histories are affected by metabolism , brain chemistry , and social behavior . Like eating behavior in mammals , foraging behavior in the honey bees is a complex syndrome influenced by genotype , physiological state , environment , and social needs . Much remains to be discovered about the behavioral physiology of food choice . This research is a priority as obesity-related disorders claim an increasing human health and economic toll . Our data are first to show that peripheral IRS expression can influence an insect's foraging choice between protein and carbohydrate sources . This finding sets the stage for comparative work that can increase our knowledge on the biology of food-related behavior .
Bees were maintained at the Honey Bee Research Laboratory at the Arizona State University Polytechnic Campus . Two high pollen-hoarding strain colonies , two low pollen-hoarding strain colonies , and two wild-type colonies were used as donors of experimental workers . To obtain the bees , queens were caged on a wax comb and allowed to lay eggs for 24 h inside the colony . Subsequently , the combs were removed and marked according to source before the brood was co-fostered in wild-type colonies . After 20 days , the combs were collected and put in an incubator where the bees emerged at 34°C and 80% relative humidity . The most recent honey bee genome assembly identifies XM_391985 as IRS . In a former genome release ( version 3 ) , IRS was identified as GB11037-RA , which differed from XM_391985 by the presence of an extra exon . We cloned IRS from total RNA isolated from several adult tissues ( worker brains , fat bodies , and ovaries ) to capture putative alternate splicing of the gene . Forward and reverse primer 5′ CACAACCGCAATCTCAGTC 3′; 5′ AACATAGTCGGCAGGTGGAC 3′ , respectively , were used . Four independent clones from each tissue were sequenced . The data confirmed that XM_391985 is a correct sequence for IRS , and did not detect alternative splicing . To produce cDNA template for double stranded RNA ( dsRNA ) synthesis , a 700 bp fragment from the open reading frame of the IRS ( XM_391985 ) mRNA sequence was cloned by forward and reverse primer 5′-TTTGCAGTCGTTGCTGGTA-3′; 5′-GCTTAAAGCCGGATAACGTG-3′ , respectively , into pCR® 4-TOPO® vector using the TOPO TA cloning kit ( Invitrogen ) . Cloning followed the instructions provided by the manufacturer . Several clones were verified by sequencing . For dsRNA synthesis , PCR primers with T7 promoter sequences ( underlined ) were used . The cloned cDNA fragment was used as a template for PCR , with 5′-TAATACGACTCACTATAGGGCGAGCGAACCGGTAGTCGTAAAG-3′ and 5′-TAATACGACTCACTATAGGGCGAGCAGTGATCAAACGTGGCTT-3′ as forward and reverse primer , respectively . The resulting product was 583 bp long . As control , green fluorescent protein ( GFP ) dsRNA was synthesized from AF097553 template , as previously described [46] , [48] , [49] . PCR products were excised from low melting temperature 1% agarose gels , purified using Qiaquick Gel Extraction Kit ( Qiagen ) . The dsRNA was then made using AmpliScribe T7 transcription kit ( Epicentre Biotechnologies ) following the manufacturer's protocol . dsRNA was purified using phenol:choloform extraction and run on a 1% agarose gel for verification of size and purity [58] . The final dsRNA concentration was adjusted to 10 µg/µl in nuclease free H2O . Newly emerged workers ( high and low pollen-hoarding strains plus wild type ) were randomly assigned treatments and marked with paint ( Testors Enamel , Testor Corporation ) to indicate treatment identity . Treated bees were injected intra-abdominally with either dsRNA against the IRS gene or , with green fluorescent protein ( GFP ) -derived dsRNA to establish a control , following general procedures for knockdown of gene expression in honey bee fat body [30] , [46] , [49] . The injection volume was 3 µl . After dsRNA injection , bees were introduced into two host colonies with a background population of about 5 , 000 wild-type bees . Fat bodies and brains were dissected from 7 day-old marked bees , and tissues flash-frozen in liquid nitrogen and stored at −80°C until use . RNA was extracted using RNeasy Mini Kit ( Qiagen ) including DNase treatment . For mRNA quantification between control and IRS knockdown workers; two step ( real time ) qRT-PCR was performed in triplicate using ABI Prism 7500 ( Applied Biosystems ) , and the data were analyzed using the Delta-Delta CT [68] method with actin ( GenBank: XM_623378 ) as housekeeper gene . This gene is stably expressed in different honey bee tissues , and provides a reference for studies of gene expression in the bee [69] , [70] . By monitoring negative control samples ( without reverse transcriptase ) and melting curves , we could verify that the qRT-PCR assay was not confounded by DNA contamination or primer dimmers [71] . In situ hybridization was performed according to a modified protocol based on Osborne and Dearden [72] and optimized for honey bee fat body . Fat bodies were fixed in buffer ( 4% formaldehyde , 20 mM KH2PO4/K2HPO4 , pH 6 . 8 , 90 mM KCl , 30 mM NaCl , 4 mM MgCl2 ) [73] at 4°C overnight with shaking , then washed three times in PBS . The samples were dehydrated through a methanol series and stored in methanol at −20°C . Rehydration was accomplished with a methanol series and followed by PTw washes ( PBS +0 . 1% Tween-20 ) . Fat bodies were digested with 20 µg/mL Proteinase K for 15 min , rinsed in PTw , and postfixed for 15 min in PTw with 4% formaldehyde . After rinsing five times in PTw , samples were transferred to 500 µl of hybridization buffer ( 50% deionized formamide , 5×SSC , 1 mg/ml yeast tRNA , 100 µg/ml salmon sperm DNA , 100 µg/ml heparin , 1xDenhardt's Solution , 0 . 1% Tween 20 , 5 mM EDTA ) and prehybridised at 60°C for 2 h . Hybridization was conducted in a hybridization buffer with 2 ng/µl specific IRS RNA probe labeled with digoxigenin ( DIG ) . To remove unbound probe , fat bodies were washed at 60°C in each of a series of pre-warmed wash solutions for 30 min in the order [74]: 75% hybridization buffer +25% 2×SSC , 50% hybridization buffer +50% 2×SSC , 25% hybridization buffer +75% 2×SSC , 100% 2×SSC , 0 . 2×SSC . Then , the samples were washed at room temperature 10 min in the following solutions: 75% 0 . 2×SSC +25% PTw , 50% 0 . 2× SSC +50% PTw , 25% 0 . 2× SSC +75% PTw , 100% PTw . The samples were blocked with 0 . 1% sheep serum in PTw for 20 min at room temperature , followed by incubation with a 1∶ 2 , 000 dilution of Anti-DIG-alkaline phosphatase conjugated Fab fragments ( Roche Molecular Biochemicals ) in blocking buffer at 4°C overnight . Tissues were then washed three times in alkaline phosphatase buffer ( 1 h ) . The color reactions were developed by BM purple alkaline phosphatase substrate precipitating at 4°C overnight . Reactions were stopped by dilution in PTw . The color reactions were developed by BM purple AP substrate precipitating at 4°C overnight . Reactions were stopped by dilution in PTw . The samples were visualized on an upright microscope ( Axio Imager A1 , Carl Zeiss Microimaging ) at 200× magnification and photographed ( Axiocam MRc5 , Zeiss Microimaging ) . Fat body and brain tissues were ground for 1 min in 150 µl and 50 µl extraction buffer , respectively ( 20 mM Tris , 150 mM NaCl and 5 mM EDTA ) supplemented with protease inhibitor ( Complete , Mini Protease Inhibitor Cocktail Tablets; Roche Applied Science ) on ice . Samples were centrifuged at 6 , 000×g for 20 min , and the supernatant was transferred into a new tube . The total protein concentration in this fraction was quantified using Bradford reagent [75] . Aliquots of individual samples , each with 100 µg protein , were then subject to SDS-PAGE on 10% gels ( Promega ) and transferred onto PVDF membrane ( Bio-Rad ) . Non-specific protein binding was blocked with 3% instant non-fat dry milk ( BestChoice ) overnight . Preabsorption was used to determine the specificity of the antibody toward IRS peptide antigen . Briefly , purified antibody ( 1 . 5 µg/ml ) and the antigen peptide ( 0 . 2 µg/ml ) were mixed in the blocking solution ( 3% Milk solution in 1XPBST ) in a total volume of 15 ml , and incubated on a rocking platform for 1 h . Membranes were probed either with this preabsorption solution for 1 h , or with purified IRS antibody ( 1∶500 ) in 15 ml blocking solution for 1 h . These incubations were followed by 3 washes with 1XPBST at 10 min interval . Membrane-bound antigen-antibody complexes were visualized with horseradish peroxidase-conjugated goat anti-rabbit IgG ( GE healthcare ) at a dilution of 1∶1 , 000 and detected with Western Lightning Chemiluminescence reagent ( PerkinElmer ) on a Versa-Doc imaging system ( Bio-Rad ) . IRS immunoreactivity identified a band of about 130 kDa , similar to the predicted molecular weight of honey bee IRS ( 129 kDa , Protein Calculator v3 . 3 , http://www . scripps . edu/~cdputnam/protcalc . html ) . dsRNA injections took place over two days for both of two experimental colonies , following the procedures described above . For every colony replicate , we prepared 150 bees from each treatment group and pollen-hoarding strain . All bees were marked with paint to indicate treatment group identity ( IRS RNAi or GFP control ) before they were introduced into the nests . Each experimental colony had a background population of about 5 , 000 wild-type bees . The experimental bees were allowed to mature . When bees from both treatment groups and genotypes were observed returning from foraging trips ( after 10 days ) , collection of foragers was initiated . Foragers were collected over a five-day period during peak foraging hours [30] . Pollen loads were removed from the left corbicula and weighed . We expelled the nectar from foragers' honey stomachs into pre-weighted capillary tubes to measure nectar load weight with a digital balance as described before [21] , [53] . Sucrose concentration was measured using a digital refractometer ( Misco ) . The same protocols for dsRNA injection ( n = 100 ) and sample collection ( above ) were used to obtain treatment and control workers for the measure of gustatory responsiveness and vitellogenin transcript levels . The 11 day-old high and low strain bees were collected in the morning and placed individually in the cylindrical mesh cages . Each bee was chilled until it showed first signs of immobility . It was then mounted in a metal holder and fixed with two strips of adhesive tape between head and thorax and over the abdomen [76] . After 1 h , gustatory responsiveness was tested using the proboscis extension response ( PER ) . The investigator was blind to the treatment identity of the bees . Each worker was tested by touching both antennae with a droplet of H2O followed by a concentration series of 0 . 1 , 0 . 3 , 1 , 3 , 10 , 30% sucrose . The inter-stimulus interval was 5–7 min . The interval was variable with the number of individuals tested at one time , usually 40–60 bees per test . A bee was observed to ‘respond’ to stimulation by fully extending its proboscis when a drop of water or sucrose was touched in turn to each antenna . The sum of the responses elicited during the test series represented the gustatory response score ( GRS ) of the bee [54] . After ending the test , the bees were assessed for their response to honey . Bees that did not respond to honey were not used in the subsequent data analysis , because we could not exclude that these workers were in poor condition or dead . For the remaining bees , GRS ranged between 0 ( response to honey , but no response to H2O and any of the sucrose solutions ) and 7 ( response to all solutions including H2O ) . For quantification of vitellogenin gene expression , mRNA was extracted from a parallel set of worker bees . As for IRS , qRT-PCR was used to quantify vitellogenin transcript levels in fat body tissue ( details above on the qRT-PCR procedure ) . Forward and reverse primer was 5′-GTTGGAGAGCAACATGCAGA-3′; 5′-TCGATCCATTCCTTGATGGT-3′ , respectively . The IRS gene expression data were log-transformed to approximate normality [70] , [77] . The resulting values conformed to assumptions of ANOVA as assessed by normal probability plots of residuals was well as by Bartlett and Levene's tests for the homogeneity of variances . A factorial ANOVA was used to validate the efficacy of RNAi . The behavioral data on nectar loads were square root transformed . A factorial ANOVA was used for initial exploration of the data on foraging behavior , which passed examination of normal probability plots on the residuals of the analysis , and also the homogeneity of variances tests ( Bartlett , Levene ) . Yet , the variables for foraging load are not independent: when a worker collects more nectar her pollen loading-capacity is reduced , causing nectar and pollen load-weights to be negatively correlated . Thus , separate main effects ANOVA's were used for the subsequent tests . Post hoc analyses were performed with the Fisher LSD test . Factorial ANOVA and Student's t-test were used for the study of GRS scores and vitellogenin gene expression ( log-transformed transcript levels ) , as the datasets conformed to assumptions of parametric tests ( see above ) . One-tailed tests were used when appropriate , i . e . , if an a prior expectation was established . All analyses were performed with STATISTICA 6 . 0 ( StatSoft ) . | Food choice , food handling , and eating are aspects of food-related behavior that can become pathological , as seen in the public health problems of obesity and diabetes . Thus , the insight that molecules from the body can bind to brain cells and signal and change food-related behavior is of biomedical interest . One such molecule is insulin , which binds to cells via receptors attached to the insulin receptor substrate protein , IRS . Insulin-like systems are found in all multi-cellular animals , and receptors with IRS are found in many cell types , including fat and muscle where receptor-binding regulates glucose uptake . However , it is unknown whether signaling to these “peripheral” tissues , in contrast to brain , have behavioral consequences . We suppress the gene encoding for IRS protein in the fat tissue of honey bees , insects with advanced food-related behavior . In response , animals collected less carbohydrate-rich food ( sugar-containing nectar ) and biased their foraging toward pollen , a protein source . Sensory sensitivity to sugar influences honey bee foraging behavior , but we show that this sensitivity remained unaltered when the IRS gene was suppressed . This study identifies a new molecular pathway that may regulate food-hoarding in bee colonies and shows that food-related behavior can be influenced by insulin-like signaling to peripheral cells . | [
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]
| 2010 | Down-Regulation of Honey Bee IRS Gene Biases Behavior toward Food Rich in Protein |
Our aim was to identify genes that influence the inverse association of coffee with the risk of developing Parkinson's disease ( PD ) . We used genome-wide genotype data and lifetime caffeinated-coffee-consumption data on 1 , 458 persons with PD and 931 without PD from the NeuroGenetics Research Consortium ( NGRC ) , and we performed a genome-wide association and interaction study ( GWAIS ) , testing each SNP's main-effect plus its interaction with coffee , adjusting for sex , age , and two principal components . We then stratified subjects as heavy or light coffee-drinkers and performed genome-wide association study ( GWAS ) in each group . We replicated the most significant SNP . Finally , we imputed the NGRC dataset , increasing genomic coverage to examine the region of interest in detail . The primary analyses ( GWAIS , GWAS , Replication ) were performed using genotyped data . In GWAIS , the most significant signal came from rs4998386 and the neighboring SNPs in GRIN2A . GRIN2A encodes an NMDA-glutamate-receptor subunit and regulates excitatory neurotransmission in the brain . Achieving P2df = 10−6 , GRIN2A surpassed all known PD susceptibility genes in significance in the GWAIS . In stratified GWAS , the GRIN2A signal was present in heavy coffee-drinkers ( OR = 0 . 43; P = 6×10−7 ) but not in light coffee-drinkers . The a priori Replication hypothesis that “Among heavy coffee-drinkers , rs4998386_T carriers have lower PD risk than rs4998386_CC carriers” was confirmed: ORReplication = 0 . 59 , PReplication = 10−3; ORPooled = 0 . 51 , PPooled = 7×10−8 . Compared to light coffee-drinkers with rs4998386_CC genotype , heavy coffee-drinkers with rs4998386_CC genotype had 18% lower risk ( P = 3×10−3 ) , whereas heavy coffee-drinkers with rs4998386_TC genotype had 59% lower risk ( P = 6×10−13 ) . Imputation revealed a block of SNPs that achieved P2df<5×10−8 in GWAIS , and OR = 0 . 41 , P = 3×10−8 in heavy coffee-drinkers . This study is proof of concept that inclusion of environmental factors can help identify genes that are missed in GWAS . Both adenosine antagonists ( caffeine-like ) and glutamate antagonists ( GRIN2A-related ) are being tested in clinical trials for treatment of PD . GRIN2A may be a useful pharmacogenetic marker for subdividing individuals in clinical trials to determine which medications might work best for which patients .
Common disorders are thought to have both genetic and environmental components . Genome-wide association studies ( GWAS ) have successfully identified numerous susceptibility loci for many common disorders ranging from behavioral traits such as addiction and substance abuse to infectious and immune-related disorders , age-related neurodegenerative disorders like Alzheimer's , Parkinson's and macular degeneration , metabolic disorders , psychiatric disorders , and many more ( for the list and results of over 800 published GWAS see http://www . genome . gov/gwastudies ) . Despite the success of GWAS , the heritability of common disorders cannot be fully explained by the genes that have been discovered [1] . GWAS are built on the notion that common alleles predispose to common disorders . Rare variants , which are probably responsible for some of the missing heritability , would not have been detected by GWAS . Sequencing the genome and novel analytical methods will help identify the rare variants . Another hiding place for the missing heritability is in interactions . Genes that impact disease through interactions with other genes or environmental factors are not detected by GWAS if their main effects are small . GWAS can only identify genes that exhibit significant main effects; genes that require the interacting factor to be included in the study to show their association with disease are missed . Inclusion of key environmental factors in genome-wide studies is anticipated to be an important next step for deciphering the genetic structure of common multifactorial disorders . Amassing sufficient analytic power for gene-environment studies , however , is a challenge . Power decreases dramatically as a function of frequency of exposure , number of parameters being estimated and sample size . Interaction studies require at least four times the sample size that standard GWAS would require to detect an effect of similar magnitude ( reviewed in [2] ) . Yet , there are fewer datasets with both DNA and environmental exposure data than those with DNA alone , and their sample sizes are often smaller . Parkinson's disease ( PD ) is a classic example of a common multifactorial disorder . PD is characterized by neurodegeneration in the substantia nigra that manifests initially as a movement disorder but often leads to cognitive and psychiatric problems as well . PD is progressive and there is no treatment currently available that could prevent or slow disease progression . PD is the second most common neurodegenerative disease after Alzheimer's disease; it affects about 5 million individuals in the 10 most populous nations and is expected to double in frequency by 2030 [3] . Until the 1990's PD was thought to be purely environmental with no genetic component . In the last decade , numerous genes have been identified , some of which can cause PD [4] and others that are susceptibility loci [5]–[10] . There are also compelling data from epidemiology that cigarette smoking and caffeinated-coffee consumption are associated with reduced risk of developing PD [11] , [12] and that exposure to environmental neurotoxins is associated with increased risk of developing PD [13] . Thus PD is a strong candidate for studying gene-environment interactions [14] . We conducted a genome-wide association and interaction study ( GWAIS ) using the joint test [15] for each SNP's marginal association and its interaction with coffee consumption on PD risk , followed by stratified GWAS in heavy and light coffee drinkers ( see Analytic Strategy in Materials and Methods section ) . Our aim was to identify genes that enhance or diminish the protective effect of caffeinated-coffee for use as biomarkers for pharmacogenetic prevention and treatment . Caffeine is an adenosine-receptor antagonist . In animal models of PD , where administration of neurotoxins is used to destroy dopaminergic neurons mimicking PD , caffeine and selective A2A-antagonists have been shown to be neuroprotective and attenuate dopamine loss [16] . Selective A2A-antagonists have been studied in human clinical trials and found to be safe , well tolerated and to provide symptomatic benefit for persons with PD [17] , [18]; however , efficacy has not been high enough in the first generation of the drugs to meet regulatory approval for use as PD drugs . We posit that subsets of patients with certain genotypes may respond well to a given treatment and others may not . When they are combined the average efficacy may be insufficient for regulatory approval , while a subgroup of patients with certain genotype might still benefit substantially . If our prediction is correct , incorporating genetics in clinical trials of PD could revolutionize PD drug development . By examining the interaction of caffeinated-coffee with 811 , 597 SNPs in a hypothesis-free genome-wide study , we discovered GRIN2A as a novel PD modifier gene . GRIN2A encodes a subunit of the NMDA-glutamate-receptor which is well known for regulating excitatory neurotransmission in the brain and for controlling movement and behavior .
Human Subject Committees of the participating institutions approved the study . The Discovery dataset was nested in the NeuroGenetics Research Consortium ( NGRC ) GWAS which successfully identified known PD genes as well as a novel association with HLA [5] which has been widely replicated [10] , [19] . For the present GWAIS , Replication samples were provided by PEG [20] ( Parkinson , Environment , and Gene ) , PAGE [21] ( Parkinson's , Genes , and Environment from the prospective NIH-AARP Diet and Health Study cohort ) , and HIHG [9] ( Hussman Institute for Human Genomics ) . Persons with PD had been diagnosed by neurologists using standard criteria [22] , control subjects self-reported as not having PD . Cases and controls were all unrelated , non-Hispanic Caucasian , from United States . The NGRC cohort was clinic-based sequentially ascertained patients , PEG and PAGE were community-based incident cases , HIHG was clinic-based and self-referral cases . The numbers of cases/controls with genotype , coffee/caffeine and key clinical and demographic data were NGRC = 1458/931 , PEG = 280/310 , PAGE = 525/1474 , HIHG = 209/133 ( Table S1 ) . NGRC , PEG and HIHG had collected lifetime caffeinated-coffee consumption data , measured as cups per day multiplied by the number of years of consumption ( ccy ) [12] , [23] . PAGE had daily mg caffeine intake from all caffeine-containing drinks and foods for 12 months prior to enrollment ( 1995–1996 ) and only incident PD cases diagnosed after 1997 were included in the analysis [24] . Despite the variation in data collection , results were consistent across studies , corroborating robustness of the interaction between coffee/caffeine and GRIN2A . We could not , and did not , attempt to distinguish the bioactive ingredient in caffeinated-coffee . Although caffeine has been shown to be neuroprotective , there may be other ingredients in caffeinated-coffee that may affect disease pathogenesis . To classify coffee/caffeine intake , each dataset was treated separately according to the measurements available . The median ccy or mg was determined for controls within each dataset ( excluding those with zero intake ) and used as the cut-off for heavy drinkers ( >median ) vs . light drinkers ( 0 to ≤median ) . The median was 67 . 5 ccy for NGRC , 74 . 0 ccy for PEG , 70 . 0 ccy for HIHG , and 237 . 8 mg/day for PAGE . For coffee dose , quartiles were defined for each dataset using the full range from zero to maximum intake in controls . Results shown for NGRC , PEG and HIHG are based on lifetime caffeinated-coffee consumption . Truncating coffee use at age-at-onset or age-at-diagnosis in patients did not affect the results . To assess the effects of caffeinated tea and soda , we performed sensitivity analysis in NGRC dataset . Caffeinated soda and tea were commonly and equally consumed by heavy and light coffee drinkers ( soda: 80% in both heavy and light drinkers; caffeinated tea: 66% in heavy coffee drinkers and 61% in light coffee drinkers ) . We repeated GWAIS and stratified GWAS with caffeinated soda and tea as covariates . We also explored association of caffeinated tea and soda with PD expecting an inverse association if caffeine were the bioactive ingredient in coffee . The source of DNA was whole blood for NGRC and HIHG , saliva for PAGE , and whole blood ( all PD and half of controls ) or saliva ( half of controls ) for PEG . NGRC was genome-wide genotyped using Illumina HumanOmni1-Quad_v1-0_B array and achieved 99 . 92% call rate and 99 . 99% reproducibility . GWAS genotyping and statistical quality control ( QC ) have been published [5] . 811 , 597 SNPs ( excluding Y chromosome SNPs because they are not amenable to sex adjustment ) passed GWAS QC and were included in GWAIS . Replication groups genotyped GRIN2A_rs4998386 . Only one SNP was genotyped for replication; we have no other undisclosed replication results . PEG and HIHG used ABI TaqMan assay-by-design ( C__28018721_20 ) , PAGE used Sequenom and all achieved call rates of 96%–99% . The first step was to test the hypothesis that the effect of coffee on PD risk is affected by a gene; ie , test statistical interaction between SNPs and coffee genome-wide . Theoretically , a test of SNP*coffee interaction would have been suitable; however , a pure test of interaction has low power; reportedly , it requires more than four times the sample size that GWAS would require to detect a main effect of similar size ( reviewed in [2] ) . We chose the joint test of SNP main effect and its interaction with coffee as proposed by Kraft et al [15] . We call the test GWAIS for genome-wide association and interaction study . The main advantage of the joint test is that it does test for interaction and it has more power than pure interaction test when there is a modest SNP marginal effect . Next we performed stratified GWAS in heavy and light drinkers to gain insight to where the interaction signal was coming from and to formulate a hypothesis for replication . We then replicated the top signal and performed pooled analysis . Methods for meta-analysis of the joint test are available [25] , [26]; however , since we had individual level data we pooled the datasets .
The most significant result was the novel appearance , on the Manhattan plot ( Figure 1A , Figure S1 ) , of a block of linked SNPs which map to the GRIN2A gene on chromosome 16 ( Figure S2 ) . This locus had not been detected in PD GWAS previously because its main effect is modest . However , when considered in the context of interaction with coffee , GRIN2A surpassed all known PD-associated genes in significance including SNCA which has been the strongest association with PD in GWAS . The signal for known PD genes were driven only by their main effects with no evidence for interaction ( Pinteraction = 0 . 5–0 . 7 ) ; whereas the signals for PD-associated SNPs in GRIN2A were enhanced by SNP*coffee interaction ( Pinteraction∼10−3 ) . The quantile-quantile ( QQ ) plot of the expected vs . observed genome-wide P values ( Figure 1B ) is also evidence for the impact of GRIN2A on PD risk . GWAIS results described above were obtained from a test that measures the combined significance of the SNP and its interaction with coffee on risk of PD [15] . The test has 2 df; hence when interaction is absent , GWAIS is less powerful than GWAS which has only 1 df . Furthermore , the sample size was smaller in GWAIS because it required not only genotypes but also coffee data , which was available for 2/3 of NGRC . Under these conditions , GWAIS produced P2df>10−6 ( Figure 1A ) for the top SNP in SNCA which had reached P = 3×10−11 in NGRC GWAS [5] . This drop in significance demonstrates the dramatic loss of power in GWAIS as compared to GWAS . Under these conditions , GWAIS yielded P2df = 1×10−6 for rs4998386 in GRIN2A ( as compared to P2df = 3×10−6 for SNCA and P2df = 7×10−5 for MAPT ) . Dominant and Additive models produced nearly identical results for GRIN2A SNPs ( Table 1 ) . Recessive model had no notable signal ( Figure S1 ) . With one goal being pharmacogenetic applications , we were interested in genes that modulate risk in people who consume caffeine , thus we stratified the subjects as heavy drinkers or light drinkers ( light includes non-drinkers ) and performed GWAS in each group ( SNP-PD test , 1 df ) . The sample size was now further reduced to only 512 cases and 387 controls who drank more than the median ( heavy drinkers ) and 946 cases and 544 control subjects who drank less than the median ( light drinkers ) . As expected due to interaction , which suggests different association patterns across categories , most of the signals seen in GWAIS ( Figure 1A ) appeared within either heavy drinkers ( Figure 2 , Table 2 , Figure S1 ) or light drinkers ( Figure 3 , Table 2 , Figure S1 ) . In heavy drinkers , the focus of this study , the most significant result was GRIN2A_rs4998386 ( P = 6×10−7 ) and 11 neighboring SNPs ( P = 10−5 to 10−6 , Table 2 ) . The QQ plots for stratified GWAS also demonstrate clearly that GRIN2A is the single primary PD associated locus in heavy coffee drinkers ( Figure 2 ) : exclusion of SNCA , HLA and MAPT did not have an impact in heavy drinkers , whereas exclusion of GRIN2A nearly abolished the extreme P values of 10−5–10−6 . No clear signals were detected in light coffee drinkers ( Figure 3 ) . The 12 GRIN2A SNPs that were associated with PD via heavy coffee consumption had similar minor allele frequencies ( MAF = 0 . 13–0 . 16 in controls ) and odds ratios ( OR = 0 . 43–0 . 51 ) and were in strong LD ( Figure S3 ) . Haplotype analysis did not strengthen the signal . Within this gene varying CNV software tools called either no CNVs or just two CNVs in controls . Thus , CNVs are unlikely to explain a large fraction of the phenotype variability . We therefore selected only the SNP with the lowest P value for replication ( GRIN2A_rs4998386 ) . Testing the association of coffee with PD in NGRC , when calculated irrespective of genotype , showed an average of 34% lower PD risk in heavy coffee drinkers than in light drinkers ( OR = 0 . 66 , P = 6×10−6 , Table 3 , Coffee irrespective of genotype ) . GRIN2A , irrespective of coffee , had a modest main effect on PD in NGRC ( Table 3 , GRIN2A rs4998386 genotype irrespective of coffee ) . A key question was if , and to what degree , GRIN2A_rs4998386 genotype modifies the effect of coffee on PD risk ( Table 3 ) : Within heavy drinkers , PD risk was 58% lower ( OR = 0 . 42 , P = 2×10−6 ) for rs4998386_TC , and 81% lower ( OR = 0 . 19 , P = 0 . 05 ) for rs4998386_TT genotype than rs4998386_CC; whereas in light drinkers genotype had no effect on risk . Similar results were obtained for Additive and Dominant models ( Table S3 ) . The joint effect comparing rs4998386_TC genotype and heavy coffee vs . rs4998386_CC genotype and light coffee was most dramatic , suggesting a highly significant 68% risk reduction ( OR = 0 . 32 , P = 7×10−11 ) in NGRC ( Table 3 , Joint effects of GRIN2A rs4998386 and coffee ) . We used GWAIS as a means to identify genes that might enhance the inverse association of coffee with PD with the goal of carrying the discovery forward as a genetic marker for use in pharmacogenetic studies . Hence , the replication hypothesis was specified a-priori , based on results of NGRC , as follows: “Among heavy coffee drinkers , carriers of rs4998386_T allele have lower risk of PD than carriers of rs4998386_CC genotype” . Although this test does not reflect our most significant results , it is the test that has the clearest interpretation because it keeps the effect of coffee constant . For example , comparing TC+heavy vs . CC+light gave larger effect size and the P value was 3-orders of magnitude lower than the specified hypothesis , however , unlike our hypothesis , the test included coffee , which would have made it difficult to draw firm conclusions about the effect of genotype on coffee's inverse association with PD . Before attempting replication , the following analyses were conducted to identify potential confounders ( Table S2 ) . We tested the frequency of rs4998386 and coffee use across disease-specific strata and population structure . There was no evidence for heterogeneity by presence or absence of family history of PD , age at onset , or recruitment site . rs4998386 frequency was different between Ashkenazi-Jewish and non-Jewish individuals ( P = 0 . 02 ) and across the European countries of ancestral origin ( P = 3×10−3 ) in cases , but not in controls , which , PD being heterogeneous , may indicate different ethnic-specific clusters of disease subtypes as has been noted for LRRK2-associated PD [34] . Not surprisingly , heavy coffee use was associated with smoking ( P<10−4 ) , which itself is inversely associated with PD risk independently of coffee [12] . Adjusting for smoking , in addition to other covariates , did not change the results ( Table S4 ) . We also repeated the analyses adjusting for caffeinated soda and caffeinated tea consumption and found the results to be robust ( Table S5 ) . Some reports suggest persons with PD are more likely to avoid sensation seeking and addictive behaviors [35] and GRIN2A polymorphisms have been implicated in predisposition to heroin addiction [36] and smoking [37] raising the concern that our results could have been confounded if the GRIN2A SNPs identified here were associated with habitual coffee drinking . However , there was no evidence for association between any of the GRIN2A SNPs and heavy vs . light coffee consumption in cases and controls combined ( OR = 0 . 95–1 . 01 , P = 0 . 61–0 . 94 ) . See Table 3 , Table S3 . The a-priori hypothesis for replication that among heavy drinkers GRIN2A_rs4998386_T carriers had a lower risk of PD than GRIN2A_rs4998386_CC was replicated comparing TC to CC ( excluding rare heterogeneous TT genotype ) : OR = 0 . 59 , P = 10−3; under Additive model ( TT vs . TC vs . CC ) : OR = 0 . 77 , P = 0 . 04; and Dominant model ( TT+TC vs . CC ) : OR = 0 . 70 , P = 0 . 01 . Note that the Additive and Dominant models included the TT genotype which is rare and its frequency varied significantly across datasets ( Table 3 , Table S6 ) . The TC vs . CC comparison is more robust for this reason; Additive and Dominant model are shown for completeness . As seen in NGRC data , genotype had no effect on risk of PD among light coffee drinkers in Replication or combined data ( OR = 1 . 0 , P = 0 . 99 ) . In pooled Replication ( without Discovery ) , the [SNP+SNP*coffee] joint test yielded P2df = 2 . 3×10−3 comparing TC to CC ( excluding rare heterogeneous TT genotype ) ; P2df = 0 . 12 for the Additive model , P2df = 0 . 02 for the Dominant model . The pooled analysis of Replication and Discovery with the [SNP+SNP*coffee] joint test yielded , P2df = 1 . 9×10−7 comparing TC to CC ( excluding rare heterogeneous TT genotype ) , P2df = 1 . 4×10−5 for the Additive model , and P2df = 8 . 6×10−7 for the Dominant model . In pooled data , compared to the light coffee drinkers with GRIN2A_rs4998386_CC genotype ( the group with highest risk ) , heavy coffee use ( with CC genotype ) reduced risk by 18% ( OR = 0 . 82 , P = 3×10−3 ) , having GRIN2A_rs4998386_T allele ( light coffee ) had no effect on risk ( OR = 1 . 0 , P = 0 . 99 ) , but the combination of heavy coffee use and GRIN2A_rs4998386_TC genotype was associated with a highly significant 59% risk reduction ( OR = 0 . 41 , P = 6×10−13 ) ( Table 3 , Joint effects of GRIN2A rs4998386 and coffee ) . See Table 4 , Table S7 . The array used in the study , Illumina OMNI-1 had nearly a million SNPs , which is a relatively dense coverage , but which could be further improved by imputing the SNPs that were not on the array using 1000 Genomes and HapMap data , a practice that has successfully aided many projects . After QC , we had over 3 . 5 million imputed and genotyped SNPs per individual in NGRC , each with information score ≥0 . 95 ( measure of imputation certainty ) , and each passing standard GWAS QC . Imputation could only be applied to NGRC ( Discovery ) because only NGRC had genome-wide data . GWAIS and GWAS analysis of the GRIN2A region with imputed SNPs uncovered a block of densely linked SNPs embedded amongst the genotyped GRIN2A , six of which achieved P2df≤5×10−8 in GWAIS ( Table 4 ) . The interaction term was OR = 0 . 44 , P = 4×10−5 ( Table 4 ) . In GWAS conducted in heavy coffee drinkers , 12 SNPs achieved P = 3×10−8 to 5×10−8 with OR = 0 . 41–0 . 42 ( Table 4 ) .
In a genome-wide gene-environment study we identified GRIN2A as a genetic modifier of the inverse association of coffee with the risk of developing PD . The discovery was made in NGRC , and replicated in independent data . Risk reduction by heavy coffee use , which was estimated to be 27% on average , was genotype-specific and varied according to GRIN2A genotype from 18% ( P = 3×10−3 ) for individuals with rs4998386_CC genotype to 59% ( P = 6×10−13 ) for those with rs4998386_TC genotype . When coffee intake was categorized in four doses , the dose trend was more prominent in individuals with rs4998386_T allele than those with rs4998386_CC genotype , with the 3rd and 4th quartiles exhibiting only 11% and 39% risk reduction for rs4998386_CC carriers , vs . 37% and 66% for rs4998386_T carriers . With imputation we uncovered a block of GRIN2A SNPs not included on the genotyping array , which achieved P = 3×10−8 to 5×10−8 . We propose GRIN2A as a new modifier gene for PD , and posit that if coffee-consumption is considered , GRIN2A may prove to be one of the most important PD-associated genes to have emerged from genome-wide studies . We base this suggestion on statistics , biology and the potential for immediate translation to clinical medicine , as we discuss below . GRIN2A had not previously been tested as a candidate gene for PD , and was not detected in PD GWAS which have all been examining gene main effects without considering interactions with relevant environmental exposures . The most significant and consistently replicated main effects detected to date are for SNCA , MAPT and HLA . Here we added , for the first time , a common and relevant environmental exposure ( coffee ) to a genome-wide study . Inclusion of coffee allowed GRIN2A to rise to the top . In the gene-environment ( GWAIS ) model , GRIN2A surpassed SNCA , MAPT and HLA in statistical significance . Among heavy coffee drinkers , the impact of GRIN2A on PD risk ( measured as OR ) was 50% greater , and 2 to 5 orders of magnitude more significant ( measured as P value ) than the strongest associations reported for SNCA , MAPT or HLA . This study is proof of concept that inclusion of environmental factors can help identify disease-associated genes that are missed in SNP-only GWAS . GRIN2A is an important gene for the central nervous system . Accelerated evolution of GRIN2A in primates is said to have contributed to the dramatic increase in the size and complexity of the human brain which defines human evolution [38] . GRIN2A encodes a subunit of the N-methyl-D-aspartate-2A ( NMDA ) glutamate receptor . It is central to excitatory neurotransmission and the control of movement and behavior [39]–[41] . The literature suggest imbalances in NMDA-dependent neurotransmission contribute to neurodegeneration in PD , possibly through massive influx of calcium and impaired mitochondrial function leading to apoptosis; and/or disruption of glutamate-mediated autophagy which is implicated in degradation and removal of proteins like α-synuclein ( see [42] for review ) . The portion of intron 3 containing SNPs with the most significant associations ( from base pair 9978046 to base pair 10128367 , Table 1 , Table 2 , and Table 4 ) includes numerous transcription factor binding sites and two peaks of enhanced histone H3K4 mono-methylation ( http://genome . ucsc . edu ) [43] . Polymorphisms throughout this region could therefore disrupt regulatory elements , potentially leading to variation in levels of GRIN2A transcript . GRIN2A is expressed at high levels in the brain , most notably in the subthalamic nucleus ( STN ) [44] . Pharmacologic inhibition of STN with an NMDA-antagonist reduces nigral neuron loss in a rodent model of PD [45] . Deep-brain-stimulation , which also targets STN , is an effective surgical symptomatic therapy for PD . The other piece of this finding is coffee/caffeine . Our study was not designed to distinguish the active ingredients in coffee . However , we note that the largest replication study ( PAGE ) measured specifically the caffeine intake in mg from all food sources ( drink , food , and chocolate ) and replicated our hypothesis and interaction robustly . We also found trends for inverse association of tea and soda with PD , and interestingly , the varied effect size and strength of association was consistent with the relative amount of caffeine in each drink ( Table S5 ) . Thus , our data are consistent with experimental observations that caffeine is neuroprotective . Caffeine is an adenosine A2A-receptor antagonist . A2A-receptor enhances calcium influx via NMDA [41] and A2A-receptor antagonists are neuroprotective in animal models of PD; they attenuate excitotoxicity by reducing extracellular glutamate levels in the striatum [46] , [47] . Thus interaction between coffee/caffeine and GRIN2A is biologically plausible , and can help formulate testable hypotheses towards a better understanding of the disease pathogenesis . GRIN2A genotyping may be useful for pharmacogenetic studies . Genetics has not yet entered drug development for PD but the time is here . We now have several susceptibility loci ( SNCA , MAPT , HLA , BST1 , PARK16 , GAK [5]–[10] ) that can help identify individuals who are at moderately increased risk of developing PD . We also have at least one neuroprotective compound ( coffee/caffeine ) which can be pharmacologically modified to alleviate its undesirable side effects . GRIN2A genotyping might also inform treatments for people who already have PD . L-DOPA , the primary PD drug for 40 years , does not slow disease progression and has serious side effects . Clinical trials for new PD drugs have not found drugs that surpass the symptomatic benefits of L-DOPA . There have been numerous drug trials for glutamate-receptor blockers as well as for selective A2A-receptor antagonists . Most were shown to be safe , well tolerated and beneficial [17] , [18] , [48]; however , the majority did not reach the regulatory threshold for efficacy to be approved as PD drugs . We wonder if some of these clinical trials will succeed if patients are subdivided by GRIN2A genotype . We acknowledge the distinction that the present study examined risk of developing PD; whereas clinical trials have thus far aimed for symptomatic improvements in patients . Nonetheless , there are sufficient parallels to suggest that GRIN2A genotype might also influence efficacy of glutamate-receptor antagonists and A2A-receptor antagonists . This is a simple and inexpensive hypothesis that can be tested in future , ongoing and even closed clinical trials that have banked DNA . Common non-coding variants in GRIN2A have been associated with Huntington disease ( HD ) [49] , [50] and schizophrenia [51] , and rare mutations have been described in patients with neurodevelopmental phenotypes [52] . Schizophrenia is associated with a ( GT ) n repeat in the GRIN2A promoter that may increase disease risk by suppressing gene expression [51] . Three GRIN2A SNPs have been associated with onset-age of HD; they are conserved and reportedly tag a binding site for CCAAT/enhancer-binding protein [49] , [50] . HD and PD are both neurodegenerative movement disorders , thus the possibility of a common genetic element was of interest . The reported HD-associated GRIN2A SNPs , rs1969060 , rs8057394 and rs2650427 , were not on the genotyping array but were imputed with high fidelity ( information score >0 . 99 ) . They map within the 150 kb region identified here for PD , they are in strong LD with PD-associated SNPs defined by D' ( 0 . 48–1 . 0 ) but not by r2 ( 0–0 . 33 ) ( Figure S4 ) . We tested the HD SNPs for association with onset age and risk of PD in NGRC while conditioning on the neighboring top PD SNP ( rs4998386 ) . One HD SNP , rs8057394 , yielded OR = 0 . 85 , P = 0 . 02 for PD overall; OR = 0 . 79 , P = 0 . 04 for heavy coffee drinkers; and OR = 0 . 90 , P = 0 . 24 for light coffee drinkers . We found no other evidence for association of HD SNPs with PD , including when we jointly tested HD SNPs and possible interaction with coffee [SNP+SNP*coffee] on risk or onset of PD . Conversely , we retested , in NGRC , the association of top genotyped PD SNP ( rs4998386 ) with PD , conditioning on HD SNP ( rs8057394 ) and found it to be robust ( P2df = 8×10−6 ) . Unlike GWAS , which is now a fully standardized practice , there is no established protocol for testing gene*environment interaction on a whole-genome scale . Our strategy of starting with the joint test ( GWAIS ) and following up with GWAS in subgroups stratified by exposure was driven by the aims of our study . In Table S7 we present a side-by-side comparison of the results for the top GRIN2A SNPs ( P<10−5 ) , when analyzed for main effect ( GWAS ) , for interaction , with Kraft's joint test , and in GWAS stratified by exposure . Amassing a large enough sample size for GWAIS is challenging . GWAIS requires larger sample sizes than GWAS yet there exist fewer samples that have data on relevant environmental exposures in addition to DNA and phenotype . To our knowledge , NGRC is the largest genetic study of PD that has collected exposure data . No other publically available PD GWAS has coffee data , eliminating the possibility of in-silico replication . We were able to identify and get access to only 3 datasets that had DNA and coffee , giving us a total sample size of 393 cases and 905 controls to replicate the GRIN2A effect in heavy coffee drinkers . In contrast , replications and meta-analyses for gene-only GWAS now have over 17 , 000 PD cases and controls [10] . We detected the known and confirmed PD-associated genes ( SNCA , MAPT and HLA ) in GWAIS but at much lower significance levels than in GWAS because of the smaller sample size with coffee data and the added degree of freedom in GWAIS . It is noteworthy , however , that at P2df = 10−6 , GRIN2A surpassed all known PD loci in significance . With the aid of imputation , we achieved P = 3×10−8 for a 2 . 4-fold difference in genotype specific effect of coffee on risk of PD . Importantly , we were able to replicate the hypothesis that we set out a-priori based on discovery . | Parkinson's disease ( PD ) , like most common disorders , involves interactions between genetic make-up and environmental exposures that are unique to each individual . Caffeinated-coffee consumption may protect some people from developing PD , although not all benefit equally . In a genome-wide search , we discovered that variations in the glutamate-receptor gene GRIN2A modulate the risk of developing PD in heavy coffee drinkers . The study was hypothesis-free , that is , we cast a net across the entire genome allowing statistical significance to point us to a genetic variant , regardless of whether it fell in a genomic desert or an important gene . Fortuitously , the most significant finding was in a well-known gene , GRIN2A , which regulates brain signals that control movement and behavior . Our finding is important for three reasons: First , it is a proof of concept that studying genes and environment on the whole-genome scale is feasible , and this approach can identify important genes that are missed when environmental exposures are ignored . Second , the knowledge of interaction between GRIN2A , which is involved in neurotransmission in the brain , and caffeine , which is an adenosine-A2A-receptor antagonist , will stimulate new research towards understanding the cause and progression of PD . Third , the results may lead to personalized prevention of and treatment for PD . | [
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]
| 2011 | Genome-Wide Gene-Environment Study Identifies Glutamate Receptor Gene GRIN2A as a Parkinson's Disease Modifier Gene via Interaction with Coffee |
The structure and composition of bacterial communities can compromise antibiotic efficacy . For example , the secretion of β-lactamase by individual bacteria provides passive resistance for all residents within a polymicrobial environment . Here , we uncover that collective resistance can also develop via intracellular antibiotic deactivation . Real-time luminescence measurements and single-cell analysis demonstrate that the opportunistic human pathogen Streptococcus pneumoniae grows in medium supplemented with chloramphenicol ( Cm ) when resistant bacteria expressing Cm acetyltransferase ( CAT ) are present . We show that CAT processes Cm intracellularly but not extracellularly . In a mouse pneumonia model , more susceptible pneumococci survive Cm treatment when coinfected with a CAT-expressing strain . Mathematical modeling predicts that stable coexistence is only possible when antibiotic resistance comes at a fitness cost . Strikingly , CAT-expressing pneumococci in mouse lungs were outcompeted by susceptible cells even during Cm treatment . Our results highlight the importance of the microbial context during infectious disease as a potential complicating factor to antibiotic therapy .
Antibiotics are indispensable for fighting bacterial infections . Yet the rapid emergence of resistance during the last decades renders current drugs increasingly ineffective and poses a serious threat to human health [1] . Drug action and bacterial resistance mechanisms are well understood in population assays of isogenic cultures in vitro . However , ecological factors and cell physiological parameters in natural environments influence the impact of antibiotics [2 , 3] . Streptococcus pneumoniae ( pneumococcus ) is an important human pathogen that resides in complex and dynamic host environments . The bacterium primarily populates the nasopharynx of healthy individuals , together with numerous commensal microbiota , and often alongside disease-associated species , including Staphylococcus aureus , Moraxella catarrhalis , and Haemophilus influenzae [4–6] . While an individual pneumococcal cell competes for limited resources with all other bacteria present in the niche , it may also benefit from a community setting . In a collective effort , bacteria become recalcitrant to antibiotics when forming biofilms that represent a physical constraint for drug accessibility [7 , 8] . Additional population-based survival strategies involve the phenotypic diversification of an isogenic population , either to preadapt for environmental changes ( bet-hedging ) or to enable division of labor [9] . Because the impact of most antibiotics is growth rate dependent [10–12] , a bifurcation into growing and nongrowing cells increases the drug tolerance for the latter fraction , commonly referred to as persisters [13 , 14] . Cell-to-cell communication represents another way to react to antibiotic inhibition by allowing bacteria to coordinate a common response; S . pneumoniae , for example , activates the developmental process of competence whereupon it may acquire resistance [15–17] . A quorum-sensing mechanism that compromises antibiotic effectiveness was also found in evolved Escherichia coli cultures , in which cells of increased resistance induce drug efflux pumps in susceptible cells via the signaling molecule indole [18] . As an alternative to reduced drug susceptibility , bacteria can also clear lethal doses of antibiotics from their environment . High cell densities and thus the presence of many drug target sites may be sufficient to lower the concentration of active compound by titration of free drug molecules [19] . Furthermore , antibiotic degradation via β-lactamase enables growth not only of resistant cells but also of susceptible cells in their vicinity [20–22] , even across species , as demonstrated for amoxicillin-resistant H . influenzae and susceptible S . pneumoniae [23 , 24] . This mechanism is of direct relevance to clinical medicine and is alternatively referred to as passive or indirect resistance ( from the perspective of susceptible cells ) or collective resistance ( from the perspective of mixed populations ) [25] . Here , we describe another mechanism by which bacteria survive antibiotic therapy without obtaining genetic resistance , with the example of the bacteriostatic antibiotic chloramphenicol ( Cm ) and the opportunistic human pathogen S . pneumoniae . We show that Cm-resistant pneumococci expressing the resistance factor Cm acetyltransferase ( CAT ) can provide passive resistance for Cm-susceptible pneumococci by intracellular antibiotic deactivation . CAT covalently attaches an acetyl group from acetyl coenzyme A ( acetyl-CoA ) to Cm [26 , 27] and thus prevents the drug from binding to bacterial ribosomes [28] . Intracellular CAT in resistant bacteria can potently detoxify an entire environment in growth culture , semisolid surfaces of microscopy slides , or in a mouse infection model , supporting the survival and growth of genetically susceptible bacteria in the presence of initially effective Cm concentrations . Our results expand recent findings on the basis of E . coli growth cultures and indicate a potential clinical relevance of passive Cm resistance [29 , 30] .
Resistances to all currently prescribed antibiotics have been identified in clinical isolate strains of S . pneumoniae [31] . Genes that transfer antibiotic resistance can be classified according to their mode of action [32] . One class keeps the cytoplasmic drug level low by preventing drug entry or by exporting drug molecules . Another class alters the targeted enzymes by modifying their drug binding sites or by replacing the entire functional unit . A third class alters the drug molecules themselves . Only members of the latter group are potential candidates for establishing passive resistance . In the pneumococcus , resistance genes that deactivate antibiotics include aminoglycoside phosphor- or acetyltransferases and cat . To date , β-lactam antibiotic-degrading enzymes have not been reported in S . pneumoniae genomes or plasmids [33] . Standard therapy of pneumococcal infections does not include aminoglycosides because of the relatively high intrinsic resistance of S . pneumoniae to members of this antibiotic family . In contrast , Cm , a member of the World Health Organization Model List of Essential Medicines [34] , is regularly prescribed throughout low-income countries for infections with S . pneumoniae and other Gram-positive pathogens due to its broad spectrum , oral availability , and excellent tissue distribution , including the central nervous system . Recently , the antibiotic was also discussed as candidate for a comeback in developed nations due to spreading resistances against first-line agents [35–37] . To test whether passive resistance emerges from antibiotic-deactivating resistance markers with S . pneumoniae , we used the drug-susceptible clinical isolate D39 [38] . We constructed an antibiotic-susceptible reporter strain expressing firefly luciferase ( luc ) and antibiotic-resistant strains expressing single-copy genomic integrated kanamycin 3′-phosphotransferase ( aphA1 ) , gentamicin 3′-acetyltransferase ( aacC1 ) , and chloramphenicol acetyltransferase ( cat ) . Resistant and susceptible cells were grown at a one-to-one ratio , and optical density ( both strains ) and bioluminescence ( emitted by susceptible cells only ) were measured ( Fig 1 ) . Expression of cat , but not aphA1 or aacCI , conferred passive resistance to susceptible cells ( as observed by increased luminescence in mixed populations compared with assays of susceptible cells only; S1 Fig ) , mirroring prior investigations of antibiotic deactivation by resistant isolates of S . pneumoniae [39] . Aminoglycosides permeate the bacterial cell only at low frequency [40]; high permeability , however , was recently shown to represent an important precondition for the establishment of passive resistance , explaining why the phenomenon could not be observed with aphA1 and aacCI expression [29] . To characterize the observed Cm collective resistance in more detail , we used the Cm-susceptible strain D-PEP2K1 ( from here on CmS ) , which constitutively expresses luc and the kanamycin resistance marker aphA1 [41] , and the Cm-resistant strain D-PEP1-pJS5 ( from here on CmR ) , which expresses cat from plasmid pJS5 [42] ( see Methods ) . Luminescence allowed for the real-time estimation of growth ( or inhibition ) of the CmS population , and kanamycin resistance allowed for the monitoring of their viable cell count by plating assays in the presence of kanamycin . Cm represses the growth of susceptible pneumococci at a minimal inhibitory concentration ( MIC ) of 2 . 2 μg ml−1 , and during Cm exposure , luminescence from luc expression of susceptible pneumococci was previously shown to decrease at a rate that depends on the applied Cm concentration [12] . However , when CmS was co-inoculated with CAT-expressing CmR , luminescence ( indicative for growth or inhibition of the CmS cell fraction ) recovered , both for a Cm concentration slightly above the MIC ( 3 μg ml−1; Fig 2A ) and even for a Cm concentration of more than two times the MIC ( 5 μg ml−1; S2 Fig ) . Luminescence recovery in mixed population assays ( CmR + CmS ) exceeded the values measured with CmS monoculture by up to 10-fold ( Fig 2A and S2 Fig ) , and plating assays ( with kanamycin ) revealed that the difference in viable cell count was 1 , 000-fold greater after 8 h of cocultivation ( Fig 2B and S2 Fig ) . Although Cm is commonly regarded as bacteriostatic , bactericidal activity has also been demonstrated against S . pneumoniae [43] , explaining the observed decrease in viability of CmS monoculture ( Fig 2B and S2 Fig ) . To confirm that CmR cells actually deactivate Cm in the growth medium , we analyzed culture supernatant ( S ) by high-performance liquid chromatography ( HPLC ) [44] . As shown in Fig 2C , within 4 h of growth , CmR cells entirely converted an initial Cm concentration of 5 μg ml−1 , as evidenced by the disappearance of the corresponding Cm peak at wavelength 278 nm . New peaks ( at later elution times ) appeared and gradually increased in HPLC profiles of S collected after 1 , 2 , and 4 h of cultivation; these peaks were previously shown to correspond to acetylated Cm derivates ( 1- and 3-acetylchloramphenicol ) [44] . Next , we focused on whether the initial amount of CAT-expressing CmR cells was important for the survival and growth of CmS cells during drug treatment . To test this , we inoculated microtiter plate wells with a fixed number of CmS cells ( inoculation at optical density [OD] 0 . 001 , corresponding to ~1 . 5 × 106 colony-forming units per ml [CFUs ml−1] ) while varying the number of CmR cells ( Fig 2D ) . High inoculation densities of CmR cells ( OD 0 . 01 ) resulted in a fast recovery of luminescence activity of CmS cells; however , the peak of luminescence was lower compared to intermediate CmR inoculation densities . This difference can be explained by cells reaching the carrying capacity of the growth medium before the pool of Cm is completely deactivated; luciferase expression activity was previously shown to slow down when cultures reach high cell densities ( above ~OD 0 . 05 ) [41] . Relatively low CmR inoculation densities ( OD 0 . 0001 ) also limited luminescence recovery of CmS cells during cocultivation . This finding likely reflects fewer CmR cells requiring more time to deactivate Cm , resulting in increased time spans of CmS drug exposure . Prolonged drug exposure of susceptible pneumococci was previously shown to result in increasing lag periods after drug removal , indicating a more severe perturbation of cell homeostasis [12] . The time span before outgrowth of CmS cells consequently consists of both the period required for drug clearance ( by CmR cells ) and the period required to reestablish intracellular conditions allowing for cell division . To test whether Cm processing by CAT is an intracellular process , or if it takes place after secretion or cell lysis , we examined the potential of the S and the cytosolic content of CmR cells to deactivate Cm ( assay scheme in Fig 2F ) . Precultured CmR cells were diluted to OD 0 . 02 and translation activity was blocked by adding 1 μg ml−1 tetracycline ( [Tc]; S . pneumoniae D39 MIC: 0 . 26 μg ml−1 ) [12] for 1 h at 37°C to prevent ongoing protein synthesis and thus CAT expression . Next , the Tc-treated culture was split into three fractions: cell pellet ( P ) and S , separated via centrifugation , and cell culture lysate ( L ) , obtained by sonication . The P was resuspended in C+Y medium containing 3 μg ml−1 Cm ( and 1 μg ml−1 Tc ) , and 3 μg ml−1 Cm was added to the S and the L , followed by incubation at 37°C . After 2 h , the remaining cells and cell debris were removed by centrifugation and filtration , and the treated medium was used to test cell growth of a Tc-resistant variant of the CmS strain . Neither the S nor the L could support growth of CmS , whereas medium preincubated with the P did ( Fig 2E ) . Together , these experiments indicate that CAT is only active inside living cells , in which acetyl-CoA is present [26 , 27] . Because the abovementioned experiments were performed in bulk assays , we wondered whether CAT-expressing bacteria would also efficiently deactivate Cm , and thus support the growth of susceptible cells , in a more complex environment , such as on semi-solid surfaces . To do so , we spotted CmR cells together with Cm-susceptible D-PEP33 cells expressing green fluorescent protein ( GFP ) on a matrix of 10% polyacrylamide C+Y medium containing 3 μg ml−1 Cm . Indeed Cm-susceptible D-PEP33 cells were able to grow and divide under these conditions ( S3 Fig ) . S . pneumoniae cohabitates the human nasopharynx with other bacteria , such as S . aureus [6] . Therefore , we investigated whether CAT-expressing S . aureus could also support growth of Cm-susceptible S . pneumoniae in environments containing Cm . As shown in Fig 3 and S1 Movie , all S . aureus cells grew and divided from the starting point of the experiment , whereas S . pneumoniae CmS cells did not grow initially . However , after 8 h , a fraction of CmS cells grew out to form microcolonies . Note that CmS cells spotted in the absence of S . aureus did not grow under these conditions ( S2 Movie ) . The observation that CmS cells grow only when Cm-deactivating cells are present in their close vicinity ( Fig 3 ) suggests that the establishment of collective resistance requires CmS and CmR bacteria to be present in the same niche . However , such coexistence is subject to ecological constraints ( e . g . , the competitive exclusion principle ) [45] , particularly if susceptible and resistant strains compete for the same limiting resource . We therefore developed an ecological model to assess the scope for coexistence between CAT-producing bacteria and an antibiotic-susceptible strain ( S1 Text ) . Consistent with this objective , we employed a minimalist modeling strategy and disentangled the qualitative effects of different factors ( antibiotic stress , relative cost of Cm degradation and density regulation by ecological resource competition ) from the interaction between CmS and CmR bacteria rather than aiming for a precise quantitative reconstruction of the experimental conditions . In fact , in contrast to natural environments ( such as the human nasopharynx ) that provide ample opportunities for coexistence because of spatial structure and concentration gradients of multiple resources , the model considers a worst-case scenario for coexistence: the two populations are assumed to grow in a well-mixed , homogeneous chemostat environment and are limited by the same resource . Nonetheless , we found that coexistence between CmR and CmS bacteria was feasible ( Fig 4A and 4B ) , albeit under a restricted range of conditions ( Fig 4C and S4 Fig ) . A mathematical analysis of the model ( S1 Text ) indicates that resistant and susceptible bacteria can establish a stable coexistence when CAT expression has a modest fitness cost . Without such a cost , the CmR strain is predicted to outcompete the CmS strain in the presence of antibiotics . Conversely , if the cost of expressing resistance is too high , the CmS strain will be the superior competitor . Interestingly , the model furthermore predicts parameter ranges that result in the extinction of mixed populations during drug treatment , while CmR populations on their own could survive ( S4 and S5 Figs ) . A second condition for coexistence demands that the CmR population has a significant impact on the extracellular Cm concentration in its ecological niche . This requires that the population density reached at steady state must be high , so that coexistence can be stabilized by frequency-dependent selection , generated by a negative feedback loop between the relative abundance of drug-deactivating cells and the level of antibiotic stress in the environment . We note that competitive exclusion acts at a local scale in structured environments , where the presence of spatial gradients in Cm and resources may help to create refuges in which either strain can escape competition from the other . In addition , we expect that coexistence between resistant and susceptible bacteria would be promoted in vivo by previously evolved ecological niche partitioning between co-occurring species . A general prediction from our mathematical model ( S1 Text ) is that coexistence of CmS and CmR in the presence of the antibiotic is precluded when the production of CAT carries no fitness cost; we expect this prediction to apply likewise in more complex environments with spatial and/or temporal heterogeneity in Cm concentrations . However , in vitro , in short-term experiments , we did not observe any obvious fitness cost for CAT expression ( such as reduced growth rates or a reduced maximum cell density; Fig 2 ) . Nevertheless , in vivo , a fitness cost might come into existence because resistant cells that grow rapidly might be preferentially targeted by the host innate immune system , as previously shown for commensal and pathogenic bacteria , including E . coli and S . aureus [46] . We tested the activity of the human antimicrobial peptide LL-37 in dependency of Cm treatment and found , indeed , increased killing efficiency against CAT-expressing S . pneumoniae ( S6 Fig ) . Furthermore , although collective resistance could be successfully demonstrated in vitro , the phenomenon might not occur in more complex environments in vivo , such as in an animal infection model , because of a different flux balance between local Cm deactivation and restoration of effective drug concentrations via diffusion from surrounding tissues . To examine whether a coexistence between CmS and CmR is possible under therapy in vivo , we performed intratracheal infection of 8-wk-old female CD-1 mice with CmS alone and the combination of CmS + CmR . In the absence of Cm treatment , we observed no significant difference in the amount of viable bacteria recovered from the lungs 24 h after infection with CmS alone versus CmS + CmR at a one-to-one ratio ( Fig 5A ) . When mice were given three doses of 75 mg kg−1 Cm once every 5 h , mice infected with CmS alone demonstrated a significant drop of one log-fold versus the untreated control . This is in contrast to mice coinfected with CmS + CmR , in which Cm treatment did not significantly reduce the number of viable bacteria recovered from the lung versus the untreated control ( Fig 5A ) . In the one-to-one mixed infection , approximately equal numbers of CmS and CmR cells were recovered in the absence of Cm treatment: 46% CmS and 54% CmR ( Fig 5B ) . Surprisingly , with Cm treatment , 6 out of 14 animals had a dramatic increase in the percentage of CmS cells versus CmR cells . No pneumococcal colonies recovered could grow in both Cm- and kanamycin-containing media , excluding the possibility that horizontal gene transfer of the cat gene occurred during coinfection . Together , these results show that passive Cm resistance and the coexistence of resistant and susceptible cells also occur in vivo , associated with a fitness cost to the CmR niche members benefiting the CmS subpopulation .
This work elucidates that CAT , which is commonly found as a resistance marker in the human microbiome [47–49] , can effectively protect Cm-susceptible pneumococci from the activity of the drug within local environments occupied by CAT-expressing cells . Because of its potency , long shelf life , and low cost , Cm remains a mainstay of broad-spectrum antibiotic therapy in several countries in Africa , the Indian subcontinent , and China [50] . The rise of multidrug resistance among human pathogens has also provoked interest in reevaluating Cm for certain serious infections in developed countries [35–37] . This work points out some caveats in using Cm to target human pathogens on mucosal surfaces because CAT-expressing commensals might provide passive resistance . CAT can only deactivate Cm inside living cells ( Fig 2E and 2F ) , presumably because it needs acetyl-CoA to acetylate and deactivate the target drug [26 , 27] . We show that Cm deactivation and collective resistance via CAT is not limited to S . pneumoniae , because CAT-expressing S . aureus can also support the local growth of pneumococci in the presence of initially effective Cm concentrations ( Fig 3 ) . Collective resistance by CAT does not only occur in vitro but also in vivo in a mouse pneumonia model ( Fig 5 ) . Strikingly , when Cm-treated mice were coinfected with CAT-expressing and Cm-susceptible pneumococci , the susceptible bacteria outcompeted the resistant ones ( Fig 5 ) . We previously showed that the susceptibility of bacteria towards antimicrobial peptides , produced by the host innate immune system , is markedly diminished in the presence of bacteriostatic antibiotics; Cm-inhibited E . coli , for example , are less efficiently cleared by the human peptide LL-37 [46] . We could demonstrate that this mechanism also takes place in S . pneumoniae ( S6 Fig ) . When Cm was added to LL-37 treatment of pneumococci , the number of CmS cells recovered was one log-fold higher compared with CmR cells ( S6 Fig ) . As shown before , this effect occurs because bacteriostatic antibiotics , such as Cm , inhibit the growth of susceptible bacteria and thereby reduce the susceptibility to host antimicrobial peptides that target bacterial division; Cm-resistant bacteria , in contrast , maintain fast growth in the presence of Cm and are therefore more rapidly killed by host antimicrobial peptides in vivo [46] . This phenomenon may therefore represent a contributing factor underlying our findings of the mouse pneumonia model . In this framework , rapidly growing CmR cells would suffer immune clearance , while the initially nongrowing CmS are less efficiently targeted by host defense factors . Once the Cm concentration has dropped sufficiently , CmS cells can outgrow and outcompete the diminished CmR population . Our work with CAT and pneumococcus extends the known phenomenon of passive resistance via β-lactamase expression and expands on recent findings of collective resistance of bacterial communities [29 , 30] . Intracellular antibiotic deactivation requires a high drug permeability , and it is worth noting that this—in general desired—drug characteristic can also represent a risk factor for the effectiveness of an antibiotic therapy . Passive resistance could also appear with other antibiotic-degrading resistance factors in other bacteria [29] and may even emerge for synthetic antibiotic compounds [51] . In light of numerous reports of prevalence of drug resistance in pathogens , successful antibiotic therapy might become increasingly complicated with the occurrence of collective resistance . The phenomena could furthermore give rise to multidrug resistance of bacterial communities , in which individual resistances are expressed in different bacterial community residents [2 , 18 , 30] . Our mathematical model , however , predicts that collective resistance is only sustainable when resistance expression comes at a ( modest ) fitness cost ( S4 Fig ) , and competitive exclusion is avoided by strong ecological feedback or alternative mechanisms ( such as spatiotemporal structure or previously evolved niche partitioning ) . Nevertheless , even if coexistence is limited , the prolonged survival of susceptible cells within resistant communities may already represent an issue by increasing the opportunity for horizontal gene transfer during antibiotic selection pressure . Passive resistance might consequently represent an important factor towards the development of genetic multidrug resistance .
S . pneumoniae CmS , a Cm-susceptible D39 derivate strain that constitutively expresses luc and a kanamycin resistance marker was used throughout . The Tc-resistant variant of this strain contained the Tc resistance gene tetM integrated at the bgaA locus , obtained via transformation with pPP1 [52] . luc has a reported half-life of 3 min in S . pneumoniae , and luminescence therefore gives real-time information on the level of gene expression activity [41 , 53] . S . pneumoniae CmR , expressing CAT from plasmid pJS5 , was used as standard for a Cm-resistant strain . Initial experiments were carried out with the Cm-resistant strain D-PEP1C3 that expresses CAT from a strong synthetic promoter . S . pneumoniae D-PEP33 expressing GFP was used as a Cm-susceptible strain in time-lapse microscopy experiments [41] . S . aureus experiments were performed with strain LAC pCM29 [54] that constitutively expresses CAT and GFP . S . pneumoniae and S . aureus cells were grown in C+Y medium ( pH 6 . 8 ) , supplemented with 0 . 5 μg ml−1 D-luciferin for luminescence measurements , at 37°C [55] . Pre-cultures for all experiments were obtained by a standardized protocol , in which previously exponentially growing cells from −80°C stocks were diluted to OD ( 600 nm , path length 10 mm ) 0 . 005 and grown until OD 0 . 1 in a volume of 2 ml medium inside tubes that allow for direct in-tube OD measurements . To determine the number of colony-forming units ( CFUs ) , S . pneumoniae cells were plated inside Columbia agar supplemented with 3% ( v v−1 ) sheep blood and incubated overnight at 37°C . Costar 96-well plates ( white , clear bottom ) with a total assay volume of 300 μl per well were inoculated to the designated starting OD value . Microtiter plate reader experiments were performed using a TECAN infinite pro 200 plate reader ( Tecan Group ) by measuring every 10 min with the following protocol: 5 s shaking , OD ( 595 nm , path length 10 mm ) measurement with 25 flashes , luminescence measurement with an integration time of 1 s . In mixed population assays ( shown in Fig 2A ) , all cultures were inoculated with CmS cells to an initial cell density of OD 0 . 001 . CmR cells were inoculated to the same density , and control cultures without CmR cells contained equal amounts of Cm-sensitive D39 wild-type cells to correct for unspecific effects such as drug-titration via cellular Cm binding . S were obtained by CmR cultivation ( inoculation at OD 0 . 001 ) in the presence of 5 μg ml−1 Cm in microtiter plates ( as described above ) . Four wells were sampled and pooled per time point ( combined volume of 1 . 2 ml ) , centrifuged to remove cells , and filtered through a 0 . 2 μm filter . HPLC analysis was carried out using an Agilent 1260 Infinity system ( Agilent Technologies ) with ultraviolet ( UV ) detection at 278 nm ( maximum absorbance of Cm ) [44] . An Aeris Peptide XB-C18 column ( Phenomenex ) with 3 . 6 μm particle and a size of 250 × 4 . 60 mm was used . Reversed-phase chromatography was carried out at a constant flow rate of 1 ml min−1 , with the mobile phase consisting of solution A: 10 mM sodium acetate buffer ( pH 6 . 0 ) containing 5% acetonitrile ( v v−1 ) and solution B: acetonitrile 0 . 1% TFA , according to the following protocol: 100 μl sample loading , 3 min 10% B , 20 min gradient 10% to 50% B , 1 min gradient 50% to 95% B , 3 min 95% B , 1 min gradient 95% to 10% B , 6 min 10% B . A Nikon Ti-E microscope equipped with a CoolsnapHQ2 camera and an Intensilight light source was used . Time-lapse microscopy was carried out by spotting pre-cultured cells on 10% polyacrylamide slides inside a Gene Frame ( Thermo Fisher Scientific ) that was sealed with the cover glass to guarantee stable conditions during microscopy . The polyacrylamide slide was prepared with growth medium containing 3 μg ml−1 Cm . Images of fluorescing cells were taken with the following protocol and filter settings: 0 . 3 s exposure for phase contrast , 0 . 5 s exposure for fluorescence at 440–490 nm excitation via a dichroic mirror of 495 nm , and an emission filter of 500–550 nm . Temperature during microscopy was controlled by an Okolab climate incubator , and images were taken every 10 min during 20 h at 37°C . The murine pneumonia model was performed with slight modifications as previously described [56] . Based on pilot experiments , we estimated that the number of animals required to observe a statistical difference between the groups would exceed the technical limit of animals that could be inoculated and treated per day . Therefore , the experiment was split into 2 d with the original pool of animals randomized to each group at the start of the multi-day experiment . Prior to statistical analysis , the data were combined . Note that all intratracheal infections were performed in a blinded fashion with respect to Cm or vehicle treatment . Eight-wk-old female CD1 mice ( Charles River Laboratories ) with an average body weight of 28 g were used . Fresh cultures of CmS and CmR were started in 10 ml of Todd-Hewitt broth containing 2% yeast extract ( THY ) and 10 ml of THY supplemented with 5 μg ml−1 Cm , respectively . Cultures were grown at 37°C in a 5% CO2 incubator until OD ( 600 nm ) 0 . 6 . Bacteria were washed twice with PBS via centrifugation at 3 , 220 × g at room temperature and concentrated in PBS to yield 3 . 5 × 107 CFU in the inoculation volume of 40 μl . For mixed infections , an equal volume of concentrated CmS and CmR pneumococci were combined . Mice were anesthetized with 100 mg kg−1 ketamine and 10 mg kg−1 xylazine . Once sedated , the vocal chords were visualized using an operating otoscope ( Welch Allyn ) , and 40 μl of bacteria was instilled into the trachea during inspiration using a plastic gel loading pipette tip . Mice were placed on a warmed pad for recovery . After 1 h , one intraperitoneal injection of Cm 75 mg kg−1 or vehicle controls was given , followed by two additional doses spaced 5 h apart . Mice were sacrificed with CO2 24 h after infection . To enumerate total surviving bacteria in the lungs , both lung lobes were removed and placed in a 2 ml sterile micro tube ( Sarstedt ) containing 1 ml of PBS and 1 mm silica beads ( Biospec ) . Lungs were homogenized by shaking twice at 6 , 000 rpm for 1 min using a MagNA Lyser ( Roche ) , with the specimens placed on ice as soon as they were harvested . Aliquots from each tube were serially diluted for CFU enumeration on THY plates . To determine whether or not a colony was CmS or CmR , individual colonies from the THY plates were picked and transferred into 100 μl of THY media in 96-well plates . The 96-well plates were incubated overnight at 37°C in a 5% CO2 incubator . After overnight incubation , wells were mixed , and 5 μl of media from each well was transferred into 100 μl of THY containing 15 μg ml−1 Cm or 100 μg ml−1 kanamycin . The 96-well plates were once again incubated overnight at 37°C in a 5% CO2 incubator , and wells were finally assessed for the presence or absence of a bacterial P . Cm ( ≥98% purity; Sigma ) for animal injection was prepared as follows: 40 mg ml−1 of Cm was dissolved in 800 μl of 70% ethanol in PBS to make a 50 mg ml−1 stock solution . This stock solution was diluted in PBS to 3 . 75 mg ml−1 for intraperitoneal injection into mice at 75 mg kg−1 . This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The corresponding protocol entitled “Mouse Models of Bacterial Infection and Innate Immunity” ( #S00227M ) was approved by the Institutional Animal Care and Use Committee of the University of California , San Diego ( Animal Welfare Assurance Number: A3033-01 ) . All efforts were made to minimize suffering of animals employed in this study . The model describes the dynamic of a coculture of CAT-expressing CmR and CmS bacterial cells growing in the presence of Cm in a chemostat environment . The two strains , with population densities xr and xs , respectively , compete for a growth-limiting resource , z . Cm is assumed to inhibit growth; we separately keep track of the intracellular concentrations of Cm ( ys in susceptible cells and yr in resistant cells ) and its concentration in the extracellular medium ym . The equations for the growth of the two bacterial populations and the growth-limiting resource are given by dxsdτ=rzkz+zhyhy+ysxs−xs , dxrdτ=ηrzkz+zhyhy+yrxr−xr , dzdτ= ( 1−z ) −crzkz+z ( xshyhy+ys−xrhyhy+yr ) , ( 1 ) where r is the maximum growth rate of CmS cells , η is the relative growth efficiency of CmR cells , c is the resource consumption rate , and kz and hy , respectively , are the half-saturation and inhibitory constants of the growth function . Time , resource concentration , and cell densities have been scaled relative to the flow rate of the chemostat , the resource concentration in the inflow medium , and the number of cells that fit in the chemostat volume , respectively , in order to reduce the number of free parameters ( see S1 Text for details ) . The concentrations of Cm in the different compartments , which have been scaled relative to the Cm concentration in the inflow medium , change according to the equations: dymdτ=11−xs−xr−pxs ( ym−ys ) +xr ( ym−yr ) 1−xs−xr−ym−ymdln ( 1−xs−xr ) dτ , dysdτ=p ( ym−ys ) −ys−ysdlnxsdτ , dyrdτ=p ( ym−yr ) −dyrky+yr−yr−yrdlnxrdτ . ( 2 ) The processes described by the terms on the right-hand side include inflow of Cm into the medium , passive transport of Cm between compartments at rate p , outflow from the chemostat , degradation of Cm by CAT in CmR cells ( according to Michaelis–Menten kinetics with maximum rate d and half-saturation constant ky ) , and concentration changes due to fluctuations in the volume of the compartments . Eqs ( 1 ) and ( 2 ) were solved numerically using Mathematica ( Wolfram ) or simulation software written in C++ ( used for the numerical bifurcation analysis , based on a Runge–Kutta integration algorithm with adaptive step-size control ) . | Antibiotic-resistant bacterial infections are on the rise and pose a serious threat to society . The influence of genetic resistance mechanisms on antibiotic therapy is well described . However , other factors , such as epigenetic resistance or the impact of the environment on antibiotic therapy , are less well understood . Here , we describe and characterize a mechanism of noninherited antibiotic resistance that enables the survival and outgrowth of genetically susceptible bacteria during antibiotic therapy . We show that bacteria expressing the resistance factor chloramphenicol ( Cm ) acetyltransferase ( CAT ) can potently deactivate Cm in their immediate environment . The reduced Cm concentration then allows for the outgrowth of genetically susceptible bacteria in the same environment . Mathematical modeling demonstrates the presence of a parameter space in which stable coexistence between Cm-susceptible and -resistant bacteria is possible during antibiotic therapy , which we validated using single-cell analyses . Strikingly , mixed culture experiments in which mice were infected with both Cm-susceptible and -resistant pneumococci revealed that Cm-sensitive “freeloader” bacteria even outcompeted resistant bacteria during antibiotic therapy . Together , we show that the microbial context during infection is a potential complicating factor to antibiotic treatment outcomes . | [
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]
| 2016 | Collective Resistance in Microbial Communities by Intracellular Antibiotic Deactivation |
The large family of Gram-positive quorum-sensing receptors known as the RNPP proteins consists of receptors homologous to the Rap , NprR , PlcR , and PrgX proteins that are regulated by imported oligopeptide autoinducers . Rap proteins are phosphatases and transcriptional anti-activators , and NprR , PlcR , and PrgX proteins are DNA binding transcription factors . Despite their obvious importance , the mechanistic basis of oligopeptide receptor regulation is largely unknown . Here , we report the X-ray crystal structure of the Bacillus subtilis quorum-sensing receptor RapJ in complex with the centrally important oligopeptide autoinducer competence and sporulation factor ( CSF , also termed PhrC ) , a member of the Phr family of quorum-sensing signals . Furthermore , we present the crystal structure of RapI . Comparison of the RapJ-PhrC , RapI , RapH-Spo0F , and RapF-ComAC crystal structures reveals the mechanistic basis of Phr activity . More specifically , when complexed with target proteins , Rap proteins consist of a C-terminal tetratricopeptide repeat ( TPR ) domain connected by a flexible helix-containing linker to an N-terminal 3-helix bundle . In the absence of a target protein or regulatory peptide , the Rap protein 3-helix bundle adopts different conformations . However , in the peptide-bound conformation , the Rap protein N-terminal 3-helix bundle and linker undergo a radical conformational change , form TPR-like folds , and merge with the existing C-terminal TPR domain . To our knowledge , this is the first example of conformational change-induced repeat domain expansion . Furthermore , upon Phr binding , the entire Rap protein is compressed along the TPR superhelical axis , generating new intramolecular contacts that lock the Rap protein in an inactive state . The fact that Rap proteins are conformationally flexible is surprising considering that it is accepted dogma that TPR proteins do not undergo large conformational changes . Repeat proteins are widely used as scaffolds for the development of designed affinity reagents , and we propose that Rap proteins could be used as scaffolds for engineering novel ligand-switchable affinity reagents .
Quorum sensing is a bacterial cell–cell communication process mediated by secreted signaling molecules . At low cell density , the concentration of the quorum-sensing signals is negligible and bacteria act as individuals . At high cell density , the concentration of the signals is sufficient to coordinate bacterial social behaviors including sporulation , virulence factor expression , motility , biofilm formation , bioluminescence , antibiotic production , and genetic competence [1] . Typically , acylated homoserine lactones are used as quorum-sensing signals by Gram-negative bacteria , whereas oligopeptides are used by Gram-positive bacteria . Despite their obvious importance , the mechanistic basis of oligopeptide receptor regulation in Gram-positive species is largely unknown . Secreted oligopeptide signals are commonly synthesized as immature pro-peptides ( Figure 1 ) [2] . The genes encoding the pro-peptides are usually encoded immediately upstream or downstream of their cognate receptor genes , forming receptor–pro-peptide gene cassettes . The immature pro-peptides are secreted from the cell and subsequently undergo proteolytic maturation [3] . The mature oligopeptides bind to and regulate transmembrane receptors such as histidine kinases , or alternatively , the mature oligopeptides are imported into the cell by oligopeptide permeases [4]–[7] . Inside the cell , the oligopeptides bind to and regulate target receptors [7]–[11] . These cytoplasmic receptors include ( 1 ) members of the RNPP protein family , consisting of receptors homologous to the Rap , NprR , PlcR , or PrgX proteins , which are widespread in Firmicutes ( e . g . , Bacillus and Enterococcus species ) [9] , [12]–[14] , and ( 2 ) the Rgg proteins , which are ubiquitous in Streptococcus and many other low G+C Gram-positive species [15] . NprR , PlcR , PrgX , and Rgg proteins are DNA binding transcription factors . In contrast , as described below , Rap proteins have diverse catalytic and noncatalytic activities , and Rap proteins are not DNA binding transcription factors . Rap proteins and their inhibitory oligopeptides , called Phr peptides , have been most extensively studied in B . subtilis , which encodes 11 Rap proteins on its chromosome and another five on plasmids [16]–[18] . The founding members of the Rap family , RapA and RapB , were shown to be response regulator aspartate phosphatases ( Rap ) [19] . Subsequently , RapE , RapH , and RapJ were demonstrated to be response regulator aspartate phosphatases [20]–[22] , and RapC , RapF , RapG , and RapH were revealed to be transcriptional anti-activator proteins that inhibit the binding of the response regulators ComA or DegU to DNA promoters [20] , [23]–[25] . Genes encoding pro-Phr polypeptides overlap with the 3′ end of rap genes , forming rap-phr gene cassettes . Mature Phr peptides are imported into the cell where each oligopeptide inhibits its cognate Rap protein ( e . g . , PhrA inhibits RapA , and PhrC inhibits RapC ) and in some cases a non-cognate Rap protein ( e . g . , PhrC inhibits RapB , which is not encoded in a cassette with a cognate phr gene ) [10] , [26] , [27] . Like rapB , rapJ is not encoded in an operon with a phr gene , and here we show that the centrally important Phr peptide PhrC , which is also commonly referred to as competence and sporulation factor [6] , [27] , binds to RapJ and inhibits its phosphatase activity . While it is well-established that Phr peptides bind to Rap proteins and inhibit their activity , how Phr peptides regulate Rap proteins remained unknown . Here we report two X-ray crystal structures containing Rap proteins: the structures of a Rap-Phr complex , RapJ-PhrC , and a Rap protein , RapI , alone . These structures and supporting in vivo and in vitro studies , together with the previously determined structure of ( 1 ) RapH complexed with the intermediate response regulator Spo0F [22] and ( 2 ) the structure of RapF complexed with the DNA binding domain of the transcription factor ComA ( ComAC ) [28] , reveal the mechanistic basis of Rap protein regulation by Phr peptides . Interestingly , our structure-function analysis shows that Rap proteins exist in radically different conformations in the target-bound , Phr peptide-bound , and ligand-free conformations . The fact that Rap proteins undergo dramatic conformational changes is particularly surprising because Rap proteins are tetratricopeptide repeat ( TPR ) proteins , which were believed to be rigid frameworks that do not undergo large conformational changes . Repeat proteins are widely used as scaffolds for the development of designed affinity reagents , and our results suggest that Rap proteins could be used as scaffolds for engineering or evolving novel ligand-switchable TPR-based affinity reagents . In addition , we note that the studies presented here set the stage for the rational development of antimicrobial peptides and peptide-mimetics targeting Rap-mediated cell–cell signaling .
Our primary goal was to determine the mechanistic basis of Rap protein regulation by Phr peptides using a combination of X-ray crystallographic , biochemical , and genetic approaches . Despite extensive efforts , we had only limited success crystallizing previously identified Rap-Phr pairs , and we therefore sought to identify new Rap-Phr pairs to target for crystallization . We previously showed in vitro that RapJ dephosphorylates Spo0F [22] , an intermediate response regulator in the B . subtilis sporulation phosphorelay pathway [29]; however , because rapJ is not encoded in an operon with a phr gene , whether a RapJ regulatory peptide existed was unknown . To determine whether previously identified Phr peptides inhibit RapJ Spo0F phosphatase activity , we measured the ability of synthetic Phr peptides to inhibit RapJ dephosphorylation of Spo0F in vivo as a function of Spo0A activation using a luciferase gene under the control of the Spo0A-driven promoter PspoIIG ( Figure 2A ) . As expected , overexpressing RapJ completely repressed PspoIIG activation ( Figures 2A and S1A ) , and deleting rapJ resulted in elevated PspoIIG expression ( Figure S1A ) . Surprisingly , however , PhrC restored PspoIIG expression to levels approaching that of the negative control—that is , when RapJ overexpression was not induced ( Figures 2A and S1A ) . In contrast , other Phr peptides tested—e . g . , PhrA ( ARNQT ) and PhrH ( TDRNTT ) —had little or essentially no effect on PspoIIG expression ( Figure 2A and unpublished data ) . To determine whether RapJ and PhrC interact directly , we incubated purified RapJ with synthetic PhrC and subjected the sample to size exclusion chromatography ( SEC ) . Using MALDI-TOF and MALDI-TOF/TOF tandem mass spectrometry , we detected PhrC complexed with RapJ ( Figure S2 ) ; control experiments confirmed that SEC separates the RapJ-PhrC complex from unbound PhrC ( Figure S3 ) . To confirm that PhrC directly inhibits RapJ dephosphorylation of Spo0F , we phosphorylated Spo0F as previously described [22] and measured the ability of synthetic PhrC to inhibit RapJ phosphatase activity in vitro . Consistent with the above in vivo results , PhrC inhibited RapJ-accelerated dephosphorylation of Spo0F ( Figure 2B ) . Other Phr peptides had no effect on RapJ Spo0F phosphatase activity ( Figure 2B and unpublished data ) . Based on the above in vivo and in vitro analyses , we conclude that PhrC directly inhibits RapJ Spo0F phosphatase activity; however , we note PhrC has at least three targets ( RapB , RapC , and RapJ ) and the physiological importance of RapJ inhibition by PhrC is unknown [10] , [27] . Following the discovery that RapJ and PhrC form a regulatory pair , we crystallized and determined the 2 . 16 Å resolution X-ray crystal structure of the RapJ-PhrC complex ( Figure 3A ) . Straightforward approaches to obtain phases for the RapJ-PhrC structure by molecular replacement using our previously determined structures of Rap proteins were unsuccessful , suggesting that PhrC binding had induced a large conformational change in RapJ; therefore , we overexpressed and purified selenomethionyl derivatized RapJ and determined phases for the RapJ-PhrC complex using the single wavelength anomalous dispersion ( SAD ) method ( Table S1 ) . Readily interpretable electron density corresponding to each residue in the PhrC pentapeptide was observed , and PhrC was modeled only after the RapJ model was nearly complete . It is also important to note that while there are two copies of RapJ-PhrC in the asymmetric unit , sedimentation equilibrium ( SE ) analytical ultracentrifugation ( AUC ) shows that RapJ is monomeric in solution in the presence and absence of PhrC ( Figure S4 ) . More specifically , the theoretical molecular weights of the RapJ and RapJ-PhrC monomers are 44 . 41 kD and 45 . 01 kD , respectively , and their molecular weight as determined by SE AUC are 46 . 0 kD and 47 . 5 kD , respectively ( Figure S4A , B ) . Consistent with these results , we previously reported that RapF , RapH , and RapK are monomeric in solution as determined by SE AUC [28] . The conformation adopted by Rap proteins in the absence of Phr peptide or target protein was unknown . To reveal the structure of a Rap protein alone , we crystallized and determined the 2 . 44 Å resolution X-ray crystal structure of RapI . As detailed in the Materials and Methods section , we obtained phases for the RapI structure by starting with a core search model consisting of a RapJ domain highly homologous to RapI , and then iteratively rebuilding and enlarging search models using phenix . mr_rosetta [30] , [31] and scoring the models based on the LLG in the Phaser [32] rotation function ( Figure 3B and Table S1 ) . While discussed extensively below , it is worthwhile to note here that there are two copies of RapI in the crystallographic asymmetric unit , and while one model is relatively complete ( Figure 3B ) , there was in fact insufficient electron density to build helix-turn-helix ( HTH ) 1 and HTH2 in the second model ( see Discussion and Materials and Methods ) . B . subtilis RapI was previously reported to stimulate the activity of the ImmA protease using an unknown mechanism , resulting in the expression , excision , and transfer of the conjugative transposon ICEBs1 [33] . However , following our structural analysis of RapI , we realized that it conserves 17 of 18 highly conserved residues in the Rap Spo0F interface ( Table S2 ) [22] , including the catalytic glutamine that inserts into the Spo0F active site . Therefore , we evaluated the ability of RapI to dephosphorylate Spo0F in vitro , and we found that RapI is indeed a Spo0F phosphatase ( Figure 4 ) . The fact that RapI activates ICEBs1 and dephosphorylates Spo0F suggests that RapI may be important for inhibiting sporulation during active ICEBs1 transposition . Furthermore , we determined that the pentapeptide DRVGA and hexapeptide ADRVGA , sequences derived from the pro-PhrI C-terminus , inhibit RapI phosphatase activity in vitro ( Figure 4 ) . We previously hypothesized that the hexapeptide form of PhrI would inhibit RapI and might also serve as the biologically important PhrI peptide [34] . Structural comparison of the Rap proteins in the previously determined structures of RapH-Spo0F and RapF-ComAC with the structure of RapJ-PhrC revealed that Rap proteins undergo radical conformational changes ( Figures 3A , B and 5 , and Movie S1 ) . We previously showed that Rap proteins are composed of two distinct domains when complexed with target proteins such as Spo0F or ComA ( Figure 5 ) [22] , [28] . These Rap domains are an N-terminal 3-helix bundle and a C-terminal TPR domain; a flexible helix-containing linker region connects the domains ( Figure 5 ) . Relative to its position in the RapH-Spo0F and RapF-ComAC structures , the entire N-terminal 3-helix bundle and linker region have dramatically flipped and repacked against the N-terminal surface of the C-terminal TPR domain in the RapJ-PhrC structure ( Figure 5 ) . In fact , the RapJ 3-helix bundle ( residues 1–70 ) and helix-containing linker region ( residues 71–94 ) merge with the C-terminal TPR domain ( residues 95–373 ) to form one extended TPR domain ( residues 1–373 ) ( Figures 3A , B , and 5 ) . Each HTH consists of an A and B helix connected by a short loop . HTH1 is formed by helices α1 and α2 ( the first two helices of the 3-helix bundle ) . HTH2 is formed by helix α3 ( the third helix of the 3-helix bundle ) and helix α4 ( the linker region helix in the RapH-Spo0F and RapF-ComAC structures ) . While HTH1 and HTH3–HTH7 conserve a number of the TPR motif signature residues , HTH2 forms a TPR-like fold but does not strictly conserve the TPR signature motif residues [35] . Similarly , comparing the structure of RapI with the structures of RapH-Spo0F and RapF-ComAC revealed that the RapI 3-helix bundle and linker region have joined the TPR domain ( Figure 5 ) ; however , it is important to note that RapI and RapJ are not in identical conformations ( Figure 3A and 3B ) . The Rap protein HTH folds assemble into a right-handed superhelical structure ( the TPR domain ) . TPR domains typically have a convex outer surface and a concave inner surface , commonly referred to as the ligand-binding groove . The RapI structure reflects this scenario , and the ligand-binding groove is in an open conformation ( Figure 3B and 3D ) . In contrast , in the RapJ-PhrC structure , the ligand-binding groove is closed ( Figure 3A and 3C ) . PhrC binding stabilizes a closed RapJ conformation that differs from the open RapI conformation due to a compression of RapJ along the TPR superhelical axis ( Figures 3 and 5 ) . To explore the physiological importance of the RapJ-PhrC interactions observed in the RapJ-PhrC structure ( Figures 3A and 6A ) , we systematically mutated RapJ residues that contribute directly to the PhrC binding surface and analyzed the mutants for sensitivity to PhrC using the in vivo PspoIIG luciferase reporter assay ( Figure 6A and 6B ) . The vast majority of mutations in the RapJ-PhrC interface resulted in RapJ proteins that were insensitive to PhrC . RapA and RapC mutations that resulted in a loss of sensitivity to PhrA or PhrC , respectively , were previously identified [10] , [19] , [24] , [36] . These mutations are in residues equivalent to RapJ residues D192 , Y224 , N225 , H228 , Q260 , and V259 ( Figure S5 ) . The RapJ-PhrC crystal structure shows that RapJ residues D192 , Y224 , N225 , H228 , and Q260 are buried in the PhrC interface ( Figure 6A ) . While V259 is not buried in the PhrC interface , we speculate that a mutation here could affect entry of Phr peptides into the binding pocket . RapJ-PhrC interface mutations E147A , Y150F , D192A , N225A , F250A , and K300E resulted in a complete loss of sensitivity to PhrC ( Figure 6A and 6B ) and , together with previous mutagenesis studies [10] , [19] , [24] , [36] , confirm the biological importance of the crystallographically identified RapJ-PhrC interface . PhrC binding to RapJ creates not only intermolecular RapJ-PhrC contacts but also new intramolecular contacts between regions of RapJ distant from the PhrC binding site . Comparison of the RapJ-PhrC , RapI , RapH-Spo0F , and RapF-ComAC structures enabled us to recognize PhrC-induced intramolecular RapJ contacts . We hypothesized that some of these RapJ contacts , in particular the contacts between HTH folds , are critical for stabilizing the catalytically inactive PhrC-bound conformation . To identify intramolecular RapJ contacts that stabilize the PhrC-bound conformation , we mutated RapJ residues that are distant from the PhrC binding site but form new intramolecular contacts upon PhrC binding and determined whether the mutant proteins were constitutively active in the presence of PhrC in vivo ( Figure 7A and 7B ) . More specifically , we targeted for mutagenesis residues that mediate PhrC-dependent contacts between HTH folds ( e . g . , a salt bridge formed between HTH2 and HTH3 ) at a distance from the PhrC binding site . RapJ residue R105 ( located in helix-α5 in HTH3 ) forms a salt bridge with E87 ( located in helix-α4 in HTH2 ) ( Figure 7A ) . RapJ-R105A displayed wild-type Spo0F phosphatase activity , and RapJ-R105A was largely insensitive to the effects of PhrC ( Figure 7B ) . RapJ residue E87 interacts with the Rap protein 3-helix bundle when the Rap proteins are bound to Spo0F or ComA . The RapJ-E87A mutant displayed a severe phosphatase defect ( unpublished data ) , and RapJ-E87 could not be evaluated for PhrC sensitivity . RapJ residue Y161 ( located in helix-α8 in HTH4 ) interacts with residues K123 and E126 ( both located in helix-α6 in HTH3 ) ( Figure 7A ) . RapJ-Y161F displayed wild-type Spo0F phosphatase activity and wild-type sensitivity to PhrC ( Figure 7B ) . However , the double mutant RapJ-R105A , Y161F displayed wild-type phosphatase activity and complete insensitivity to PhrC ( Figure 7B ) . Consistent with the fact that these contacts are distant from the PhrC binding site , using MALDI-TOF and MALDI-TOF/TOF tandem mass spectrometry we detected PhrC complexed with RapJ-R105A , Y161F following SEC of RapJ-R105A , Y161F incubated with PhrC ( Figure S6 ) . RapJ residues R105 and Y161 are highly conserved among the B . subtilis Rap proteins ( Figure S5 ) , and the fact that RapJ-R105A , Y161F is competent to bind PhrC but insensitive to its inhibitory effects confirms the biological importance of the RapJ conformation observed in the RapJ-PhrC crystal structure . Furthermore , consistent with the possibility that the RapJ conformation observed in the RapJ-PhrC crystal structure is the inactive conformation adopted by other Rap proteins upon binding to Phr peptides , RapH-R105A was insensitive to the effects of PhrH in vivo ( Figures 7C and S1B ) .
Sequence and structural analysis shows that the Rap proteins are repeat proteins consisting of nine HTH TPR or TPR-like folds , which pack together to form a right-handed superhelical TPR domain ( Figure 3A and 3B ) [8] , [22] , [28] . TPR proteins are the most common repeat proteins in bacteria , comprising 14% of all bacterial repeat proteins , which make up greater than 5% of the bacterial proteome [37] . Repeat proteins like the Rap proteins form extended structures; thus , they have a larger surface area to volume ratio than globular proteins . Due at least in part to this large surface area to volume ratio , repeat proteins commonly mediate protein–protein and protein–peptide interactions . It is accepted dogma that repeat proteins do not undergo large conformational changes upon protein or peptide ligand binding ( for review , see [38] ) . This concept has been widely accepted simply because there were no data suggesting that repeat proteins are particularly flexible or undergo large conformational changes . Upending this widely held belief , comparison of the structures of RapI alone , RapJ-PhrC , RapH-Spo0F , and RapF-ComAC reveals that Rap proteins can undergo enormous conformational changes ( Figure 5 and Movie S1 ) . In fact , as discussed below , our data show that Rap proteins can exist in single-domain or dual-domain forms . When complexed with a target such as Spo0F or ComA , Rap proteins have a distinct N-terminal 3-helix bundle , helix-containing linker region , and C-terminal TPR domain ( Figure 5 ) . In the case of RapF , the 3-helix bundle and linker region form the ComAC binding surface . In the case of RapH , the C-terminal TPR domain and 3-helix bundle make critical contacts with Spo0F; in fact , the 3-helix bundle contains the catalytic glutamine that inserts into the Spo0F active site . Comparison of the RapJ-PhrC , RapH-Spo0F , and RapF-ComAC structures reveals the mechanism of Phr regulation . The Phr-induced conformational change ( detailed below ) not only simultaneously results in a total rearrangement of both the ComAC and Spo0F binding sites , but also ( 1 ) buries ( renders inaccessible ) RapJ residues corresponding to RapF residues that bind ComAC , including Phe24 and Leu67 , which were previously shown to mediate critical interactions with ComAC [28] , and ( 2 ) splits and displaces the Spo0F binding surface on the 3-helix bundle ( including the catalytic glutamine ) and TPR domain to opposite sides of the Rap protein where they cannot interact concurrently with Spo0F . In both the RapH-Spo0F and RapF-ComAC structures , the Rap linker region helix lies at a ∼45° angle to the 3-helix bundle ( Figure 5 ) . In comparison to the Spo0F and ComAC bound structures , in both the RapI and RapJ-PhrC structures , the N-terminal 3-helix bundle has rotated ∼180° and the linker region helix has rotated ∼135° ( Figure 5 ) . Together , the rotation of the 3-helix bundle and linker region helix creates two HTH folds ( HTH1 and HTH2; Figure 5 ) that pack against the existing C-terminal TPR domain , resulting in the extension of the TPR domain fold by two HTH repeats . To our knowledge , this represents the first example of conformational change-induced repeat domain expansion . Structural alignment of the RapI and RapJ-PhrC structures shows that RapJ in the PhrC bound conformation is compressed along the TPR superhelical axis , causing the disappearance of the concave groove that exists in the structures of RapI alone , RapH-Spo0F , and RapF-ComAC . New intramolecular RapJ contacts that form as a result of the PhrC-induced compression stabilize the RapJ closed conformation and are necessary for PhrC inhibition of RapJ Spo0F phosphatase activity . The fact that the R105A mutation resulted in a severe loss of RapJ and RapH sensitivity to PhrC and PhrH , respectively , suggests that this residue is a hotspot contributing significantly to the binding energy of the intramolecular interface formed in the closed , Phr peptide-bound conformation . We speculate that the N-terminal 3-helix bundle moves into the intermediate open conformation when Phr peptides dissociate from Rap proteins in the closed conformation or when Spo0F or ComA dissociate from Rap proteins in the fully open conformation ( Figure 5 ) . While additional studies are required to determine whether the conformation of the 3-helix bundle and linker region in the RapI alone crystal structure represents a stable Rap protein conformation adopted in the absence of Phr peptide or target protein , existing data suggest that this is not likely to be the case . More likely is the possibility that the structure of RapI alone depicts one of many transient conformations that the 3-helix bundle and linker region can adopt in the absence of a Phr peptide or target protein . In fact , as mentioned above , there are two copies of RapI in the crystallographic asymmetric unit , and while the model of one molecule is relatively complete and includes the majority of the 3-helix bundle and linker region , there was in fact insufficient electron density to build the N-terminal 3-helix bundle and linker region in the other molecule ( see Materials and Methods ) . In the more complete RapI model , where the N-terminal 3-helix bundle and linker region are included , the N-terminal 3-helix bundle , linker region , and C-terminal TPR domain together bury 857 Å2 surface area at their interface . However , consistent with the idea that the structure of RapI depicts one of many transient conformations , we note that the position of the N-terminal 3-helix bundle and linker region in the more complete RapI model is likely influenced by crystal contacts with symmetry-related copies of RapI . The above results suggest that in the absence of a bound target such as Spo0F or ComA , or a bound Phr peptide such as PhrC , the N-terminal 3-helix bundle and linker region may adopt different conformations relative to the C-terminal TPR domain . What is the structural basis of Rap-Phr binding and selectivity ? The residue at position −4 from the C-terminus of the Phr penta and hexapeptides is basic ( Arg or Lys ) in all of the identified B . subtilis Phr peptides; it is Arg in eight of nine B . subtilis Phr peptides , and it is Lys in only one instance , PhrG [9] . The RapJ-PhrC structure revealed that RapJ residue Asp192 forms a salt-bridge with PhrC residue Arg2 ( Figure 6A ) . With the exception of RapG , every B . subtilis Rap protein contains Asp at the position structurally equivalent to RapJ Asp192; in RapG , this residue is Glu ( Figure S5 ) . We propose that the salt bridge between RapJ-D129 and PhrC-R2 is conserved in every B . subtilis Rap-Phr complex . This hypothesis is supported by previous analysis of Rap proteins containing mutations in the position equivalent to RapJ Asp192 [10] , [19] , [24] , [36] , as well as by studies analyzing the effects of substitution mutations in Phr peptides at the position equivalent to PhrC Arg2 [7] . We propose that Phr peptides recognize their cognate Rap protein by first scanning the surface for a pocket of complementary shape . Second , the Rap-Phr salt bridge equivalent to the interaction of RapJ Asp192 and PhrC Arg2 is a hotspot contact that anchors the peptide-receptor complex . With the exception of the basic residue conserved at the position −4 from the C-terminus , the remaining B . subtilis Phr residues are poorly conserved and each could contribute to the determination of Rap-Phr interaction specificity . While there are not yet enough known Rap-Phr pairs to use multiple sequence alignment to identify covarying residues at the Rap-Phr interface , we have identified a number of B . subtilis Rap-Phr interactions that likely contribute to the determination of interaction specificity . For example , the PhrC residue Glu1 sidechain hydrogen bonds with the Rap Tyr297 and Lys300 sidechains ( Figure 6 ) . Sequence analysis of the B . subtilis Rap-Phr pairs shows that when the Phr residue equivalent to PhrC residue Glu1 is Glu or Asp , then the position equivalent to RapJ Tyr297 is Tyr and the position equivalent to RapJ Lys300 is Lys . However , when the Phr residue equivalent to PhrC residue Glu1 is Ala or Ser , then the Rap position equivalent to RapJ Tyr297 is Phe and the position equivalent to RapJ Lys300 is Leu . Similarly , the PhrC Gly3 mainchain nitrogen hydrogen bonds with the RapJ Tyr150 sidechain ( Figure 6 ) , and this appears to be the case for every B . subtilis Rap-Phr pair with the following exception . In the RapK-PhrK pair the Phr residue equivalent to PhrC residue Gly3 is Pro , and RapK encodes Thr at the position equivalent to RapJ Tyr150 . Likewise , the PhrC Met4 mainchain nitrogen and carbonyl form hydrogen bonds with the RapJ Asn225 sidechain ( Figure 6 ) , and this Asn is conserved in every B . subtilis Rap protein . Thus , this Rap-Phr interaction contributes to the binding energy but not to the determination of binding specificity in B . subtilis . Finally , we note that when the Phr residue equivalent to PhrC residue Met4 is Met , then the positions in RapB , RapC , and RapF equivalent to RapJ Tyr224 and Phe250 are Tyr and Phe , respectively ( Figure 6 ) . Ongoing computational modeling and peptide docking studies guided by the RapJ-PhrC structure as well as complementary genetic and biochemical analysis of the calculated Rap-Phr interactions will test the importance of the above observations and reveal more broadly the Rap and Phr contacts that dictate Rap-Phr interaction specificity at the atomic level . Finally , analogous to antibodies and their hypervariable complementarity determining regions , repeat proteins such as the TPR proteins can be imagined as a structurally conserved backbone decorated with functional residues . This is exemplified by the variable lymphocyte receptors ( VLRs ) in jawless fish . Instead of the immunoglobulin-based antigen receptors created by V ( D ) J recombination in jawed vertebrates , the VLRs result from the combinatorial assembly of leucine-rich repeats [39] . Studies of VLRs and numerous other repeat proteins such as TPR and ankyrin repeat proteins have provided tremendous insight into repeat protein function while also advancing our ability to evolve or engineer repeat proteins displaying new functions [40]–[48] . In fact , repeat proteins are now widely used as scaffolds for the development of designed affinity reagents—for example , Designed Ankyrin Repeat Proteins ( DARPins ) and TPR-based recognition modules ( T-Mods ) —which can substitute for antibodies in chromatographic , diagnostic , co-crystallization , and therapeutic applications . In comparison to antibodies , repeat proteins can offer a number of advantages including elevated solubility , high production yields in microbial expression systems , protease resistance , and thermal stability . Antibodies are particularly difficult to manufacture since they are glycosylated and contain disulphide bonds . The stability of even truncated forms of antibodies , such as scFv and Fab fragments , relies on the formation of intradomain disulphide bonds [49] , also limiting their application . Due to the development of powerful selection techniques ( e . g . , ribosome display ) , the effort required to generate alternative binding reagents with prescribed target-binding specificity is quickly approaching that required to create conventional antibodies [40] . Both the oligopeptide binding site on the Rap protein TPR concave surface and the multiple target protein binding sites on the Rap protein TPR convex surface could be engineered to bind different oligopeptides and proteins , respectively . Furthermore , the RapJ-PhrC X-ray crystal structure shows a channel leading into the oligopeptide binding site , suggesting that the RapJ peptide interaction surface could be engineered to bind the flexible C-terminus of a target protein . As discussed above , prior to our studies , repeat proteins were not known to undergo large ligand-induced conformational changes . Consequently , the use of peptides or other ligands to regulate the target binding of designed affinity reagents has not been previously explored . We propose that Rap proteins could serve as scaffolds for engineering or evolving ligand-switchable TPR-based affinity reagents .
RapJ was amplified from B . subtilis strain 168 genomic DNA using Phusion High-Fidelity DNA Polymerase and the primer pair RapJ-Fwd and RapJ-Rev ( Table S3 ) . The PCR product was cloned into the SapI and XhoI sites of pTB146 using the In-Fusion method ( Clontech ) to give pTB146J [50] . His-Sumo-RapJ was overexpressed in E . coli strain BL21 ( DE3 ) by first growing the cells at 37°C in LB medium containing 100 µg/ml ampicillin to OD600 = 0 . 6 and then inducing expression with 0 . 1 mM isopropyl β-D thiogalactopyranoside ( IPTG ) for 16 h at 16°C . All subsequent purification steps were carried out at 4°C . The cells were collected by centrifugation and lysed in buffer A ( 20 mM Tris-HCl , pH 8 . 0 , 250 mM NaCl , 50 mM KCl , 10 mM MgCl2 , 10 mM β-ME , 10% glycerol ) supplemented with 1 µM Pepstatin , 1 µM Leupeptin , 20 µg/ml DNase , and 1 mM PMSF . Lysate supernatant was applied to His-60 Ni resin ( Clontech ) equilibrated in buffer A . The His-60 resin was then washed in buffer A and resuspended in 65 mM Tris-HCl ( pH 8 . 0 ) , 325 mM NaCl , 35 mM KCl , 7 mM MgCl2 , 3 . 5 mM DTT , 10% glycerol , and 0 . 2% NP-40 . SUMO protease was then added at 4 mg/ml His-60 resin and incubated at 4°C for 16 h . RapJ contained no heterologous residues following removal of the N-terminal His-Sumo fusion . RapJ was eluted with buffer A and diluted 3-fold with buffer B ( 20 mM Tris-HCl , pH 8 . 0 , 10 mM MgCl2 , 5 mM DTT , and 10% glycerol ) , passed through a 0 . 45 µm filter , and loaded onto an anion exchange column ( Source 15Q; GE Healthcare ) equilibrated in buffer B containing 50 mM KCl . RapJ was then eluted in a 50–1 , 000 mM KCl gradient of buffer B . Fractions containing RapJ were pooled , concentrated by ultrafiltration through a 30 kDa filter , and further purified by gel filtration using a Superdex 200 ( GE Healthcare ) 16/70 column equilibrated in buffer C ( 20 mM Tris-HCl , pH 8 . 0 , 150 mM KCl , 5 mM MgCl2 , and 5 mM DTT ) . RapJ was concentrated to 1 . 58 mM and stored at −80°C . Expression of selenomethionyl RapJ was in E . coli strain B834 ( DE3 ) grown in M9 medium [51] . A total of 10 mM dithiothreitol was present throughout the purification , which was otherwise performed as described above for native RapJ . Selenomethionyl RapJ was concentrated to 1 . 35 mM before storing at −80°C . RapI was amplified from B . subtilis str . 168 genomic DNA using primers RapI_Fwd_Infusion and RapI_Rev_XhoI and cloned into the SapI and XhoI sites of pTB146 using the In-Fusion method to give pTB146I ( Table S3 ) . ImmA was amplified from B . subtilis str . 168 genomic DNA using primers ImmA-pBB-NdeI-Fwd and ImmA-pBB-EcoRI_rev and cloned into NdeI and EcoRI sites of pBB75 by the In-Fusion cloning to give pBBA . ImmR was amplified from B . subtilis str . 168 genomic DNA using primers ImmR1_F_pCOLANcoI_Inf and ImmR1_R_pCOLANotI_Inf and cloned into NcoI and NotI sites of pACYCDuet-1 ( Novagen ) by the In-Fusion method to give pACYCR . His-Sumo-RapI , untagged ImmA , and untagged ImmR were overexpressed following co-transformation of pTB146I , pBBA , and pACYCR in E . coli strain BL21 ( DE3 ) by first growing the cells at 37°C in LB medium containing 100 µg/ml ampicillin , 30 µg/ml kanamycin , and 17 µg/ml chloramphenicol to OD600 = 0 . 8 and then inducing expression with 1 mM IPTG for 16 h at 18°C . Cells were lysed in Buffer D ( 20 mM Tris , pH 8 . 0 , 250 mM NaCl , 50 mM KCl , 20 mM βME , 10 mM MgCl2 , and 10% glycerol ) supplemented with 1 µM Pepstatin , 1 µM Leupeptin , 20 µg/ml DNase , and 1 mM PMSF . Lysate supernatant was applied to His-60 resin equilibrated in buffer D . His-Sumo-RapI bound to the His-60 resin , while ImmA and ImmR were not retained . The Ni resin was then washed in buffer D and eluted with buffer D containing 20 , 50 , 100 , 200 , or 500 mM imidazole . The 50–500 mM imidazole-containing fractions were pooled and 50 µg SUMO protease was then added per 1 mg of total protein in 40 mM Tris , pH 8 . 0 , 0 . 2% NP40 , 50 mM NaCl , 190 mM KCl , 1 mM DTT , 16 mM βME , 8 mM MgCl2 , and 8% glycerol and incubated at 4°C for 16 h . RapI contained no heterologous residues following removal of the N-terminal His-Sumo fusion . Protein was centrifuged at 15 , 000 rpm for 10 min to remove precipitated protein , passed through a 0 . 45 µm filter , diluted 3-fold with buffer E ( 20 mM Tris-HCl , pH 8 . 0 , 10 mM MgCl2 , 5 mM DTT , and 10% glycerol ) and loaded onto a Source15Q column equilibrated in buffer E containing 50 mM KCl . RapI was then eluted in a 50–1 , 000 mM KCl gradient of buffer E . Fractions containing RapI were pooled , concentrated by ultrafiltration through a 30 kDa filter , and further purified by gel filtration using a Superdex 200 16/70 column equilibrated in buffer F ( 20 mM Tris-HCl , pH 8 . 0 , 150 mM KCl , 5 mM MgCl2 , and 5 mM DTT ) . RapI was concentrated to 608 µM and stored at 4°C for less than 10 d prior to its use in crystallization experiments . Native RapJ-PhrC crystals were produced by the vapor diffusion method at 20°C using a 1∶1 mixture of RapJ-PhrC ( 250 µM RapJ and 1 . 24 mM PhrC in 17 . 7 mM Tris-HCl , pH 8 . 0 , 133 . 2 mM KCl , 4 . 4 mM DTT , 4 . 4 mM MgCl2 , and 2% Benzamidine hydrochloride hydrate ) and well solution ( 8 . 8% [w/v] PEG 3000 , 290 mM magnesium chloride , and 100 mM sodium cacodylate , pH 6 . 4 ) . RapJ-PhrC crystals were soaked and cryoprotected in mother liquor solution containing 3% , 7 . 5% , and 14% glycerol for ∼5 s each followed by 4 h soaking in mother liquor solutions containing 20% glycerol . RapJ-PhrC crystals containing the selenomethionyl derivatized RapJ protein were produced by the vapor diffusion method at 20°C using a 1∶1 mixture of RapJ-PhrC ( 250 µM RapJ and 1 . 24 mM PhrC in 17 . 7 mM Tris-HCl , pH 8 . 0 , 133 . 2 mM KCl , 8 . 8 mM DTT , 4 . 4 mM MgCl2 , and 2% Benzamidine hydrochloride hydrate ) and well solution ( 8 . 8% [w/v] PEG 3000 , 250 mM magnesium chloride , and 100 mM sodium cacodylate , pH 5 . 4 ) . RapJ-PhrC crystals were soaked and cryoprotected in mother liquor solution containing 3% , 7 . 5% , and 14% glycerol for ∼5 s each followed by 5 min soaking in mother liquor solutions containing 20% glycerol . Single-wavelength anomolous dispersion ( SAD ) and native data on nitrogen-cooled crystals were collected at NSLS beamline X29 and processed using the HKL software package [52] . RapI crystals were produced by the vapor diffusion method at 20°C using a 1∶1 mixture of RapI ( 145 µM in 20 mM Tris-HCl , pH 8 . 0 , 150 mM KCl , 5 mM DTT , 5 mM MgCl2 ) and well solution ( 17% [w/v] PEG 3350 , 200 mM lithium nitrate , and 100 mM Tris-HCl , pH 7 . 8 ) . RapI crystals were soaked and cryoprotected in mother liquor solutions containing 3 . 0% , 7 . 5% , and 14 . 0% glycerol for ∼5 s each followed by 5 min soaking in mother liquor solutions containing 20% glycerol . Native data on nitrogen-cooled crystals were collected at NSLS beamline X29A and processed using the HKL software package . The RapJ-PhrC crystal structure was determined by the SAD method using crystals of selenomethionyl RapJ bound to PhrC that were isomorphous to the native RapJ-PhrC crystals . PHENIX ( AutoSol ) was used to locate heavy atom positions , calculate phases , and generate an initial model at 2 . 21 Å resolution [53] . This model was then refined against 2 . 16 Å native data in PHENIX . The final model was generated through iterative cycles of building in COOT [54] and refinement in PHENIX . The RapJ and PhrC models were built de novo into the SAD-phased map . The earliest rounds of refinement in PHENIX employed simulated annealing as well as individual atomic coordinate and individual B-factor refinement . The later rounds of refinement in PHENIX employed individual atomic coordinate and individual B-factor refinement , as well as a TLS model whose initial parameters were guided by the TLS Motion Determination ( TLSMD ) server [55] . During the final rounds of refinement in PHENIX , the stereochemistry and ADP weights were optimized—that is , the weights yielding the lowest Rfree value were used for refinement . PhrC molecules were added only after the RapJ models were built , and then water molecules were added . Insufficient electron density was observed for the following residues and they were omitted from the model: RapJA 1–5 , 72–77 , and 90–92; RapJB 1–6 and 72–77 . Two chlorine atoms were built into clear electron density during the final stages of refinement . The RapI crystal structure was determined by molecular replacement . A conserved region of RapJ consisting of residues 217–365 was used as an initial search model for molecular replacement . Phenix . mr_rosetta and RapI sequence alignment was then used to rebuild the starting model , resulting in 1 , 000 new models . The Phaser LLG score was used to identify the best model . The model identified here was then extended to include residues 175365 , and the model was then subjected to another cycle of rebuilding using phenix . mr_rosetta . Phaser was then used to identify the best model and also place a second copy in the crystallographic asymmetric unit . Arp/wArp was then used to improve the model and map . Insufficient electron density was observed for the following residues , and they were omitted from the model: RapIA 1–100 and 375–391; RapIB 1–13 , 73–77 , and 378–391 . Ramachandran statistics were calculated in Molprobity [56] . Molecular graphics were produced with PyMOL [57] . The B . subtilis IS75 rapJ markerless deletion strain was constructed by amplifying a region upstream of rapJ using the primer pair ΔrapJ_5′_Inf_F and ΔrapJ_5′_Inf_R and a region downstream of rapJ using primer pair ΔrapJ_3′_Inf_F and ΔrapJ_3′_Inf_R ( Table S3 ) . To generate pMiniRapJ , In-Fusion cloning was used to simultaneously ligate the PCR products and insert them into the EcoRI and SalI sites of pMini-MADII ( a kind gift from Dan Kearns , Indiana University ) , which carries a temperature-sensitive origin of replication and an erythromycin resistance cassette . The ΔrapJ , PspoIIG::luc strain VP068 was constructed by transforming pMiniRapJ into the PspoIIG::luc strain PP533 ( a kind gift from David Dubnau , PHRI ) . Growth on LB agar containing 0 . 5 µg/ml erythromycin and 2 . 5 µg/ml lincomycin at the restrictive temperature ( 37°C ) that inhibits plasmid replication selected single-crossover events integrating pMiniRapJ into the chromosome . To evict the plasmid , the strain was incubated in 3 ml LB broth at a permissive temperature for plasmid replication ( 22°C ) for 14 h , diluted 30-fold in fresh LB broth , and incubated at 22°C for another 8 h . Dilution and outgrowth were repeated two more times . Cells were then serially diluted and plated on LB agar at 37°C . Fifty individual colonies were patched in duplicate on LB agar alone and LB agar containing 0 . 5 µg/ml erythromycin and 2 . 5 µg/ml lincomycin to identify colonies that had evicted the plasmid . Chromosomal DNA from colonies that had excised the plasmid was purified and screened by PCR using primers ΔrapJ_5′_Inf_F and ΔrapJ_3′_Inf_R to determine which isolate had retained the ΔrapJ allele . The rapJ deletion was confirmed by Western blotting using anti-RapJ rabbit antisera . To generate VP068 strains expressing wild-type and mutant RapJ proteins , rapJ was PCR amplified from B . subtilis strain IS75 chromosomal DNA using primers pHyspank_rapJ_F and pHyspank_rapJ_R . The PCR product was cloned into the SalI and SphI sites in pDR111 ( a kind gift from D . Rudner , Harvard Medical School ) using the In-Fusion method . The resulting plasmid , pDRJ1 , was then mutagenized using the appropriate mutagenic primers ( Table S3 ) and either the ChangeIT Mutagenesis ( USB ) or Quikchange II XL Mutagenesis ( Agilent technologies ) protocols . RapH-R105A was similarly generated using pDRH1 [22] . DNA sequencing confirmed that the pDRH1- and pDRJ1-derived plasmids were free of mutations other than those introduced by site-directed mutagenesis . The pDRJ1- and pDRH1-derived plasmids were then transformed into the ΔrapJ , PspoIIG::luc strain VP068 or the ΔrapHphrH , PspoIIG::luc strain BD5035 [22] , respectively . Double-crossover recombination at the VP068 amyE locus yielded strains that express wild-type or mutant RapJ proteins under the control of the IPTG-inducible hyperspank promoter . Double-crossover recombination at the BD5035 amyE locus yielded strains that express wild-type or mutant RapH proteins under the control of the IPTG-inducible hyperspank promoter . The reporter strains were grown in LB medium to OD600≈2 , centrifuged , and resuspended in fresh Sporulation Medium ( DSM ) [58] to OD600 = 2 . The cultures were then diluted 20-fold in fresh DSM . For the RapJ assays , the cultures were supplemented with 60 µM IPTG and PhrC at the concentrations indicated . For the RapH assays , the cultures were supplemented with 100 µM IPTG and 20 µM PhrH . 200 µl were dispensed per well in duplicate in 96-well black plates ( Corning ) . 10 µl of luciferin was added to each well at a final concentration of 4 . 7 mM . The cultures were then incubated at 37°C under agitation in a PerkinElmer Envision 2104 Multilabel Reader . The plate lids were heated to 38°C to avoid condensation . Relative Luminescence Unit ( RLU ) and OD600 were measured at 3 min intervals . RapJ and RapI were overexpressed and purified as described above with the exception that dialysis rather than gel filtration was used to exchange the proteins into 20 mM Tris-HCl ( pH 8 . 0 ) , 150 mM KCl , 5 mM MgCl2 , 5 mM DTT , and 10% glycerol . RapJ and RapI were stored at −80°C . Spo0F containing a C-terminal fusion to hexahistidine was purified as previously described [59] . KinA containing an N-terminal fusion to hexahistidine was purified as previously described [22] . Spo0F labeling and in vitro phosphatase assays were performed as described previously [22] except that final reaction conditions were 32 . 5 µM DRVGA , 32 . 5 µM ADRVGA , 310 µM TDRNTT , 310 µM ARNQT , or 310 µM ERGMT; 6 . 5 µM RapJ or 6 . 5 µM RapI; 6 . 0 µM radiolabeled Spo0F∼P and 24 µM Spo0F; and 2 . 85 µM KinA , 14 . 55 mM Tris ( pH 8 . 0 ) , 50 mM EPPS ( pH 8 . 5 ) , 0 . 1 mM EDTA , 100 mM KCl , 23 mM MgCl2 , 3 mM DTT , 11 . 6% glycerol , 0 . 04 µM [γ-32P] ATP , and 1 mM ATP . SE AUC measurements were carried out as previously described [22] with the following modifications: gel purified RapJ was used at 50 µM , PhrC was used at 500 µM , the samples were prepared in buffer C , and the rotor speed was 13 , 000 rpm . Wild-type RapJ and RapJ-R105A , Y161F were overexpressed and purified as they were for X-Ray Crystallography with the following changes to the protein purification protocol . Subsequent to the SourceQ purification step , the samples were dialyzed against buffer H ( 20 mM Tris-HCl , pH 8 . 0 , 50 mM KCl , 5 mM MgCl2 , 5 mM DTT ) . PhrC was then added to obtain a final concentration of 8 . 5 mM PhrC and 425 µM wild-type RapJ or RapJ-R105A , Y161F . The RapJ-PhrC complexes were then loaded to a Superdex 200 16/70 column equilibrated in buffer H . The peak fractions were concentrated to 900 µM and stored at −80°C . In the case of the PhrC alone control , PhrC was loaded to a Superdex 200 16/70 column equilibrated in buffer H , and the fractions corresponding to elution volume of the RapJ-PhrC complexes were pooled and analyzed for the presence of PhrC . MALDI-TOF and MALDI-TOF/TOF MS analyses of the above samples were carried out by first diluting them 100-fold with 5% acetonitrile , and then mixing the diluted samples with an equal volume of matrix solution containing 7 mg/ml alpha-cyano-4-hydroxycinnamic acid , 5 mM of ammonium monobasic phosphate , and 0 . 1% trifluoroacetic acid in 50% acetonitrile . The mixture was spotted onto a MALDI plate and analyzed with a 4800 Proteomics Analyzer tandem mass spectrometer ( AB SCIEX , Framingham , MA , USA ) in positive ion mode ( m/z 500–700 ) . Spectra were analyzed using Data Explorer v4 . 5 ( Applied Biosystems ) . MALDI-TOF/TOF analysis was performed on m/z 593 . 27 ( PhrC[M+H]+ ) . Synthetic oligopeptides PhrC ( NH2-ERGMT-COOH ) , PhrA ( NH2-ARNQT-COOH ) , PhrH ( NH2-TDRNTT-COOH ) , PhrI-5mer ( NH2-DRVGA-COOH ) , and PhrI-6mer ( NH2-ADRVGA-COOH ) were purchased from LifeTein ( South Plainfield , NJ ) at 95% purity . The lyophilized oligopeptides were reconstituted as 10 mM stocks in H2O for use in crystallographic assays and in vitro phosphatase assays , or the oligopeptides were reconstituted as 10 mM stocks in DSM for use in the vivo PspoIIG-luciferase reporter assays . Aliquots of the synthetic oligopeptides were stored at −20°C . Atomic coordinates and structure factors for RapJ-PhrC and RapI have been deposited in the Protein Data Bank under accession codes 4GYO and 4I1A , respectively . | The bacterial cell–cell communication process known as quorum sensing regulates important social behaviors including antibiotic production , motility , virulence , biofilm formation , sporulation , bioluminescence , and genetic competence . Gram-positive bacteria secrete oligopeptide quorum-sensing signals that bind to membrane-bound and cytosolic receptors . How oligopeptide quorum-sensing signals regulate the activity of their target receptors was previously largely unknown . Here we show that proteins belonging to the family of bacterial quorum-sensing receptors known as the Rap phosphatases undergo a remarkable regulatory conformational change upon binding oligopeptide signals . More specifically , in the absence of the oligopeptide signal , Rap proteins consist of two distinct domains: an N-terminal domain consisting of a three-helix bundle , and a superhelical C-terminal domain comprising an array of seven similar helix-turn-helix repeats . A flexible helix-containing linker region connects these domains . In complex with the regulatory oligopeptide , however , the Rap protein domains and linker region rearrange , merging to form a single continuous superhelical structure consisting of nine helix-turn-helix repeats . To our knowledge , this represents the first example of conformational change-induced repeat domain expansion . The structure-function studies presented here set the stage for the rational development of antimicrobial peptides and peptide-mimetics capable of targeting cell–cell signaling mediated by Rap proteins and similar bacterial receptors . | [
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| 2013 | Conformational Change-Induced Repeat Domain Expansion Regulates Rap Phosphatase Quorum-Sensing Signal Receptors |
Systemic lupus erythematosus ( SLE ) is an inflammatory autoimmune disease with a strong genetic component . African-Americans ( AA ) are at increased risk of SLE , but the genetic basis of this risk is largely unknown . To identify causal variants in SLE loci in AA , we performed admixture mapping followed by fine mapping in AA and European-Americans ( EA ) . Through genome-wide admixture mapping in AA , we identified a strong SLE susceptibility locus at 2q22–24 ( LOD = 6 . 28 ) , and the admixture signal is associated with the European ancestry ( ancestry risk ratio ∼1 . 5 ) . Large-scale genotypic analysis on 19 , 726 individuals of African and European ancestry revealed three independently associated variants in the IFIH1 gene: an intronic variant , rs13023380 [Pmeta = 5 . 20×10−14; odds ratio , 95% confidence interval = 0 . 82 ( 0 . 78–0 . 87 ) ] , and two missense variants , rs1990760 ( Ala946Thr ) [Pmeta = 3 . 08×10−7; 0 . 88 ( 0 . 84–0 . 93 ) ] and rs10930046 ( Arg460His ) [Pdom = 1 . 16×10−8; 0 . 70 ( 0 . 62–0 . 79 ) ] . Both missense variants produced dramatic phenotypic changes in apoptosis and inflammation-related gene expression . We experimentally validated function of the intronic SNP by DNA electrophoresis , protein identification , and in vitro protein binding assays . DNA carrying the intronic risk allele rs13023380 showed reduced binding efficiency to a cellular protein complex including nucleolin and lupus autoantigen Ku70/80 , and showed reduced transcriptional activity in vivo . Thus , in SLE patients , genetic susceptibility could create a biochemical imbalance that dysregulates nucleolin , Ku70/80 , or other nucleic acid regulatory proteins . This could promote antibody hypermutation and auto-antibody generation , further destabilizing the cellular network . Together with molecular modeling , our results establish a distinct role for IFIH1 in apoptosis , inflammation , and autoantibody production , and explain the molecular basis of these three risk alleles for SLE pathogenesis .
Systemic lupus erythematosus ( SLE , [MIM 152700] ) is a clinically heterogeneous autoimmune disease with a strong genetic component , characterized by inflammation , dysregulation of type-1 interferon responses and autoantibodies directed towards nuclear components . SLE overwhelmingly targets women , and its incidence and clinical course differ dramatically between ethnic populations . In particular , SLE occurs with at least 3–5 times higher prevalence and more severe complications in African-Americans ( AA ) compared to Americans with European ancestry ( EA ) [1] . However , the genetic basis of this increased risk is largely unknown . The recently “admixed” AA population is likely to provide critical information necessary to identify chromosomal regions that harbor variants associated with SLE and provide insights about allele frequency differences among distinct ancestral populations ( i . e . , European and African ) . Admixture mapping ( AM ) has proven to be a powerful method to leverage ancestry information to identify chromosomal segments linked to disease [2]–[9] . For instance , AM has helped identify the risk gene MYH9 in idiopathic focal segmental glomerulosclerosis in AA [10] , and risk alleles in several genes associated with breast [11] and prostate cancer [12] . In addition to the greater lupus incidence , studying AA populations offers a second advantage . Africans have the smallest haplotype blocks of all human populations: African average population recombination distance is 6 kb , while it is 22 kb in Europeans and Asians [13] , [14] . This 3-fold smaller haplotype size gives rise to correspondingly tighter genomic associations in admixed populations such as AA , making causal mutations easier to decipher . Although several genes for SLE susceptibility have been found through candidate gene analysis and genome wide association scans ( GWAS ) , none or very few causal mutations have been identified in each gene . In this study we employed AM in AA to identify admixture signals , and performed a follow-up association study on AA and EA to further identify and localize variants associated with SLE . We experimentally validated predicted variants with biochemistry , cell culture experiments and sequencing of patient-isolated samples . We showed distinct functions of two coding SNPs including changes in gene expression . Through electrophoretic mobility shift assays ( EMSAs ) , protein identification and in vitro protein binding assays , we determined that the intronic SNP disrupts function of a transcriptional enhancer of the IFIH1 locus . Taken together , these results explain the effects of three independent causal mutations on SLE , and begin to elucidate the disparity in disease prevalence between different human populations .
Since case-only analysis has greater statistical power than case-control , we first performed a case-only admixture scan [3] , [6] , [8] on 1032 AA SLE cases ( Figure 1 , Table S1 ) . Individual admixture estimates and genome scans for admixture mapping were analyzed using STRUCTURE [15] and ANCESTRYMAP [7] and later verified with ADMIXMAP [6] . As expected , a two-ancestral population model ( African and European ) best explained the population structure of these samples . By applying the ANCESTRYMAP software , we identified seven potential admixture signals that exceeded our predefined LOD threshold of 2 ( Figure 2A , Table S2 ) . Specifically , we identified a genomewide significant association [7] of SLE risk with European ancestry at 2q22–q24 ( highest LOD = 6 . 28 was achieved between rs6733811 and rs4129786 ) using a weighted prior risk model; the strongest association at the same locus was observed at a fixed prior risk of 1 . 5 , which represents a 1 . 5-fold increased risk of SLE due to one European ancestral allele at this locus . To evaluate how the prior risk model could influence the ANCESTRYMAP results , we also applied a uniform prior risk model and found consistent genomewide evidence for association at 2q22–q24 ( LOD = 5 . 86 ) . We also reassessed the strength of the admixture signal at 2q22–24 using alternate markers ( LODodd = 3 . 65 , LODeven = 4 . 32 ) , and computer simulation ( P = 0 . 02 ) . We also validated case-only admixture signals with a case-control admixture scan with 800 ancestry informative markers ( AIMs ) , using 1726 controls from the Dallas Heart Study ( DHS ) ( Table S2 ) . We next repeated the admixture scan using ADMIXMAP , which uses a classical ( non-Bayesian ) hypothesis test ( i . e . , score tests for allelic associations with the trait , conditional on individual admixture and other covariates ) . All the admixture signals identified by the case-only design using ANCESTRYMAP were strongly validated by ADMIXMAP ( Table S2 ) . The strongest peak was identified at 2q22–24 through ADMIXMAP ( P = 2 . 99×10−8 , Table S2 ) . Both AM programs found that the strongest effect was on the AIM rs6733811 ( P = 2 . 99×10−8 , LOD = 5 . 65 ) . Two weaker signals were also found on chromosome 2 ( LOD = 3 . 61 , P = 4 . 82×10−3; and LOD = 3 . 52 , P = 1 . 64×10−4 ) , as well as on chromosomes 7 ( LOD = 3 . 26 , P = 6 . 49×10−6 ) , 9 ( LOD = 3 . 43 , P = 1 . 41×10−5 ) , 14 ( LOD = 2 . 44 , P = 3 . 84×10−5 ) and 19 ( LOD = 3 . 20 , P = 1 . 15×10−5 ) ( Table S2 ) . To identify SLE-susceptibility gene ( s ) within 2q22–24 , we performed a follow-up case-control ( CC ) association study in two ethnically diverse groups: CCAA ( 1525 cases , 1810 controls ) and CCEA ( 3968 cases , 3542 controls ) ( Figure 1 , Table S1 ) . Individual ancestry was estimated using 216 highly informative AIMs . Case-control association tests were performed using 284 SNPs from 20 plausible candidate genes spanning ∼21 megabases of 2q22–q24 ( 95% CI ( 142 . 4–163 . 6 ) , Table S3 ) . Forty-two SNPs from 10 genes ( IFIH1 , CACNB4 , ACVR1C , KCNH7 , NEB , STAM2 , ZEB2 , NMI , ARHGAP15 , and ACVR2A ) showed significant association for the allelic test ( Puncorrected<0 . 05 ) in CCAA , whereas 23 SNPs ( in IFIH1 , CACNB4 , NEB , ARHGAP15 , and TNFA1P6 ) showed significant association in CCEA ( Table S4 ) . The strongest associations occurred at IFIH1 ( interferon-induced helicase 1; PAA = 3 . 52×10−5 , PEA = 8 . 82×10−5 ) and CACNB4 ( voltage-gated calcium channel , beta subunit; PAA = 9 . 07×10−5 , PEA = 2 . 61×10−2 ) , which are separated by 10 . 2 Mb . Among the 22 SNPs tested within IFIH1 , 13 were significantly ( P<0 . 05 ) associated in AA and 4 in EA . Among 23 CACNB4 SNPs , 12 were significant in AA and 2 in EA . Considering the number of associated SNPs , level of replication and involvement in autoimmune phenotypes [16]–[20] , we considered IFIH1 as the strongest candidate to explain the admixture-mapping signal . Of the 13 SNPs significantly associated in AA , a preliminary imputation-based association analysis and comparing linkage disequilibrium ( LD ) determined that 11 were sufficient to tag the 13 associated SNPs ( Table S6 ) . To increase the statistical power to detect variants associated with SLE , we genotyped these 11 IFIH1 SNPs in 949 healthy AA controls from the DHS , along with additional out-of-study controls ( Figure 1 , Table S1 ) . Using single SNP analysis ( allelic and genotypic models ) , followed by conditional analysis and LD analysis across two populations , we detected three SNPs with potentially independent SLE association ( Table 1 , Figure 2B and 2E ) . Based on an allelic model , intronic variant rs13023380 [PAA = 4 . 33×10−5 , PEA = 9 . 52×10−11; Pmeta = 5 . 20×10−14; OR = 0 . 82 ( 0 . 78–0 . 87 ) ] , and a missense ( Ala946Thr ) variant rs1990760 [PAA = 2 . 02×10−4 , PEA = 1 . 22×10−4; Pmeta = 3 . 08×10−7; OR = 0 . 88 ( 0 . 84–0 . 93 ) ] , were associated with SLE in both AA and EA . Another non-synonymous missense variant ( Arg460His ) , rs10930046 , was initially associated only with SLE in AA ( PAA = 1 . 81×10−7; OR = 0 . 80 ( 0 . 73–0 . 87 ) ) , where the best fit genetic model was identified as dominant ( Pdom = 1 . 16×10−8 , OR = 0 . 70 ( 0 . 62–0 . 79 ) ) ( Table 1 , Table S5 ) . This SNP is rare in EA , having a minor allele frequency ( MAF ) of only 1 . 3% in controls and 1 . 6% in cases ( PEA = 0 . 086 ) ( Table 1 ) . However , after conditioning on the other two associated SNPs ( rs13023380 and rs1990760 ) , the rs10930046 became marginally significant in EA ( PEA = 0 . 017; OR = 1 . 2 ) . These SNPs remained significant in both AA and EA after adjusting for ancestry ( Table 1 ) . Strikingly , the ancestral alleles at these three SNPs ( all ‘G’ ) are the minor alleles in at least one population: all three ancestral alleles are the minor alleles in EA; the ancestral rs10930046 allele is minor in AA as well . We analytically estimated the joint population attributable risk ( PAR ) [21] using these three SNPs ( rs13023380 , rs10930046 and rs1990760 ) for AA ( 18 . 1% ) and EA ( 14 . 7% ) . Most of the increased PAR ( % ) in AA was attributable to rs1090046 ( 12 . 5% PAR ) , whereas for EA very little was attributable to this SNP ( 0 . 3% PAR ) , likely due to the extremely low MAF . For AA , we also sought to determine how much of the European ancestry risk ratio ( λ = 1 . 5 , estimated by ANCESTRYMAP ) was attributable to the three SNPs at 2q22–24 . Using the estimated ORs in AAs and the SNP allele frequencies of the two ancestral populations ( YRI , the Yoruba people of West Africa , was used as an African ancestral population; CEPH , Utah residents with Northern and Western European heritage , was used as an European ancestral population ( Table S8 ) , we estimated the locus-specific ancestry risk ratio ( λ; see Methods ) for each SNP ( λrs1990760 = 1 . 12 , λrs10930046 = 1 . 15 , λrs13023380 = 1 . 12 ) . Assuming that each SNP contributes to the ancestry risk ratio independently , about 80% of the increased risk due to one copy of the European ancestry alleles estimated from ANCESTRYMAP ( ∼1 . 5 ) can be explained by the three SNPs at 2q22–24 , reinforcing our conclusion from admixture mapping that local European ancestry increases the disease risk at 2q22–24 . We also repeated our admixture mapping by stratifying the cases by three genotype ( ‘AA’ , ‘AG’ and ‘GG’ ) at the most differentiated ( FstCEPH-YRI = 0 . 38 ) SNP , rs10930046 ( NAA = 279 , NAG = 323 , NGG = 114 ) . Even with the small samples , we found a dramatically increased risk of European ancestry at rs10930046 ( LOD = 10 . 78 ) in the homozygous ‘AA’ compared to the other genotypes , where ancestry association is insignificant ( LOD for ‘GG’ = −6 . 46 and ‘AG’ = −3 . 38 ) . To identify additional SLE-associated variants , we performed an imputation-based association analysis in and around IFIH1 using MACH [22] with reference data from AA ( 207 controls ) and EA ( 594 controls ) using genotyping data from the ImmunoChip ( Figure 2B , 2E and Tables S6 , S7 ) . Using stringent predefined criteria for imputation , there were 61 additional SNPs for AA , but only 1 for EA later used for conditional analysis . Inefficiency of EA imputation was mainly due to presence of many low frequency ( <1% ) alleles and strikingly different LD structure ( Figure 2D and 2G , Table S7 ) . A pair-wise logistic regression analysis conditioned on each SNP revealed that the three previously identified SNPs were each independently associated with SLE . While in AA , rs13023380 , rs10930046 and rs1990760 accounted for the entire association spanning the whole gene ( Figure 2B , 2C ) , in EA , rs13023380 and rs10930046 were independently associated with SLE and accounted for the association ( Figure 2E , 2F ) . Finally , comparing LD ( r2 ) between these three SNPs across nine datasets from seven ethnic populations , we concluded that these three SNPs are also physically independent ( Figure S2 ) . Interestingly , using D′ we found that these SNPs are on the same haplotype in EA and AA , but most likely they are not in the ancestral populations ( Figure S2 ) . In order to discover the ancestral origin of the risk ( ‘A’ in each of the 3 SNPs ) and protective ( ‘G’ in each case ) alleles for these three SNPs , we estimated local ancestry around the SNPs , then compared ( by allele frequency and fixation index ) AA individuals whose both haplotypes were European ( AAEUR , N = 129 ) or African ( AAAFR , N = 2124 ) , and to individuals from HapMap populations CEPH and YRI ( Table S8 ) . Risk allele frequencies derived from the haplotypes were similar between AAEUR and CEPH , and between AAAFR and YRI . Alignment of the human genome with other genomes strongly suggests that the protective alleles ( ‘G’ ) are ancestral , and that the risk ( ‘A’ ) alleles are derived . For the two coding SNPs , the ‘G’ allele of rs1990760 ( and the resulting alanine amino acid ) is ∼100% conserved across 34 mammalian genomes ( Table S9 ) ; the ‘G’ allele of rs10930046 ( and the resulting arginine amino acid ) is ∼100% conserved across 50 vertebrate genomes ( Table S10 ) . Introns are typically less conserved than protein-coding sequence , and accordingly the intronic sequence surrounding the rs13023380 SNP is only strongly conserved in primates; the base corresponding to rs13023380 is ‘G’ in each case ( Figure S6 ) . In AA , only the rs10930046 risk allele is major; interestingly , all three ‘A’ risk , derived alleles are the major alleles in EA and the rs10930046 risk allele is almost fixed ( Table 1 ) . This suggests a strong selective pressure against the SLE-protective alleles in humans [23] , which is not manifest in other animal species . Given the strong association of these three SNPs in IFIH1 with SLE , we evaluated their effect on the function of the IFIH1 gene . IFIH1 has been implicated in binding with dsRNA complexes generated as replication intermediates during RNA viral infections , leading to inflammation and apoptosis [24] , [25] . The full length IFIH1 protein contains 1025aa in the following domains: caspase recruitment ( CARD ) ( aa115–200 ) , helicase ATP-binding ( aa305–493 ) , helicase C-terminal ( aa743–826 ) and RIG-I regulatory ( aa901–1022 ) ( Figure 5A ) . Deletion of the ATP-binding domain , which includes rs10930046 , induces apoptosis in melanoma cells [26] . The RIG-I regulatory domain , which includes rs1990760 , recognizes dsRNA , upon which the helicase domains are activated [27] . Apoptosis has been associated with SLE pathogenesis in humans and mice [28] . Furthermore , Ingenuity Pathway Analysis ( IPA ) indicates that IFIH1 interacts with several genes involved in apoptosis and inflammation ( Figure S3 ) . To assess the effects of coding variants in apoptosis and inflammation , we mutagenized IFIH1 cDNA cloned in a mammalian expression vector with a poly-cistronic ( IRES ) GFP marker at the C-terminus . We over-expressed IFIH1 in a K562 leukemia cell line and measured cell death for each risk SNP , comparing with the ancestral protective allele . The rs10930046 risk allele ‘A’ significantly increased apoptosis over the protective allele ‘G’ ( 14 . 6% average increase at each time point between 44 and 92 hours , P = 0 . 014 ) ( Figure 3A ) . In contrast , the risk allele ‘A’ of rs1990760 had little impact on apoptosis ( P = 1 . 0 ) , as expected since it is located in the RIG-1 regulatory domain , which is not involved in apoptosis . To assess the effect of these polymorphisms on expression of downstream genes , additional transfected K562 cells were sorted for GFP+ cells by FACS and total RNAs were isolated from these cells . These were subjected to RT-qPCR of 11 genes related to apoptosis , inflammation or viral response: NFκ-B1 , NFκ-B2 , RELA , CASP8 , CASP9 , TNFα , MAPK8 , MAVS , IFNA , IFIT1 and MX1 . Gene expression analysis showed that over-expression of the ‘A’ allele of rs10930046 significantly increased expression of NFκ-B1 ( >2 . 8-fold , P = 2 . 1×10−2 ) , CASP8 ( >1 . 8-fold , P = 4 . 5×10−4 ) , CASP9 ( >3 . 5-fold , P = 7 . 2×10−6 ) and MAVS ( >2-fold , P = 9 . 6×10−3 ) compared to the ‘G’ allele ( Figure 3B , 3D , 3E , 3H ) but did not affect expression of NFκ-B2 ( P = 0 . 14 ) , TNFα ( P = 0 . 7 ) or MAPK8 ( P = 0 . 9 ) ( Figure 3C , 3F , 3G ) . While the ‘A’ allele of rs1990760 had no significant effect on expression of NFκ-B1 ( P = 0 . 14 ) or CASP9 ( P = 0 . 08 ) , it showed a significant decrease of TNFα ( >5-fold , P = 1 . 8×10−7 ) , NFκ-B2 ( >3-fold , P = 1 . 3×10−3 ) and CASP8 ( >1 . 5-fold , P = 1 . 6×10−6 ) expression ( Figure 3B , 3C , 3D , 3E , 3F ) . The rs1990760 risk allele also significantly increased expression of MAPK8 ( >2-fold , P = 6 . 2×10−3 ) and MAVS ( >1 . 6-fold , P = 8 . 5×10−3 ) ( Figure 3G , 3H ) . Strikingly , interferon alpha ( IFNA ) expression was reduced for both risk alleles ( rs10930046 , >2-fold , P = 4 . 2×10−3; rs1990760 , 2-fold , P = 9 . 1×10−3 ) ( Figure 3I ) . Reduced IFNA expression in SLE patients had been predicted for the risk allele of rs1990760 [29] . Our results not only confirmed this but also showed that expression of the risk allele of rs10930046 similarly reduced IFNA expression ( Figure 3I ) . Similarly , expression of IFIT1 was also reduced ( rs10930046 , >0 . 75-fold , P = 8 . 7×10−3; rs1990760 , >3 . 5-fold , P = 9 . 1×10−7 ) ; and MX1 expression was decreased for rs10930046 ( >2 . 5-fold , P = 2 . 1×10−3 ) but was increased for rs1990760 ( >1 . 5-fold , P = 3 . 3×10−4 ) ( Figure 3J , 3K ) . Following induction of transfected cells with Type-1 interferon IFN beta ( IFNB ) , IFIT1 and MX1 showed strong up-regulation ( Figure 3L , 3M ) by both SNPs ( IFIT1: rs10930046 , >2-fold , P = 2 . 0×10−2; rs1990760 , >2-fold , P = 2 . 7×10−2; MX1: rs10930046 , >1 . 3-fold , P = 3 . 1×10−2; rs1990760 , >2 . 2-fold , P = 4 . 7×10−5 ) . RELA expression did not change significantly ( for rs10930046 , P = 0 . 61 and for rs1990760 , P = 0 . 77 ) for either risk allele ( not shown ) . In our expression analysis , significant up-regulation of CASP8 , CASP9 and NFκ-B1 ( and unchanged NFκ-B2 and TNFα levels ) by the rs10930046 risk allele would be expected to dramatically increase apoptosis , as observed . For rs1990760 , levels of these five pro-apoptotic factors are dramatically lowered , consistent with absence of an apoptosis phenotype . MAVS ( mitochondrial antiviral-signaling protein ) expression was increased for both risk alleles . MAVS is an antiviral protein in the host defense system whose virus-triggered cleavage is necessary to attenuate apoptosis [30] , [31] . However , without viral attack MAVS induces apoptosis through caspase and NFKB activation [30] . In our case , it could promote apoptosis , particularly for the risk allele of rs10930046 . In terms of inflammation , the expression data shows some interesting effects . For rs10930046 , neither TNFα nor MAPK8 changed , but for rs1990760 , TNFα decreased while MAPK8 increased leading to inflammation signaling through non-apoptotic pathways . We next examined known transcriptional networks in the context of our expression data . At the root , IFIH1 and type-1 interferons constitute a positive-feedback loop ( Figure 3P ) . We verified this in our cellular model: indeed , in control cells , IFIH1 expression increased several hundred-fold upon IFNB treatment ( Figure 3N ) , and in IFIH1 over-expressing cells , IFNA expression increased ( Figure 3O ) . Taken together , our results support the predicted IFIH1-Type1 interferon feedback loop through IRF7 [32] and MAVS [33] . IFNA and TNFα are known to drive IFIT1 expression [34] , and the IFIH1 SNP-driven decrease in IFIT1 may be mediated through decreased IFNA and/or TNFα . MX1 ( interferon-induced GTP-binding protein ) is also driven by IFNA and TNFα [35] . Surprisingly , although both IFNA and TNFα decreased in the presence of rs1990760 ‘A’ , MX1 was significantly up-regulated . This result is similar to a recent paper [29] , which showed that when SLE patients' cells were induced with IFNA , the rs1990760 ‘A’ risk allele displayed higher levels of MX1 than ‘G’ allele patients , even though these patients had lower circulating IFNA levels . Our data suggest that IFIH1 risk alleles at these two coding SNPs down-regulate IFNA expression ( either through reduced expression or activity of IFIH1 ) and , in turn , IFNA down-regulates interferon regulatory antiviral genes , potentially conferring viral susceptibility . It is also possible that the rs1990760 risk variant in IFIH1 may increase sensitivity of cells to IFNA pathway activation and subsequent IFN-induced gene transcription [36] . The intronic variant rs13023380 could influence IFIH1 function either by producing a functional miRNA or by altering the binding efficiency to one or more nuclear regulatory proteins . Through bioinformatic analyses ( miRBase: http://www . mirbase . org/ ) we confirmed that no reported or predicted miRNA-producing or binding sites were present in these sequences . To address whether rs13023380 alters nuclear protein-DNA interaction , we performed EMSAs on nuclear protein extracts from K562 and JURKAT cell lines , using 150-bp PCR products amplified from genomic DNA of ‘AA’ and ‘GG’ homozygous patients . Both PCR products containing the ‘A’ risk sequences or ancestral ‘G’ sequences bound to nuclear protein extract , but DNA containing ‘A’ sequences consistently showed ∼2-fold reduced binding efficiency to a protein complex compared to ‘G’ sequences ( Figure 4A , Figure S4F ) . To identify any DNA-bound proteins , we performed mass spectrometric sequencing ( MALDI-TOF ) on the protein/DNA complexes isolated using two separate methods: 2D electrophoresis and protein pull-down . In 2D electrophoresis , the visible DNA-bound protein complex in EMSA was excised from a native PAGE gel ( Figure S4A–S4C ) and sequenced directly , which identified lupus autoantigen Ku70/80 ( XRCC5/6 ) , nucleolin ( NCL ) and HSP90AA1/AB1 as the major constituents of the DNA–protein band ( Table S11 ) . Using the second method , we performed EMSA with biotinylated PCR products and pulled down the DNA-bound proteins using immobilized streptavidin-coated agarose beads . Subsequent fractionation by SDS-PAGE ( Figure S4D ) , and sequencing of two distinct visible protein bands ( not present in the control pull-down product ) , confirmed NCL and HSP90AB1 ( Table S11 ) . We did not identify Ku70/80 in the streptavidin method , possibly because these two proteins were washed off or were present in insufficient quantities to detect and sequence . However , when we performed “super-shift” assays with antibodies to these proteins , surprisingly , anti-NCL and anti-Ku70/80 antibodies released EMSA-bound DNA instead of super-shifting the complex ( Figure S4E ) . It is possible that the antibodies either induce conformational changes in their targets to release DNA or compete with target proteins for DNA binding . Autoantibodies against NCL and Ku70/Ku80 are characteristic features of SLE [37] , [38] and release of free DNA from EMSA-bound DNA in vitro implies that autoantibodies in vivo could impair the function of these proteins by disrupting the binding of bound proteins from target DNA , including the rs13023380 locus . In light of the observed competition of added antibodies to protein-DNA binding , we determined whether purified recombinant proteins of NCL and Ku70/80 bound to these DNAs . Both recombinant proteins , purified from insect cells , produced identical gel shifts as the nuclear extract ( Figure 4B , 4C ) , but again the risk ‘A’ allele bound to the recombinant proteins with ∼2-fold decreased efficiency relative to the protective ‘G’ allele . These results prompted us to enquire whether DNA sequence containing rs13023380 and its surroundings could act as a transcriptional regulatory element ( TRE , e . g . enhancer/silencer ) in vivo , and if the risk allele has any effect on transcription . The same sequences used for EMSA were cloned before a minimal TKmin promoter and a luciferase reporter gene , and luminescence assays were performed . Both sequences increased reporter gene activity over the core vector , suggesting that the rs13023380 locus contains a transcriptional enhancer . The risk allele-carrying sequences showed almost a 2-fold reduction ( Figure 4D ) in luciferase activity compared to those with the ancestral allele . Taken together , these results suggest that the rs13023380 locus recruits transcriptional activity of IFIH1 through binding of Ku70/80 , NCL and HSP90AA1/AB1 ( and potentially more proteins ) , and that the risk allele at this base position interferes with this enhancer activity , potentially decreasing IFIH1 transcript levels . The absolute conservation of Ala946 ( rs1990760 , Ala946Thr ) in all sequenced mammalian genomes , with diverse codons , strongly suggests selection at the amino acid level ( Table S9 ) . We performed molecular modeling of IFIH1-Thr946 , based on the protein structure of the IFIH1 C-terminal domain ( PDB 2RQB ) , and the full-length structure of the homologous enzyme RIG-I , bound to dsRNA ( PDB 3TMI ) . Ala946 is placed directly at the mouth of the helicase active site; in RIG-I this region makes contact with the helicase “cap” , which mediates dsRNA entry and processing [27] . Mutation of alanine to the bulker threonine side-chain ( Figure 4E , 4F ) may alter the sterics and/or dynamics of this protein region , leading to loss-of-function . Similarly , Arg460 ( rs10930046 , Arg460His ) is conserved in all vertebrate genomes sequenced , with diverse codons , again implying amino acid-level selection ( Table S10 ) . Comparison with RIG-I ( PDB 3TMI ) suggests that in the ancestral protein , Arg460 may form hydrogen bonds with the 419–433 loop , most likely with the strictly conserved acidic side-chains of Glu425 and Glu428 , and the conserved Gln433 ( Figure 4E , 4G ) . Intriguingly , the crystal structure of the human IFIH1 ATP-binding ( DECH ) domain ( PDB 3B6E ) incorporates the pervasive rs10930046 risk mutation . In this structure the His460 side-chain does not make favorable contacts with the 419–433 loop and much of this loop is poorly structured . Loss of stabilizing interactions of Arg460 might lead to weakened structural integrity of the helicase ATP-binding domain ( the 3B6E domain is internally shifted ∼1 . 5 Å relative to the RIG-I structure; Figure 4G ) , and subsequently with the helicase C-terminal and RIG-I regulatory domains . DsRNA binding , which occurs at a site proximal to the rs10930046 mutation ( Figure 5A ) , leads to RIG-I dimerization [39] . The disruptive nature of the rs10930046 risk allele on overall protein structural integrity apparently decreases dimerization , as the 3B6E structure was determined as a monomer ( all related structures are dimers ) . Indeed it is likely that the rs10930046 risk allele structure “poisons” an ancestral binding partner , leading to a dominant negative phenotype , consistent with the genetically dominant model , especially in AA . The intronic rs13023380 risk allele has no effect on the protein-coding sequence of IFIH1 . The region directly surrounding rs13023380 is rich in strongly conserved C/G bases ( Figure S6A ) . Given the binding of the locus to NCL and other nuclear regulatory proteins , we hypothesized that the site might play a role in mRNA processing . Modeling of the region around rs13023380 predicts a highly structured pre-mRNA , with strongly favorable folding free energies ( CentroidFold , ncRNA . org ) ( Figure S6B ) . In the ancestral pre-mRNA , the rs13023380 base is part of a highly structured 7-mer RNA stem with a 7-base loop ( Figure S6B ) . In the risk allele pre-mRNA , mutation of the conserved rs13023380 base disrupts RNA stem formation , and likely perturbs structure and stability of the loop ( Figure S6C , S6D ) , which might disrupt the binding of RNA-binding proteins ( such as NCL [40] ) , impairing pre-mRNA trafficking and processing .
Our whole genome admixture scan identified 7 admixture peaks associated with SLE in AA , with the strongest at 2q22–24 , containing the IFIH1 gene . Three SNPs ( two coding: rs1990760 and rs10930046 , and one intronic: rs13023380 ) accounted for the increased risk . IFIH1 has been associated with Type 1 diabetes ( T1D ) [41] , IgA deficiency [18] , Graves' disease [17] , and suggestively linked to SLE [20] , [42] . The role of IFIH1 in apoptosis and inflammation makes it potentially critical for SLE progression . Moreover , allele frequency differences in associated and non-associated SNPs ( high FST values ) , together with the differences in the number of rare variants between EA and AA , imply a strong positive selection in EA ( intriguingly , for the SLE-risk alleles at all three positions ) , as previously suggested [23] . In AA , local European ancestry at these loci correlates with increased risk . Variant rs1990760 has been recently reported to affect expression of viral resistance genes IFIT1 and MX1 in SLE patients [29] . The risk allele of rs1990760 positively correlated with interferon-induced gene expression in SLE patients who were positive for anti-dsDNA antibodies [29] . Another report on rs1990760 suggested that the risk allele correlated with increased expression of IFIH1 in T1D patients [32] . The rs10930046 risk allele has been implicated in psoriasis susceptibility [43] . Here we have systematically examined the effects of the two coding SNPs on immune cell biology , and demonstrated that the rs10930046 risk allele dramatically increases apoptosis , and that both significantly perturb inflammatory gene profiles . The intronic risk allele disrupts a transcriptional enhancer that recruits nucleolin , lupus autoantigen Ku70/80 and HSP90 , potentially decreasing IFIH1 transcript levels . Combined with molecular modeling , our results strongly suggest that these effects are due to several specific amino acid and nucleotide substitutions , rather than to indirect effects due to LD with other SNPs . SLE is commonly identified with an up-regulation of the interferon pathway [44] . Intriguingly , our results suggest that the two non-synonymous IFIH1 mutations down-regulate interferon signaling . However , recent findings demonstrated that SLE patients with anti-DNA antibodies have lower serum IFNA levels [29] , and this dose-dependent decrease suggests that there exists a sub-population of SLE patients with lower serum IFNA levels with increased IFN sensitivity [36] . Heterogeneity is also observed in clinical TNFα levels; rs1990760 would seem a likely candidate to be associated with low TNFα levels in this patient sub-population [45] . The intronic SNP ( rs13023380 ) discovered in this study has not been previously implicated in SLE or any other medical condition . The transcriptional enhancer uncovered in this genomic region , and the risk allele's disruption of its activity , opens up new avenues for investigation . Nucleolin , in addition to contributing to RNA polymerase 1 function [46] , is known to be a principal component of the B-cell transcription factor complex LR1 [47] , which binds the Ig heavy chain switch region and functions in Ig recombination . Disruption of nucleolin binding to the rs13023380 risk allele may dysregulate polymerase binding , IFIH1 transcription , autoantibody production and interaction . The region surrounding rs13023380 is rich in highly conserved C/G bases , which are preferentially recognized by NCL and Ku70/80 [48] , [49] . In addition to perturbing transcription at the locus , molecular modeling implicates the base substitution in destabilizing non-spliced mRNA , further altering proper regulation of expression levels . Nucleolin , as a matrix-binding protein [50] , could also provide a scaffold for matrix and DNA during immunoglobulin hyper-recombination . Thus in SLE patients , a positive feedback loop potentially exists where genetic susceptibility creates a biochemical imbalance , dysregulating NCL , which may then promote antibody hypermutation and autoantibody production , further destabilizing the cellular network . Similarly , Ku70/80 facilitates DNA repair and promotes transcription initiation by complexing with RNA polymerase 1 . Disruption of Ku70/80 binding to the rs13023380 locus would be expected to have similar consequences for autoantibody production and interaction . Additionally , Ku70/80 mediates the predominant pathway of non-homologous end joining ( NHEJ ) during immunoglobulin class switch recombination ( CSR ) [51] , [52] . Our discovery that antibodies directed against NCL and Ku70/80 promote release of dsDNA by nuclear proteins suggests that in SLE , hallmark autoantibodies against these two proteins may alter their activity . Thus in SLE patients , genetic susceptibility could create a biochemical imbalance that dysregulates NCL , Ku70/80 , or other nucleic acid regulatory proteins binding to the rs13023380 locus ( and other DNA sequences ) . Follow-up studies could systematically explore the effect of both antibodies and SNP-induced protein mutations on the DNA-binding and transcriptional properties of a number of gene products implicated in SLE and other diseases . From an evolutionary point of view , evidence suggests that IFIH1 is under strong positive selection , especially in EA . The derived allele of rs10930046 ( risk in AA ) is highly differentiated in ethnically diverse populations and allele frequency increases from AA ( 60% ) to EA ( >98% ) , and may be acting as a protective allele ( for some condition other than SLE ) . This indicates that at some time point , the risk allele may have offered competitive advantage to individuals by increasing apoptosis in defense to new threats of infection encountered during migrations to the New World from Africa [53] . This selection is evident in an observed gradient of geographical distribution ( Figure S5 ) of the allele frequency [53] . Therefore , the different haplotype block structure between the two groups derived from African and European homozygotes is expected . To evaluate the epidemiological significance of IFIH1 polymorphism in the genetic and ethnic background of SLE in AA and EA populations , we estimated the joint PAR . The joint PAR from the three SNPs for AA and EA are 18 . 1% and 14 . 7% , respectively . Most of the increased PAR in AA was attributable to rs1090046 ( 12 . 5% ) . Interestingly , the admixture peak at 2q22–24 is associated with increased local European ancestry , suggesting that European ancestry at this locus confers a higher risk of SLE compared to African ancestry . Indeed , we observed the strongest locus-specific LOD score at 2q22–24 using a fixed prior risk of 1 . 5 , meaning that carrying one European ancestry allele confers 1 . 5 fold increased risk of SLE relative to having no European ancestry alleles . Furthermore , we addressed the question whether the locus-specific ancestry risk ratio calculated from the estimated OR of the three SNPs in AA and their allele frequencies in ancestral populations accounts for the European ancestry risk ratio of ∼1 . 5 estimated from the admixture scan . The total increased ancestry risk ( 45% ) due to these three SNPs ( λrs1990760 = 1 . 12 , λrs10930046 = 1 . 12 , λrs13023380 = 1 . 16 ) were close to the increased risk 50% estimated from our admixture mapping ( λ = 1 . 5 ) . Therefore , the locus-specific ancestry risk ratio corroborates the ancestry risk ratio estimated from the admixture scan . In summary , to our knowledge this is the first study to use a whole-genome admixture mapping design to identify SLE susceptibility loci , confirm case-control association analysis in AA and EA , and identify novel variants within IFIH1 associated with SLE susceptibility . We report three independently associated IFIH1 variants with significant ethnic variation , providing a possible basis for differences in SLE risk between ethnically diverse populations . In addition , we show allele-specific differential cellular signaling and predict an in vivo role of Ku70/80 and NCL autoantibodies that could impair function of IFIH1 by disrupting DNA binding . Therefore , these results clearly establish IFIH1 as an SLE susceptibility gene and provide mechanisms for the IFIH1 variants in SLE etiopathogenesis .
We designed our study in four stages: In Stage 1 , we performed a case-only admixture scan on 1032 AA SLE cases ( Figure 1 , Table S1 ) followed by exploring the largest admixture peak at 2q22–24 with a more focused candidate gene analysis in both AA and EA populations ( Stage 2 ) , including out-of-study controls from dbGaP for AA ( 2675 ) and EA ( 6208 ) to boost statistical power . In Stage 3 , an imputation-based analysis was performed to fine-map our selected gene using SNPs from ImmunoChip arrays [54] . In Stage 4 , we experimentally tested the biochemical function of associated SNPs in cellular models , in vitro protein experiments and molecular modeling . For our admixture mapping ( AM ) , we used 1032 African-American ( AA ) SLE cases and 1726 AA controls ( Table S1 ) . Individuals were recruited by the coordinating institutions: Lupus Family Repository and Registry ( LFRR ) at the Oklahoma Medical Research Foundation ( OMRF , 540 AA ) , and the University of Alabama at Birmingham ( UAB ) ( 492 AA ) through the PROFILE study group . Cases fulfilled at least 4 of 11 criteria from the American College of Rheumatology ( ACR ) [55] , [56] based on medical record review . In the follow-up case control study ( CC ) , we used both AA ( CCAA: 1525 cases and 1810 controls , ) and European-Americans ( EA ) ( CCEA: 3968 cases and 3542 controls ) . There was an overlap of 737 cases between AM and CC . We increased the sample size using out of study controls from publicly available datasets in dbGaP [57] ( dbGaP see Web Resources ) , including 942 AA and 2267 EA controls from the Study of Addiction ( SAGE ) ; 784 AA and 1449 EA controls from Health ABC ( HABC ) ; 2492 EA controls from the Wellcome Trust Consortium ( WTCCC see Web Resources ) ; 949 AA controls from the Dallas Heart Study ( DHS see Web Resources ) provided by Dr . Helen H . Hobbs . AA and EA samples were collected by the coordinating institutions: LFRR , BIOLUPUS , Medical University of South Carolina , the PROFILE study group , the Oklahoma Lupus cohort , the Feinstein Institute of Medical Research and ODRCC . All individuals were de-identified prior to being genotyped . Within each stage of this experiment , all cases and controls were independent . This study was approved by the Institutional Review Boards of the OMRF or the ethical committees at the institutions where subjects were recruited . Rigorous quality control ( QC ) was applied to all data used in this study . Subjects were excluded from analysis if they had <95% genotyping success or were population-stratification outliers . Using ancestral informative markers ( AIMs ) , we performed principal components analysis ( PCA ) using EIGENSOFT [58] ( EIGENSOFT see Web Resources ) and STRUCTURE [59] ( STRUCTURE see Web Resources ) to identify outliers , hidden population structure and estimate individual ancestry proportions ( European ) . Relatedness between individuals was calculated using PLINK [60] ( PLINK see Web Resources ) and GCTA [61] . All related and duplicate individuals ( r>0 . 25 ) were removed . SNPs were removed for >10% missing genotyping , being out of Hardy-Weinberg equilibrium ( HWE , P<0 . 001 in controls ) or for poor clustering . SNPs were also removed for minor allele frequency ( MAF ) <1% . We used AIMs which passed QC , had a minimum intermarker distance >1 Megabase and were not in linkage disequilibrium ( LD ) in the ancestral populations . In addition to these QC measures , imputed SNPs were included in the analysis only if Rsq >0 . 90 . This ensures the all high quality imputed SNPs were included in the analysis . Genomic DNA samples in AM from SLE patients were genotyped at OMRF using Affymetrix MALD 3K panel , including 2154 SNPs that passed QC . Fourteen duplicate individuals were removed . DHS samples were genotyped by Perlegen Sciences including 800 AIMs and 10 SNPs within 2q22–24 . Samples in the follow-up CC study were genotyped on a custom Illumina iSelect platform . SAGE , HABC and WTCCC genotypes were merged with our data for further QC . PCA was performed for all samples using 401 SNPs to detect PCA outliers from SAGE , HABC and WTCCC . We used 163 AIMs to compare our samples with SAGE , and 755 AIMs to compare our samples with DHS ( Figure S1 ) . After QC , individuals were removed from analysis if they were related or duplicate ( 57 AA and 138 EA ) , had a missing genotype call rate >5% ( 19 AA and 361 EA ) , or were within 2 standard deviations of the mean first eigenvector ( 30 AA and 51 EA ) ( Figure S1 ) . Since we used several thousand out of study controls from different publicly available sources [dbGaP ( SAGE , HABC ) and WTCCC] , we had to take very strict QC . Some from the out of study control samples were identified as outliers by PCA; these individuals were mainly the AA samples with >75% European ancestry , and EA samples with <90% European ancestry . All SNPs that passed QC were used in the analysis . After QC our analysis included 1525 AA SLE cases and 1810 AA healthy controls . We genotyped 347 highly informative AIMs to detect hidden population structure and correct for spurious associations . To assess robustness of these results we analyzed 3968 EA cases and 3542 EA . In order to follow the admixture signal within 2q22–24 , we genotyped 284 SNPs from 20 candidate genes . The peak and 95% CI of the 2q22–24 region spanned about 21 MB . We used logistic regression in PLINK , with individual admixture estimates as a covariate to identify and remove individual outliers , and to correct for admixture and population stratification in our data analysis . We increased our sample sets using out-of-study controls for a total of 1525 cases and 4485 controls in AA , and 3968 cases and 9750 controls in EA . The URLs for the data presented here are as follows: PLINK , http://pngu . mgh . harvard . edu/~purcell/plink STRUCTURE , http://pritch . bsd . uchicago . edu/structure . html EIGENSOFT , http://genepath . med . harvard . edu/~reich/Software . htm ANCESTRYMAP , http://genepath . med . harvard . edu/~reich/Software . htm ADMIXMAP , http://homepages . ed . ac . uk/pmckeigu/admixmap MACH , http://www . sph . umich . edu/csg/abecasis/MACH/index . html HAPMAP , http://www . hapmap . org dbGaP , http://www . ncbi . nlm . nih . gov/gap Wellcome Trust Consortium , http://www . wtccc . org . uk DHS , http://clinicaltrials . gov/ct2/show/NCT00344903 Ingenuity Pathway Analysis , http://www . ingenuity . com Capital Bioscience , MD: http://www . capitalbiosciences . com/ Becton Dickinson Bioscience: http://www . bd . com/ Stratagene: www . stratagene . com/ Invitrogen , USA: www . invitrogen . com Totallabquant: http://www . totallab . com UCSC Genome Browser: http://genome . ucsc . edu NCBI sequencing Trace Archive: http://blast . ncbi . nlm . nih . gov ClustalW: http://www . clustal . org/clustal2/ Protein Data Bank: http://www . rcsb . org PyMOL: http://www . pymol . org/ CentroidFold: http://www . ncrna . org LMBCR: www . ouhsc . edu/lmbcr miRBase: http://www . mirbase . org/ | African-Americans ( AA ) are at increased risk of systemic lupus erythematosus ( SLE ) , but the genetic basis of this risk increase is largely unknown . We used admixture mapping to localize disease-causing genetic variants that differ in frequency across populations . This approach is advantageous for localizing susceptibility genes in recently admixed populations like AA . Our genome-wide admixture scan identified seven admixture signals , and we followed the best signal at 2q22–24 with fine-mapping , imputation-based association analysis and experimental validation . We identified two independent coding variants and a non-coding variant within the IFIH1 gene associated with SLE . Together with molecular modeling , our results establish a distinct role for IFIH1 in apoptosis , inflammation , and autoantibody production , and explain the molecular basis of these three risk alleles for SLE pathogenesis . | [
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| 2013 | Admixture Mapping in Lupus Identifies Multiple Functional Variants within IFIH1 Associated with Apoptosis, Inflammation, and Autoantibody Production |
Individuals with fast nicotine metabolism typically smoke more and thus have a greater risk for smoking-induced diseases . Further , the efficacy of smoking cessation pharmacotherapy is dependent on the rate of nicotine metabolism . Our objective was to use nicotine metabolite ratio ( NMR ) , an established biomarker of nicotine metabolism rate , in a genome-wide association study ( GWAS ) to identify novel genetic variants influencing nicotine metabolism . A heritability estimate of 0 . 81 ( 95% CI 0 . 70–0 . 88 ) was obtained for NMR using monozygotic and dizygotic twins of the FinnTwin cohort . We performed a GWAS in cotinine-verified current smokers of three Finnish cohorts ( FinnTwin , Young Finns Study , FINRISK2007 ) , followed by a meta-analysis of 1518 subjects , and annotated the genome-wide significant SNPs with methylation quantitative loci ( meQTL ) analyses . We detected association on 19q13 with 719 SNPs exceeding genome-wide significance within a 4 . 2 Mb region . The strongest evidence for association emerged for CYP2A6 ( min p = 5 . 77E-86 , in intron 4 ) , the main metabolic enzyme for nicotine . Other interesting genes with genome-wide significant signals included CYP2B6 , CYP2A7 , EGLN2 , and NUMBL . Conditional analyses revealed three independent signals on 19q13 , all located within or in the immediate vicinity of CYP2A6 . A genetic risk score constructed using the independent signals showed association with smoking quantity ( p = 0 . 0019 ) in two independent Finnish samples . Our meQTL results showed that methylation values of 16 CpG sites within the region are affected by genotypes of the genome-wide significant SNPs , and according to causal inference test , for some of the SNPs the effect on NMR is mediated through methylation . To our knowledge , this is the first GWAS on NMR . Our results enclose three independent novel signals on 19q13 . 2 . The detected CYP2A6 variants explain a strikingly large fraction of variance ( up to 31% ) in NMR in these study samples . Further , we provide evidence for plausible epigenetic mechanisms influencing NMR .
Nicotine is a neuro-stimulant with high addiction potential [1] . Similar to other drugs causing dependence , nicotine increases dopamine levels in the nucleus accumbens and activates the mesolimbic brain reward pathway [2] . Nicotine metabolism rate varies significantly between individuals and is strongly correlated to total nicotine clearance thus altering nicotine levels from a given intake [3] . Smokers are known to titrate their nicotine levels via cigarette consumption , number and volume of puffs , and depth of inhalation , to achieve and maintain desired levels; thus , nicotine clearance rate influences smoking behavior [4] . Individuals with fast metabolism typically smoke more , are less likely to succeed in quitting , and are more prone to nicotine dependence ( ND ) [5] . Consequently , those who smoke more have a greater risk for smoking-induced diseases [6] . Beyond the evident toxic effects of tobacco smoke , there is increasing evidence that , in addition to the known acute effects of nicotine [7] , chronic nicotine exposure as such may also increase the risk of cancer by multiple mechanisms [8] . With the advent of other nicotine delivery devices than tobacco , such as e-cigarettes , the need to understand the long-term consequences and action mechanisms of nicotine and its metabolism are of high public health relevance . A member of the cytochrome P450 family , CYP2A6 , is the main metabolic enzyme for nicotine accounting for up to 80% of nicotine clearance [9] . A large number of distinct CYP2A6 ( ENSG00000255974 ) alleles have been identified , including SNPs , duplications , deletions , and conversions ( www . cypalleles . ki . se/cyp2a6 . htm ) . CYP2A6 variations have been phenotypically grouped as slow ( <50% of activity ) , intermediate ( 80% of activity ) , and normal ( 100% of activity ) metabolizers . Another member of the cytochrome P450 family , CYP2B6 , has an approximately 10% catalytic efficiency of the CYP2A6 enzyme in vitro in nicotine c-oxidation , and may play a minor role in nicotine clearance at higher nicotine levels [10] or in the absence of functional CYP2A6 . While CYP2A6 is expressed primarily in the liver , CYP2B6 ( ENSG00000197408 ) is expressed at higher levels in the brain , where it may influence localized metabolism of nicotine [11 , 12] . Cytochrome P450 drug metabolizing enzymes are rarely highlighted in genome-wide association studies ( GWAS ) as the allele frequencies of the functional variants are low in most populations [13] . However , in 2010 a very large GWAS meta-analysis of smoking quantity revealed associations of CYP2A6 and CYP2B6 with SNPs that are in strong linkage disequilibrium ( LD ) with known functional variants [14] . Nicotine metabolism involves multiple steps and several enzymatic pathways . Up to 75% of nicotine is converted to cotinine mainly by CYP2A6 , 15% of nicotine is metabolized through other metabolic pathways , and a minor fraction ( 10–15% ) is excreted to urine unchanged . The majority of cotinine is further converted to 3-hydroxycotinine exclusively by CYP2A6; up to 40% is excreted to urine as 3-hydroxycotinine while 10% is further metabolized into 3-hydroxycotinine-glucuronide by UGT enzymes prior to excretion . Approximately 15% of cotinine is converted to cotinine-glucuronide by UGT enzymes , and the remaining is metabolized through other pathways . [9] Cotinine is a relatively stable compound with a half-life of 15–20h , and is superior as a biomarker of nicotine intake compared to self-reported smoking quantity ( cigarettes per day , CPD ) [15] . The ratio of 3-hydroxycotinine/cotinine ( i . e . nicotine metabolite ratio , NMR ) is an established biomarker of CYP2A6 activity , as well as nicotine metabolism rate , and it correlates strongly with total nicotine clearance [3] . Twin studies suggest an important genetic contribution to nicotine metabolism , measured using the nicotine metabolite ratio . Swan and colleagues reported that the estimated additive genetic effects on plasma NMR were 0 . 67 ( 95% CI 0 . 56–0 . 76 ) , dropped to 0 . 61 ( 95% CI 0 . 48–0 . 71 ) after adjusting for non-genetic covariates , and further reduced to 0 . 49 ( 95% CI 0 . 33–0 . 63 ) when CYP2A6 was adjusted for [16] , suggesting that known CYP2A6 variants identified at the time accounted for approximately 15–20% of NMR heritability . In addition to the strong effect of CYP2A6 and other genetic influences , NMR is also influenced by various demographic and hormonal factors . According to a recent study [17] , factors affecting NMR include ethnicity , likely due to the varying frequency of reduced activity CYP2A6 variants among different ethnicities [13] . Other affecting factors include sex , hormone replacement therapy , and use of estrogen containing contraceptive pills , all related to CYP2A6 being induced by estrogen [18] , as well as body mass index ( BMI ) , alcohol consumption , and cigarette consumption [17] . Altogether these above mentioned factors were estimated to account for approximately 8% of inter-individual variance in NMR [17] . In addition to significantly affecting smoking behavior , nicotine clearance rate has been shown to contribute to the efficacy of cessation pharmacotherapy in various retrospective studies [19–21] . In a recent randomized prospectively NMR-stratified placebo-controlled clinical trial , varenicline ( a prescription medication for smoking cessation ) was more efficacious for normal metabolizers compared to nicotine patch [22] . Nicotine patch was equally effective in slow metabolizers with less side-effects than in varenicline treatment , suggesting that tailored pharmacotherapy , i . e . stratifying on NMR level , can be one approach for improving smoking cessation rates [22] . This first clinical trial to take nicotine metabolism rate into account is a landmark paper highlighting the importance of the metabolic component cigarette smoking and use of other nicotine products . GWAS using metabolites measured from serum have been highly successful in identifying underlying genes [23 , 24] , highlighting the power of informative phenotypes . Our objective was to utilize NMR , a genetically informed biomarker of nicotine metabolism , in a GWAS meta-analysis of cotinine-verified ( ≥10ng/ml ) current smokers from three Finnish cohorts to identify novel genetic variants influencing nicotine metabolism rate . In Caucasians , with up to 90% of individuals being normal metabolizers , a minor fraction of variance in inter-individual differences in nicotine metabolism is accounted for by known reduced activity CYP2A6 variants . Thus , we expected that other contributing factors , such as other genes , but also novel regulators of CYP2A6 action or expression , including epigenetic mechanisms , are bound to exist . Our data highlighted novel genetic and epigenetic influences on NMR , deepening our understanding on factors influencing nicotine metabolism and providing valuable guidelines for further focused studies .
Written informed consent , according to the current edition of the Declaration of Helsinki , was obtained from all subjects who were interviewed and/or gave DNA samples before the beginning of the studies . The collection of blood samples followed the recommendations given in the Declaration of Helsinki and its amendments . For the FinnTwin12 , FinnTwin16 , and NAG-FIN studies , data collection has been approved by the hospital district of Helsinki and Uusimaa , the ethics committee for epidemiology and public health ( HUS-113-E3-01 , HUS-346-E0-05 , HUS 136/E3/01 ) . FinnTwin12 and FinnTwin16 have also been approved by the IRB of Indiana University at Bloomington , Indiana , while NAG-FIN was also approved by the IRB of Washington University ( St . Louis ) . Young Finns Study data collection has been approved by the hospital district of Southwest Finland ethics committee ( ETMK: no . 88/180/2010 ) . The ethics approvals for FINRISK have been obtained from the Coordinating Ethics Committee of Helsinki and Uusimaa Hospital District . Cotinine and 3-hydroxycotinine phenotypes were measured from frozen serum samples using liquid chromatography/tandem mass spectrometry ( University of Toronto , prof . Rachel Tyndale’s laboratory ) for the FinnTwin12 , FinnTwin16 , and YFS samples , as previously described [33] , and using gas chromatograph-mass spectrometer ( at the National Institute for Health and Welfare , Helsinki , Finland ) for the FINRISK2007 sample , as previously described [34]; these assays for NMR were demonstrated to be fully concordant ( R2 = 0 . 93 , P<0 . 0001 ) [35] . Cotinine threshold of ≥10ng/ml was applied to restrict the analyses to regular smokers . Several potential factors influencing on NMR ( sex , age , BMI , smoking quantity , alcohol consumption , genotyping batch ) [17] were considered as covariates . Sex and BMI were associated with NMR ( p<0 . 05 ) in the initial linear univariate regression models , and were selected as covariates for the study specific GWAS . Further , age was selected as a covariate due to the sampling design of the population-based samples . Neither smoking quantity , alcohol consumption , nor genotyping batch , were significant confounders ( p>0 . 05 ) and thus were not included as covariates . Details of phenotype and covariate distributions in the three samples are presented in Table 1 . Genotyping was performed with the Human670-QuadCustom Illumina BeadChip at the Welcome Trust Sanger Institute , and with the Illumina Human Core Exome BeadChip at the Welcome Trust Sanger Institute and at the Broad Institute of MIT and Harvard . Standard post-genotyping quality control thresholds were applied for SNPs ( minor allele frequency ( MAF ) <0 . 01 , SNP call rate <0 . 95 , and Hardy Weinberg Equilibrium ( HWE ) p<1E-06 ) . Further , subjects with a call rate <0 . 95 were excluded , and a sample heterozygosity test , as well as sex and Multidimensional Scaling ( MDS ) outlier checks were done . Pre-phasing of the data was done with SHAPEIT2 [36] and imputation with IMPUTE2 [37] using the 1000 Genomes Phase I integrated haplotypes ( produced using SHAPEIT2 ) reference panel [38] . For data generated with the Human670-QuadCustom Illumina BeadChip the following post-imputation exclusion criteria were applied for SNPs: MAF<0 . 01 , SNP call rate <0 . 95 ( <0 . 99 for SNPs with MAF<0 . 05 ) , HWE p<1E-06 , and imputation info <0 . 4 . For data generated with the Illumina Human Core Exome BeadChip the SNP exclusion criteria were otherwise identical , except that a threshold of minor allele count <2 was applied instead of a MAF cut-off . Further , the same sample quality thresholds as in post-genotyping quality control were applied . Quality controls and imputation for all Finnish GWAS data were done centrally at the Institute for Molecular Medicine , University of Helsinki , Helsinki , Finland .
MZ and DZ twins of the FinnTwin12 and FinnTwin16 cohorts were used to provide heritability estimates for NMR . The intraclass correlation for NMR was 0 . 80 for MZ and 0 . 26 for DZ pairs . The pattern of correlations suggested that in addition to additive genetic effects , dominance effects may be present but shared environmental were unlikely to be present . The data were consistent with both AE and ADE models , but not the ACE model as the MZ correlation was much larger than twice the DZ correlation ( in such a situation the C component will be zero ) . In the AE model , A effects accounted for 0 . 81 ( 95% CI 0 . 70–0 . 88 ) of variance in NMR . The ADE model fit difference was not statistically significant from the more parsimonious AE model ( p = 0 . 087 , Δχ2 = 2 . 94 , Δdf = 1 ) ; in the ADE model A effects accounted for 0 . 20 ( 95%CI 0 . 00–0 . 85 ) and D effects for 0 . 62 ( 95%CI 0 . 00–0 . 88 ) of the variance in NMR . According to the power analyses we had inadequate power to detect signals with rare SNPs ( MAF <5% ) unless they have very large effect sizes , but high power to detect signals with common SNPs ( MAF >5% ) that have medium to high effect sizes ( beta>±0 . 6 ) ( S1 Table ) . Each of the three Finnish cohorts independently showed genome-wide significant association on 19q13 . 2 . In our GWAS meta-analysis 719 SNPs exceeded the genome-wide significance threshold within a 4 . 2 Mb region on 19q13 . 2 ( chr19:39546965–43710562; according to GRCh37/hg19 ) ( S2 Table ) . Manhattan and QQ plots are presented in Figs 1 and 2 . The strongest evidence for association emerged for CYP2A6 ( minimum p = 5 . 77E-86 for rs56113850 , in intron 4 ) ( S3 Table ) . Other genes of relevance with genome-wide significant signals included CYP2B6 ( minimum p = 1 . 95E-24 for rs7260329 , in intron 8 ) , CYP2A7 ( ENSG00000198077 ) ( minimum p = 1 . 43E-38 for rs28602288 , beta = -0 . 48 , -83C>T , within predicted promoter region ) , EGLN2 ( ENSG00000269858 ) ( minimum p = 7 . 87E-16 for rs76443752 , in intron 3 ) , and NUMBL ( ENSG00000105245 ) ( minimum p = 1 . 40E-20 for rs4802082 , in intron 5 ) ( S2 Table ) . Conditional analyses of the 19q13 . 2 locus revealed three independent genome-wide significant signals tagged by rs56113850 , rs113288603 , and esv2663194 ( Fig 3 and Table 2 ) . The same independent SNPs emerged in all the three cohorts . A fourth independent signal ( rs12461964 ) emerged in the conditional analysis of the FINRISK2007 sample; however , this was not seen in the meta-analysis of the three samples . All the independent SNPs are imputed , and are located either within CYP2A6 or at most at a distance of 8 kb from the gene . For all the independent SNPs the minor allele decreases NMR , i . e . decreases nicotine clearance rate ( Table 2 ) . None of the independent SNPs have predicted functional effects . A plausible interplay between the top-SNP rs56113850 and rs113288603 was detected . When analysed within the largest of our cohorts ( YFS ) the minor allelic effect size of rs113288603 increased from non-significant ( beta = 0 . 05 , p = 0 . 522 ) to highly significant ( beta = -0 . 47 , p = 1 . 32E-09 ) when rs56113850 was added to the model; when an interaction term ( rs56113850*rs113288603 ) was also added to the model the effect size further increased ( beta = -0 . 62 , p = 1 . 32E-03 ) . Similarly , the effect size of rs56113850 increased from -0 . 67 ( p<2E-16 ) to -0 . 83 ( p<2E-16 ) when rs113288603 was added to the model; addition of an interaction term ( rs56113850*rs113288603 ) to the model did not further affect the results ( beta = -0 . 82 , p<2E-16 ) . According to LD estimation in the large FINRISK sample ( N = 19857 ) , rs56113850 and rs12461964 share an LD block with the known reduced activity allele CYP2A6*2 , and esv2663194 shares a block with the known reduced activity allele CYP2A6*9 , while rs113288603 is located outside these LD blocks ( S5 Fig ) . Based on pairwise LD values both CYP2A6*2 and CYP2A6*9 exhibit strong LD with all four independent SNPs ( S4 Table ) , and after conditioning on the top-SNP ( rs56113850 ) neither were genome-wide significant ( S5 Table ) . Age , sex , and BMI explained altogether 8 . 9% , 6 . 1% , and 0 . 53% of the variance in NMR in FinnTwin , YFS , and FINRISK2007 samples , respectively . The top-SNP rs56113850 alone explains 14–23% of the variance in NMR in the three cohorts ( Table 2 ) . Further , the percentage of variance explained by the three independent SNPs ( rs56113850 , rs113288603 , and esv2663194 ) when jointly included in the model was 20 . 8% in FinnTwin , 31 . 4% in YFS , and 26 . 3% in FINRISK ( increasing to 27 . 7% when rs12461964 was included ) . In the wGRS analyses highly similar results were obtained for the 3-SNP and 4-SNP wGRSs; only results for the 4-SNP wGRS are presented . For ease of interpretation , the wGRS was constructed as a weighted sum of major alleles ( all of which increase nicotine clearance rate ) . In the wGRS meta-analysis of 3954 current smokers statistically significant association was detected for quantitative CPD ( beta = 0 . 10 , 95% CI 0 . 04–0 . 16 , p = 0 . 0019 ) , suggesting that individuals with faster metabolism smoke more . The wGRS meta-analysis for current ( N = 3954 ) vs . former ( N = 3543 ) smoking showed association with increased likelihood of being a former smoker ( OR = 1 . 39 , 95% CI 1 . 09–1 . 76 , p = 0 . 007 ) . Similarly , the major allele of CYP2A6*2 showed a trend of association with increased likelihood of being a former smoker ( OR = 1 . 03 , 95% CI 0 . 72–1 . 47 , p = 0 . 89 ) , although the results were not statistically significant . To scrutinize potential confounders for cessation , we utilized available questionnaire data and excluded NAG-FIN individuals who either have a DSM-IV major depression disorder diagnosis or report quitting due to adverse health consequences and FINRISK individuals who have a relevant somatic diagnosis , are on disability pension , or rate their health as ‘poor’ . Further , we adjusted the analyses for alcohol consumption . After these exclusions , 2946 current and 2591 former smokers remained , and the wGRS result no longer was statistically significant ( OR = 1 . 30 , 95% CI 0 . 95–1 . 78 , p = 0 . 10 ) . We also tested these models with the rs56113850*rs113288603 interaction term included as a covariate; in all the models the interaction term was non-significant ( p>0 . 05 ) and so a more complex model was not justified . In the meQTL analyses of the 719 genome-wide significant SNPs and 158 CpG sites within the target region , methylation values of 16 CpG sites showed statistically significant association with 173 of the SNPs ( FDR corrected p<0 . 05 ) ( S6 Table ) . Among the highlighted genes were EGLN2 , CYP2A7 , CYP2F1 ( Cytochrome P450 , Family 2 , Subfamily F , Polypeptide 1 ) ( ENSG00000197446 ) , and DLL3 ( Delta-like 3 ( Drosophila ) ) ( ENSG00000090932 ) ( S7 Table ) . To distinguish between our hypotheses B ( ‘SNPs affect NMR via methylation’ ) and C ( ‘SNPs affect methylation via NMR’ ) we performed CIT , and confirmed that methylation at the CpG site tagged by cg08551532 ( in DLL3 ) mediates the effect of SNPs on NMR ( S8 Table ) . We detected no evidence supporting hypothesis C .
Nicotine metabolism rate is one of the key factors affecting smoking behavior , and has also been shown to contribute to the efficacy of cessation pharmacotherapy [22] . Many smokers find it exceedingly difficult to succeed in quitting , even when they have a major cardiovascular illness and smoking cessation would greatly improve their prognosis [53] . Current pharmacotherapies and behavioral counselling enhance smoking cessation rate on average by only approximately 2-fold [54] , highlighting the need for more effective cessation support . Unraveling the genetic architecture of nicotine metabolism may enhance development of tailored smoking cessation pharmacotherapies . MZ and DZ twins of FinnTwin12 and FinnTwin16 cohorts yielded a heritability estimate of 0 . 81 for NMR , confirming that genetic effects are major determinants of inter-individual variance in NMR . This estimate is higher than previous estimates obtained in an experimental setting [16] , perhaps due to less heterogeneity in the Finnish sample . Our aim was to identify novel genetic variants affecting nicotine metabolism . We utilized NMR , a biomarker of nicotine metabolism , in a GWAS meta-analysis of three Finnish cohorts , and identified association on 19q13 . 2 . This locus harbours a number of genes , including several members of the cytochrome P450 gene family . Three independent genome-wide significant signals were detected , all located either within or in the immediate vicinity of CYP2A6 , the gene encoding the main metabolic enzyme for nicotine . A fourth independent signal in CYP2A6 emerged in one of the samples ( FINRISK2007 ) ; however , this was not seen in the meta-analysis . The minor alleles of all of the independent variants associated with decreased NMR values , i . e . decreased nicotine clearance rate . All the independent variants are novel signals and have not been previously been highlighted in any smoking-related GWAS . Our top-SNP ( rs56113850 ) is located in intron 4 of CYP2A6 , has a high minor allele frequency ( MAFFINRISK = 0 . 44 ) and a prominent effect size ( beta = -0 . 65 ) , and alone accounts for a substantial percentage of variance ( 14–23% ) in NMR in the three Finnish cohorts . Our second independent SNP ( rs113288603 ) is located 5 . 9 kb upstream of CYP2A6 , seems to be enriched in the Finnish population ( MAFFINRISK = 0 . 15 vs . MAFEUR = 0 . 09 ) , has a small effect size ( beta = -0 . 02 ) , and accounts for less than 1% of variance in NMR in the three cohorts . Neither of these variants has predicted functional consequences . Interestingly , our data support a plausible interplay between rs113288603 and rs56113850 , as the effect sizes significantly increase when the other SNP is added to the model . These findings are in line with our GWAS data , as rs113288603 shows no association in any of the cohort-specific GWAS or in the meta-analysis , but in an analysis conditioned on rs56113850 it emerges as the second independent genome-wide significant SNP . The plausible mechanism underlying the interplay remains to be determined . Our third independent variant ( esv2663194 ) tags a 32 kb deletion that affects both CYP2A6 and CYP2A7 . Esv2663194 has a low minor allele frequency ( MAFFINRISK = 0 . 03 ) and a prominent effect size ( beta = -1 . 08 ) , and accounts for 3–8% of variance in NMR in the three cohorts . The 32 kb ( chr19:41355715–41387669 , according to GRCh37/hg19 ) deletion abolishes exons 1–2 in CYP2A6 and exons 2–9 in CYP2A7 when compared to the reference sequence; both genes have multiple isoforms and the consequence of the deletion varies between the isoforms . The 32 kb deletion may produce a similar construct as CYP2A6*12 , which is a hybrid allele formed by an unequal crossover between CYP2A6 and CYP2A7 . CYP2A6*12 is composed of the 5′-regulatory region and exons 1–2 of CYP2A7 and exons 3–9 and the 3′-regulatory region of CYP2A6 , and harbors 10 amino acid differences when compared to the wild-type CYP2A6 allele , with an allele frequency of 2 . 2% reported among Spaniards [51] . CYP2A6*12 is shown to have reduced enzyme activity in vivo [55 , 56]; in line with this , the minor allele of esv2663194 associates with decreased clearance rate . Further studies are needed to confirm whether the 32 kb deletion tagged by esv2663194 indeed creates a construct with properties similar to CYP2A6*12 . Our fourth independent SNP ( rs12461964; detected in the FINRISK2007 sample ) is located 8 . 2 kb downstream of CYP2A6 , has a high minor allele frequency ( MAFFINRISK = 0 . 45 ) and a prominent effect size ( beta = -0 . 61 ) . Rs12461964 is in high LD with the top-SNP rs56113850 ( D’ = 0 . 85 ) ; although it was identified as an independent SNP in conditional analyses in the FINRISK2007 sample , the detected association likely reflects LD with rs56113850 . To date , the most widely studied CYP2A6 variants include CYP2A6*2 ( rs1801272 , L160H ) which encodes a catalytically inactive enzyme , a whole gene deletion allele CYP2A6*4 , CYP2A6*9 ( rs28399433 , -48T>G ) , which has an alteration in the TATA box resulting in lower expression of a structurally normal protein , and CYP2A6*12 discussed above [13] . Of these characterized reduced-activity variants , our data included CYP2A6*2 and CYP2A6*9 , both of which showed genome-wide significant association but were not identified as independent signals in conditional analyses . A previous very large ( N = 85997 ) meta-analysis of self-reported CPD showed genome-wide significant association on 19q13 . 2 , with strongest evidence obtained for rs4105144 , which is in LD with CYP2A6*2 ( D′ = 1 . 0 in CEU ) [14] . Similarly , LD between our novel independent signals and both CYP2A6*2 and CYP2A6*9 was high ( D’ = 0 . 89–1 . 00 ) . The percentage of variance in NMR explained by CYP2A6*2 and CYP2A6*9 in our study cohorts was up to 6% and 12% , respectively . A previous study using an ethnically diverse sample suggested that 15–20% of variance in NMR is accounted for by the four characterized reduced-activity variants ( CYP2A6*2 , CYP2A6*4 , CYP2A6*9 , and CYP2A6*12 ) [16]; much of the genetic variation would not have been tested for in this study ( i . e . CYP2A6*10 –*35 ) . Interestingly , in our study CYP2A6*9 alone explained a large fraction of variance in NMR , possibly due to the higher minor allele frequency in the Finnish cohorts ( MAF 0 . 12–0 . 14 ) compared to that reported in the Caucasian population ( MAFEUR = 0 . 07 ) . In addition to CYP2A6*2 and CYP2A6*9 , our data set included three additional known alleles , CYP2A6*14 ( rs28399435; S29N ) , CYP2A6*18 ( rs1809810; Y392F ) , and CYP2A6*21 ( rs6413474; K476R ) . Although they are non-synonymous variants , their effect on nicotine clearance likely is minimal . CYP2A6*14 does not appear to affect enzyme activity [57 , 58] , and although CYP2A6*18 has a decreased activity towards another substrate , coumarin , activity towards nicotine is unaffected [59] . Based on in vivo studies , CYP2A6*21 showed normal activity in a Caucasian population [60] . After conditioning on our top-SNP ( rs56113850 ) , none of the five known alleles were genome-wide significant , suggesting that rs56113850 captures information on all of these alleles . The percentage of variance explained by our novel independent SNPs was very high ( up to 31% ) , exceeding the estimates for the four functional variants frequently genotyped in Caucasians [16] . Our novel variants likely tag multiple functional variants , both known and unidentified ones , and thus capture information on relevant haplotypes . The population-attributable effect of the novel independent variants thus is far greater than that of the known functional variants , and for the purpose of estimating the rate of nicotine metabolism our novel SNPs are more informative than the known previously characterized reduced-activity variants . Based on our twin modelling , the heritability of NMR is 0 . 81 . Although our novel independent SNPs capture a strikingly large fraction of this , major fraction still remains unaccounted for . This may be due to limitations of GWAS with lack of coverage of CYP2A6 duplications , translocations , and rare variants . Further , the high sequence homology between CYP2A6 , CYP2A7 , and CYP2A13 may prohibit detection of variants within homologous regions . SNP genotyping technologies using short probes will not be able to detect these variants with high specificity; most likely such variants will fail the HWE threshold , and thus will be excluded from analyses . In addition , limitations of statistical power result in inability to detect all relevant signals . In our GWAS meta-analysis we had high power to detect signals with common SNPs ( MAF >5% ) that have medium to high effect sizes . This is reflected in our top-SNPs , three of which are not only common but also have large effect sizes , and one that is rare ( MAF = 0 . 03 ) but has a large effect size ( beta = -1 . 08 ) . We had very low power to detect signals with rare SNPs that have low effect sizes , and it is likely that we missed those signals; larger studies are needed to capture these signals . Ethnicity has been reported as a prominent factor affecting NMR; however , all the subjects in the current study were Caucasian ( Finns ) . Age , sex , and BMI accounted for 8 . 9% and 6 . 1% of variance in NMR in the FinnTwin and YFS samples , respectively . This is in line with a previous study showing that various non-genetic factors account for altogether 8% of inter-individual variance in NMR [17] . However , in the FINRISK2007 sample age , sex , and BMI only accounted for 0 . 5% of variance in NMR , which may be due to older age and longer overall duration of smoking with more cessation than among the young adult FinnTwin and YFS samples . Although only variants in CYP2A6 were highlighted as independent signals , other interesting genes , such as CYP2B6 , CYP2A7 , EGLN2 , and NUMBL , reside within the 19q13 locus , and showed genome-wide significant association in our GWAS meta-analysis . Our top-SNP in CYP2B6 ( rs7260329 ) has been highlighted in the large GWAS meta-analysis of CPD [14] . CYP2B6 can also catalyze nicotine metabolism to cotinine and to nornicotine [9]; the CYP2B6 gene sits adjacent to CYP2A6 and they share some common regulation . CYP2B6 also metabolizes several other drugs of abuse , as well as bupropion , an atypical antidepressant also used as a smoking cessation aid [5] . Several functional CYP2B6 variants have been identified . The most prevalent and clinically important variant is CYP2B6*6 , characterized as a haplotype consisting of two linked non-synonymous variants CYP2B6*4 ( rs2279343 ( K262R ) ) and CYP2B6*9 ( rs3745274 ( Q172H ) ) , resulting in a splice site variant with reduced function [61] . In the current study the non-synonymous SNPs defining CYP2B6*4 , CYP2B6*9 , as well as CYP2B6*5 ( rs3211371 ( R487C ) ) did not show genome-wide significant results , but were in high LD with the top-SNP in CYP2B6 ( rs7260329 ) ( S9 Fig ) . Further , rs7260329 shows modest LD with our independent SNPs rs56113850 ( D’ = 0 . 40 ) , esv2663194 ( D’ = 0 . 65 ) , and rs12461964 ( D’ = 0 . 43 ) , but not with rs113288603 ( D’ = 0 . 04 ) ; thus , it is unclear whether the detected association simply reflects LD with the independent variants residing in CYP2A6 . The interpretation of the detected CYP2A7 association is challenging , as the possible role of CYP2A7 in nicotine metabolism is unknown . Our top SNP in CYP2A7 ( rs28602288 ) is located within a predicted promoter region . It is plausible that the association between rs28602288 and NMR reflects the regulation of CYP2A6*12 ( a hybrid allele of CYP2A6 and CYP2A7 ) or similar constructs , such as the one generated by the 32 kb deletion tagged by esv2663194 , rather than implies a role for CYP2A7 in nicotine clearance . Rs28602288 is in LD with all of the independent variants ( D’ = 0 . 67–1 . 00 ) . Two additional interesting genes were highlighted . Egl-9 Family Hypoxia-Inducible Factor 2 ( EGLN2 ) is a key component of the oxygen-sensing pathway that regulates the expression of various downstream genes , and responses e . g . to carbon monoxide ( CO ) and cigarette smoke exposure . In a recent study , EGLN2 was associated with CPD and breath CO independent of CYP2A6 , although it was not associated with nicotine metabolism [62] . The top-SNP in EGLN2 ( rs76443752 ) is in LD with all of the independent CYP2A6 variants ( D’ = 0 . 67–1 . 00 ) . Another potentially interesting candidate is Numb Homolog ( Drosophila ) -Like ( NUMBL ) which encodes a protein that maintains progenitor cells during cortical neurogenesis [63] and has previously been implicated in lung cancer [64 , 65] . The top-SNP in NUMBL ( rs4802082 ) is in LD with all of the independent variants ( D’ = 0 . 48–0 . 96 ) . We constructed wGRS using the independent 19q13 . 2 SNPs and tested whether the wGRS predicts smoking behavior in two independent Finnish samples . The wGRS constructed of major alleles ( increasing the metabolism rate ) was associated with increased smoking quantity . This is in line with previous evidence of faster metabolizers smoking more [66] . Adding the rs56113850*rs113288603 interaction term to the wGRS analyses did not improve model fit , suggesting that the simpler model with main effects was sufficient . Our unadjusted wGRS results for current vs . former smoking suggested that major alleles that associate with faster metabolism associate with increased odds of being a former smoker; after adjustment and exclusion for potential confounders the wGRS result no longer was significant . Although the sample size decreased , the confidence intervals did not significantly increase , suggesting that the change in results is due to reduced confounding rather than reduced power . Many clinical trials and epidemiological studies indicate that slow metabolizers quit more often than normal metabolizers [20 , 67 , 68] . Possibly our wGRS does not capture all the relevant aspects of measured NMR or the cross-sectional nature of the FINRISK smoking status data did not permit us to fully replicate earlier findings . We followed up the 719 genome-wide significant SNPs by meQTL analyses in order to annotate their potential functional consequence . As smoking is known to induce significant changes in methylation patterns [69] , only cotinine-verified current smokers ( cotinine≥10ng/ml ) were included in the analyses . Several SNPs showed significant association with methylation values of 16 CpG sites located within the target region . The 16 CpG sites overlap with relevant genes , such as members of the cytochrome P450 gene family ( CYP2F1 and CYP2A7 ) and EGLN2 . According to CIT , methylation in one CpG site mediate the effect of SNPs on the variance observed in NMR . This CpG site ( tagged by cg08551532 ) is located in DLL3 , which is involved in neurogenesis via its role in the Notch signaling pathway [70] . Further , DLL3 has been shown to be silenced by methylation in human hepatocellular carcinoma ( HCC ) , leading to restricted growth of cancer cells [71] . Expression of NOTCH3 , encoding for a receptor for DLL3 , has been shown to be influenced by cigarette smoke [72] . The potential mechanism how DLL3 may affect nicotine metabolism remains to be determined . To our knowledge this is the first GWAS of NMR reported to date . Despite a relatively small sample size ( N = 1518 ) , we detected a multitude of genome-wide significant signals on 19q13 . 2 , highlighting the power of informative biomarkers in GWAS and demonstrating the power of Finnish population samples to identify novel genes even with a modest sample size . Our genetic and epigenetic analyses enclosed several genes as potential players in regulating NMR . Four members of the cytochrome P450 gene family ( CYP2A6 , CYP2B6 , CYP2A7 , and CYP2F1 ) were highlighted , although only CYP2A6 encompassed independent signals . Three additional highlighted genes ( DLL3 , NUMBL , and EGLN2 ) are functionally linked according to the GeneMania database ( http://www . genemania . org/ ) , suggesting that an interplay between these genes may influence NMR . Future studies are needed to elucidate the potential role of these genes in NMR . The detected novel CYP2A6 variants explain a strikingly large fraction of variance ( up to 31% ) in NMR in the current sample , suggesting that they tag both known and unidentified functional variants . The population-attributable effect of the detected independent variants is thus far greater than that of the four functional variants frequently genotyped in Caucasians ( CYP2A6*2 , *4 , *9 , and *12 ) . Further , we enclose evidence for plausible epigenetic mechanisms on 19q13 . 2 influencing NMR . | Nicotine metabolism rate significantly varies between individuals and affects smoking behavior . Individuals with fast nicotine metabolism typically smoke more and thus have a greater risk for smoking-induced diseases . Further , the efficacy of smoking cessation pharmacotherapy is dependent on nicotine metabolism rate . Twin and family studies have shown that genes influence nicotine metabolism; however , only a minor fraction of variance in inter-individual differences in nicotine metabolism is accounted for by known reduced activity variants in CYP2A6 , the main metabolic enzyme for nicotine . Here we utilized a biomarker of nicotine metabolism ( nicotine metabolite ratio , NMR ) in a genome-wide association study of three Finnish cohorts to identify novel genetic variants influencing nicotine metabolism rate . Our results enclose three independent novel signals in CYP2A6 . The detected variants explain a strikingly large fraction of variance ( up to 31% ) in NMR in the study samples . A genetic risk score constructed using the independent signals predicts smoking quantity in two independent Finnish samples . Further , we enclose evidence for plausible epigenetic mechanisms influencing NMR . With the advent of other nicotine delivery devices than tobacco , such as e-cigarettes , the need to understand the long-term consequences and action mechanisms of nicotine and its metabolism are of high public health relevance . | [
"Abstract",
"Introduction",
"Material",
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"Discussion"
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| []
| 2015 | A Genome-Wide Association Study of a Biomarker of Nicotine Metabolism |
SARS-coronavirus ( SARS-CoV ) replication and transcription are mediated by a replication/transcription complex ( RTC ) of which virus-encoded , non-structural proteins ( nsps ) are the primary constituents . The 16 SARS-CoV nsps are produced by autoprocessing of two large precursor polyproteins . The RTC is believed to be associated with characteristic virus-induced double-membrane structures in the cytoplasm of SARS-CoV-infected cells . To investigate the link between these structures and viral RNA synthesis , and to dissect RTC organization and function , we isolated active RTCs from infected cells and used them to develop the first robust assay for their in vitro activity . The synthesis of genomic RNA and all eight subgenomic mRNAs was faithfully reproduced by the RTC in this in vitro system . Mainly positive-strand RNAs were synthesized and protein synthesis was not required for RTC activity in vitro . All RTC activity , enzymatic and putative membrane-spanning nsps , and viral RNA cosedimented with heavy membrane structures . Furthermore , the pelleted RTC required the addition of a cytoplasmic host factor for reconstitution of its in vitro activity . Newly synthesized subgenomic RNA appeared to be released , while genomic RNA remained predominantly associated with the RTC-containing fraction . RTC activity was destroyed by detergent treatment , suggesting an important role for membranes . The RTC appeared to be protected by membranes , as newly synthesized viral RNA and several replicase/transcriptase subunits were protease- and nuclease-resistant and became susceptible to degradation only upon addition of a non-ionic detergent . Our data establish a vital functional dependence of SARS-CoV RNA synthesis on virus-induced membrane structures .
Following infection and genome translation , positive-strand RNA ( +RNA ) viruses establish a cytoplasmic enzyme complex that directs the amplification and expression of their genome . The viral RNA-dependent RNA polymerase ( RdRp ) is the central enzyme of this ‘replication/transcription complex’ ( RTC ) , but it also may include other viral non-structural proteins ( nsps ) and host factors that cooperate to synthesize viral RNA . Over the past decade , it has become clear that +RNA virus RTCs are invariably associated with virus-induced membrane structures , which are poorly characterized but presumably provide a framework for RNA synthesis by facilitating the concentration and cooperation of viral macromolecules on a dedicated membrane surface . They may also protect the viral RNA from nucleases in the cytoplasm of the host cell , aid in shielding the double-stranded RNA intermediates of virus replication from the host cell's innate immune system , or contribute to the coordination of the viral life cycle in time and space . These membrane-bound RTCs are the molecular machines that drive the RNA synthesis and evolution of +RNA viruses . Clearly , unraveling their structure and function will be critical to understand the biochemistry of +RNA virus replication and develop novel antiviral control strategies . The RTC of coronaviruses , including that of SARS-coronavirus ( SARS-CoV ) , the causative agent of the life-threatening severe acute respiratory syndrome ( for a review , see reference [1] ) , stands out for a number of reasons . First , at 27–32 kb , the polycistronic coronavirus genome is by far the largest genome among currently known RNA viruses [2] . Second , the viral RNA-synthesizing machinery not only amplifies the genome , but also directs the synthesis of a set of subgenomic ( sg ) mRNAs ( eight in the case of SARS-CoV; RNA2 to RNA9 ) to express the viral accessory and structural protein genes . The latter are produced from a corresponding set of subgenome-length negative strand RNAs , which derive from discontinuous negative-strand RNA synthesis [3] , [4] . Third , the viral replicase/transcriptase ( which will be referred to as “replicase” for brevity ) is of unprecedented size and complexity [5] , [6] . It is produced by translation of the partly overlapping open reading frames ( ORF ) 1a and 1ab , with expression of the latter requiring a -1 ribosomal frameshift near the end of ORF1a . In this manner , SARS-CoV genome translation yields the large replicase polyproteins pp1a ( 4 , 382 aa ) and pp1ab ( 7 , 073 aa ) . Extensive autoproteolytic processing , mediated by two ORF1a-encoded protease domains [7]–[10] , ultimately generates 16 nsps [5] , [6] , [11] , [12] . These include key replicative enzymes ( e . g . the nsp12-RdRp [13] , and the nsp13-helicase [14] ) , a variety of subunits containing presumed accessory functions for viral RNA synthesis ( e . g . the nsp8-primase [15] , [16] , nsp14-exoribonuclease [17] , [18] , and nsp15-endoribonuclease NendoU [19]–[22] ) and several predicted multi-spanning membrane proteins ( nsp3 , nsp4 and nsp6; [23] , [24] ) that presumably modify cellular endomembranes and target the RTC to this scaffold . Immunofluorescence microscopy previously revealed that newly synthesized SARS-CoV RNA and several nsps colocalize in perinuclear foci in SARS-CoV-infected cells [8] , [14] , [25]–[27] . Electron microscopy established the presence of typical paired membranes , membrane whorls , and double-membrane vesicles ( DMVs ) , which labeled for nsps [26]–[29] and viral RNA [27] and were therefore proposed to carry the SARS-CoV RTC . The endoplasmic reticulum ( ER ) was identified as the most likely membrane donor [26] and recent electron tomography studies indeed revealed a network of SARS-CoV-induced membrane structures that is continuous with this organelle ( Knoops et al . , in preparation ) . In the past four years , substantial progress has been made in the characterization of individual replicase subunits using enzymatic assays , reverse and classical genetics , bioinformatics and structural studies . However , the composition and mechanistics of the native ribonucleoprotein complexes , in which these different components interact to drive coronavirus replication and transcription , have remained completely uncharacterized thus far . We therefore set out to isolate active RTCs from SARS-CoV-infected cells and used those to develop an in vitro system that faithfully reproduced the synthesis of both genomic and sg RNAs , mainly of positive polarity . RTC activity cosedimented with newly synthesized viral RNA and several replicase subunits in a dense membrane fraction containing structures that could be labeled for nsp3 and nsp4 . The in vitro activity of the pelleted RTC depended on the presence of a cytoplasmic host factor . Furthermore , RTC activity was destroyed by addition of ( non-ionic ) detergents , which also released replicase subunits and ( mainly ) sg RNA from the membrane fraction . Protease and nuclease protection experiments indicated that viral RNA and nsps were protected by membranes , thus further substantiating the functional connection between SARS-CoV RNA synthesis and virus-induced membrane structures that appear to be essential for RTC activity .
In order to characterize isolated SARS-CoV RTCs , we developed an in vitro RNA synthesis assay ( IVRA ) to study their activity in vitro . In this reaction , the incorporation of [α-32P]CTP into viral RNA was analyzed in a mixture containing NTPs , Mg2+ , an ATP-regenerating system , and an inhibitor of cellular transcription ( Actinomycin D ) . The RTC activity in cytoplasmic extracts prepared from SARS-CoV-infected Vero-E6 cells produced a set of 32P-labeled RNA molecules with sizes corresponding to those of the SARS-CoV genome and all eight sg RNAs ( Fig . 1 ) . These products were not detected when using mock-infected cell lysates ( Fig . 1A , mock ) , demonstrating that SARS-CoV RTC activity was responsible for their synthesis . Reaction conditions were optimized by varying several parameters , including the composition of the reaction mixture , incubation time , temperature , and the Mg2+ concentration ( Fig . 1 and data not shown ) . In a time course experiment , in vitro synthesized viral RNA accumulated up to 100 min into the reaction ( Fig . 1A ) , after which a decrease was observed , probably due to declining RTC activity in combination with continued RNA degradation by cellular nucleases . The optimal reaction temperature was 30°C ( Fig . 1B ) . RTC activity was strongly dependent on the Mg2+ concentration and was maximal when 2 mM of Mg2+ was added to the reaction ( Fig . 1C ) . Manganese could not replace Mg2+ , as IVRAs containing Mn2+ only yielded a ladder of small radiolabeled RNA molecules with aberrant sizes ( Fig . 1D ) , suggesting an effect on RdRp processivity . Addition of ionic ( SDS and deoxycholate ( DOC ) ) or non-ionic detergents ( Nonidet P40 ( NP-40 ) and Triton X-100 ( TX-100 ) ) to the post-nuclear supernatant ( PNS ) prior to the IVRA completely abolished the accumulation of radiolabeled viral RNA , suggesting that the integrity of membranes is an important factor for SARS-CoV RTC activity ( Fig . 1D ) . To determine the polarity of the in vitro produced RNAs , the 32P-labeled products of an IVRA were hybridized to a membrane containing immobilized RNA probes specific for SARS-CoV positive- or negative-stranded RNA ( Fig . 1E ) . A strong hybridization with the positive strand-specific probe was observed , demonstrating that the RTC mainly synthesized RNA of positive polarity in vitro . After longer exposure times , a small quantity of radiolabeled material hybridizing to the negative strand-specific probe became visible , but a similar signal was observed with the negative control RNA ( Fig . 1E ) . This indicated that the quantity of in vitro synthesized negative-stranded RNA was very small ( less than 2% of the total RNA ) , which is in line with the large excess of positive over negative strands that is commonly observed in vivo . To assess whether protein synthesis occurred during IVRAs , we determined whether 35S-labeled amino acids were incorporated into proteins during a 100-min reaction . When using the PNS of uninfected cells , SDS-PAGE revealed a smear of 35S-labeled material ( Fig . 2A , lane 2 ) . These products were absent when the PNS was heated to 96°C for 5 min prior to the assay ( Fig . 2A , lane 1 ) , suggesting they resulted from translation under IVRA conditions . When using the PNS of SARS-CoV-infected cells , we observed incorporation of radiolabel also into a set of discrete polypeptides ( Fig . 2A , lane 4 ) , including species with sizes matching those of the SARS-CoV membrane ( M ) and nucleocapsid ( N ) proteins . This was likely due to the fact that the lysate contained large amounts of the sg mRNAs encoding these proteins , possibly in combination with the virus-induced shut-off of host cell translation [30] . Protein synthesis was completely inhibited when the translation inhibitors cycloheximide or puromycin were present during the IVRA ( Fig . 2A , lanes 5 and 6 ) , but this did not affect in vitro RTC activity since the quantity of radiolabeled RNA products was unchanged ( Fig . 2B ) . To further characterize the active RTC , the PNS of SARS-CoV-infected cells was subjected to differential centrifugation . A 10 , 000×g supernatant fraction ( S10 ) showed no RTC activity ( Fig . 3 , lane 2 ) , but only a trace amount of the original activity was recovered in the 10 , 000×g pellet fraction P10 ( Fig . 3 , lane 4 ) . Surprisingly , RTC activity in this P10 fraction could be largely restored by adding an aliquot of the cytoplasmic S10 fraction ( Fig . 3 , lane 5 ) . An S10 fraction prepared from mock-infected cells was equally capable of restoring the RTC activity in P10 , indicating that a cytoplasmic host factor was required ( Fig . 3 , lane 6 ) . Routinely , about 50% of the RTC activity that was originally present in the PNS could be recovered in the P10 fraction ( in assays supplemented with S10 ) . Remarkably , virtually all replicative activity was lost , while transcription was only 2- to 3-fold decreased , in the P10 fraction depleted of the host factor ( Fig . 3 , lane 4 ) . The sedimentation of the RTC activity at 10 , 000×g suggests that it is associated with heavy membrane structures . The P10 fraction , which contained the bulk of RTC activity , was analyzed by electron microscopy ( negative staining ) in combination with an immunogold labeling for the ( putative ) transmembrane proteins nsp3 , nsp4 , and nsp6 ( Fig . 4 and data not shown ) . Clusters of vesicles ( with diameters between 100 and 350 nm ) were observed , which appeared to be associated with more tubular or flattened membrane structures . A strong immunolabeling of these structures for SARS-CoV nsp3 ( Fig . 4A ) and nsp4 ( Fig . 4B ) was observed . Membrane structures immunoreactive for nsp3 ( Fig . 4C ) or nsp4 ( data not shown ) were not detected in a control P10 fraction prepared from mock-infected cells . Occasionally , double membranes could be distinguished ( Fig . 4B , arrow ) . These observations are consistent with the notion that the P10 fraction is enriched for SARS-CoV-induced nsp-containing membrane structures that have been documented in infected cells . The distribution of newly synthesized SARS-CoV RNAs between the RTC-containing P10 and cytoplasmic S10 fractions was analyzed by fractionation of PNS after an IVRA ( Fig . 5A ) . The bulk ( 76% ) of newly made genomic RNA was recovered from the P10 fraction , suggesting it remained associated with the heavy membrane structures . In contrast , newly synthesized sg RNAs were , depending on their size , progressively more abundant in S10 , suggesting their release from the RTC . To further investigate the role of membranes in RNA localization , an IVRA was performed with PNS , after which 0 . 5% TX-100 was added and the distribution of viral RNAs between P10 and S10 was analyzed ( Fig . 5B ) . The bulk of the smaller RNA species ( RNA5-9 ) was now recovered from the S10 fraction . In contrast , one-half of the genomic RNA remained associated with the P10 fraction after detergent treatment , suggesting product-specific differences in RTC operation and organization , which appears to include partly detergent-resistant structures . For selected nsps , for which suitable antisera that are reactive in Western blot experiments were available , the distribution between the cytoplasmic S10 fraction and the RTC-containing P10 fraction was analyzed . This revealed that these RTC subunits were enriched or mainly present in the P10 fraction ( Fig . 6 ) . The bulk of nsp3 was in the P10 fraction and nsp5 was found almost exclusively in the P10 fraction ( Fig . 6 , lane 3 ) . Most of nsp8 was detected in the P10 fraction although also a substantial amount was found in the cytoplasmic fraction ( Fig . 6 , lane 2 & 3 ) . Treatment of PNS with 0 . 5% TX-100 prior to P10-S10 fractionation , led to the redistribution of nsp3 , nsp5 , and nsp8 , which were no longer found in the P10 fraction , but were recovered at increased levels in the S10 fraction ( Fig . 6 , lanes 4 & 5 ) . This suggests that their direct or indirect association with membranes caused them to cosediment with the RTC activity in the P10 fraction . To further assess the role of membranes in SARS-CoV RNA synthesis , it was investigated whether they protect the RTC . A standard 100-min IVRA was performed , followed by treatment with the nuclease Bal31 , a non-specific nuclease that degrades both single- and double-stranded RNA , in the presence or absence of 0 . 5% TX-100 . After fractionation of the samples into P10 and S10 , the quantity of in vitro synthesized radiolabeled RNA in each fraction was analyzed ( Fig . 7A ) . In untreated control samples , newly made viral RNA was found both associated with the RTC in the P10 fraction ( predominantly genomic RNA ) as well as released in the cytoplasmic S10 fraction ( enriched in sg RNA; Fig 7A , lane 1 & 2 ) . The newly made viral RNA in the cytoplasm was completely degraded upon nuclease treatment ( Fig 7A , lane 3 ) , while the RNA associated with the RTC was protected ( Fig 7A , lane 4 ) . The latter products only became susceptible to nuclease treatment upon addition of 0 . 5% TX-100 , suggesting that the replicating RNA is enclosed by membranes ( Fig 7A , compare lanes 4 and 6 ) . To determine whether also replicase subunits were protected by membranes , PNS was treated with proteinase K , either in the absence or presence of 0 . 5% TX-100 . Protease-treated samples and untreated controls were subsequently fractionated into P10 and S10 , after which the presence of nsp3 , nsp5 , and nsp8 was probed by Western blotting ( Fig . 7B ) . Both cytoplasmic nsp3 in S10 and pelleted nsp3 in P10 were susceptible to protease treatment ( Fig . 7B , top panel ) . The nsp5 subunit , which mainly cosedimented with the RTC in P10 , was largely resistant to protease treatment ( Fig 7B , middle panel , lane 5 ) , but it was degraded in the presence of TX-100 . The observed protease-resistance of nsp5 is not due to a lack of proteinase K activity , since both nsp3 and a host protein cross-reacting with the nsp5 antiserum were completely degraded in this same sample . Likewise , nsp8 in the P10 fraction was resistant to protease treatment , and , surprisingly , this was also true for the nsp8 that was present in the S10 fraction ( Fig . 7B , lower panel ) . Both forms of nsp8 were susceptible to the protease in the presence of a non-ionic detergent ( TX-100 ) . These data suggest that nsp5 and nsp8 were enclosed by membranes . In agreement with the membrane topology predictions for the nsp3 domains used to raise our antiserum [5] , [23] , a major part of nsp3 was exposed on the surface of these membrane structures .
The SARS-CoV RTC , like the RTCs of other +RNA viruses [31]–[33] , is believed to be associated with virus-induced structures derived from intracellular membranes . The coronavirus RTC is composed of an unusually large number of subunits , including several nsps with unique enzyme functions [2] , [34] . Despite steady progress , the functional characterization of the 16 SARS-CoV nsps , including the RdRp and helicase enzymes that are central to replication , is still in an early stage . To investigate the details of the molecular interplay between these subunits , the viral RNA template , and host factors , in vitro assays for viral RNA synthesis will be indispensable . By now , the soluble expression and purification of several individual coronavirus nsps has proven to be problematic . In combination with the membrane-associated nature of the complex , this suggests that the reconstitution of the RTC from its purified components , remains a distant perspective . As a complementary approach , we therefore set out to isolate the active SARS-CoV RTC from the only currently available source: virus-infected cells . The newly developed IVRA described in this paper ( Fig . 1 ) will allow us to obtain more insight into the architecture and function of the SARS-CoV RTC as a whole , and may aid to address the poorly defined role of cellular membranes . Although RdRp activity in cell lysates was previously reported for the coronaviruses mouse hepatitis virus and transmissible gastroenteritis virus [35]–[40] , this is to our knowledge the first description of a robust in vitro system for coronavirus RNA synthesis that produces the full spectrum of viral mRNAs ( both genomic and sg RNAs ) generated in infected cells . A similar in vitro system was recently developed for the distantly related arterivirus equine arteritis virus ( manuscript in preparation ) , suggesting that our method may be generally applicable to nidovirus RTCs . Protein synthesis occurred in our lysates under the IVRA conditions used , but its inhibition did not affect in vitro RTC activity ( Fig . 2 ) . This suggests that , in contrast to what was described for cells infected with mouse hepatitis virus [41]–[43] or SARS-CoV ( our unpublished data ) , continued translation is not required for RTC activity in vitro . Likely , inhibition of protein synthesis does not influence the activity of the preformed , active RTCs present in our PNS , which are mainly synthesizing RNA of positive polarity ( Fig . 1E ) . Currently , suitable small SARS-CoV RNA replicons , which could be added to an IVRA as exogenous template and be distinguished from natural viral RNAs on the basis of size , are not available . Consequently , addressing the question whether de novo initiation of RNA synthesis occurs in our system must wait until further technical advances ( in this area ) have been made . Still , a potential complication may be the inability of such exogenous templates to enter the membrane-protected RTC , as also observed in this study for molecules like Bal31 nuclease and proteinase K ( Fig . 7 ) . SARS-CoV RTC activity was recovered in a 10 , 000×g heavy membrane pellet ( P10 ) , but the isolated RTCs had to be supplemented with an S10 fraction from infected or uninfected cells to regain activity ( Fig . 3 ) . This indicates that , besides the host factors possibly associated with the RTC in the P10 fraction , also a cytoplasmic host factor is required for SARS-CoV RNA synthesis . The nature of this host factor is currently being analyzed . Replication appeared to be particularly dependent on the presence of this host factor . While transcription was only 2- to 3-fold reduced , replication was barely detectable in the P10 fraction depleted of the host factor ( Fig . 3 ) . Whether this difference is merely due to the larger size of the genomic RNA and/or reflects a higher demand or specific role for the host factor in replication remains to be investigated in more detail . The RTC activity cosedimented with newly synthesized viral RNA , several replicase subunits and nsp3- , nsp4- and nsp6-containing membrane structures . The latter proteins are ( putative ) multi-spanning transmembrane proteins [23] , [24] , [44] presumed to be important in the induction of the RTC-related membrane rearrangements that accompany SARS-CoV infection [26] , [27] . Furthermore , the cosedimentation of nsp3 with the RTC ( Fig . 6 ) may indicate that one or multiple of the enzymatic activities of this multidomain protein [5] are important for RNA synthesis . All of the nsp5 main proteinase copurified with RTC activity in the P10 fraction , although it remains to be investigated whether this finding is directly related to the site of replicase polyprotein processing . The RTC's core enzyme , the nsp12-RdRp , has been postulated to work in concert with a unique second RdRp activity that was recently identified in nsp8 . Its proposed RNA primase activity [15] , [45] would be consistent with the ( partial ) cosedimentation of nsp8 with the RTC that we observed in this study ( Fig . 6 ) . After TX-100 treatment , nsp3 , nsp5 , and nsp8 no longer cosedimented in P10 , suggesting they had been released from the membrane structures . In addition , protease protection experiments in the absence and presence of detergents revealed that nsp5 and ( part of ) nsp8 were shielded by membranes , while the predicted cytoplasmic domains of nsp3 were not [23] . The experiments in Fig . 7 indicated that a cytoplasmic form of nsp8 ( in S10 ) was also shielded from protease activity by membranes . This suggests the existence of membrane structures distinct from the RTC-containing complexes in P10 . The cosedimentation of nsp3 , nsp5 , and nsp8 with RTC activity is in line with their colocalization in specific structures in the perinuclear region of SARS-CoV-infected cells , as observed by immunofluorescence microscopy [25] , [26] . Free RNA of transmissible gastroenteritis virus was previously found to be susceptible to nuclease treatment , whereas most negative-stranded RNAs , and a small fraction of ( probably nascent ) positive-stranded RNAs , were present in membrane-protected complexes [46] . In our study , non-ionic detergents rendered SARS-CoV RNA susceptible to nuclease digestion ( Fig . 7 ) and destroyed all RTC activity ( Fig . 1 ) . This again signifies the importance of intact membrane structures for viral RNA synthesis . Their disruption may have dissociated the active enzyme complex and/or changed the RTC's microenvironment , or may have provided access to cytoplasmic nucleases . The bulk of newly synthesized SARS-CoV genome remained associated with the RTC-containing heavy membrane structures , while sg RNAs appeared to be more readily released from the structures in which they had been synthesized . In previous studies with transmissible gastroenteritis virus , it was also found that preferentially sg RNAs were no longer associated with the membrane-associated complexes [46] . The released RNA molecules might represent a pool of mRNAs destined for translation into structural and accessory proteins ( sg RNAs ) and additional replicase proteins ( RNA1 ) , while the RTC-associated RNAs might be engaged in replication and/or packaging . In this manner , the intracellular compartmentalization mediated by the formation of specialized membrane structures might also serve to coordinate different steps in the viral life cycle and/or enhance their specificity for viral RNA . Surprisingly , after treatment with 0 . 5% TX-100 , a large fraction of genomic RNA remained in the 10 , 000×g pellet , suggesting it is associated with detergent-resistant structures . This might indicate that , as postulated for hepatitis C virus replication complexes [47]–[49] , the SARS-CoV RTC is associated with lipid rafts or lipid droplets , a feature that could also explain the proposed role of lipid rafts during the early stages of SARS-CoV replication [50] . If SARS-CoV RTCs , as this study suggests , are enclosed by membranes that may provide an optimal environment for viral RNA synthesis , this raises the question of how newly synthesized RNA products are released from these structures . Moreover , the fact that RTC activity depends on a cytoplasmic host factor that does not cosediment with the complex is an additional indication that crosstalk between cytoplasm and RTC-containing membrane structures must occur , e . g . via channels that may facilitate transport across membranes . Taken together , our data support the existence of a functional link between SARS-CoV RNA synthesis and the unusual membrane structures induced upon coronavirus infection .
Vero-E6 cells were infected with SARS-CoV ( strain Frankfurt 1 ) at a multiplicity of infection of 5 as described previously [26] . All procedures involving live SARS-CoV were performed in the biosafety level 3 facility at Leiden University Medical Center . Rabbit antisera recognizing nsp3 , nsp5 , and nsp8 were described previously [26] . Antisera against nsp4 and nsp6 were raised in New Zealand White rabbits using as antigens the bovine serum albumin-coupled synthetic peptides FSNSGADVLYQPPQTSITSAVLQ and LNIKLLGIGGKPCIKVATVQ , representing the C-terminal sequences of nsp4 and nsp6 , respectively . SARS-CoV- or mock-infected cells ( eight 175 cm2 flasks ) were harvested by trypsinization at 10 hours post infection . To inhibit cellular transcription , 2 µg/ml actinomycin D was present in all solutions used for harvesting and washing of the cells . After washing with PBS , cells were resuspended in 2 ml ice-cold hypotonic buffer ( 20 mM HEPES , 10 mM KCl , 1 . 5 mM MgOAc2 , 1 mM DTT , 133 U/ml RNaseOUT ( Invitrogen ) and 2 µg/ml actinomycin D , pH 7 . 4 ) and incubated for 10 min at 4°C . Cells were disrupted using a Dounce homogenizer by giving 30 strokes with a tight fitting pestle . Isotonic conditions were restored by adding HEPES , sucrose , and DTT , which resulted in a final lysate containing 35 mM HEPES , pH 7 . 4 , 250 mM sucrose , 8 mM KCl , 2 . 5 mM DTT , 1 mM MgOAc2 , 2 µg/ml actinomycin D , and 130 U/ml RNaseOUT . Nuclei , large debris , and any remaining intact cells were removed by two successive 5-min centrifugations at 1 , 000×g , and the resulting PNS was either assayed immediately for RTC activity or stored at −80°C . The SARS-CoV titer present in PNS was approximately 108 plaque-forming units per ml . Plaque assays performed before and after IVRAs revealed that no measurable de novo virus production occurred during this assay ( data not shown ) . A 10 , 000×g pellet ( P10 ) and supernatant ( S10 ) fraction were prepared from PNS by centrifugation at 10 , 000×g for 10 min . The pellet was resuspended in dilution buffer ( 35 mM HEPES , 250 mM sucrose , 8 mM KCl , 2 . 5 mM DTT , 1 mM MgOAc2 , pH 7 . 4 ) , in 1/10 of the original PNS volume from which the pellet had been prepared . In some experiments , PNS was incubated for 15 min at 4°C with 0 . 5% TX-100 prior to the preparation of P10 and S10 fractions . Assays were performed using either 25 µl PNS , 20 µl S10 , 5 µl P10 , or 5 µl P10 supplemented with 20 µl S10 . When required , the total volume was adjusted to 25 µl with dilution buffer . The subsequent addition of reaction components yielded a 28 µl final reaction volume , containing 30 mM HEPES pH 7 . 4 , 220 mM sucrose , 7 mM KCl , 2 . 5 mM DTT , 2 mM MgOAc2 , 2 µg/ml actinomycin D , 25 U RNaseOUT , 20 mM creatine phosphate ( Sigma ) , 10 U/ml creatine phosphokinase ( Sigma ) , 1 mM ATP , 0 . 25 mM GTP , 0 . 25 mM UTP , 0 . 6 µM CTP and 0 . 12 µM and 10 µCi [α-32P]CTP ( GE Healthcare ) . Unless otherwise indicated , IVRAs were performed for 100 min at 30°C . Reactions were terminated by adding 60 µl of a mixture containing 5% lithium dodecyl sulfate , 0 . 1 M Tris-HCl , pH 8 . 0 , 0 . 5 M LiCl , 10 mM EDTA , 5 mM DTT , and 0 . 1 mg/ml proteinase K , and incubating at 37°C for 10 min . When protein synthesis was tested , [α-32P]CTP was replaced with 14 . 3 µCi of Promix ( GE Healthcare ) , containing a mixture of [35S]methionine and [35S]cysteine . To assess the effect of translation inhibition , 70 µg/ml of cycloheximide or 350 µg/ml of puromycin were added . RNA was isolated from IVRA reaction mixtures by acid phenol extraction and isopropanol precipitation . Reaction products were analyzed by denaturing formaldehyde agarose gel electrophoresis essentially as described previously , except that a 1% agarose gel was used [51] . Radiolabeled in vitro synthesized RNA was detected by exposing a PhosphorImager screen directly to the dried gel , after which screens were scanned with a Personal Molecular Imager FX ( Bio-Rad ) and data were analyzed with Quantity One version 4 . 5 . 1 ( Bio-Rad ) . Unlabeled endogenous SARS-CoV RNA was detected by hybridization with a 32P-labeled oligonucleotide SARSV002 ( 5′-CACATGGGGATAGCACTAC-3′ ) , which is complementary to a sequence present in the 3′-end of all SARS-CoV RNAs [5] . In vitro transcribed RNAs ( 0 . 75 µg ) corresponding to nt 29 , 364-29 , 727 of the 3′-terminal region ( 3′-TR ( + ) ) or complementary to nt 1-378 ( 3′-TR ( − ) ) of the SARS-CoV genome were immobilized to Hybond N+ membrane ( GE Healthcare ) . As negative controls , RNAs corresponding to nt 12 , 313–12 , 660 ( ctrl . a ) of the equine arteritis virus genome or its complementary sequence ( ctrl . b ) were included . The membrane with the immobilized probes was prehybridized for 4 hours in a hybridization mixture containing 5×SSPE ( 750 mM NaCl , 50 mM NaH2PO4 , 5 mM EDTA , pH 7 . 0 ) , 0 . 05% SDS , 5x Denhardt and 100 µg/ml homomix I . Subsequently , the membrane was hybridized with half of the 32P-labeled RNA recovered from a 28-µl IVRA in 0 . 8 ml hybridization mix , which was first heat denatured at 70°C for 15 min . After hybridization for 16 h at 56°C , membranes were washed twice for 20 min at 56°C with 4 ml of 5x SSPE , 0 . 05% SDS , and the hybridization signal was quantified by PhosphorImager analysis . Proteins were separated by SDS-PAGE and transferred to Hybond-P PVDF membrane ( GE Healthcare ) by semi-dry blotting . After blocking with 1% casein in PBS containing 0 . 1% Tween-20 ( PBST ) , membranes were incubated with anti-nsp3 , anti-nsp5 or anti-nsp8 rabbit antisera , diluted 1∶2000 in PBST with 0 . 5% casein and 0 . 1% BSA . Peroxidase-conjugated swine anti-rabbit IgG antibody ( DAKO ) and the ECL-plus kit ( GE Healthcare ) were used for detection . Protease protection experiments were done by incubating PNS ( 50 µl ) for 10 min at 20°C with 20 µg/ml of proteinase K either in the absence or presence of 0 . 5% TX-100 . After inactivation of the protease by addition of 2 mM PMSF and fractionation into a 10 , 000×g pellet ( P10 ) and supernatant ( S10 ) , samples were analyzed by Western blotting . For nuclease protection assays , a standard 100-min IVRA was performed with the PNS , after which 5U of Bal31 nuclease was added , either in the presence or in the absence of 0 . 5% TX-100 . After a 10-min incubation , samples were fractionated into S10 and P10 fractions . Radiolabeled RNA was isolated from the fractions and analyzed as described above . One volume of 6% paraformaldehyde in 60 mM PIPES , 25 mM HEPES , 2 mM MgCl2 , 10 mM EGTA , pH 6 . 9 was added to P10 fractions . Formvar-coated grids were placed on 10-µl drops of these fixed P10 fractions and incubated at room temperature for 1 min . After blocking with 1% BSA in PBS , grids were incubated for 30 min with rabbit antisera directed against nsp3 , nsp4 or nsp6 ( 1∶200 ) in PBS containing 1% BSA . Bound rabbit IgG was detected with protein A carrying 15-nm gold particles . After negative staining with 2% phosphotungstic acid , grids were viewed in a FEI T12 transmission electron microscope at 120 kV . | The SARS-coronavirus ( SARS-CoV ) , which causes the life-threatening severe acute respiratory syndrome , replicates in the cytoplasm of infected host cells . A critical early step in the SARS-CoV life cycle is the formation of a replication/transcription complex ( RTC ) that drives viral genome replication and subgenomic mRNA synthesis . Virus-encoded enzymes form the core of this RTC , which is believed to be associated with characteristic virus-induced membrane structures derived from modified host cell membranes . To investigate the connection between these membrane structures and SARS-CoV RNA synthesis , and to characterize RTC composition and function , we isolated these complexes and developed the first in vitro assay to study their activity . SARS-CoV genomic RNA and all eight subgenomic mRNAs were synthesized in this in vitro reaction . By centrifugation , RTC activity could be isolated from the cytoplasm , together with membrane structures , viral enzymes , and RNA . The activity of these isolated RTCs was dependent on a cytoplasmic host factor . RTC activity was destroyed by detergent treatment , suggesting a critical role for membranes that appeared to protect the complex against protease and nuclease digestion . Our data establish a functional connection between viral RNA synthesis and intracellular membranes and show that host factors play a crucial role in SARS-CoV RNA synthesis . | [
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| 2008 | SARS-Coronavirus Replication/Transcription Complexes Are Membrane-Protected and Need a Host Factor for Activity In Vitro |
Herpes simplex virus-1 ( HSV-1 ) causes lifelong infection affecting between 50 and 90% of the global population . In addition to causing dermal lesions , HSV-1 is a leading cause of blindness resulting from recurrent corneal infection . Corneal disease is characterized by loss of corneal immunologic privilege and extensive neovascularization driven by vascular endothelial growth factor-A ( VEGF-A ) . In the current study , we identify HSV-1 infected cells as the dominant source of VEGF-A during acute infection , and VEGF-A transcription did not require TLR signaling or MAP kinase activation . Rather than being an innate response to the pathogen , VEGF-A transcription was directly activated by the HSV-1 encoded immediate early transcription factor , ICP4 . ICP4 bound the proximal human VEGF-A promoter and was sufficient to promote transcription . Transcriptional activation also required cis GC-box elements common to the VEGF-A promoter and HSV-1 early genes . Our results suggest that the neovascularization characteristic of ocular HSV-1 disease is a direct result of HSV-1's major transcriptional regulator , ICP4 , and similarities between the VEGF-A promoter and those of HSV-1 early genes .
Herpes simplex virus-type 1 ( HSV-1 ) is a neurotropic member of the alpha herpesvirus family with worldwide seroprevalence rates ranging from between 50–90% . [1] , [2] . Primary infection is usually mild or asymptomatic in the immunocompetent host and typically occurs in childhood or early adolescence following inoculation of mucosal epithelial surfaces . During initial infection , virions gain access to sensory nerve fibers and are transported to neuronal cell bodies in the trigeminal ganglia where HSV-1 establishes a latent infection [3] . Although treatable , infection is life-long as a result of the sequestration of latent virus from immunological surveillance [3] . Latency may be broken during times of stress or immunological suppression resulting in the resumption of the lytic viral replication cycle . Newly produced virions migrate down trigeminal nerve fibers to epithelial surfaces where the reactivated virus resumes lytic viral replication and infectious virions are released . Symptoms of reactivation may be as mild as dermal vesicles or as severe as herpes simplex encephalitis , the most common cause of sporadic viral encephalitis in the world [4] . Despite the familiarity of dermal HSV-1 lesions , the most significant clinical consequence of HSV-1 infection is secondary to ocular HSV-1 infection . The trigeminal nerve provides sensation to the lips , nose , and eye . Although the skin about the orofacial region is the most frequent target of viral reactivation , all areas innervated by the trigeminal nerve branches are susceptible and recurrent bouts of corneal reactivation are not uncommon [3] , [5] . Repeated incidents of corneal infection lead to the breakdown of corneal immunologic privilege and the development of an immunoinflammatory disorder termed herpetic stromal keratitis ( HSK ) . Chronic inflammation elicits extensive corneal opacification driven by host CD4+ T cells and neovascularization secondary to disruption of the normal equilibrium between corneal angiogenic and anti-angiogenic factors [5] . The immunoinflammatory nature of HSK is particularly vexing , as patients refractory to treatment with antiviral medication may require corneal transplantation [5] . Inflammation and corneal vascularization promote corneal graft failure [5] . Thus , the HSK-associated inflammatory processes which necessitate corneal transplantation also substantially increase the risk of transplant rejection in HSK patients [5] . Several different mechanisms contribute to corneal immunologic privilege including the expression of immunosuppressive factors , specialized tolerance-promoting DC populations , and the avascular nature of the cornea [6]–[8] . Corneal avascularity may play an important role during HSK , as corneal neovascularization is highly predictive of future graft failure in HSV-1 affected patients [5] . Furthermore , avascular tissues are universally immunologically privileged [9]–[11] . The cytokine vascular endothelial growth factor-A ( VEGF-A ) plays a particularly crucial role in HSV-1-induced corneal neovascularization and drives both angiogenesis and lymphangiogenesis [9] , [12]–[14] . A recent study in our laboratory revealed that VEGF-A is expressed by HSV-1 infected corneal epithelial cells due to increased accumulation of VEGF-A mRNA in HSV-1 infected cells [12] . However , the mechanism by which VEGF-A expression is induced remains unclear . In the current study , we report the discovery that transcriptional up-regulation of VEGF-A is dependent on HSV-1's major transcriptional regulator , infected cell protein 4 ( ICP4 ) . ICP4 binds the proximal human VEGF-A promoter and is sufficient for transcriptional up-regulation of VEGF-A . Additionally , VEGF-A expression requires a tract of GC-rich sequences with high homology to the promoters of HSV-1 early ( E ) genes that ICP4 normally transactivates . Our results indicate that HSV-1 promotes expression of VEGF-A via ICP4-dependent transactivation , and this may be the result of sequence similarity between HSV-1 E genes and the human proximal VEGF-A promoter .
The human cornea is normally devoid of blood and lymphatic vessels due to low expression of ( lymph ) angiogenic cytokines and abundant expression of anti- ( lymph ) angiogenic factors [6] , [8] , [10] , [11] . Our group has previously described the expression of the pro- ( lymph ) angiogenic cytokine VEGF-A by HSV-1 infected corneal epithelial cells which drives lymphangiogenesis following HSV-1 infection [12] . Increased expression of VEGF-A was at least partially the result of transcriptional up-regulation as mice expressing GFP under the proximal human VEGF-A promoter showed selective induction of GFP within HSV-1 antigen-positive cells at 36 hours post infection ( PI ) with HSV-1 strain McKrae ( Figure 1A ) . Likewise , GFP reporter expression was detectable by 12 hours PI ( Figure 1B ) . Real time PCR analysis of VEGF-A mRNA relative to the housekeeping genes β-actin , TBP , and PPIA demonstrated transcriptional up-regulation following HSV-1 infection in Tert-immortalized human corneal epithelial cells ( THCE ) ( Figure 1C , p<0 . 01 ) . Likewise , HSV-1 infection of the human embryonic kidney fibroblast 293 cell line yielded a similar transcriptional up-regulation of VEGF-A in human cells ( Figure 1D ) . VEGF-A was also detected in cytoplasmic extracts of 293 cells with the highest detectable concentration being found at 6 hours PI ( Figure 1E ) . VEGF-A promoter activity was also assayed using a reporter vector driving firefly luciferase under the proximal human VEGF-A promoter . Infection of 293 cells with three different wild-type HSV-1 strains ( -McKrae , KOS , and SC16 ) , and the homologous HSV-2 virus , all significantly induced expression of the VEGF-A promoter luciferase reporter gene ( Figure 1F ) . Reporter expression peaked at 12 hours PI , which was 6 hours after VEGF-A levels peaked in the cytoplasm , possibly due to the differential secretion of VEGF-A versus the non-secreted luciferase reporter which was retained in cells . Thus , HSV-1 infection drove transcriptional up-regulation of VEGF-A and protein expression both in vitro and in vivo . VEGF promoter-driven GFP expression was only detected in HSV-1 antigen-positive cells within the cornea during acute infection [12] . However , HSV-1 antigen-negative cells expressing GFP reporter were observed in limbal tissues , proximal to the cornea ( data not shown ) . Neutrophils contain preformed stores of VEGF-A and could release VEGF-A during HSV-1 infection without detectably expressing reporter for VEGF-A transcription [15] . To determine the relative contribution of HSV-1 infected versus uninfected cells to VEGF-A production in vivo , we utilized the Cre-lox system to selectively excise the VEGF-A gene from HSV-1 infected cells . Either C57BL/6 controls or mice with a floxed VEGF-A gene ( Flxd-VEGFA ) were infected with either wild type HSV-1 strain SC16 or a Cre-expressing HSV-1 recombinant virus , which was derived from strain SC16 and expressed Cre under the control of the HSV-1 ICP0 promoter ( Figure 2A and B ) . In C57BL/6 control mice , corneal VEGF-A concentrations did not significantly differ at day 3 PI between mice inoculated with wild type HSV-1 versus the Cre-expressing HSV-1 recombinant ( Figure 2C , p>0 . 05 ) . However , in Flxd-VEGFA mice , VEGF-A concentrations at day 3 PI were 75% lower in animals infected with the Cre-expressing ICP0 recombinant virus relative to Flxd-VEGFA miceinfected with wild type HSV-1 ( Figure 2D ) . Based on the fact that selective Cre-mediated excision of the VEGF-A gene reduced VEGF-A levels by 4-fold , it appeared that HSV-1 infected cells were the predominant source of VEGF-A during acute HSV-1 infection . VEGF-A is expressed during a wide range of inflammatory processes including wound healing , psoriasis , and following the ligation of some toll-like receptors ( TLR ) [16] , [17] . HSV-1 infection stimulates the TLRs 2 , 3 , 4 , and 9 [18] . All TLRs depend on the MyD88 and/or TRIF adaptor proteins for signal transduction [19] . Therefore , we tested whether VEGF-A was TLR-dependent using mice deficient in MyD88 ( MyD88−/− ) or TRIF ( TRIF−/− ) . Corneas of MyD88−/− mice and TRIF−/− or their respective C57BL/6 and B6129 controls strains , were scarified and inoculated with PBS or 105 PFU of HSV-1 . Corneas were harvested at 24 hours PI and assayed for VEGF-A levels by cytokine bead array . HSV-1 infection induced VEGF-A expression to high and statistically equivalent levels in the corneas of C57BL/6 , MyD88−/− , B6129 , and TRIF−/− mice ( Figure 3A and B ) . While VEGF-A levels were slightly higher in TRIF−/− mice relative to B6129 controls , this difference was not significant ( Figure 3B , p>0 . 05 ) . In addition , VEGF-A dependent corneal lymphangiogenesis was equivalent in MyD88−/− and TRIF−/− mice at day 5 PI relative to wild type control mice ( data not shown ) . MAP kinase activation up-regulates VEGF-A expression in tumors and following TLR ligation [20]–[22] . Activation of MEK following Helicobacter pylori infection induces VEGF-A expression through the transcription factors Sp1 and Sp3 [23] . The HSV-1 immediate early ( IE ) gene product , ICP27 activates JNK and p38 pathways [24] , and the HSV-2 homolog of the HSV-1-encoded protein kinase , US3 , directly activates the MEK pathway [25] . Therefore , we sought to clarify if any of these signal transuction pathways might play a role in HSV-1 stimulated VEGF-A expression . Using 293 cells , we tested if HSV-1 induction of VEGF-A mRNA accumulation was dependent on MEK1/2 , p38 , or JNK1/2 signal transduction pathways using their respective inhibitors , U1026 , SB206580 , and SP600125 ( Figure 3C ) . Treatment of cells with 10 µM of each inhibitor ( ≥100 times the IC50 values of each drug ) had no discernable effect on the up-regulation of VEGF-A mRNA levels 12 hours PI relative to vehicle- treated cells ( Figure 3C ) . Therefore , neither MyD88- , TRIF- , MAP kinase- , MEK1/2- , p38- , or JNK1/2-signal transduction pathways appeared to be required for HSV-1 infection to induce VEGF-A accumulation in HSV-1 infected cells . To clarify which promoter elements were required for HSV-1 to induce expression from the VEGF-A promoter , we utilized luciferase reporter plasmids containing human VEGF-A promoters that ranged in size from 2068 base pair ( bp ) promoter that spanned −2018 to +50 bp ( relative to the transcription start site ) to a minimal 102-bp VEGF-A promoter that spanned −52 to +50 bp ( Figure 4A ) . Transfected 293 cells were then assayed for luciferase expression at 12 hours PI with HSV-1 strain McKrae ( Figure 4A ) . Expression depended on a short stretch of DNA from −85 to −52 bp relative to the transcription start site . The absence of HIF-1α elements ( −975 to −968 bp ) or STAT3 elements ( −848 to −840 bp ) , which mediate VEGF-A transcriptional up-regulation in response to hypoxia and IL-6 respectively [25] did not prevent HSV-1 from inducing a 10-fold increase in luciferase expression from the −790 to +50 BP promoter ( Figure 4A ) . EMSA analysis also demonstrated a distinct alteration in the binding of nuclear proteins to biotinylated probe spanning −88 to +55 bp following infection with HSV-1 . In uninfected 293 cells , nuclear protein extracts bound probe in discrete shifts , possibly corresponding to either multimeric structures or distinct complexes ( Figure 4B ) . In contrast , nuclear protein extracts harvested 6 hours after HSV-1 McKrae infection contained proteins that primarily bund the VEGF-A promoter probe in a in a broad band of lower mobility . The same probe was not bound to detectable levels by nuclear extracts harvested from HSV-1 strain McKrae-infected cells at 12 hours PI ( Figure 4B ) . However , during infection with the less virulent strain HSV-1 strain KOS , an EMSA shift was still detectable at 12 hours PI ( data not shown ) . The original construction of the luciferase reporter plasmid driven by the −85 to 50 bp segment of the human VEGF-A promoter resulted in the loss of the pGL3 multiple cloning site ( MCS ) . A new and equivalent luciferase expression vector was constructed that retained a MCS , and thus allowed site-directed mutagenesis of the proximal VEGF-A promoter from −88 to +55 bp relative to the transcription start site . The resulting plasmid was denoted [pVA8855] and performed equivalently to the original plasmid vector . Three consensus “GC box” sequences are present between 85 to −52 bp of the VEGF-A promoter , and which may serve as binding sites for transcription factors of the Sp family [22] ( Figure 4C ) . We tested the relevance of these GC box sequences in the VEGF-A promoter by mutating individual GGGCGG consensus sequences , or combinations thereof , to the mutant sequence AACACA ( Figure 4C ) . Human 293 cells were transiently transfected with the wild type VEGF-A promoter construct , pVA8855 , or 1 of 8 GC-box mutant plasmids . After 48 hours , cells were mock-inoculated or inoculated with 3 pfu per cell of HSV-1 strain McKrae , and luciferase levels were compared at 12 hours PI ( Figure 4D ) . Mutation of GC-box 1 alone ( −85 to −80 bp ) or GC-box 2 alone ( −74 to −69 bp ) did not preclude HSV-1 induction of the luciferase reporter gene ( Figure 4D ) . In contrast , deletion of GC-box 3 ( −58 to −53 bp ) or any permutation of two or more GC-boxes ablated the capacity of HSV-1 to induce luciferase expression from the minimal VEGF-A promoter represented by −88 to +50 bp relative to the transcriptional start site ( Figure 4D ) . Although GC box sequences were required for VEGF-A promoter transcriptional up-regulation , we did not detect binding of nuclear proteins to GC boxes using proximal VEGF-A promoter probes . EMSA assays of native or GC box mutated probes ( Figure S1A ) spanning −88 to −50 bp relative to the transcription start site did not show differential EMSA shifts between probes using either uninfected or 6 hour PI nuclear extracts ( Figure S1B ) . However , we presume this negative result was due to extensive secondary structure within this region of the human VEGF-A promoter blocking transcription factor binding as probe containing an isolated GC box probe was shifted using nuclear extracts from 0 , 6 , and 12 hours PI ( Figure S1C ) . HSV-1 infection up-regulates the transcription factor EGR-1 which promotes transcription at sites within the HSV-1 genome bearing the core consensus sequence GCGGGGGCG [26] . The region of the human VEGF-A promoter spanning −85 to −52 bp contains two EGR-1 binding sequences , and EGR-1 drives expression of VEGF-A following growth factor stimulation [22] . Western blot analysis of HSV-1 infected 293 cells for EGR-1 did not demonstrate up-regulated expression of EGR-1 until 12 hours PI ( Figure 5A and B ) . In contrast , VEGF-A transcript was significantly augmented ( p< . 01 ) as early as 3 hours PI suggesting that an EGR-1 independent pathway activated the VEGF-A promoter in HSV-1 infected cells ( Figure 5C ) . To test for the possibility of either an indirect or direct role for EGR-1 , we used siRNA to reduce EGR-1 . Transfection with siRNAs against EGR-1 ( 4538 and 4539 ) blocked EGR-1 up-regulation following HSV-1 infection with EGR-1 expression reduced by roughly 89% ( comparison between HSV-1 infected non-transfected cells versus HSV-1 infected cells treated with siRNA 4539 , p<0 . 01 , Figure 5D ) . However , even in the absence of EGR-1 up-regulation VEGF-A transcript expression was up-regulated following HSV-1 infection in siRNA 4539-treated cells and tended to increase following siRNA 4538 treatment as well ( Figure 5E ) . EGR-1 is constitutively expressed in 293 cells , and EGR-1 affects HSV-1 gene expression [26] . Thus , EGR-1 knockdown may indirectly affect pathways that could influence VEGF-A transcription . We tested for a direct role for EGR-1 via site specific mutagenesis of EGR-1 binding sequences within the VEGF-A promoter . EGR-1 is a C2H2-type three zinc finger transcription factor with the fingers binding the triplets GCG , G/T GG , and GCG respectively [27] . EGR-1 and GC box elements within the −85 to −52 bp region of the VEGF-A promoter overlap so only modification to the first triplet of EGR-1 sites could be made without also altering GC box sequences ( Figure 5F ) . In fact , mutation of the first triplet is sufficient to abrogate EGR-1 binding [28] , [29] . Mutation of EGR-1 consensus sequences had no effect on VEGF-A promoter driven luciferase expression following HSV-1 infection ( pVA8855 versus ΔEGR-1 or promoterless reporter vector pGL3 , Figure 5G ) . Although EGR-1 could conceivably contribute to transcription of the VEGF-A gene at late stages of HSV-1 infection , we did not observe a requirement for either EGR-1 up-regulation or the presence of EGR-1 consensus sites for transcriptional up-regulation of VEGF-A . Two experiments were conducted with biochemical inhibitors to determine if HSV-1's capacity to induce the VEGF-A promoter was dependent upon the de novo synthesis of viral or cellular proteins in HSV-1 infected cells . The first experiment used the protein synthesis inhibitor cycloheximide . When protein translation was allowed to occur , 3 pfu per cell of HSV-1 McKrae induced a 10-fold increase in VEGF-A mRNA levels in THCE cells at 12 hours PI ( Figure 6A ) . A second experiment was conducted with the guanosine analogue acyclovir to determine if restriction of HSV-1 DNA synthesis and ∼445 HSV-1 late ( L ) proteins affected HSV-1's capacity to induced VEGF-A synthesis [30] , [31] . Infection with HSV-1 McKrae induced VEGF-A mRNA to high and equivalent levels in THCE cells treated with vehicle or 200 µM acyclovir ( Figure 6B ) . These data excluded the possibility that HSV-1 virion attachment and/or entry were sufficient to induce VEGF-A mRNA accumulation . Rather , the data suggested that de novo protein synthesis during the initial hours of HSV-1 infection was necessary to induce VEGF-A gene expression in HSV-1 infected cells . To explore the possibility that one or more HSV-1 proteins might contribute to VEGF-A transcriptional up-regulation , the capacity of wild type HSV-1 strain KOS to induce VEGF-A mRNA accumulation was compared to two well characterized HSV-1 KOS-derived mutants , HSV-1 n12 [32] and HSV-1 hr94 [33] . HSV-1 n12 is an ICP4− null virus that fails to encode HSV-1's major transcriptional regulator , infected cell protein 4 ( ICP4 ) , and consequently fails to exit the IE phase of protein accumulation [32] . As a result , HSV-1 ICP4− null viruses over express four IE proteins ( ICP0 , ICP22 , ICP27 , and ICP47 ) and fail to efficiently synthesize the other ∼70 E and L proteins encoded by the HSV-1 genome . The second HSV-1 mutant , hr94 , fails to encode HSV-1 origin-binding protein ( OBP ) , which is necessary for the onset of viral DNA synthesis [33] . As a result , HSV-1 OBP− null viruses efficiently synthesize ∼30 IE and E proteins , but exhibit more restricted expression of ∼45 HSV-1 L proteins . The efficiency of VEGF-A mRNA induction was compared in 293 cells inoculated with 3 pfu per cell of wild type HSV-1 KOS versus the KOS-derived ICP4− null ( n12 ) or OBP− null ( hr94 ) viruses . HSV-1 KOS induced an average 8-fold increase in VEGF-A mRNA levels at 12 hours PI ( Figure 6C ) . Likewise , the HSV-1 OBP− null virus induced an average 6-fold increase in VEGF-A mRNA levels ( Figure 6C ) . In contrast , infection with an HSV-1 ICP4− null virus did not detectably induce VEGF-A mRNA accumulation ( Figure 6C ) . An experiment was conducted to determine if ICP4 played a role in the capacity of HSV-1 to induce the minimal VEGF-A promoter represented by −88 to +55 bp in luciferase reporter plasmid construct pVA8855 ( Figure 4D ) . Specifically , 293 cells were transfected with pVA8855 or a promoterless control plasmid , pGL3 , for 48 hours , and then the cells were infected with 3 pfu per cell of wild type , ICP4− null , or OBP− null HSV-1 . Luciferase reporter gene expression was significantly elevated in 293 cells inoculated with HSV-1 KOS or the HSV-1 OBP− null virus ( Figure 6D , **p< . 01 ) . In contrast , inoculation with the HSV-1 ICP4− null virus failed to induce luciferase expression from the VEGF-A promoter in pVA8855 ( Figure 6D ) . Tests were conducted to determine if the IE transactivator ICP0 was necessary for HSV-1 to induce VEGF-A mRNA accumulation . Human 293 cells infected with an HSV-1 ICP0− null virus , 7134 , transcribed VEGF-A mRNA at levels equivalent to cells infected with the parental virus HSV-1 KOS ( Figure 6E ) . Likewise , HSV-1 KOS and HSV-1 ICP0− null virus were compared for their ability to induce VEGF-A production in vivo in the corneas of interferon-signaling deficient CD118−/− mice , in which HSV-1 ICP0− null viruses may replicate to nearly wild type levels during the first 24 hours PI [34] . In these in vivo tests , VEGF-A levels were significantly up-regulated at 24 hours PI in the corneas of mice inoculated with wild type HSV-1 KOS or the ICP0− null virus relative to mock-infected controls ( Figure 6F , **p< . 01 ) . Thus , ICP0 is not essential for HSV-1 infection to induce VEGF-A mRNA or protein accumulation . We verified these conclusions in an in vivo test using pVEGFA-GFP reporter mice . Following inoculation of mouse corneas with HSV-1 ICP4− null virus , HSV-1 antigen positive cells were detectable in the cornea at 12 hours PI , but these sites did not co-localize with induction of the pVEGFA-GFP reporter in these transgenic mice ( Figure 6G ) . In contrast , mouse corneas inoculated with wild type KOS ( Figure 6G ) or HSV-1 OBP− null virus ( data not shown ) exhibited a clear co-localization of HSV-1 antigen-positive cells and induction of the pVEGFA-GFP reporter gene . Although low level GFP expression was observed in HSV-1 antigen-positive cells in reporter mice infected with ICP4− null virus , expression was extremely faint compared to that observed in KOS infected cells and was comparable to the transient GFP expression observed following corneal scarification procedure required for HSV-1 infection . As an additional control we tested the ability of an HSV-1 ICP27− null virus , d27-1 [35] , to induce the minimal VEGF-A promoter in plasmid pV8855 . ICP27 , like ICP4 , is a viral IE protein that is required for the efficient expression of E and L proteins [36] . Hence , HSV-1 ICP27− null viruses over express viral IE proteins including ICP4 ( Figure S3A and B ) , but fail to express most of other ∼70 E and L HSV-1 proteins [35] . Consistent with the observed over expression of ICP4 , cells inoculated with an HSV-1 ICP27− virus induced the VEGF-A promoter in pV8855 to express 7-fold and 60-fold higher levels of luciferase than was observed in cells infected with wild type HSV-1 or an HSV-1 ICP4− virus , respectively ( Figure S3C ) . This correlation between ICP4 over expression and elevated luciferase reporter induction in ICP27− virus-infected cells suggested that the major transcriptional regulator of HSV-1 , ICP4 , might play a direct role in transcriptional induction of the VEGF-A promoter rather than acting via an indirect mechanism that required the synthesis of HSV-1 E or L proteins . To test for a role for ICP4 in the complex binding the proximal VEGF-A promoter , nuclear extracts were harvested from 293 cells following infection with HSV-1 KOS , or ICP4− null virus or OBP− null virus . Infection with HSV-1 KOS or the OBP− null virus led to a change in the EMSA shift of VEGF-A −88 to +55 probe within 6 hours PI that remained through 12 hours PI but no change in the probe shifts was observed in ICP4− virus-infected nuclear extracts relative to uninfected extracts ( Figure 7A ) . The ICP4 binding consensus DNA sequence A/GTCGTCNNNNYCGRC ( N = any nucleotide , Y = pyrimidine , R = purine ) is not present in the human VEGF-A promoter region [37] , [38] . However , ICP4 undergoes extensive post-translational modifications that alter sequence affinity and ICP4 binds a wide variety of sequences with no apparent relation to its consensus sequence [37]–[40] . To determine if ICP4 bound the human VEGF-A promoter , −88 to +55 base pair probe was incubated with nuclear protein extracts harvested from cells at 6 hours PI and assayed for mobility supershift following addition of monoclonal antibody against HSV-1 ICP4 . Addition of anti-ICP4 retarded probe/nuclear protein mobility establishing binding of ICP4 to the VEGF-A proximal promoter region ( Figure 7B ) . ICP4 interaction with the proximal human VEGF-A promoter did not require GC box sequences from −85 to −52 bp relative to the transcription start site ( Figure S2A ) . Furthermore , EMSA supershift analysis of probe spanning either −88 to −50 bp or probe spanning −50 to +55 bp indicated that the ICP4 binding site or sites were localized to −50 to +55 base pairs relative to the transcription start site ( Figure S2B and C ) . ICP4 binding to the proximal human VEGF-A promoter was specific . Binding was not observed using oligo probe spanning an irrelevant sequence of the VEGF-A ( bp −1513 to −1338 relative to the transcription start site , Figure 7C ) . In addition , nuclear protein complexes from HSV-1 infected cell extracts were also competitively disassociated from −88 to +55 bp probe by molar excesses of unlabeled oligo containing the ICP4 consensus sequence ( Figure 7D ) . We next sought to determine if ICP4 expression was sufficient to drive transcriptional enhancement of VEGF-A . As with 293 and THCE cells , luciferase reporter activity for the human proximal VEGF-A promoter is up-regulated in human primary dermal keratinocytes ( HPKs ) in an ICP4-dependent fashion ( Figure 8A ) . To determine if ICP4 was sufficient for reporter expression , HPKs were transfected with either promoterless luciferase vector pGL3 or pVA8855 and transduced with adenoviral vector expressing the reverse tetracycline-controlled transactivator protein ( AdrtTA ) and either a negative control adenvoviral vector AdNull or vector expressing ICP4 under the control of a tetracycline response element ( AdICP4 ) . After incubation for 30 hours in the presence or absence of 3 µm doxycycline , cells were assayed for luciferase activity . The reporter was significantly up-regulated in AdICP4 transduced cells treated with doxycycline relative to AdICP4+ vehicle-treated and AdNull-treated controls ( Figure 8B ) . We also tested ICP4 sufficiency for VEGF-A promoter activation in vivo by cornea stromal co-injection of either AdrtTA and AdNull or AdrtTA and AdICP4 into pVEGFA-GFP reporter mice . Mice were kept on doxycycline treated water ( 2 mg/mL ) for 5 days before eyes were harvested and examined for GFP expression by confocal microscopy . The VEGFA-GFP reporter was not detectably induced in mouse corneas transduced with AdNull , but was abundantly expressed in AdICP4-transduced corneas ( Figures 8C and D , respectively ) . Thus , ICP4 was required for transcriptional up-regulation of VEGF-A during HSV-1 infection and sufficient to augment transcription at the proximal human VEGF-A promoter . Blockade of VEGF-A and its receptor VEGFR-2 are sufficient to block HSV-1- induced lymphangiogenesis but not angiogenesis [5] , [12] , [41] , [42] . Lymphatic vessels typically express the VEGF family receptors VEGFR-2 and VEGFR-3 [9] while blood vessels express VEGFR-1 and VEGFR-2 [9] , both of which bind VEGF-A [13] . We hypothesized that the mere partial dependence on blood vessel growth for VEGF-A may be due to the production of other VEGFR-1 ligands during HSV-1 infection . Transcript abundance for all five human VEGFs; VEGF-A , VEGF-B , VEGF-C , VEGF-D , and PGF was assayed by real-time RT-PCR in human primary keratinocytes infected with HSV-1 KOS , ICP4− null , or OBP− null virus . Transcripts encoding the VEGFR-3 ligands VEGF-C and VEGF-D were not detected by RT-PCR ( Figure 8E ) . Likewise , the VEGFR-1 ligand VEGF-B was not up-regulated HSV-1 infection ( Figure 8E ) . However , transcript for the VEGFR-1 ligands VEGF-A and PGF significantly increased following inoculation of human primary keratinocytes with HSV-1 KOS or OBP− null virus ( Figure 8E ) . As with VEGF-1 mRNA levels , increased transcript abundance of PGF mRNA was also ICP4-dependent ( Figure 8E ) . At the time of writing it is unclear whether ICP4-dependent transcription of PGF occurs in the same direct fashion as with VEGF-A or whether ICP4 and HSV-1 indirectly activate transcription at the PGF promoter . However , our results indicate that HSV-1 infection induces mRNA transcription from cellular genes that encode two VEGFR1 ligands , and this induction is ICP4-dependent . Further testing will be required to determine if chemical or antibody-mediated blockade of VEGF-A and PGF ligands is sufficient to stop the angiogenic sequelae characteristic of ocular HSV-1 infection .
Despite the longstanding awareness of the impact of angiogenesis and contribution of VEGF-A during ocular HSV-1 infection [5] , [41] , [42] , recognition of HSV-1 infected cells as a source of VEGF-A has occurred only recently [12] . Our study suggests that the preeminent source for VEGF-A during acute ocular infection is HSV-1 infected cells on the basis co-localization of VEGFA-GFP reporter with HSV-1 antigen ( Figure 1 , [12] ) and experiments using HSV-1 infected cell-specific deletion of VEGF-A ( Figure 2A ) . We had initially hypothesized that VEGF-A expression was driven by TLR or other pattern recognition receptors . TLR ligation drives expression of VEGF-A as well as other VEGF family members [16] , [43] , [44] . All TLRs signal through either MyD88 and/or TRIF [19] . Yet , MyD88 and TRIF deficiency had no impact on VEGF-A expression following HSV-1 inoculation . This result could be reconciled with functional redundancy between MyD88 and TRIF signaling TLRs allowing expression in the sole absence of either MyD88 or TRIF , but not in animals deficient in both genes . However , that appears unlikely as corneal infection with vesicular stomatitis virus does not drive detectable pVEGFA-GFP reporter expression in reporter mice [12] despite activation of MyD88- and TRIF-dependent TLRs [45] , [46] as well as other pattern recognition receptors such as LRRFIP1 and RIG-I [14] , [47] , [48] . The possibility of TLR-driven VEGF-A expression during HSV-1 infection would also be particularly unlikely in human cells as the preeminent TLR mediating HSV-1 recognition in humans is TLR3 [49] . TLR3/TRIF signaling profoundly suppresses VEGF-A through interferon pathways [50] and is thus , unlikely to make a positive contribution during HSV-1 infection . Conceivably non-TLR pattern recognition receptors could contribute to VEGF-A expression . However , ICP4- HSV-1 would be expected to activate similar pattern recognition receptors as parental HSV-1 yet did not up-regulate VEGF-A transcript . A null adenoviral vector also did not stimulate reporter for VEGF-A , while AdICP4 drove reporter expression in vitro and in vivo . Thus , two different model systems with considerable overlap for stimulation of pattern recognition receptors did not drive VEGF-A expression without concurrent expression of ICP4 . In fact , innate pattern recognition may decrease VEGF-A expression . Viral pattern recognition receptors almost universally activate interferon pathways [51] , [52] , and interferon inhibits expression of VEGF-A [50] . Transcriptional up-regulation of VEGF-A was dependent on the HSV-1 transactivator ICP4 . This observation and sequence similarities between the VEGF-A promoter and HSV-1 E gene promoters suggests that rather than being an innate response to viral infection , the HSV-1 transcriptional regulation program drives VEGF-A expression . VEGF-A mediates a number of responses that may be beneficial to the pathogen such as suppression of DC maturation [53] as well as growth and chemotaxis of neuronal axons [54] , the ultimate target of HSV-1 . All wild-type HSV-1 strains tested to date have maintained the ability to up-regulate VEGF-A transcript and promoter reporter . The highly homologous virus HSV-2 , whose ICP4 is functionally interchangeable with HSV-1 ICP4 [55] , also drove expression of VEGF-A . Shared expression of VEGF-A by these disparate viruses is suggestive of a natural selection mechanism . But activation of transcription of VEGF-A was maintained despite repeated passage within Vero cells , a fibroblast line expected to be deficient in the receptors for VEGF-A; VEGFR-1/2 , though not specifically tested . In the absence of VEGFR-1/2 no conceivable survival benefits of VEGF-A expression would exist in culture , and a specifically evolved pathway would presumably be lost . Thus , rather than being an evolved pathway HSV-1 induced VEGF-A expression could be an incidental consequence of similarity between VEGF-A and HSV-1/2 promoters . ICP4 bound the VEGF-A promoter independently of the three GC box sequences required for transcriptional up-regulation . In addition , we were not able to detect nuclear protein binding to these motifs in the VEGF-A promoter probe . We detected binding of nuclear proteins to probe containing a single isolated GC box ( Figure S1 ) . Therefore , we presume the lack of nuclear protein binding to GC boxes within VEGF-A promoter probe was due to the extensive secondary structure present within the VEGF-A promoter probe from −85 to −52 bp . The most obvious GC box binding candidate is the endogenous transcription factor Sp1 which is heavily phosphorylated during HSV-1 infection and plays a role in IE and E gene transcription [38] , [56] , [57] . However , it should be noted other members of the Sp transcription factor family also bind GC box sequences . ICP4 bound within a region spanning bp −50 to +55 relative to the VEGF-A transcription start site ( Figure S2 ) . There are no sequences within this region with perfect homology to the ICP4 consensus binding sequence ATCGTCNNNNYCGRC ( N = any nucleotide , Y = pyrimidine , R = purine , 12 ) . This point is not a cause for concern as ICP4 affinity is degenerative with strong interactions involving sequences with no resemblance to the core consensus sequence [37]–[40] , [58] . ICP4 and DNA interactions are also affected by oligomerization of ICP4 and extensive post-translation modification , at least some of which are dependent on additional HSV-1 proteins such as ICP27 [39] . As adenoviral expression of ICP4 was sufficient to drive transcription at the proximal VEGF-A promoter ( Figure 7F–H ) . Therefore , it appears that post-translational modifications of ICP4 mediated by other HSV-1 proteins are not required for transcription initiation of VEGF-A . Instead , native ICP4 interaction with non-consensus elements or additional , possibly constitutively expressed DNA binding factors are sufficient to allow ICP4-dependent transcription of VEGF-A . The effects of VEGF-A differ appreciably depending on isoform [7] , [13] , [59] . VEGF-A isoforms are generated by alternative splicing and vary in both extracellular matrix affinity and receptor signaling cascades elicited [7] , [13] . HSV-1 ICP27 inhibits splicing [60] and may alter VEGF-A isoform expression from infected cells . VEGF-A expression during HSV-1 infection has been shown to drive both hem- and lymph-angiogenesis . The hemangiogenic consequences of VEGF-A expression are to be expected but the strongly pro-lymphangiogenic impact of HSV-1 elicited VEGF-A is at odds with studies showing only mild lymphodilation following expression of the most common VEGF-A isoforms [14] . VEGF-A expressed by HSV-1 infected cells may differ in either isoform or post-translation modifications and these differences may be responsible for disparate lymphangiogenic effects . Alternatively , expression of additional cytokines may alter the effects of VEGF-A . IL-1 and IL-6 promote angiogenesis during HSV-1 keratitis [42] . Moreover , we have identified another cytokine required along with VEGF-A for lymphangiogenesis during infection . VEGF-A affects several systems that are not directly related to ( lymph ) angiogenesis . VEGF-A modifies dendritic cell function through ligation of DC expressed VEGFR-2 , which inhibits maturation [53] . VEGF-A suppresses CCL21 expression by lymphatic endothelial cells , reducing DC migration to lymphatics [61] , and ligation of neuronal- expressed VEGF receptors induces the growth of neuronal axons [54] . It remains to be seen whether HSV-1 elicited VEGF-A differs from naturally expressed forms with regards to suppression of DC maturation or neuronal axon growth . In summary , our data provide conclusive evidence that HSV-1 drives expression of VEGF-A promoting angiogenic sequelae characteristic of ocular HSV-1 infection [8] , [12] , [41] . Not only was VEGF-A transcription dependent on the HSV-1 transactivator ICP4 , the VEGFR-1 ligand PGF was also transcriptionally up-regulated in an ICP4- dependent fashion . The promoters between VEGF-A and PGF are highly homologous [62] . Future studies will examine the role of ICP4 in the expression of other PGF and the impact of ICP4 dependent expression of VEGFs on the host immune response , viral replication , and angiogenesis .
Animal treatment was consistent with the National Institutes of Health Guidelines n the Care and Use of Laboratory Animals . All experimental procedures were approved by the University of Oklahoma Health Sciences Center and Dean A . McGee Eye Institutes' Institutional Animal and Care Use Committee under the approved IACUC protocol number 10-024 . Corneas were prepared and stained for specific antigens as previously described [63] . The sources of antibodies used are as follows; rabbit anti-HSV-1 and mouse anti-HSV-1 ICP4 ( Abcam ) , goat anti-GFP ( Serotec ) , Dylight 549 donkey anti-rabbit and FITC bovine anti-goat ( Jackson ImmunoResearch ) . Images were taken using an Olympus IX81-FV500 epifluorescence/confocal laser-scanning microscope . Human and mouse VEGF-A levels were measured by cytokine bead array using a Bio-plex suspension array system ( Bio-Rad ) as previously described [64] , with multiplex kits provided by Millipore . Male C57BL/6 , B6129 , MyD88−/− and TRIF−/− mice were purchased from Jackson Laboratories . Transgenic reporter mice expressing GFP under the proximal VEGF-A promoter were the generous gift of Dr . Brian Seed ( Harvard University ) and were constructed on an FVB background as previously described [65] . Mice with a floxed VEGF-A allele were generated as previously described [66] . A breeder pair was provided by Genentech . CD118−/− mice were maintained in the OUHSC barrier . Anesthetized mice were infected with HSV-1 by scarifying the cornea with a 25 gauge needle and applying 105 pfu of HSV-1 per cornea . GFP reporter expression was visualized by secondary detection of GFP . Mice used were between 6 weeks and 6 months old and age matched controls were employed in each experiment . THCE cells were the generous gift of Dr . Jerry Shay ( UT Southwestern ) and were maintained in keratinocyte serum free media ( Invitrogen ) supplemented with 0 . 15 ng/ml EGF and 0 . 25 µg/ml bovine pituitary extract . Human 293 cells were maintained in DMEM ( Gibco ) supplemented with 10% FBS ( Gibco ) . Primary human dermal keratinocytes ( Lifeline Cell Technology ) were maintained in complete Dermalife medium ( Lifeline Cell Technology ) . HSV-1 virus stocks were propagated using Vero cells . The HSV-1 ICP4− null virus , n12 , is a nonsense mutant with a premature stop codon that only encodes the N-terminal 25% of the ICP4 protein [67] ( generously provided by Dr . Neal Deluc , University of Pittsburgh ) . The HSV-1 OBP− null virus , hr94 , contains a lacZ reporter gene insertion that disrupts the origin binding protein ( UL9 ) open-reading frame [59] ( generously provided by Dr . Sandra Weller , University of Connecticut Health Sciences Center ) . The HSV-1 ICP0− null virus , 7134 , contains a lacZ open-reading grame exchanged in place of the ICP0 open-reading frame ( generously provided by the late Dr . Priscilla Schaffer ) [68] . HSV-1 d27-1 ( ICP27− ) null virus was a kind gift of Steve Rice ( University of Minnesota Medical School , Minneapolis ) [35] . HSV-1 expressing Cre recombinase under the ICP0 promoter was generated as described [69] . The parental strain HSV-1 SC16 was a gift from Dr . Weiming Yuan ( University of Southern California , Los Angeles , CA ) . AdrtTA as well as AdNull and the TRE-regulated ICP4-expressing vector AdICP4 were constructed as previously described [68] . Luciferase reporter plasmids based on Promega's pGL3 luciferase reporter vector with luciferase driven by the sequences −2048/+50 , −1290/+50 , −790/+50 , −415/+50 , −268/+50 , −85/+50 , −52/+50 relative to the transcription start site of the human VEGF-A gene were constructed as previously described [70] and generously provided by Dr . Paul Fox ( The Cleveland Clinic ) . For assays testing the importance of specific promoter elements within luciferase reporter plasmid −85/+50 , a new vector driven by bp −88 to +55 of the human VEGF-A transcription start site was constructed as the original construction of −88/+50 removed the MCS site of pGL3 , complicating further manipulations . The new vector , pVA8855 , was created by amplifying the −88 to +55 base pair region of pLuc2098 using the primers . VAProx8855-XhoI-5′-ACTGAACTCGAGCCCGGGGCGGGCCGGG-3′ . VAProx8855-HindIII-Rev-5′-TTCAGT AAGCTT CCCCCAGCGCCACGACCTCC-3′ and digesting the resulting PCR fragment and vector pGL3 in XhoI and HindIII ( Promega ) and ligated with T4 DNA ligase ( Promega ) . The sequence integrity of the resulting plasmid was verified and luciferase expression following HSV-1 infection between pLuc135 and pVA8855 did not significantly differ . Site specific mutations of either EGR-1 consensus sequences or of the three GC boxes within pVA8855 were generated by PCR directed mutagenesis using Hotstart Turbo PFU DNA polymerase ( Stratagene ) and degradation of the original plasmid using Dpn I ( Promega ) . The base sequence within pVA8855 corresponding to the region between −88 to −50 bp of the human VEGF-A promoter and sequences following mutation are listed below , all of which were verified by DNA sequencing . pVA8855 ( −88/−50 ) CCCGGGGCGGGCCGGGGGCGGGGTCCCGGCGGGGCGGAG ΔEGR-1 CCCGGGGCGGGCTAGGGGCGGGGTCCCTAAGGGGCGGAG ΔGC Box1 CCCAACACAGGCCGGGGGCGGGGTCCCGGCGGGGCGGAG ΔGC Box2 CCCGGGGCGGGCCGGAACACAGGTCCCGGCGGGGCGGAG ΔGC Box3 CCCGGGGCGGGCCGGGGGCGGGGTCCCGGCAACACAGAG ΔGC Box1+2 CCCAACACAGGCCGGAACACAGGTCCCGGCGGGGCGGAG ΔGC Box1+3 CCCAACACAGGCCGGGGGCGGGGTCCCGGCAACACAGAG ΔGC Box2+3 CCCGGGGCGGGCCGGAACACAGGTCCCGGCAACACAGAG ΔGC Box1+2+3 CCCAACACAGGCCGGAACACAGGTCCCGGCAACACAGAG The primers used to generate above mutants are as follows; ΔEGR-1 For 5′-GCGGGCTAGGGGCGGGGTCCCTAAGGGGCGGAG-3′ Rev 5′-CTCCGCCCCTTAGGGACCCCGCCCCTAGCCCGC-3′ ΔGC Box1 For 5′-GGGCTCGAGCCCAACACAGGCCGGGGGCG-3′ Rev 5′-GCC CCCGGCCTGTGTTGGGCTCGAGCCCG-3′ ΔGC Box2 For 5′- GGGCGGGCGGAACACAGGTCCCGGCG-3′ Rev 5′-GCCGGGACCTGTGTTCCGGCCCGCCCCG-3′ ΔGC Box3 For 5′-GGTCCCGGC AACACAGAGCCATGCGCC-3′ Rev 5′-GGCGCA TGGCTCTGTGTTGCCGGGACC-3′ ΔGC Box1+2 For 5′-GGGCTCGAGCCCAACACAGGCCGGAACACAG-3′ Rev 5′-CTGTGTTCC GGCCTGTGTTGGGCTCGAGCCC-3′ Luciferase assays were performed by plating 25 , 000 293 cells or primary human dermal keratinocytes per well of a 96 well plate and transfecting cells the following day with 250 ng of plasmid per well and Lipofectamine 2000 ( Invitrogen ) per to the manufacturer's instruction . At 48 hours after transfection , transfected cells were infected with 3 pfu per cell of HSV-1 and luciferase activity was determined at the indicated times post infection relative to HSV-1 infection using Promega's firefly Luciferase Assay System per the manufacturer's instructions . Nuclear and cytoplasmic extracts of 293 cells were harvested using NE-PER nuclear and cytplasmic protein extraction kit ( Thermo Scientific ) . Protease activity was inhibited by addition of 1× Calbiochem Protease Inhibitor Cocktail . Protein concentration of the extracts was measured using BioRad BCA protein assay . The protein concentrations of nuclear and cytoplasmic extracts were normalized to 1000 µg/mL using NE-PER nuclear and cytoplasmic extraction buffers , respectively . Oligonucleotides for EMSA were either chemically synthesized or generated by PCR amplification using Hotstart Turbo PFU DNA polymerase . Oligonucleotides of pVA8855 , pVA8855 ΔEGR-1 , pVA8855 ΔGC Box 1 , pVA8855 ΔGC Box 2 , pVA8855 ΔGC Box 3 , pVA8855 ΔGC Boxes 1 and 2 , pVA8855 ΔGC Boxes 1 and 3 , pVA8855 ΔGC Boxes 2 and 3 , pVA8855 ΔGC Boxes 1 and 2 and 3 , were generated using primers biotinylated primers; biotin-5′-TAGCCCGGGCTCGAGCC-3′ and biotin-5′-GAATGCCAAGCTTCCCCCAG-3′ which amplifies a fragment corresponding to bp −88 to +55 relative to the transcription start site and biotin-5′-GCGGAGCCATGCGCCC-3′ and biotin-5′-GAATGCCAAGCTTCCCCCAG-3′ which amplifies a fragment corresponding to bp −50 to +55 relative to the transcription start site . Specific binding was verified by analyzing nuclear protein binding to biotinylated oligonucleotides spanning a sequence of the human VEGF-A promoter that was irrelevant to HSV-1 induced transcription of VEGF-A ( −1513 to −1338 bp relative to the transcription start site ) , generated using the primers Biotin-5′-AGGCCTCAGAGCCCCAACTTTG-3′ and biotin-5′-CCTTACCTCCAAGCCCCCTTTTCC-3′ . To analyze EMSA shifts corresponding to base pairs −88 to −50 bp of the human VEGF-A promoter , the wild-type oligonucleotide −88-50WT 5′-AGCCCGGGGCGGGCCGGGGGCGGGGTCCCGGCGGGG CGGAGCCAT-3′ , or the GC box mutated corresponding oligonucleotide −88-50ΔGC 5′- AGCCCAACACAGGCCGGAACACAGGTCCCGGCAACACAGAGCCAT-3′ . Binding of HSV-1 infected cell nuclear protein extracts was competitively inhibited using the ICP4 consensus containing oligonucleotide 5′-CAC TAT CGT CCA TAC CGA CCA CAC CGA CGA A-3′ . EMSAs were performed using Thermo Scientific's LightShift Chemiluminescent EMSA Kit according to the manufacturer's protocol with the exception that instead of the included EMSA binding buffer , the binding buffer used was composed as follows; 10 mM Tris-HCl , 5 mM MgCl2 , 0 . 035% β-mercaptoethanol , 0 . 1% Triton X-100 , and 2 . 5% glycerol ( Sigma ) at pH 7 . 5 . Briefly , the procedure was as follows . Probe ( 20 femtomoles per 20 µL reaction volume ) was incubated for 20 minutes in binding buffer with 2 µg of nuclear protein extract with or without 1 . 5 µL of anti-ICP4 antibody or isotypic control . Following binding , the reaction was electrophoresed in 0 . 5× TBE buffer in a 5% polyacrylamide gel ( BioRad ) using the BioRad Criterion cell system . Electrophoresed probe was transferred to a BioRad Zeta-probe nylon membrane and biotinylated oligonucleotide was detected via Streptavidin-horseradish peroxidase-mediated luminescence . Luminescence was detected with a FLUOstar Omega plate reader ( BMG Labtech ) . Luminescence was normalized as luciferase units relative to the intensity of luminescence detected in non HSV-1 infected 293 cells transfected with the promoterless luciferase control vector pGL3 . Corneas were harvested , and RNA was isolated at indicated time points PI using Trizol as per the manufacturer's instructions ( Invitrogen ) . After RNA isolation , 2 µg RNA per sample was converted to cDNA using an RT system ( Promega ) using random primers according to the manufacturer's instruction . Samples were then analyzed via real-time PCR using Sybr Green supermix ( Bio-Rad Laboratories ) via an iCycler ( Bio-Rad Laboratories ) as previously described [71] . The abundance of VEGF-A/C/D cDNA relative to the housekeeping gene β-actin was calculated as 2−ΔΔCt . For analysis of VEGF-A up-regulation in THCE cells , VEGF-A fold induction was determined using a panel of three housekeeping genes to control for effects of HSV-1 on housekeeping gene expression . VEGF-A , B , C , D , or PGF cDNA relative to β-actin , TBP , or PPIA was calculated as described for β-actin , and the geometric mean of the threefold induction values was taken as being the individual fold induction for the respective sample . VEGF-C and VEGF-D were not detected in human 293 cells or HPKs in our hands . The validity of these primers has already been verified previously by other groups [72] , [73] . THCE cells were plated at densities of 3×105 cells per well in 12-well plates and then infected with 3 pfu per cell HSV-1 the next day before harvesting RNA at the indicated time point using Trizol . Primers were purchased from Sigma-Aldrich and sequences used were as follows: Mu VEGF-A forward , 5′-CTGCTGTACCTCCACCATGC-3′; Mu VEGF-A reverse , 5′-TCACTTCATGGGACTTCTGCTCT-3′; Mu VEGF-C forward , 5′-CTGfGGAAATGTGCCTGTGAATG-3′; Mu VEGF-C reverse , 5′-ATTCGCACACGGTCTTCTGTAAC-3′; Mu VEGF-D forward , 5′-CAAGACGAGACTCCACTGCC-3′; Mu VEGF-D reverse , 5′-GCACTCACAGCGATCTTCATC-3′; Mu β-actin forward , 5′-CTTCTACAATGAGCTGCGTGTG-3′; Mu β-actin reverse , 5′-TTGAAGGTCTCAAACATGATCTGG-3′; Hu VEGF-A forward , 5′-AGGAGGAGGGCAGAATCATCA-3′; Hu VEGF-A reverse , 5′-CTCATTGGATGGCAGTAGCT-3′; Hu VEGF-B forward , 5′-TCGCCGCACTCCTGCAGCTG-3′; Hu VEGF-B reverse 5′-CGAGTATACACATCTATCCATGACAC-3′; Hu VEGF-C forward 5′-TCAAGGACAGAAGAGACTATAAAATTTGC-3′ validated in reference 72; Hu VEGF-C reverse 5′-ACTCCAAACTCCTTCCCCACAT-3′; Hu VEGF-D forward 5′-ATGGACCAGTGAAGCGATCAT-3′ , validated in reference 78; Hu VEGF-D reverse 5′-CAGCTTCCAGTCCTCCAGAGTGA-3′; Hu PGF forward 5′-GGAACGGCTCGTCAGAGGTG-3′; Hu PGF reverse 5′-CGACGTCCACCAGCCTCTC-3′; Hu β-actin forward , 5′-AGCCTCGCCTTTGCCGA-3′; Hu β-actin reverse , 5′-CATGTCGTCCCAGTTGGTGAC-3′; Hu TBP forward , 5′-TGCACAGGAGCCAAGAGTGAA-3′; Hu TBP reverse , 5′-CACATCACAGCTCCCCACCA-3′; Hu PPIA forward , 5′-GTCAACCCCACCGTGTTCTT-3′ , PPIA reverse , 5′-CTGCTGTCTTTGGGACCTTGT-3′ , Hu EGR-1 For 5′-CAGCCCTACGAGCACCTGACC-3′ , Hu EGR-1 Rev 5′-GAGTGGTTTGGCTGGGGTAAC-3′ . For assays of VEGF-A transcript following infection with HSV-1 and treatment with specific inhibitors , the following concentrations were used throughout the 12 hour course of the assay; cycloheximide ( Sigma ) 100 µg/mL , acyclovir ( Sigma ) 200 µM , and U0126 , SB206580 , and SP600125 ( all purchased from Sigma ) were used at concentrations of 10 µM . To reduce EGR-1 , 293 cells were plated in 24 plates at a density of 105 cells per well in DMEM without antibiotics . Cells were treated with either siPort Amine transfection reagent , or with transfection reagent and either negative control siRNA ( 4390843 Ambion ) , or siRNAs against EGR-1 ( 4538 and 4539 Ambion ) with siRNAs applied at a final concentration of 20 nM in serum free Optimem ( Invitrogen ) . After a 24 hour incubation , medium was removed and replaced with DMEM without antibiotics , and the plate was used after an additional 24 hour incubation . Reduction of EGR-1 was verified by real time RT-PCR to be greater that 60% in both 4538 and 4539 uninfected groups relative to negative siRNA controls and EGR-1 transcript remainded below levels in negative control-treated uninfected cells even 12 post HSV-1 infection . Comparisons between multiple treatment groups were performed using one-way analysis of variance and Tukey's multiple comparison tests . All statistical analysis was performed with GBSTAT ( Dynamic Micro Systems ) . | Herpes simplex virus-type 1 is the leading cause of infectious corneal blindness in the industrialized world . Most of the morbidity associated with the virus is due to the host response to episodic reactivation of latent virus . Corneal immunologic privilege is associated with a number of factors including the absence of blood and lymphatic vessels . Conversely , corneal hem ( blood ) - and lymph-angiogenesis driven by inflammation correlate with the loss of privilege . Neovascularization is a common phenomenon in HSV-1 keratitis that correlates with poor prognosis . We have previously discovered HSV-1 elicits corneal lymphangiogenesis through a unique mechanism involving vascular endothelial growth factor ( VEGF ) -A independent of that described for other insults including transplantation or bacterial infection . However , the viral-encoded product ( s ) that elicit host production of VEGF-A is ( are ) unknown . In this paper , we have identified infected cell protein-4 ( ICP4 ) as the primary virus-encoded product that drives VEGF-A expression . As VEGF-A is involved in driving neovascularization associated with tumor growth and metastasis , proteins that influence transcriptional regulation of VEGF-A may be useful in the development of adjunct therapy for such disparate diseases as cancer and HSV-1 keratitis . | [
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| 2011 | The Herpes Simplex Virus-1 Transactivator Infected Cell Protein-4 Drives VEGF-A Dependent Neovascularization |
Insertion sequences ( IS ) are the simplest and most abundant form of transposable DNA found in bacterial genomes . When present in multiple copies , it is thought that they can promote genomic plasticity and genetic exchange , thus being a major force of evolutionary change . The main processes that determine IS content in genomes are , though , a matter of debate . In this work , we take advantage of the large amount of genomic data currently available and study the abundance distributions of 33 IS families in 1811 bacterial chromosomes . This allows us to test simple models of IS dynamics and estimate their key parameters by means of a maximum likelihood approach . We evaluate the roles played by duplication , lateral gene transfer , deletion and purifying selection . We find that the observed IS abundances are compatible with a neutral scenario where IS proliferation is controlled by deletions instead of purifying selection . Even if there may be some cases driven by selection , neutral behavior dominates over large evolutionary scales . According to this view , IS and hosts tend to coexist in a dynamic equilibrium state for most of the time . Our approach also allows for a detection of recent IS expansions , and supports the hypothesis that rapid expansions constitute transient events—punctuations—during which the state of coexistence of IS and host becomes perturbated .
Transposable elements ( TE ) are pieces of DNA that encode the enzymatic capability to change location and proliferate within the host genome through a process called transposition . They are widely distributed in prokaryotes and eukaryotes , and in some cases they constitute substantial fractions of the genome [1] . Due to their relative autonomy , proliferative ability , and apparent lack of a useful function , they were considered for some time a paradigm of selfish DNA , i . e . a molecular parasite that proliferates at the cost of the genome it “infects” [2] , [3] . Nowadays , the relationship between TE and host genomes is known to be much more complex . Particular TE insertions may be beneficial for the host , for instance by inactivating genes whose expression is no longer required [4] , [5] , acting as a vehicle for the exchange of useful genes , or facilitating adaptation to fast environmental changes [1] , [6] , [7] . Even if TE did not play any beneficial role , hosts often possess regulatory mechanisms that keep TE under control and minimize the risk of possibly deleterious insertions [8] , [9] . Because of their ability to promote recombination , TE are key contributors to the plasticity of genomes [10] , [11] . Hence , understanding the dynamics of TE in different organisms is relevant to the comprehension of genome architectures . Insertion sequences ( IS ) are the simplest form of TE , as they often code for only one gene responsible for their mobility machinery ( the transposase gene ) [9] . IS first enter host genomes through lateral gene transfer ( LGT ) and they can increase their copy number via transposition . The broad diversity of effects that IS exert on their hosts has turned the fate of this relationship —long-term coexistence or eventual extinction of the host due to IS proliferation— , into a matter of debate [12] . Moreover , relatively recent cases of rapid IS expansions in bacterial genomes , which have been attributed to episodes of host restriction and environmental change , raise additional questions on the causes and nature of such IS expansions [11] , [13] , [14] . As of today , the mechanisms by which environmental perturbations cause IS expansions , the role played by selection in controlling IS copy number , or the significance of decreases in host population sizes in the expansion of IS are mostly unsolved issues . Even more interestingly , could IS expansions represent transitory punctuations with a relevant role on host evolution ? [15]–[17] . A better understanding of the evolutionary forces that control the IS dynamics is required in order to shed light on all these questions [18] . The first works aiming at analyzing TE dynamics date back to the decade of 1980 [19]–[22] . Inspired by the idea that TE are selfish elements , they depicted a scenario where TE spontaneously tend to proliferate and either host regulatory mechanisms or purifying selection keep TE numbers under control [21] , [23] . Due to the limited data on TE abundance and distribution available at that time , those works either remained mostly theoretical or mainly addressed eukaryotic TE [24] . In recent years , however , the ever increasing number of sequenced genomes has provided us with an unprecedented amount of data on the abundance and distribution of prokaryotic TE . This has permitted the evaluation of a series of hypotheses concerning IS dynamics [14] , [25]–[28] . In particular , a high homology of IS copies within genomes has been reported [25] and interpreted on the basis of a fast proliferation dynamics following the arrival of an IS element , ultimately leading to the extinction of the host . This view has been challenged [27] by the large proportion of IS remnants in Wolbachia genomes , implying that IS proliferation does not necessarily lead to extinction . Statistical approaches directed at identifying the causes behind IS abundance have found that it correlates with genome size but not with LGT rate , host pathogenicity or lifestyle [26] . Estimations of the fitness cost of IS elements by comparing a simple model with the genomic data available for the IS5 family [28] have found that the fitness cost is small enough to assume that , in practice , IS may be neutral or almost neutral for the host genome . In this study , we take advantage of the large amount of genomic data currently available and analyze the abundance distributions of 33 IS families in 1811 bacterial chromosomes . This allows us to test and compare two simple models of IS spreading , namely a neutral model and a model with purifying selection , which are introduced in the next section . By fitting those models to the genomic data we obtain estimates for the proliferation , loss and LGT rates , as well as the fitness cost associated to an IS copy . The joint evaluation of such estimates and the original data allows us to disentangle the general forces that control IS dynamics in the long-term and explore the possibility that IS and hosts coexist in an equilibrium state punctuated by transient episodes of IS proliferation .
The models here used are aimed at capturing the main mechanisms that are responsible for the proliferation , spreading and loss of IS within and among genomes . We first introduce a neutral model that takes into account the following key processes: ( a ) the IS ability to proliferate , ( b ) IS deletion , and ( c ) IS incorporation through lateral gene transfer ( LGT ) . As an alternative to this neutral model , we also consider the case of IS entailing a fitness cost . The processes of proliferation , deletion , and LGT , complemented with a fitness cost that is proportional to the IS copy number , define a model of IS dynamics with selection . A schematic of the models is shown in Fig . 1 . The rules of the models and the associated parameters should be understood in an effective manner , and in agreement with the procedure used to detect and classify IS sequences ( see Methods ) . The duplication rate in our model applies to those insertion events that are not lethal for the host genome . From the perspective of a neutral scenario any observable insertion is assumed to be neutral or quasi-neutral: genomes hit by a lethal or highly deleterious insertion die shortly afterwards and do not further contribute to the population dynamics . The duplication parameter r is an effective measure of the duplication rate of an IS family . In this sense , it includes functional IS copies but also tolerates a fraction of non-functional ( in the sense of non-duplicating ) IS copies that might be detected in the genome and ascribed to that family . Similarly , the effective deletion parameter d embraces actual deletions , but also excisions that do not reinsert and sequences that , due to mutation accumulation , can no longer be detected . Finally , the LGT parameter h can only take into account those transfer events that conclude with the insertion of the IS in the genome . Though preventing lethal insertions of IS elements originated by duplication or LGT is a form of purifying selection , this mechanism acts on each element independently , and is thus included in the neutral model . Purifying selection that acts to streamline genomes represents a different mode of action which is included in the model incorporating selection , together with any other selective mechanism that penalizes the genome proportionally to its IS content . The key processes in the neutral model can be summarized into two parameters: the duplication-deletion ratio α , and the LGT-deletion ratio β . The model with selection includes an extra parameter , the fitness cost-deletion ratio σ . The advantage of working with relative ratios becomes clear given the difficulty of obtaining reliable estimates of the actual duplication , deletion and LGT rates , which greatly vary depending on the experimental methodology and environmental conditions [8] , [29] , [30] . Furthermore , the duplication-deletion ratio can be easily interpreted in terms of a proliferation or deletion bias at the level of IS dynamics , as later discussed . Both models can be solved to obtain the expected abundance distribution of an IS family in the long-term stationary state ( see Methods ) . The models provide , for each IS family , the probability of finding a genome with a given number of copies . By comparing that probability with the observed IS abundances it is possible to estimate values for the model parameters and test whether the neutral model or the model with selection are valid to explain the genomic abundances of IS . Data on the classification and distribution of bacterial IS elements was taken from [31] ( see Methods for further details ) . Starting from a dataset of 1811 bacterial chromosomes harboring at least one IS element , we selected 1079 of them by choosing randomly only one chromosome per species , in order to minimize redundancy . For each IS family , its abundance distribution was fitted to both models by means of a maximum likelihood approach . Most of the 33 IS families show abundance distributions that are well fit by the neutral model ( Fig . 2 shows a representative example ) . This assertion is supported by the goodness of fit tests , that render non-significant values even if no correction for multiple comparisons is applied . The only exception is IS21 ( ) , but the fit to this case becomes non-significant once corrected for the 33 comparisons . The detailed results of the fits are provided in the SI . It is remarkable that a simple , neutral model is able to explain the data with only two free parameters . We have checked whether the use of two different LGT rates , one for genomes where the corresponding IS family is absent , and a different one for genomes where the family is present improves the fits to the data . That is not the case for 31 of the 33 families , once corrected for multiple comparisons , thus suggesting that LGT rates to genomes where a given IS family is either absent or present are similar . Next , we took the values of the duplication-deletion ratio α estimated in the neutral model and tried to refine the fits by adding fitness cost and selection . We found that the optimal values of the selection parameter σ were close to zero . In concordance , selection does not significantly improve the fit for any of the IS families ( detailed results in the SI ) . This fact remains true even if small changes in α are considered . As an alternative , we also explored the selection model by adopting a completely different range of values of α , between 102 and 103 , as suggested by [28] . In that scenario , duplications are overwhelmingly more frequent than deletions , and negative selection is the only factor able to prevent an explosive proliferation of the IS . As in the previous case , no improvement in the fits with respect to the neutral model is observed . It is worth mentioning that the estimated selection parameter σ is typically tenfold smaller than the duplication-deletion ratio . Taken together , our results show that selection needs not be invoked to explain the abundance and distribution of IS . In the following paragraphs , we face the estimates of the neutral model to the genomic data in order to further explore the possibility that IS behave neutrally . A global analysis of the estimated parameters for the whole set of IS families reveals that most families behave in a strikingly similar way , with the duplication-deletion ratio close to 0 . 9 ( Fig . 3 ( a ) ) . Noticeable exceptions are Tn3 and Tn7 , for which significantly smaller values are found . In order to evaluate the relevance of LGT in determining the IS abundance , we studied the correlation between the LGT rates of a family ( measured as parameter β ) and the corresponding fraction of genomes that host that family ( Fig . 3 ( b ) ) . A strong correlation exists ( Spearman's , ) , confirming the fact that the entry of new IS families into the genome totally relies on LGT . In contrast , as shown in Fig . 3 ( c ) , LGT rates do not correlate ( Spearman's , ) with the mean number of copies within “infected” genomes ( those genomes with at least one copy for a given family ) . This is in agreement with the idea that duplication-deletion processes , rather than LGT , is what determines the copy number once the genome has become “infected” [26] . We also studied whether the host genome size affects IS duplication and LGT rates . To that end , chromosomes in the database were classified into three subsets according to their sizes ( smaller than 2 . 6 Mbp , between 2 . 6 and 4 . 2 Mbp , and larger than 4 . 2 Mbp ) . These cut-off points yield equal size subsets with approximately 350 chromosomes each . The model parameters were recalculated for each data subset and IS family ( Fig . 4 ) . We found no significant differences in the duplication-deletion ratios among the three size groups ( Friedman test , ) . By contrast , LGT-deletion ratios show a significant increase in larger genomes ( Friedman test , ) . In order to complete our analysis , we also fitted the data to the selection model with a strong proliferation bias ( ) and found that the selection coefficients do not vary with the genome size ( Friedman test , ) . A major issue concerning transposable elements is whether they can coexist with their host for long periods of time or their proliferation ultimately leads to host invasion and death . Long-term coexistence of IS and hosts becomes possible if proliferative and reductive forces compensate each other , so that the IS copy number remains stable on average . Stability is meant in a statistical sense , since the process is affected by large fluctuations . In the framework of the neutral model , this equilibrium condition can be translated into a mathematical relationship among model parameters: , where is the mean copy number of IS in the population of genomes ( see SI ) . That expression represents a critical balance between duplication and LGT rates on the one side and deletion on the other side that permits a stable , long-lasting coexistence between IS and host ( recall that and ) . In contrast , situations where the relation above is not fulfiled lead to IS expansions or declines . Specifically , if , the IS proliferates “explosively” , whereas if , the IS gets quickly extinct . We explored the empirical relation between the estimated parameters α and for all the IS families in the dataset . As Fig . 5 ( a ) reveals , there is a trend of the data to be located close to the dashed line that represents the critical balance condition ( coefficient of determination ) . Empirical data obeying it suggest that IS and hosts have evolved stabilizing mechanisms that prevent both IS extinction and unbound proliferation in most genomes . Parameters α and were estimated independently in order to ensure that the observed trend is not a product of the fitting algorithm ( see Methods ) . If parameters are estimated jointly , the agreement between the empirical data and the critical balance condition rises even higher ( ) . Interestingly , this approach based on the critical balance allows for discrimination between equilibrium and IS states of exponentially fast proliferation or decline . To check for that , we generated datasets by mimicking situations where the LGT rate remains stable while the duplication rate increases ( IS unbound growth ) or decreases ( IS decline ) . We found strong deviations from the critical balance , even if the simulated values of α and β were kept inside the previously observed range ( Fig . 5 ( b ) ) . The models developed in this work account for the dynamics of IS in an equilibrium state . The fact that real abundance distributions are well fit by the theoretical curves means that IS are in equilibrium in most genomes . Conversely , we can take advantage of the theoretical distributions to detect outliers , i . e . genomes that show an abnormally large copy number for a given IS family ( see Methods for further details on the detection procedure ) . From the perspective of the neutral model , outliers can be interpreted as the result of transient imbalances in duplication , deletion and/or LGT rates , which break down the critical balance . The search for outliers gave as a result a set of 35 strains ( of a total of 1685 ) , that span over a small number of species . It is relatively common that the same genome behaves as an outlier with respect to more than one IS family . For instance , all 12 strains of Yersinia pestis are outliers with respect to IS200 , and three of them also with respect to IS21 . Genomes belonging to the genus Shigella ( S . boydii , S . dysenteriae , S . flexneri and S . sonnei ) are overcrowded with IS1 , IS3 and IS4a . Other examples are Xanthomonas oryzae ( outlier for IS1595 , IS5a , IS5b and IS701 ) and Salmonella enterica subsp . enterica ( outlier for IS200 ) . A summarized list can be found in Table 1 , while a comprehensive list is provided in the SI .
By fitting the genomic data to a neutral duplication-deletion-LGT model , we were able to observe two general trends: first , the estimated duplication rates are typically one order of magnitude greater than the estimated LGT rates; second , the LGT rate correlates with the number of genomes that host a given IS family , but does not correlate with the IS genomic abundance . These findings together let us conclude , in agreement with [26] , that transposition and LGT play different roles in the dynamics of IS . Whereas LGT determines the spreading of IS across genomes , it only plays a minor role once a genome already contains a given IS family . Inside such infected genomes , the abundance of IS copies is mainly driven by stochastic duplications and deletions . When looking at the duplication-deletion ratio , we found that it takes a value slightly smaller than one , which can be interpreted in terms of a deletion bias at the level of IS [35] , [36] . Such a deletion bias makes LGT essential for the long term persistence of IS: in the absence of an external income via LGT , IS copies tend to be deleted faster than they duplicate and , eventually , they disappear . This mechanism offers a possible explanation to the loss of IS in organisms whose life conditions limit their LGT rates , e . g . in anciently host-restricted endosymbionts [13] . Some authors have reported a correlation between genome size and IS content [1] , [26] , which motivated us to test whether duplication and LGT rates vary in genomes of different sizes . In disagreement with the prevailing idea that larger genomes withstand greater IS proliferation rates , we found no significant differences in duplication rates among genomes of different sizes . On the other hand , the LGT rate becomes greater in larger genomes ( Fig . 4 ( b ) ) , which opens a new path to explain the above-mentioned correlation . Actually , an observed correlation between bacterial ecology and genome size [37] suggests that prokaryotic ecological niches might be the proximate cause that determines LGT rate values . Our results show that purifying selection at the host level needs not be invoked to explain the abundance and distribution of IS , because the genomic data are fully compatible with a neutral scenario . In fact , the small differences in the distributions derived from neutral and selection models may be insufficient to discriminate between both scenarios . There are , however , some clues that challenge the prevailing role traditionally ascribed to selection . First , provided that there is a deletion bias , purifying selection is no longer essential to control IS . Second , the fact that IS in larger genomes—those with a presumably smaller fraction of essential genes—do not show reduced fitness cost challenges the view that interruption of essential genes by IS insertions generates an efficient selection pressure against IS . Third , even if there were no deletion bias and duplications greatly overwhelmed deletions , the values we found for the selection-deletion ratio—typically ten-fold smaller than the duplication-deletion ratio—bring along the possibility that IS control takes place in a weak selection scenario . This same idea had been pointed out in [28] , where the abundance distribution of IS5 under the assumption of a strong proliferation bias was studied . In a context of weak selection , the composition of the host population experiences random variations that allow for fixation of slightly deleterious genotypes [38] . Hence , when the host population dynamics is taken into account , opposite predictions are derived from deletion and proliferation biased scenarios ( see Table 2 ) . In the former case , the IS copy number is controlled by deletions , and selection may be neglected , thus resulting in an effectively neutral dynamics . In the latter case , explosive IS proliferation would be the expected outcome because weak purifying selection is unable to compensate for IS duplications ( see the SI for analytical calculations ) . Therefore , finding weak selection rates in a proliferation biased scenario necessarily implies that host genomes are out of equilibrium and in their way to becoming fully invaded by IS [12] , [25] . At odds with the aforementioned scenario of non-equilibrium proliferative dynamics , our results point towards a stable coexistence of IS and hosts . Despite the fact that molecular mechanisms of transposition vary [9] , all of the 33 IS families considered show strikingly similar values of the dynamical parameters . Even more , duplication , deletion , and LGT rates balance according to a critical condition that allows for evolutionary persistence without explosive proliferation . Such a narrow range of parameter values suggests an implicit role of stabilizing selection acting on IS and promoting those that behave like mild , persistent parasites [39] . In fact , IS mutants that fall below the critical condition are doomed to disappear; those that excede it proliferate quickly and—even if they entail a minimal fitness cost—eventually kill their local host populations , thus causing their own extinction [40] . Degenerated IS copies constitute a hallmark of the neutral dynamics based on deletion bias: IS are controlled via deletions , which turn functional IS copies into degenerated ( or vestigial ) ones . In contrast , if IS are to be controlled via purifying selection , whole genomes rich in IS tend to disappear , without generation of any IS remnants . On this point , it is worth discussing the case of Wolbachia , a genus of anciently host-restricted endosymbiotic bacteria . Wolbachia endosymbionts have reduced genomes ( ∼1 Mbp ) and their effective population sizes are thought to be very small . The strains of Wolbachia that are associated to arthropods ( e . g . Drosophila melanogaster and Culex quinquefasciatus ) are known to coinfect hosts and undergo LGT [41] , [42]; while those associated to filarial nematodes ( Brugia malayi and Onchocerca ochengi ) seem to be transmitted in a strictly vertical way , which greatly limits LGT [43] . In agreement with the idea that LGT is essential for the maintenance of IS , only the arthropod-associated Wolbachia strains host functional IS copies [44] , [45] . Importantly , the comparative analysis of IS in Wolbachia reveals that more than 70% of IS copies in arthropod-associated strains are nonfunctional [27] , [46] . Those nonfunctional copies belong to several IS families , which are also represented in nematode-associated Wolbachia with no functional copies . Large amounts of partial IS copies have also been found in a recent study dealing with thermophilic cyanobacteria of the genus Synechococcus [47] . These facts suggest that nonfunctional , fragmentary IS copies may be prevalent in bacterial genomes , even if they have experienced strong reductions in size , and that deletions are an important force leading to the loss of IS . In contrast , group II introns—another kind of TE in prokaryotes—display a smaller fraction of fragmentary copies and their dynamics are possibly driven by selection [48] . The neutral dynamics that we present here can give rise to punctuated events of IS proliferation . They occur whenever the LGT , duplication and deletion rates become imbalanced and the critical condition breaks down . We have identified some of those events by applying an outlier detection algorithm on the abundance distributions . According to our analysis , the fraction of such outliers is small , hence confirming that non-equilibrium states are the exception rather than the rule . Some of the outliers that we have detected have already been noticed and interpreted in the literature as IS expansions [14] , supporting the idea that outliers truly represent genomes that have experienced an episode of IS proliferation . It is not rare that multiple IS families show expansions within the same genome , which suggests that the causes of IS punctuations do not lie at the IS but at the host level . Indeed , some IS expansions have been associated to episodes where bacteria underwent host restriction [11] , [13] , [14] . Traditionally , the reduced efficiency of purifying selection in smaller populations has been invoked to explain such expansion events . There are other mechanisms , though , that may account for IS punctuations in the absence of selection . Transitory alterations in the deletion and LGT rates may play the same role , as well as stress induced downregulation of host regulatory mechanisms limiting IS transposition [17] , [29] . In an indirect way , ecological changes—such as host restriction—may imply reductions in the fraction of essential genes [49] , [50] , which would lead to a higher probability of IS insertions being non-lethal , and eventually to increases in the effective duplication rate [26] . In sum , our results indicate that the persistence of IS in bacterial genomes are the outcome of a neutral process , with little role for purifying selection . Let us emphasize that the absence of selection here reported should be interpreted as a general trend in the whole set of genomes , averaged over long periods of time . Sporadic cases of IS insertions affected by selection may occur , but the neutral behavior dominates at large evolutionary scales . Most genomes contain IS abundances compatible with an equilibrium state , albeit punctual imbalances in the LGT , duplication and deletion rates—but not necessarily in the host population size—may produce transient IS expansions . In the light of the important role of transposable elements in adaptation and genome evolution [4] , [6] , [17] , [51] , a better understanding of the actual causes behind IS expansions becomes an appealing challenge . From an “ecological” perspective , most IS families share closely similar values of the relevant dynamical parameters , suggesting that IS and host genomes have coevolved towards a state of stable coexistence . The apparent equivalence of different IS families brings to mind the concept of a neutral ecosystem [52] . Hence , it would be interesting to further explore the parallelisms between IS dynamics and neutral ecology , which could provide us with novel insights into the processes that rule the architecture of genomes .
We used the catalog in [31] as a source of information regarding bacterial IS classification and distribution . IS catalog construction is briefly summarized in the following . In a preliminary study , transposases and other IS-encoded proteins collected from Pfam ( v2 . 6 ) [53] and ISfinder [54] ( a specialized database focused on prokaryotic IS elements ) were used to generate a manually curated list of protein architectures ( protein domain organization descriptions ) associated to IS-encoded proteins . Listed architectures represented , by extension , IS-associated genes . Simultaneously , a table describing the correspondence between gene combinations ( represented by protein architecture strings ) and IS elements classified according the the IS finder classification scheme , was built . Then , chromosomal and predicted protein sequences , as well as protein translation tables ( gene coordinate files ) for 2074 bacterial chromosomes were downloaded from the NCBI Genome database on October 2012 . A computational pipeline written in Perl directed the execution of HMMER 3 . 0 and other in-house developed applications to detect , classify and count IS elements in complete genomes . First , the protein architecture for the complete set of proteins predicted for all bacterial genomes was reconstructed on the basis of HMMER alignments against the Pfam database . Then , IS-associated genes were identified by comparison with the previously generated list of protein architectures . Once IS-associated genes had been identified , the system assigned individual genes , or clusters of adjacent genes , to IS elements using the correspondence table also established in the preliminary study . The system attempted to resolve IS elements located in tandem , as well as to identify complete IS elements that could exist within gene clusters originated by nested insertions . To do so , clusters of IS-associated genes were segmented into all possible collections of adjacent gene subclusters , which were then classified , when possible , as belonging to a certain IS family . The segmentation scheme used maximized the total length of successfully classified subclusters . As result , 69 , 438 IS associated genes , corresponding to 57 , 515 IS elements in 1 , 1811 chromosomes , were identified . The overall IS detection and classification strategy aimed at reducing the number of wrongly classified genes at the expense of a slight decrease in sensitivity . With this purpose , the system was based on NCBI published gene predictions and only individual or adjacent gene clusters that could be unequivocally assigned to IS elements belonging to canonical IS families or groups were considered . Two approaches were followed to evaluate the quality of the annotations generated by the IS detection and classification pipeline . For the first approach , the set of genes annotated in the NCBI database as encoding for transposases was compared against the set of IS-associated genes detected by the pipeline . Out of the 65 , 230 genes annotated with the keyword ‘transposase’ at the NCBI database , 85% were correctly identified by the pipeline . For the second approach , IS family affiliation was compared for the sets of IS-associated genes described both in the genomic component of ISfinder ( ISbrowser [55] ) and in the annotations generated by the pipeline . At a global level , IS family affiliation agreed for 88% of the 866 shared IS-associated genes . At the level of individual IS families , the fraction of genes that were affiliated to the same IS family by both systems had average and median values of 79% and 100% , respectively . We studied the neutral evolution of the number of copies in the genome as a generalized birth and death process ( Fig . 1 ( a ) ) . A complete analysis of this kind of processes applied to the study of proteomes has been carried out in [56] . The neutral model focuses on a particular IS family in a single genome . Elements belonging to the family are duplicated at a rate r and deleted at a rate . In addition , new copies can be inserted through lateral transfer at a rate . We define the state of the genome as the number of copies that it carries , with no upper limit for such copy number . A genome with copies will turn into a state with copies after duplication or LGT . Under the assumption that copies behave independently and LGT rate is a constant , the transition rate is equal to . On the other side , the transition rate due to copy deletion is equal to . As described in Fig . 1 , the relevant parameters in this case are ( duplication-deletion ratio ) and ( LGT-deletion ratio ) . From a formal perspective , working with those ratios simply amounts to measuring time in units of IS deletion events . The duplication , deletion and transfer processes reach a stationary state where the probability of finding a genome with copies follows [56] ( 1 ) The duplication-deletion ratio , , plays a central role in the dynamics . If the number of copies inside the genome increases steadily until it invades the genome . This proliferation-biased scenario , is unrealistic in the absence of purifying selection . In contrast , if duplications are slower than deletions and the copies inside the genome tend to disappear . In this deletion-biased scenario the extinction of the IS is prevented by the external contribution of lateral transfer . Adding selection to the model requires considering a whole population of genomes instead of a single genome . Inside each genome the dynamics of duplication , deletion and lateral transfer remains the same as in the neutral model . In addition , the IS copy number determines a fitness cost on the host genome . A schematic of the resulting process is depicted in Fig . 1 ( b ) . For simplicity we assume that the fitness cost is linear in the number of copies , , and define the cost-deletion ratio . From a mathematical point of view , the model with selection can be seen as a multitype branching process whose stationary behavior is described by its generating matrix [21] , [28] . ( 2 ) where . The population evolves according to the following dynamical equation: ( 3 ) The stationary composition of the population is described by the eigenvector associated with the greatest real eigenvalue of . The stationary abundance distribution is equal to the component of . ( Note that takes values from , which corresponds to the first component of ) . It is worth mentioning that the neutral model can be derived from the selection model in the limit . In order to compare the genomic data with the models we assume that the dynamics of a particular IS family is similar in all genomes , while different families behave independently . Therefore , the genomic frequencies observed for a given IS family can be interpreted on the basis of the probability of finding a genome with copies in a population of independent genomes . This is an assumption supported by empirical and theoretical arguments . Indeed , even in strains of the same species , the abundance of an IS family varies broadly [27] . On the theoretical side , IS dynamics are a kind of branching processes [57] , where information on initial conditions is lost exponentially fast [58] –for all practical purposes , within a few duplication cycles . This nonetheless , and in order to minimize the possible bias introduced by closely related strains , we restricted our analysis to a dataset composed of only one strain per species . Although genomes from distinct species may be not completely independent , the averaging on many non-related groups compensates for that . As a confirmation , taking one genome per genus and repeating the analysis did not change our results . The full dataset with multiple strains per species was only used to detect outliers . Absence of a particular IS family in a genome may have two causes . One is the dynamics described by our models , which include the temporal extinction of an IS family . Another one is the possibility , that we cannot discard , that a specific genome is non-invadable by that family . Since we cannot distinguish between both mechanisms , we excluded from this study those genomes which do not contain any IS family at all . The remaining dataset ( provided as Table 4 in the SI ) contains bacterial chromosomes ( harboured by species ) . As it is quite a large number , special cases of genomes that may be non-invadable by certain IS families are not expected to introduce a significant bias into the estimation of . Alternatively , IS familes that are very specific to certain genomes can be detected through their poor fits . IS families that appear in fewer than genomes were discarded to prevent unreliable estimates associated to small datasets . The following parameter estimation was done independently for each of the remaining IS families . First , the frequency distribution of the family was extracted from the genomic data . Then , for each model a maximum likelihood approach was applied to determine the parameters that best fit the model to the data . As a numerical optimization algorithm , we used the simplex method implemented in MATLAB ( MATLAB version 7 . 6 . 0 . 324 ( R2008a ) . Natick , Massachusetts: The Mathworks Inc . ) . The robustness of our qualitative results against the split of IS families into different subfamilies was also tested . Additional fits to IS copy number abundance were carried out for three families that can be clearly separated into groups: IS4 , IS5 and IS66 . Some care must be taken in order to evaluate the role of selection . The key difficulty is the fact that parameter estimation in the selection model is confused by multiple local maxima in the likelihood function . Since local maxima with similar values are distributed along the whole parameter range , parameter estimation becomes strongly dependent on their initial guesses . As a result , an a priori estimation of some parameters is required before the selection model can be fitted to the data . Because the neutral model is a particular case of the selection model , we took from the neutral setting and tried to refine the fit by adding selection . Alternatively , we explored the selection model by choosing a qualitatively different range of values of , between and ( as suggested in [28] ) ; and also the case of a small ( but greater than one ) . The goodness of the fits was evaluated by means of a likelihood ratio test that compared the observed and expected frequencies for each abundance interval . This test is similar to a Chi-square test , but more suitable if any of the differences between the observed and expected frequencies is greater than the expected frequency . Different abundance intervals have been defined for each IS family in such a way that at least two occurrences are expected for each interval ( alternative criteria have been tried without major changes in results ) . The values associated to the test statistics have been numerically computed by simulating a sampling process on the expected distribution . Comparison between neutral and selection models was done in terms of the corrected Akaike Information Criterion [59] , both models containing two degrees of freedom ( because is fixed in the model with selection ) . The detailed results of the fits to the neutral and selection models are provided in the Supplementary Information . For each IS family , outliers are genomes that contain a large copy number , so large that it cannot be explained by any of the models . Specifically , let us define as the probability of having or more copies , . The probability that a genome with or more copies is found in a sample of genomes is . The value of is indeed the significance level , already corrected by the sample size [60] . It can be set to the desired value in order to numerically obtain the copy threshold . Thus , genomes with more than copies are outliers at a corrected significance level . Copy thresholds are different accross IS families , thus detection of outliers was carried out independently for each family . We tried and with similar results . As we looked for outliers in the full dataset ( including more than one strain per species ) , we took a sample size chromosomes . That is a conservative choice , since the actual number of independent instances in the dataset may be smaller; however , similar results were obtained by setting ( the number of different species ) . Notice that the correction for sample size implies that the significance threshold per genome , in all these conditions , is close to . The critical condition sets an implicit constraint if a stationary abundance distribution is to be established . When it comes to study the condition above , such a constraint may give rise to a false correlation if the fitting algorithm estimates and jointly . In order to avoid the introduction of spurious correlations , we used an alternative approach that provides an independent ( although less precise ) estimation of the parameters . First , the LGT-deletion ratio was estimated as , where and are the frequencies of genomes with one and no copies , respectively . Next , we discarded genomes with no copies and estimated α only from “infected” genomes . These parameter values were used to test the critical balance . By simulating non-stationary genomes we checked that the independent estimation algorithm does not give rise to false correlations . | Insertion sequences ( IS ) are mobile genetic elements found in most prokaryotic genomes . They are able to autonomously change position and proliferate in chromosomes . The nature of the coevolutionary dynamics of IS with the genome that hosts them is a matter of debate: Do IS proliferate to the point of causing the extinction of the host ? Is it possible that IS and hosts stably coexist ? Can environmental perturbations cause IS expansions ? What is the role of selection in controlling IS copy number ? In this study , we have analysed abundance patterns of IS families to test two different evolutionary hypotheses: in the first one IS evolve neutrally , while in the second case they are affected by selection . Our results indicate that , most of the time , IS and their hosts coexist stably in a neutral scenario where the proliferation of IS through duplications and lateral gene transfer is balanced by regular deletions . Occasionally , though , this balance may be disrupted , causing temporary explosions of IS abundance . | [
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]
| 2014 | Large-Scale Genomic Analysis Suggests a Neutral Punctuated Dynamics of Transposable Elements in Bacterial Genomes |
Spinal muscular atrophy is a severe motor neuron disease caused by inactivating mutations in the SMN1 gene leading to reduced levels of full-length functional SMN protein . SMN is a critical mediator of spliceosomal protein assembly , and complete loss or drastic reduction in protein leads to loss of cell viability . However , the reason for selective motor neuron degeneration when SMN is reduced to levels which are tolerated by all other cell types is not currently understood . Widespread splicing abnormalities have recently been reported at end-stage in a mouse model of SMA , leading to the proposition that disruption of efficient splicing is the primary mechanism of motor neuron death . However , it remains unclear whether splicing abnormalities are present during early stages of the disease , which would be a requirement for a direct role in disease pathogenesis . We performed exon-array analysis of RNA from SMN deficient mouse spinal cord at 3 time points , pre-symptomatic ( P1 ) , early symptomatic ( P7 ) , and late-symptomatic ( P13 ) . Compared to littermate control mice , SMA mice showed a time-dependent increase in the number of exons showing differential expression , with minimal differences between genotypes at P1 and P7 , but substantial variation in late-symptomatic ( P13 ) mice . Gene ontology analysis revealed differences in pathways associated with neuronal development as well as cellular injury . Validation of selected targets by RT–PCR confirmed the array findings and was in keeping with a shift between physiologically occurring mRNA isoforms . We conclude that the majority of splicing changes occur late in SMA and may represent a secondary effect of cell injury , though we cannot rule out significant early changes in a small number of transcripts crucial to motor neuron survival .
Autosomal recessive Spinal Muscular Atrophy ( SMA ) is a leading genetic cause of infant mortality , with a carrier frequency of 1∶50 and an annual incidence of 1 in 10 , 000 live births [1] . Affected individuals develop symmetrical , proximal weakness resulting from neurogenic muscle atrophy , and ultimately leading , in the most severely affected individuals , to death from respiratory failure . The pathological correlate of these symptoms is selective loss of large alpha motor neurons in the ventral horn of the spinal cord . The vast majority of cases are caused by homozygous deletion of the survival motor neuron 1 ( SMN1 ) gene [2] with subsequent reduction in levels of the SMN protein [3] . SMN is highly conserved in evolution and ubiquitously expressed . Complete loss of SMN , which is incompatible with life [4] , is prevented by production of SMN from the SMN2 gene , a near identical paralogue of SMN1 which has arisen from an inverted duplication event in recent evolution . The presence of a translationally silent C-T transition in SMN2 exon 7 , however , leads to disruption of an exonic splice-enhancer ( ESE ) element and exon 7 skipping in the majority of SMN2 derived transcripts [5] , [6] . The functional consequence is that SMN2 produces only very little full length SMN ( FL-SMN ) , while the SMND7 isoform is translated into an unstable protein that is rapidly degraded [7] . Disease severity is broadly proportional to residual SMN levels , which is a function of SMN2 copy number , although other modifying factors are involved in some cases [8] , [9] . The mechanism by which SMN deficiency leads to selective lower motor neuron loss in SMA is poorly understood and difficult to reconcile with its ubiquitous expression unless either SMN has a motor neuron-specific function or motor neurons are selectively vulnerable to a deficiency in the general function of SMN common to all cells . Currently , the best characterised function of SMN is as part of a multi-protein complex which is critical for the core steps in the assembly of small nuclear ribonuclear proteins ( snRNPs ) , components of the spliceosome , the cellular machinery that controls splicing of pre-mRNAs [10] , [11] . In particular , SMN acts in the cytoplasmic assembly of Sm core proteins on snRNAs , which is a prerequisite for import of snRNPs into the nucleus [12] . SMN levels and the activity of snRNP assembly vary during development and according to tissue type . In the mouse spinal cord , snRNP assembly is highest during embryogenesis and early postnatal development and then falls to a baseline level when myelination occurs [13] . Mouse models of SMA , which have reduced SMN levels , show a drop in snRNP assembly activity as measured by in vitro assays , while steady state snRNP levels measured in tissues are only mildly reduced [14] . Interestingly , a subset of snRNPs belonging to the minor spliceosome seems to be differentially affected [14] , [15] . Several strands of evidence support the notion that reduced snRNP assembly is associated with motor neuron degeneration . The subtle motor neuron loss that has been reported by some investigators at late stages in mice heterozygous null for Smn can be accelerated by crossing with mice heterozygous null for Gemin2 , another core component of the SMN complex . This is associated with a reduction in snRNP assembly [16] . In zebrafish , a failure of embryonic motor axon growth can be induced by silencing not only SMN , but also gemin2 . The observed defects can be rescued by direct injection of U snRNPs [17] . However , other studies in zebrafish indicate that the axonal degeneration phenotype is not coupled to U snRNP assembly [18] . Reducing SMN levels in HeLa cells by RNAi leads to an increased error rate in splice-site pairing , which has also been observed in fibroblasts from SMA patients [19] . Finally , a recent study in SMA mice found that at end-stage , widespread splicing abnormalities can be found in several tissues including the spinal cord . Importantly , different transcripts were found to be altered in a tissue-specific manner [15] . While both snRNP assembly dysfunction and splicing abnormalities have been documented in models of SMA , several questions remain . It is still unclear whether splicing abnormalities cause motor neuron loss in SMA , or whether they are a late occurrence in disease , either as a consequence of spliceosome dysfunction or the severe physiological alterations secondary to respiratory distress , hypoxia and malnutrition . If spliceosome dysfunction is critical for disease pathogenesis , the mechanism of splicing alterations needs to be further delineated . In addition , the role of SMN in spliceosome assembly is only one of many functions that are potentially altered in SMA , including roles in transcription regulation [20]–[22] , axonal transport of mRNA and RNPs [23] , [24] as well as regulation of local translation at the neuromuscular junction [25] . This study had two aims . First , we wanted to address the question of whether abnormal splicing events are a cause or consequence of SMA . We utilised an exon-specific microarray to examine the transcriptome of spinal cord samples harvested from SMA and control mice at pre-symptomatic , early symptomatic and late-symptomatic stages , to test the hypothesis that widespread alteration of splicing precedes disease onset . Secondly , we used the difference in temporal mRNA expression pattern between SMA and control mice to identify neuronal pathways disturbed by SMN deficiency .
The mouse model used in this study ( Smn−/−; SMN2; SMNΔ7 ) is the most commonly used model of severe SMA and has been described previously [26] . The maximum lifespan of SMA mice was 14 days . Subtle differences in weight compared to the control littermates ( Smn+/+; SMN2; SMNΔ7 ) were discernible before P7 , but were not reliably predictive of genotype in individual mice ( Figure 1A and 1B ) . SMN protein levels were markedly reduced in SMA mice at all time points as measured by Western blot ( Figure 1C ) and immunohistochemistry ( Figure 1D ) . At P7 a failure in the righting reflex became apparent . Importantly , this coincided with a drop in numbers of large motor neurons in the spinal cord ( Figure 2A and 2B ) . No discernible differences in phenotype or pathology were present at P1 , indicating that , in the SMNΔ7 mouse model of SMA , the disease develops in a motor system in which embryonic development has been relatively normal . By P13 , mice appeared emaciated , were unable to right themselves , and showed signs of respiratory distress ( Figure 1B ) . There was a corresponding loss of >30% of lower motor neurons from the ventral horn in SMA mice . At late-symptomatic stage ( P13 ) , a similar relative loss of motor neurons was evident across the entire length of the spinal cord , ( Figure 2C ) , justifying the use of whole spinal cord for RNA analysis . However , absolute numbers differed depending on the region examined , reflecting the differential innervation of limb and trunk musculature by motor neurons originating in the cervical , thoracic or lumbar cord . To understand the scale of change in mRNA expression in the spinal cord , we first performed gene-level comparisons between genotypes at each time point , using the core probe sets on the array and GeneSpring software ( Agilent Technologies , Santa Clara , CA , USA ) . When using an arbitrary p-value threshold of 0 . 05 and a fold-change threshold of 1 . 5 as cut-off for biological significance , the expression of 142 genes was increased or decreased in the spinal cord of SMA mice compared with their control littermates at late-symptomatic stage ( P13 ) , with a maximal fold-change of 3 . 9 ( Table S1 ) . Importantly , the degree of change between SMA and control mice was much smaller at the pre-symptomatic ( P1 ) and early-symptomatic ( P7 ) stages , with only 12 and 23 genes changed , respectively ( Table 1 and Table 2; Figure 3A ) . This finding argues against a critical function of SMN in transcription regulation , but also shows that if widespread splicing changes occur in SMA , this does not lead to a pre-symptomatic systemic change in whole transcript level mRNA expression , which might be expected if mis-spliced transcripts are subject to nonsense-mediated decay . An additional data analysis , which we refer to as the ENSG analsysis , was performed in which probes were grouped into sets , each corresponding to a gene in the Ensembl ( version 49 ) annotation database [27] . Time point-specific differential expression between cases and controls was quantified by fitting a linear model on a gene-by-gene basis [28] , [29] . Of 21 , 911 genes examined , 693 genes exhibited case/control differences at P13 , as opposed to 92 at P7 , and 83 at P1 ( Figure S1 , Tables S2 , S3 , S4 , see Text S1 for details of the linear model ) . This is in accordance with the parallel analysis described above . We next examined changes between time points for each genotype . Overall , the number of genes differentially expressed between time points in control mice was higher by a factor of ten compared to the number of genes differentially expressed between genotypes at individual time points ( Figure 3B ) , suggesting that the immediate post-natal period is associated with major changes in spinal cord gene expression in normally maturing mice . A key aim of this study was to assess the amount of splicing variation in the spinal cord of an SMA mouse model compared to control mouse spinal cord at several time points during disease progression . Because exon-specific microarrays are relatively novel , and analysis methods have not been fully developed and validated , we used two complementary statistical approaches to investigate the number of differentially expressed exons . First we used an ANOVA test in GeneSpring ( Agilent ) to select exons which show a significant difference between exon-level and transcript-level signal and then calculated the splicing index ( SI ) [30] , for these exons . The SI is the logarithm of the ratio of array probe set intensities ( corresponding to expression levels of individual exons ) to overall gene-level expression . So an SI value of 0 indicates equal expression of a particular exon in relation to the gene as a whole between genotypes i . e . no differential alternative splicing . Using a splicing index of |SI|≥0 . 5 as an arbitrary threshold we identified 252 potential alternative splicing events at the late-symptomatic stage ( P13 ) , but only 5 at the early symptomatic ( P7 ) and 16 at the pre-symptomatic ( P1 ) stage ( Figure 4A ) . This initial analysis suggests that alternative splicing events are a consequence of disease progression in SMA , rather than the primary cause . Since the splicing index method is known to lead to inaccuracies if complex splicing patterns are present , such as splicing of multiple exons in one gene , and might thus underestimate the level of differential exon use present , we next performed an analysis comparing expression levels of individual exons between genotypes at each time point . The rationale for this is that each instance of differential splicing between genotypes will lead to at least one exon being differentially expressed . So the number of differentially expressed exons provides an upper bound for the number of differential splicing events . In this analysis , which we refer to as the ENSE analysis , probes were grouped into sets , each corresponding to an Ensembl exon [27] . Time point-specific differential expression between cases and controls was quantified by fitting a linear model on an exon-by-exon basis [28] , [29] . The p-value cut-off for significant differences between genotypes at the exon-level was chosen to balance sensitivity with a reasonable false discovery rate ( FDR ) , as estimated by a permutation-based analysis ( Text S1 and Table S5 ) . At a p-value threshold of 1e-4 , there were 812 significantly differentially expressed exons at P13 , compared to 66 at P7 , and 72 at P1 ( Figure 4B , Tables S6 , S7 , S8 ) ; a total of 211 , 567 exons were examined . See Materials and Methods and Text S1 for further details of this analysis . This provided additional evidence that the vast majority of alternative splicing events occur at late-stage disease in the SMNΔ7 mouse model of SMA . We further observed that for genes with at least one differentially expressed probe set under the ENSE annotation , but no significant gene-level change under the ENSG annotation , i . e . genes for which there is some evidence of differential splicing events , it is mostly one , and never more than two , exons that are significantly altered . To assess whether the differentially expressed exons were associated with a particular intron type , we cross-referenced our data with a publicly available database of U12 introns , i . e . sequences recognised by the U12 snRNA containing minor spliceosome , but not the U2 snRNA containing major spliceosome [31] . Overall , the frequency of U12 introns in genes containing exons differentially expressed between genotypes was 0 . 19% , as opposed to the expected frequency of approximately 0 . 35% ( 0 . 13% at P1 , 0% at P7 , 0 . 22% at P13 ) . Moreover , only one gene ( Vash1 , ENSG000000712460 , Vasohibin-1 ) contained a differentially expressed exon directly adjacent to an U12 type intron , while all other exons were remote from U12 type introns . This analysis suggests that genes spliced by the minor spliceosome are not preferentially affected by SMA , even though components of the minor spliceosome were shown to be disproportionately affected by SMN deficiency [14] . While the results obtained from the SMNΔ7mouse model afforded important insights into the dynamics of gene- and exon-level expression changes over time , to ensure that our findings were not restricted to this particular transgenic model of SMA , but applicable to SMA mouse models in general , we performed a similar analysis on the more severe but genetically less complicated Smn−/−;SMN2 mouse model of SMA [32] . The Smn−/−;SMN2 animals , in which complete absence of mouse Smn is rescued by the introduction of the human SMN2 transgene , have a maximum lifespan of 6 days . Previous studies showed that at P1 , there are normal motor neuron numbers , whereas at P5 , there is about a 35% loss compared to litter mates with normal mouse Smn ( Smn+/+;SMN2 ) [32] . Early synaptic abnormalities are seen from P2 in this model [33] , although neuromuscular junctions appear normal at P1 , indicating normal pre-symptomatic development [34] . Exon-array analysis of spinal cord harvested from pre-symptomatic ( P1 ) and late-symptomatic ( P5 ) animals mirrored the principal findings in the SMNΔ7 mouse at both gene and exon level . When examining gene expression in Genespring using core probe sets , more changes were present at late-symptomatic stage than at the pre-symptomatic stage ( 3 genes up- or down-regulated at P1 , 160 genes up- or down-regulated at P5 with p≤0 . 05 and fold change >1 . 5 ) . Even more striking was the result of the splicing index analysis , which showed 27 potential alternative splicing events at P5 , but none at P1 when choosing a splicing index cut-off of 0 . 5 . These results showed that our findings in the SMNΔ7 mouse model were not caused by a specific effect of the SMNΔ7 transgene but are likely to be a generalised phenomenon in SMA . To validate the findings of our microarray experiments , we performed semi-quantitative and/or quantitative RT-PCR focussing on genes that showed at least one exon with differential expression at all time points ( Cdkn1 , Snrp1a , Chodl , Mccc2 , Uspl1 , ChAT , Figure S2 ) . In addition , we performed qRT-PCR on spinal cord samples obtained from E16 embryos for some of the targets to examine whether changes were already present pre-natally . All qRT-PCR results matched the changes at gene-level seen in the array experiments , underlining the robustness of the array findings . Chodl , the gene encoding chondrolectin is a C-type lectin with unknown in vivo function . Interestingly , in situ hybridisation shows that this gene is highly expressed in anterior horn cells ( Allen Brain Atlas http://mousespinal . brain-map . org ) and might have important , if currently unknown , motor neuronal functions . We observed a progressive reduction in Chodl expression over time . Of note , the exon array data were indicative of differential expression of the 3′ end of Chodl , and validation by qRT-PCR using primers spanning both constitutive exons as well as two alternative 3′-terminal exons was in keeping with a preferential loss of the Chodl -001 isoform ( ENSMUST23568 ) and relative sparing of Chodl-002 ( ENSMUST69148 ) ( Figure 5A and 5B ) . The relative reduction of Chodl expression appeared to be spinal cord specific and could not be demonstrated in either skeletal muscle or kidney . However , even these tissues showed a trend towards increased expression of the Chodl-002 isoform in SMA compared to control mice ( Figure 5C ) . Immunohistochemistry for Chodl showed strong immunoreactivity of anterior horn cells , in keeping with the published in situ data . There was reduced Chodl immunoreactivity in SMA mice , but remaining anterior horn cells retained substantial Chodl staining , which indicated that the reduced Chodl expression is at least partially due to loss of motor neurons ( Figure S4 ) . Uspl1 ( ubiquitin specific peptidase like 1 ) , a gene encoding part of the ubiquitin-dependent protein degradation pathway was found to be up-regulated in SMA at all time points . In addition , Uspl1 showed a consistent change in both splicing index and Ensembl exon-level analysis of differential splicing . Uspl1 exon 2 ( ENSMUSE00000351955 ) is a cassette exon absent in transcripts Uspl1-006 ( ENSMUST00000121416 ) and -007 ( ENSMUST00000117878 ) . The increased use of Uspl1 exon 2 in the SMA mice led to an isoform shift with relatively higher levels of exon 2 containing transcripts Uspl1 -001-005 ( Figure 6 ) . Of note , this pattern was much more pronounced in muscle than in spinal cord , and less evident in kidney ( Figure 6B ) . In spinal cord samples , the degree of isoform shift was more pronounced at symptomatic than at pre-symptomatic stages ( Figure 6C ) . Immunohistochemistry for Uspl1 showed ubiquitous cytoplasmic staining in all spinal cord cells with grey matter preference . In keeping with the only mild overall increase in Uspl1 expression at mRNA level ( Figure S2F ) , no difference in immunoreactivity was evident between genotypes Figure S4 ) and Western blotting showed no significant difference in the main protein isoform identified between genotypes . Further interesting changes at all time points were detected including the whole-gene up-regulation of Snrpa1 ( average 1 . 8 fold ) . Snrpa1 encodes one of the many protein components of the spliceosomal A complex [34] , which is associated with the U2 snRNA . Western blotting showed a consistent small expression increase ( Figure S4 ) . While no other spliceosomal components were differentially expressed , the Snrpa1 change might reflect a compensatory response of the cell to Smn deficiency . Another change included down-regulation of isoforms ENSMUST00000091326 and ENSMUST00000022148 of Mccc2 , the gene encoding the methylcrotonoyl-CoA carboxylase beta chain , a mitochondrial enzyme involved in amino acid metabolism ( Figure S2D ) . Interestingly , the array data suggest that , while two Mccc2 isoforms were expressed at lower levels in SMA compared to controls , a third isoform ( ENSMUST00000109383 ) was in fact up-regulated . This was confirmed by qRT-PCR and semi-quantitative PCR ( Figure S3 ) . In the SMNΔ7 mouse model , the early postnatal days appeared particularly relevant to disease development . Our earlier finding of massive gene expression changes between time points in post-natal wild-type mice ( Figure 3B ) indicates that events relevant to disease in the SMN Δ7 mouse model coincide with transcriptome changes associated with normal post-natal development or maturation . To analyse which pathways were physiologically activated during this time , we first compared gene expression in control mice ( Smn+/+;SMN2;SMNΔ7 ) between P1 and P7 , and subsequently between P7 and P13 . Using GO-Elite software , we identified pathways enriched with genes involved in spinal cord cell proliferation , axon development , oligodendrocyte development and myelination as significantly altered , reflecting physiological events during the rapid growth of the spinal cord . When the same analysis was performed for the SMA mice , a strikingly different pattern emerged . With the exception of two GO IDs pertaining to nervous system development , all physiologically activated pathways were absent in both the P1 v P7 and P7 v P13 analyses Table 3 ) . To investigate whether this dramatic change in gene expression pathways was mirrored by a difference in proliferating spinal cord cells , we performed Western blotting and immunohistochemistry for the proliferation marker PCNA . Our preliminary results indicate that there is indeed a reduction in the number of proliferating cells in the SMA spinal cord ( Figure S5 ) . At P13 , genes relating to cellular responses to DNA damage became prominent in the SMA mice , which could not be explained by a significant amount of spinal cord gliosis ( Figure S5 ) . Of note , the gene with the highest-fold change between genotypes at P13 was Cdkn1a , a cyclin dependent kinase inhibitor activated by p53 in response to DNA damage . This analysis suggested that in the SMNΔ7 mouse model there is an inhibition or a failure of activation of the normal physiological pathways of post-natal spinal cord maturation .
In this study we undertook a detailed assessment of transcriptional changes over time in the spinal cord of a commonly used severe mouse model of SMA , using time points correlated with key phenotypic and pathological changes . We identified alterations in a subset of genes involved in post-natal neurodevelopmental pathways in SMA , and showed that splicing alterations are only a late occurrence in disease . Survival , weight development and motor phenotype of our SMNΔ7 mouse colonies were similar to that described by others [26] , [35] , with a change in outward appearance and development of motor deficits apparent at P7 . Recent studies have identified morphological changes at the neuromuscular junction as early events in SMA [33] , [35] with neurofilament accumulation occurring as early as P5 in the SMNΔ7 model . This structural change at the distal end of the motor neuron is closely followed in our study by a significant loss of large motor neurons at P7 , indicating that although synaptic changes are the earliest identified feature of SMA it ultimately is a disease of the entire lower motor neuron . Of note , motor neuron loss was present to a similar degree across the entire spinal cord , in contrast to the previous finding of a rostral-caudal gradient with relative sparing of the lumbar region [35] . Motor neuron loss at P7 was reflected in the reduction at transcript level of choline acetyl transferase ( ChAT ) , the key enzyme in motor neuronal synthesis of acetylcholine ( Figure S2E ) . This finding , as well as that of reduced levels of Chodl mRNA , which seems to be highly expressed in anterior horn cells , shows that even though whole spinal cord was used for the array , important cell-type specific changes were detectable . The key finding of this study is that alternative splicing events are a late occurrence in SMA , and are therefore unlikely to contribute to early disease pathogenesis . Zhang et al . [15] described widespread splicing abnormalities in several tissues including the spinal cord at end-stage disease in the same mouse model employed in this study and attributed this finding to dysfunction of the spliceosome secondary to SMN deficiency . Importantly , even though the time point of analysis is not absolutely identical to ours , there is considerable overlap between Zhang et al . 's data set obtained at P11 and our P13 data set , when raw data are analysed in the same way ( Figures S6 , S7; Tables S1 , S9 , S10 , S11 ) . However , it remains unclear from these data whether splicing abnormalities had preceded the onset of symptoms , as would be predicted from the crucial role of SMN in spliceosome assembly , particularly in embryonic and early post-natal development [13] . In addition , the presence of widespread splicing defects in organs other than the spinal cord is difficult to interpret in the light of apparent tissue specificity of the disease if splicing abnormalities are indeed thought to be relevant to the mechanism of motor neuron degeneration . To determine the degree of variation in splicing between SMA and control mice , we utilised the splicing index , but also examined changes at individual exons as a measure of the maximum number of alternative splicing events present . The absolute number of changes found in late-symptomatic mice was not large given the large number of exons examined ( >200 , 000 ) . When the same analysis was performed , comparing between genotypes at pre-symptomatic and early symptomatic time points , only very few exon-level changes were present . Importantly , we were able to confirm a similar pattern of exon expression changes in the more severe Smn−/−;SMN2 mouse model , corroborating our main finding in the SMNΔ7 mice . Overall , our data indicate that the majority of splicing changes are not a direct consequence of SMN deficiency , but rather a consequence of disease progression , probably representing physiological isoform-shifts in response to cell stress . There is evidence that oxidative stress can induce shifts in alternative splicing , and that neurons may be more vulnerable to this process than other cells [36] . We would therefore argue that SMN deficiency to the degree observed in either the SMNΔ7 or Smn−/−;SMN2 mouse , although associated with severe reduction in snRNP assembly capacity in vitro [14] , does not lead to a systemic breakdown of splicing fidelity in vivo until the disease is well established . While our results do not support the hypothesis that widespread , systematic splicing abnormalities cause SMA , we cannot rule out the possibility that splicing of one or several transcripts is critically affected by SMN deficiency , and that the few splicing changes observed early in our mice contribute to SMA pathogenesis , followed by a cascade of loss of splicing fidelity or secondary effects . In fact , at least one of the genes identified by our array ( Uspl1 ) is differentially spliced between SMA and control mice even at the embryonic phase , albeit to a lesser degree than at the symptomatic stages . Of note , the isoform shift observed in this particular gene is more pronounced in muscle than in spinal cord , and less marked in kidney , an organ not affected by SMA pathology . Independent of whether or not splicing changes are ultimately responsible for SMA disease initiation , our study identified several pathways that might shed light on SMA pathogenesis and disease progression . Analysis of transcriptional changes between genotypes in this study took place on a background of large scale physiological changes between time points , reflecting rapid neuronal development in the early post-natal days . Analysis of the changes between time points identified several pathways related to normal neuronal development . Surprisingly , in the SMA mice the majority of physiological transcriptional changes seen in control mice were absent even between the early time points P1 and P7 . This finding not only indicates that abnormal post-natal neuronal development might underlie early events in SMA but might explain general delayed growth and failure to thrive in the SMA mice . In this study , we examined gene expression in the entire spinal cord and are unable to distinguish which cell types contribute most to expression changes , even though changes that plausibly originate from motor neurons , such as ChAT and Chodl , are clearly present . The neurodevelopmental pathways identified as altered in our study can be associated with other cell types , including oligodendrocytes and cells in the posterior spinal cord . While this is a preliminary finding , it clearly warrants further studies examining the role of non-motor neuronal cells in SMA . Human autopsy cases indicate involvement of sensory neurons in the dorsal root ganglia , Clarke's column and the thalamus in SMA [37]–[40] , although the majority of studies were undertaken before the molecular diagnosis of SMA was available . More recent clinical studies showed that severe SMN-related SMA is associated with widespread neuronal degeneration , including sensory pathways [41] . Subtle sensory neuron abnormalities have also been detected in a severe mouse model of SMA [42] . To our knowledge , however , there is no study systematically investigating the role of non-motoneuronal cells in SMA spinal cord . In conclusion , our data show that alternative splicing events predominantly occur late in SMA , while alterations of post-natal neurodevelopmental pathways precede overt symptom onset . Further studies should continue to focus on the role of SMN in the post-natal maturation and development of the neuromuscular system including spinal cord motor neurons .
Transgenic Smn+/−;SMN2;SMNΔ7 [26] mice were maintained as heterozygous breeding pairs in standard animal facilities in Oxford . Homozygous Smn−/−; SMN2; SMNΔ7 mice reached the disease end-point by post-natal day 13 ( P13 ) . Five mice of each genotype were sacrificed at age P1 , P3 , P5 , P7 , P9 , P11 and P13 for motor neuron counts , and 4 mice of each genotype at P1 , P7 and P13 for RNA and protein extraction . Mice were genotyped using DNA extracted from tail-tips and standard PCR procedures . For motor neuron counts , mice were terminally anaesthetised with i . p . pentobarbitone and transcardially perfused with phosphate-buffered saline ( PBS ) followed by 4% paraformaldehyde ( PFA ) in PBS . For RNA and protein extraction , mice were killed by i . p . injection of pentobarbitone . Smn+/−;SMN2 mice ( Jackson labs strain no . 005024 ) were maintained as heterozygote breeding pairs under standard SPF conditions in animal care facilities in Edinburgh [43] . Litters produced from SMA colonies were retrospectively genotyped using standard PCR protocols ( JAX® Mice Resources ) . All animal breeding and procedures were performed in accordance with Home Office and University guidelines . Spinal cords were dissected , post-fixed in 4% PFA for 2 hours , cryoprotected in 30% sucrose overnight , embedded in OCT medium and rapidly frozen in liquid nitrogen-cooled isopentane . 20 µm thick horizontal sections were cut on a cryostat and stained with 0 . 5% Cresyl violet with 0 . 04% acetic acid . A minimum of 30 non-adjacent sections covering the entire spinal cord segment of interest were scrutinised for large , polygonal , Nissl positive cells in the ventral horn of the spinal cord anterior to the central canal . Only cells with a clearly present nucleolus were counted to avoid double counting of neurons . Motor neuron counts were performed blinded to genotype . At P13 , cords were macroscopically divided into cervical , thoracic and lumbar segments using the cervical and lumbar enlargements as landmarks . For the time-course , only lumbar spinal cord was utilised . 6 µm thick paraffin section were cut and stained with standard Haematoxylin and Eosin . For immunohistochemistry , sections were incubated with the primary antibody ( mouse anti-SMN antibody ( 1∶320 , BD Transduction lab ) , rabbit anti-Uspl1 ( 1∶600 , Santa Cruz ) , mouse anti-Chodl ( 1∶200 , abcam ) , rabbit anti-PCNA ( 1∶2500 , abcam ) , goat anti-Chat ( 1∶400 , Chemicon ) for 40 minutes at room temperature or at 4°C overnight . Antibody binding was visualised using a Dako REAL EnVision kit according to manufacturer's instructions . Immunohistochemistry was carried out on several sections taken from two different paraffin blocks for two animals per genotype . Representative images are shown . Whole spinal cords were rapidly dissected and snap-frozen on dry ice . RNA was extracted using the Qiagen RNeasy Mini RNA extraction kit according to the manufacturer's instructions . The quality and RNA integrity was assessed on a BioAnalyzer; all samples had a RNA Integrity Number ( RIN ) ≥9 ( Agilent Laboratories , US ) . 1 ug starting RNA was ribosome depleted using the Ribominus Human/MouseTranscriptome Isolation kit ( Invitrogen ) . Labelled sense ssDNA for hybridization was generated with the Affymetrix GeneChip WT sense target labelling and control reagents kit ( Affymetrix , UK ) according to the manufacturer's instructions . Sense ssDNA was fragmented and the distribution of fragment lengths was measured on the BioAnalyzer . The fragmented ssDNA was labelled and hybridized to the Affymetrix GeneChip Mouse Exon 1 . 0 ST Array ( Affymetrix ) . Chips were processed on an Affymetrix GeneChip Fluidics Station 450 and Scanner 3000 . For the gene-level analysis , core probe sets which map to the same transcript cluster were grouped together and RMA ( Robust multi-array analysis ) [44] normalised in GeneSpring GX10 . 1 . 02 . Differentially expressed genes were identified using an unpaired t-test; selecting genes with 1 ) a p-value less than or equal to an arbitrary threshold of 0 . 05 and 2 ) a fold change difference between genotypes ≥1 . 5 . The selected genes were sorted according to gene ontology using GenMAPP's GO-Elite ( http://www . genmapp . org/go_elite/go_elite . html ) . Only MAPPFinder ontologies with ≥3 genes changing and a permuted p-value of ≤0 . 05 were reported . At the exon level , core probe sets were PLIER ( Probe Logarithmic Intensity Error ) normalised in GeneSpring GX 10 . 1 . 02 ( Agilent ) . Transcript probe sets that had detection above background ( DABG ) p-value≤0 . 05 in both SMA and control groups were retained . An ANOVA test was used to identify significant differences between exon-level signal and transcript level signal . As recommended in the Affymetrix White Paper [44] , [45] , exon level probe sets exhibiting exon-to-transcript intensity ratios >5 were excluded from the ANOVA ( where log2[exon-to-transcript ratio] = log2[exon expression]−log2[transcript expression] ) . This filter removed probes with high background and cross-hybridisation potential . The p-value threshold for the ANOVA was selected to control for a false discovery rate of 0 . 05 using the Benjamini-Hochberg multiple testing procedure [46] . For exons selected on the basis of the ANOVA , the spicing index , SI ( log2[exon-to-transcript expression ratio] ) , was calculated and used as a measure of differential splicing between genotypes . See [45] , [47] for further details . A significantly differentially spliced exon was defined to be one having both an FDR-controlled ANOVA p-value≤0 . 05 and |SI|≥0 . 5 ( on the log scale , corresponding to a fold change up or down of approximately 1 . 4 in the absolute exon/transcript ratio between genotypes ) . CEL files were preprocessed using RMA without background correction ( see Text S1 ) . Publicly available custom chip-definition files ( CDFs ) ( http://brainarray . mbni . med . umich . edu/Brainarray/Database/CustomCDF/CDF_download . asp ) were used to group probes into sets . Parallel analyses , based on two different CDFs were performed . The first CDF , referred to as ENSE , defines a probe set for each Ensembl exon . The second , ENSG , defines a probe set for each Ensembl gene [27] . There were 211 , 567 and 21 , 911 probe sets for the ENSE and ENSG analyses respectively . At each probe set , a linear model was fitted using the limma package ( version 2 . 16 . 5 ) , to quantify evidence of genotype differences within each time point ( see Text S1 for details ) . A permutation-based analysis was conducted to estimate the FDR at a variety of p-value thresholds ( see Text S1 and Table S5 ) . The p-value thresholds selected were 1e-4 for ENSE , and 1e-3 for ENSG , as these thresholds controlled the FDR at a reasonably low level . Statistical analyses were performed using R [28] , version 2 . 8 . 1 . Snap frozen whole spinal cords were homogenised in RIPA lysis buffer ( 50 mM Tris-Cl , pH 7 . 5 , 150 mM NaCl , 0 . 1% ( w/v ) SDS , 1% ( v/v ) sodium deoxycholate , 1% ( v/v ) TX-100 ) and 1% ( v/v ) protease inhibitors by sonication at 50% output for 15 sec . Homogenates were chilled on ice for 20 min and clarified by centrifugation at 15 , 800 g for 20 min at 4°C . Protein samples ( 50 µg/well ) were electrophoresed through 12% SDS polyacrylamide gels and transferred to 0 . 2 µm nitrocellulose membranes ( Millipore ) . Membranes were blocked with 5% ( w/v ) milk powder in TBS-T , pH 8 . 0 , for 1 hr and incubated with the primary antibody ( mouse anti-SMN ( 1∶1 , 000 , BD Transduction labs ) , mouse anti-actin ( 1∶1 , 000 , Abcam ) , rabbit anti-SNRPA1 ( 1∶1000 , abcam ) , goat anti-CHAT ( 1∶500 , Chemicon ) , rabbit anti-USPL1 ( 1∶500 , Santa Cruz ) , rabbit anti-PCNA ( 1∶200 , abcam ) in 3% ( w/v ) BSA in TBST overnight at 4°C . Blots were probed with HRP-conjugated antibodies ( 1∶10 , 000 , Amersham ) and developed using ECL reagents ( Amersham ) . Western blots were repeated three times on biological replicates , and representative blots are shown . RNA was reverse transcribed into cDNA using random hexamer primers ( Invitrogen ) and Expand Reverse Transcriptase ( Roche ) under standard conditions . RT-PCR was performed using Taq DNA Polymerase ( Sigma ) or Expand High Fidelity PCR system ( Roche ) . Real-time PCR was performed using Fast SYBR Green chemistry ( Applied Biosystems ) and a StepOnePlus Real-time PCR machine ( Applied Biosystems ) . Real-time PCR Primers were designed with Primer Express software ( Applied Biosystems ) . Primer concentrations were optimised to yield low Ct values and minimal primer dimer formation ( commonly 300 nM for both forward and reverse primers ) . GAPDH was used as the endogenous control , as there was no differential expression between genotype in the array and in qRT-PCR experiments . All primer pairs were tested to have similar amplification efficiency to GAPDH when tested on serial cDNA dilutions over 4 log . Fold change was calculated using standard ΔΔCt calculations . The average fold change per time point was calculated from four biological replicates at each time point , and an unpaired two-tailed t-test was used to test for significantly different gene expression at each time-point . Error bars represent the standard deviation of the mean . | The identification of mutations in the Survival Motor Neuron ( SMN ) gene as the cause of the severe motor neuron disorder spinal muscular atrophy is one of a number of discoveries implicating selective motor neuron vulnerability to defects in processing of RNA and its associated ribonucleoprotein complexes . An unresolved issue is whether loss of the general cellular function of SMN in spliceosomal assembly , which is predicted to result in widespread defects in mRNA splicing , is directly responsible for motor neuron death . We have used exon-specific microarrays to assess the degree of altered splicing in the spinal cord in a mouse model of SMA . Our finding that the vast majority of splicing changes are a late feature of the disease and may represent a shift to alternative isoform expression , rather than loss of splicing fidelity , provides evidence that widespread splicing disturbance is not a primary feature of the disease pathogenesis but a secondary effect of cell injury in a late phase of the disease . However , our study cannot rule out a role for subtle early changes in one or a few transcripts crucial to motor neuron survival expressed at low levels or in only in a sub-population of spinal cord cells . | [
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| 2009 | Alternative Splicing Events Are a Late Feature of Pathology in a Mouse Model of Spinal Muscular Atrophy |
Target Product Profiles ( TPPs ) are process tools providing product requirements to guide researchers , developers and manufacturers in their efforts to develop effective and useful products such as biologicals , drugs or diagnostics . During a WHO Stakeholders Meeting on Taenia solium diagnostics , several TPPs were initiated to address diagnostic needs for different stages in the parasite’s transmission ( taeniasis , human and porcine cysticercosis ) . Following the meeting , draft TPPs were completed and distributed for consultation to 100 people/organizations , including experts in parasitology , human and pig cysticercosis , diagnostic researchers and manufacturers , international organizations working with neglected or zoonotic diseases , Ministries of Health and Ministries of Livestock in some of the endemic countries , WHO regional offices and other interested parties . There were 53 respondents . All comments and feedback received were considered and discussions were held with different experts according to their area of expertise . The comments were consolidated and final TPPs are presented here . They are considered to be live documents which are likely to undergo review and updating in the future when new knowledge and technologies become available .
Neurocysticercosis ( NCC ) is a Neglected Tropical Disease caused by infection with the cestode parasite Taenia solium . NCC is endemic in many low- and middle-income countries [1] and is the most frequent cause of acquired epilepsy in those countries and probably the world [2] . In endemic areas , 29% of neurological seizures are attributable to NCC [3] . The lifecycle of T . solium involves two hosts . Humans harbour the sexually reproducing mature tapeworm in the small intestine ( taeniasis ) . Eggs released with the faeces are infectious if eaten by pigs in which the larval parasite ( cysticercus ) encysts in the muscle tissues and brain , causing porcine cysticercosis . Ingestion of poorly cooked , infected pig meat by humans leads to growth of the tapeworm , completing the lifecycle . Eggs in the faeces of a patient infected with the adult tapeworm are also infectious if they are ingested by humans , in which case the cysticerci may lodge in muscle and other tissues , including the central nervous system causing NCC . Symptoms of NCC vary depending , among other things , on the number , size and location of the cysts . They include headache , blindness , convulsions , epilepsy and death . The disease usually affects very poor communities , where pigs live in close contact with humans , and hygiene , sanitation and education are limited . The disease has a very strong social impact; epilepsy sufferers are often stigmatised , especially if sufferers are women and girls , as it is linked with witchcraft . Diagnosis of NCC is complicated , and neuroimaging is frequently required for a definite diagnosis [4 , 5] . Currently there are no standard treatment guidelines for NCC , and treatment is tailored to the individual cases [2 , 6 , 7] , depending on factors such as location and viability of the cysts . Therapeutic approaches might include symptomatic therapy , anthelmintic treatment , or surgery , and often more than one of these options is needed [8] . The administration of anthelmintic drugs may elicit or increase pre-existing cerebral oedema and therefore is contraindicated in cases with cysticercotic encephalitis , increased intracranial pressure and subarachnoid NCC in close proximity to blood vessels [6 , 8] , thus it is very important to have the neuroimaging diagnostic before starting the treatment of living cysts . Suitable imaging diagnostic facilities are not readily available in the poor rural communities where the disease is endemic , and there is a need for better diagnosis tools to identify patients in rural and remote communities with viable cysts that need to be referred to neuroimaging before treatment . There are several options for NCC disease control at population level , aimed at breaking the parasite’s transmission cycle . They include identification and treatment , or mass treatment for taeniasis ( infection in humans with the adult tapeworm ) , chemotherapeutic treatment of porcine cysticercosis , pig vaccination , improvements in pig housing and management , meat inspection , improved human hygiene and sanitation , health education , etc . [9] . Efforts to control the disease have usually included several of these options , with the particular choices varying depending on the setting and the resources available . One of the challenges faced by efforts to control T . solium is a need for better diagnostic tools to monitor the outcome of control efforts [10] . Evaluation of control programs can be achieved by monitoring taeniasis or monitoring porcine cysticercosis . Monitoring control programs through changes in the prevalence of NCC is not recommended due to the persistence of NCC infections over long periods [10] . For Taenia solium , and generally for programs addressing neglected tropical diseases targeted for control and elimination [11] , it is crucial to have high-quality , low-cost diagnostic tools available and deployable in low-resource settings . In December 2015 the World Health Organization ( WHO ) Department of Control of Neglected Tropical Diseases ( WHO/NTD ) and the Special Programme for Research and Training in Tropical Diseases ( WHO/TDR ) convened a stakeholder meeting [12] to discuss the difficulties with the diagnosis of T . solium infections , to coordinate and harmonize the search for appropriate diagnostic tests and to overcome the challenges in doing so . During the meeting , diagnostic priorities for different stages of the parasite’s transmission ( taeniasis , human and porcine cysticercosis ) were defined , and it was decided to initiate several Target Product Profiles ( TPPs ) . An important step early in the process of designing diagnostic tools is defining their essential features , to allow researchers and diagnostic manufacturers to develop solutions that meet the needs in specific settings and for defined purposes . A TPP can be described as an important strategic document doing exactly this—describing key characteristics of a product . TPPs are used by the pharmaceutical and diagnostic industries , but also by organizations working on developing tools for the control of neglected diseases . Here we describe the development of four TTPs for T . solium taeniasis/cysticercosis .
Following the Stakeholder Meeting on T . solium Taeniasis/Cysticercosis Diagnostic Tools held at WHO headquarters , Geneva in December 2015 , four TPPs were prioritised and progressed , and final drafts were elaborated . The attributes selected were based on previously published diagnostic TPPs produced by organizations such as FIND ( www . finddx . org ) and PATH ( www . path . org ) , as well as the attributes suggested by the WHO [13] . Two TPPs were produced for taeniasis , one for NCC and one for porcine cysticercosis , as follows . TPPs for other use cases , such as screening of human populations , or detection of positive pigs through the pork chain , were not prioritised at this point in time . The draft TPPs were circulated to an extensive group of stakeholders for consultation . These included a wide range of experts in parasitology , human and pig cysticercosis , diagnostic experts , diagnostic manufacturers , organizations working with neglected diseases , organizations working with zoonotic diseases , some Ministries of Health and Directors of Veterinary services , WHO regional offices , as well as other interested parties . Efforts were made to ensure individuals and/or organizations from all T . solium endemic regions [1] were included . In total , 100 persons/institutions were contacted directly , and those receiving the drafts were requested to pass them to their colleagues if they thought appropriate . The experts/organizations contacted included 22 from Africa , 21 from the Americas , 28 from Asia-Pacific , 21 from Europe and 8 from International Organizations . Experts were given one month to provide feedback . The consultation took place during March 2017 , and it was extended until Mid-April 2017 . All comments and suggestions received were considered . Clarifications and follow up discussions were held with almost all contributors according to their area of expertise , to reach a consensus or arrive at a decision in relation to the various aspects of the final TPPs . In many cases , compromises needed to be made in order to keep the TPP attributes realistic and to account for the different opinions and needs as best as possible .
Currently available methods for the diagnosis of T . solium taeniasis lack either sensitivity , specificity , or both ( 10 ) . Current methods include faecal microscopy to identify parasite eggs , detection of coproantigens or copro-DNA , and detection of specific antibodies in serum . Faecal microscopy fails to identify pre-patent infections and is not species specific , antibody tests continue to test positive after treatment and currently available nucleic acid amplification tests ( NAAT ) are not suitable for application at point-of-care . Specificity of some tests might be high , such as the coproantigen test described by Guezala et al [16] , however the test is not commercially available . Two TPPs were developed for the diagnosis of T . solium taeniasis . The rationale for having two tests was that they differ fundamentally in their intended use ( surveillance versus monitoring a control program , and monitoring the initial stages in a disease control program versus monitoring later stages ) and this connotes important differences in some attributes , such as the use case ( attribute 1 . 2 ) , location of use ( attribute 1 . 5 ) , as well as test specificity ( attributes 3 . 1 and 3 . 4 ) . A point-of-care antigen ( Ag ) test ( TPP1 ) was recommended for use at the initial stages of an intervention when specificity for T . solium would not be critical . The second test , a more specific test ( TPP2 ) , was formulated for use in the later stages of an intervention in order to monitor the effectiveness of intervention procedures . If necessary , this second test could be performed in a specialist laboratory ( does not need to be a point-of-care test ) . At the later stages of a control intervention the prevalence of taeniasis would be expected to be very low ( prevalence in many highly endemic areas is commonly only 1–2% [17 , 18] although higher prevalences have been reported [19 , 20] , and species specificity would be important to avoid confounded data being obtained due to the presence of other taeniid cestode infections such as Taenia saginata . While some respondents suggested that a single test for taeniasis would suffice , it was considered that the different operational requirements for diagnosis of taeniasis in respect of a screening program , as distinct from use in monitoring the effectiveness of an intervention program , would be better reflected in two different diagnostic tests . However , if a point-of-care test with a high level of specificity and sensitivity were developed , it would be suitable for use in both circumstances . The Minimal test attributes for TPP2 were based on a currently existing , non-commercial coproAg test [21] , but with some attributes modified to fit the use case . The draft circulated TPPs were expecting all the tests to be species specific for T . solium . However , a large majority of the feedback received recommended that the Minimal requirements for a point-of-care test should not include species specificity because it would also be beneficial for the diagnosis and treatment of infections with other Taenia spp . Based on this feedback , the final version presented here for the Minimal attributes of a point-of-care test allows detection of other Taenia spp . This being the case , the Minimal attributes for the point-of-care test are interrelated , for example species differentiation ( attribute 3 . 1 ) is linked to the use case ( attribute 1 . 2 ) and specificity ( attribute 3 . 4 ) amongst others . The draft circulated for the Optimal attributes for a point-of-care test ( TPP1 ) was based on a lateral flow assay . In response to the feedback received , the format ( attribute 2 . 1 ) was changed to a Rapid Diagnostic Test , multiple formats allowed , in order to provide greater flexibility in relation to the technologies that could be adopted . A number of comments to the draft TPPs concerned the time period after a patient was successfully treated for taeniasis before the test should be expected to revert to being negative . A 6-day period was chosen as being realistic based on the performance of the current coproAg test [21] . Several respondents questioned the age at which humans can be infected with taeniasis . A consensus was reached that , although young children have been occasionally found with T . solium taeniasis , it was considered unusual to have children younger than 2 years of age infected . Ethical implications of testing young children , including the feasibility of treatment , need to be considered when using the test in the field . Both the sensitivity and specificity for the taeniasis test TPPs ( attributes 3 . 3 and 3 . 4 ) circulated as drafts were altered in the final versions based particularly on feedback that highlighted a need for very high test specificity for monitoring control programs . Specificity was prioritized for all taeniasis tests , except for the Minimal attributes point-of-care test , in which sensitivity was prioritized considering that the test would be used for surveillance , and not for monitoring an intervention . While sensitivity and specificity are inherent test characteristics , they should be defined in the TPP taking into account the expected prevalence of the condition to be diagnosed in the conditions of use , which will determine the predictive value of a positive ( PPV ) and a negative ( NPV ) test . There is limited data about the prevalence of T . solium as much of the published information refers to taeniasis without defining the species . However , the starting prevalence of T . solium taeniasis is expected to be around 1–2% , and up to approx . 5% [17–19] although some papers mention hotspots of up to 26% [20] . Within the 1–5% prevalence range , the PPV of the optimal TPP1 and both the TPP2 tests will be 49%-83% ( specificity 99% ) and the NPV 100% ( sensitivity 95% ) . The minimal TPP1 test too will have 100% NPV ( sensitivity 99% ) but its PPV will be much lower 5%-21% ( specificity 80% ) ( Fig 2 ) . This means that a test with sensitivity of 95% or more is adequate within the expected prevalence range , and that , if it is also 99% specific , it would be suited for both the initial and the later stages of an intervention . The reference test ( attribute 3 . 6 ) selected for T . solium taeniasis [14] was chosen on the basis that the test has high sensitivity and specificity , has shown to detect immature tapeworms and has been validated extensively . The relevant range of cost ( price to end-user , attribute 4 . 1 ) , was based on what was considered realistic based on the feedback provided , but no real cost analysis was done . Current tests for the diagnosis of NCC include antibody detection ( enzyme-linked immunoelectrotransfer blot , ELISA ) and antigen detection ( ELISA ) tests . A challenge with the currently available tests is that a proportion of people living in endemic areas might test transiently positive on either antibody or antigen detection tests , apparently in the absence of mature or clinically-relevant cysticercosis [22–25] . Antibody tests do not differentiate between patients having viable cysts from those with non-viable cysts . Comments were received and discussions held on the need to include also detection of antibodies , in order to detect patients with only non-viable/calcified cysts . Viable cysts degenerate over time , and some may eventually be calcified [17 , 26] . In cases where cysts become non-viable , inflammation , degeneration and possible calcification occur , although precise staging is not always clear because changes in and around the parasite occur as a continuum [8] and this is reflected in the performance of currently available diagnostic tests ( Table 5 ) . Overall , consensus was reached that the priority was for a point-of-care test to be used in symptomatic patients in order to detect those with viable cysts who needed to be referred for imaging and further management , including treatment with anthelmintics . Anthelmintic treatment for NCC should not be initiated on the basis of a serology result alone , as cyst development/inflammation and the presence of oedema need to be determined prior to the initiation of treatment [6] . It was considered that differential diagnosis of symptomatic patients with non-viable cysts was less of a priority because their treatment would not be significantly different to other cases of epilepsy . Similarly , detection of non-symptomatic cases of subjects with viable cysts was not considered a priority as many lesions may remain asymptomatic ( between 50–80% of those affected ) or resolve spontaneously without symptoms [6 , 27] . As many NCC patients are poor and live in remote rural areas , priority was given to the detection of only symptomatic patients who could be helped by anthelmintic treatment of viable cysts and for whom their investment in attending a medical center for brain imaging and treatment warranted the expense . Test attributes were selected to avoid identification of other NCC patients for whom the expense of travelling to a medical center and medical imaging would not greatly assist their medical prognosis . The analytical sensitivity/limit of detection ( the smallest amount of substance in a sample that can accurately be measured by an assay ) was a frequent topic of discussion . A consensus emerged that the Optimal attribute should specify to detect patients with a single intracranial cysticercus , including both intraparenchymal and extra-parenchymal cysts . However , several opinions were received regarding the Minimal requirements . Eventually a compromise was reached to prioritise the detection of high-risk patients , defined as patients with 5 or more parenchymal cysticerci , or with a single ventricular or subarachnoid cysticercus . Respondents considered that it would be useful if a test could be used to monitor the effectiveness of anthelmintic treatment of NCC , particularly in specific scenarios such as patients with subarachnoid NCC [28] . Hence this was added to the Optimal use case . This necessitated the test being quantitative or at least semi-quantitative , and appropriate changes were made in the final TPP . An important and complex issue was how to deal with the existence of cases where transient serologically positive responses occur ( both for antibody and antigen ) in people living in T . solium endemic areas . These responses could potentially be due to people being exposed to T . solium eggs , with the parasite possibly undergoing early development sufficient to induce a detectable serologic response , but not completing development and hence not causing detectable or symptomatic NCC . However , the precise cause of these transient responses has not been determined and it could be due to some cause unrelated to T . solium . This is particularly the case for the currently used test for circulating antigen which is known not to be specific for T . solium . In the case of the Minimal attributes for NCC , the existence of transiently positive reactions is included and , based upon the feedback that was received , a consensus was reached that a positive test would require to be repeated with a second sample collected after three months in order to confirm a case of persistent NCC . However , depending on the history and severity of the signs , the physician might decide to refer the patient immediately without repeat testing . Optimal sensitivity and specificity was reviewed , increasing the sensitivity and decreasing the specificity suggested in the draft , based on the need to detect the patients to be referred , but also accounting that the test would be used in a pre-selected population ( symptomatic patients ) , so the test would still have a high positive predictive value . The definitive method for the diagnosis of porcine cysticercosis is detection of cysts with a full carcass muscle and brain dissection; this is both time consuming and expensive , particularly in the case of light infections . Serological tests ( both for circulating antigen and antibody ) are available but specificity can be problematic when used for monitoring a control program . It is important that tests do not cross react with other taeniid cestode parasites to which pigs are likely to be exposed . Additionally , many pigs in endemic areas that are serologically positive have no cysts at necropsy [29–31] . It is therefore critical that any test developed is validated in endemic areas , in pig populations similar to the ones that would be targeted in a control program . The Minimal attributes were based on a currently available commercial antigen detection test for T . solium , with some attributes modified to fit the use case . The occurrence of transient positive responses in animals that do not have mature cysts were not considered acceptable , unless it could be demonstrated unequivocally that these responses only occurred in animals exposed to T . solium and are not induced by any other cause such as exposure to any other parasites . Discussions were held with a number of experts on the period over which the test would revert to being negative after a successful chemotherapy . The drug most commonly used to treat porcine cysticercosis is oxfendazole [32 , 33] . There is limited published evidence on the dynamics of circulating cysticercal antigen after oxfendazole treatment [34 , 35] . Sikasunge et al . [34] found a statistically significant reduction in circulating antigen level at 8 weeks post-treatment . For this reason , a Minimal period of 10 weeks , and an Optimal period of 2 weeks for the test to revert to negative were considered appropriate . Oxfendazole is highly effective for the treatment of cysticerci in muscle tissues , although efficacy is limited in the treatment of brain cysts [36] . It is unclear if cysticercal antigens in pigs cross the brain-blood barrier . Dorny et al [37] found that one pig having 16 viable cysts only in the brain was negative for the presence of T . solium circulating antigens , however there are few data on serological responses in animals having only brain cysts . With these considerations in mind , in order to provide flexibility in the development of diagnostics for porcine cysticercosis the TPP’s Minimal indication for treated animals ( attribute 1 . 1 ) was worded in a way such that it would not be restricted if the test were positive in animals having only viable brain cysts . A consensus was reached based on feedback from respondents that a qualitative test would be fit for purpose , hence this was changed from the proposal for a quantitative test that was included in the draft TPP which had been circulated . Analytical sensitivity was defined at 1 cyst for the Optimal attribute . A variety of views were expressed in this respect for the Minimal attribute , and a decision was taken to define the limit of detection for this test to also be 1 cyst . Sensitivity and specificity were also reviewed based on the feedback provided . Based on the use case , priority was determined to be specificity and hence this was maintained at a high level as per the original draft . Sensitivity was reviewed down , because of the challenges in achieving good sensitivity in an antigen test . A test with a relatively low sensitivity would nevertheless be useful in monitoring progress of control efforts , so long as the specificity was high .
The TPPs were finalized based on the contributions of many subject matter experts serving different roles in academia , government , industry , NGOs and international organizations . Often a variety of views were expressed about a particular test attribute and , through additional discussions , in most cases a consensus was determined and this then formed the attribute included in the final TPPs . Each TPP is likely to need to be reviewed and updated in the future , as knowledge and technology progresses . It is expected that the TPPs presented here will assist researchers and diagnostic developers in guiding their efforts and ultimately development of the tools needed to assist in the control of T . solium transmission and a consequent reduction in the incidence of human NCC . | Diagnosing the different manifestations of Taenia solium ( taeniasis and neurocysticercosis in humans , and pig cysticercosis ) presents many challenges . The current paper describes the Optimal and Minimal attributes of the diagnostic tests that would be fit for a variety of diagnostic purposes , presented in the form of Target Product Profiles ( TPPs ) . Four TPPs were drafted , distributed for consultation , and the feedback received was consolidated to produce the final versions presented here . | [
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| 2017 | Target product profiles for the diagnosis of Taenia solium taeniasis, neurocysticercosis and porcine cysticercosis |
Allele age has long been a focus of population genetic research , primarily because it can be an important clue to the fitness effects of an allele . By virtue of their effects on fitness , alleles under directional selection are expected to be younger than neutral alleles of the same frequency . We developed a new coalescent-based estimator of a close proxy for allele age , the time when a copy of an allele first shares common ancestry with other chromosomes in a sample not carrying that allele . The estimator performs well , including for the very rarest of alleles that occur just once in a sample , with a bias that is typically negative . The estimator is mostly insensitive to population demography and to factors that can arise in population genomic pipelines , including the statistical phasing of chromosomes . Applications to 1000 Genomes Data and UK10K genome data confirm predictions that singleton alleles that alter proteins are significantly younger than those that do not , with a greater difference in the larger UK10K dataset , as expected . The 1000 Genomes populations varied markedly in their distributions for singleton allele ages , suggesting that these distributions can be used to inform models of demographic history , including recent events that are only revealed by their impacts on the ages of very rare alleles .
The age of an allele of a given frequency can be reveal the forces acting upon it , with rare alleles being particularly sensitive to recent evolutionary processes [1] . A functional allele that is younger than expected given its frequency is likely to have been under directional selection . This is not surprising for favored alleles , but it is also true for harmful alleles [2] , including those with negative impacts on health that are under negative selection [3] . If researchers are able to estimate allele age , they could combine this with other information ( e . g . allele frequency , geographical distribution , functional annotation ) to improve predictions of an allele’s effect on human health . Alternatively , an allele that is older than expected given its frequency is also a candidate for having an interesting history , as functional alleles older than expected can be the result of balancing or negative frequency-dependent selection [4] . As population genomic samples grow in size , the density of variable sites rises approximately in proportion to the log of the sample size [5] , and very large data sets will have large numbers of SNPs and other variants in every gene . If information could be gleaned on the ages of a large number of variants for a functional region of the genome , this could be used to develop a detailed portrait of the history of natural selection specifically on that region . Allele ages are also shaped by processes that act in aggregate across the genome . The overall distribution of ages will be strongly shaped by the demographic history of the population , and for the rarest alleles , that distribution will be acutely sensitive to recent admixture [6] . The age spectrum will also fluctuate spatially along the genome , both stochastically and as a function of the intensity of background selection [7] . In developing a way to study allele age , we considered several constraints . An estimator should not be a function of allele frequency , as we wish to glean information about allele history that is distinct from its frequency . We also prefer an approach that is not a function of the demographic history of the population , as some estimators are [8 , 9] . An estimator that can get close to the true value of the unknown , regardless of demographic history , enables analyses in cases when the history is not known and it enables comparisons between populations that are not confounded by errors in our knowledge of the demographic history of the populations . We also wish to be able to study the age of the very rarest alleles , including those that appear only once in a sample ( singletons ) . This last criterion leads to an approach that is different from existing methods that are based on the variation observed among copies of an allele , or in flanking regions [8–13] . An estimator should also be applicable for very large sample sizes for which it becomes increasingly possible to find low frequency alleles that arose by multiple mutations [14] . For these cases , as with singletons , we need a method that is applicable for each individual gene copy . Finally , an estimator should not be highly sensitive to the details of the bioinformatics pipeline used to process the data , such as whether the data were statistically phased . We developed a new estimator that focuses , not directly on allele age , but rather on the time when a base position in a particular chromosome first coalesces in the genealogy . The mutation causing a derived allele at that base will have occurred since this first coalescent time , and so first coalescent time can be used as a proxy for allele age . For example , a neutral singleton allele will have a uniform probability of having arisen anywhere along an external branch , and therefore an expected age of half the first coalescent time . We assessed performance using simulated data , and show that it performs well and substantially overcomes the challenges describe above . We also applied it to SNP alleles from the 1000 Genomes Project [15] and the UK10K genome panel [16] . For these data sets , we assessed basic predictions regarding population-specific variation in the ages of rare alleles , the ages of private alleles and alleles shared by populations , and we compared ages of alleles that are expected a priori to have functional impacts with those that are not . Our estimator shares structural similarities with existing methods developed to estimate demographic histories from shared-haplotype tract-length distributions [17–20] , but is uniquely able to discern the specific histories of individual rare alleles .
Consider one chromosome ( the focal chromosome ) from a sample of chromosomes drawn at random from a population , and an individual base position on that chromosome ( the focal base ) . As shown in Fig 1 , the focal base can be thought of as the terminal point on a branch A of the genealogy of the sample of chromosomes at that base position . If the focal base is a singleton variant ( i . e . a derived allele that occurs only once in the sample ) , then the mutation causing that allele must have occurred on this branch . Also shown in Fig 1 is that branch A connects the focal chromosome to the most recent common ancestor of that chromosome and a sister branch S , which is ancestral to one or more sister chromosomes of the focal chromosome . Branches A and S share their most recent common ancestor at a time tc generations prior to the sample generation , and branch A is therefore tc generations long . We denote the length of branch S as φ generations . If the sample contains a single unique sister chromosome ( that is , the focal chromosome is also the closest relative of the sister at the focal base ) , then φ is equal to tc . When multiple chromosomes are all equally and most closely related to the focal chromosome at the focal base , they form a clade of sisters , and φ < tc . Consider first the case when there is no recombination and the focal chromosome has just a single sister . In this situation , any differences between the focal and sister chromosomes will have been caused by mutations on branches A and S . We model mutation as a Poisson process , where each base has a constant probability , μ , of mutating in each generation . Treating a chromosome as a continuous line , and considering events just to one side of the focal base ( in either the 5’ or 3’ direction ) , the probability density of distance x from a focal base to the first base that is not identical between the two chromosomes can be approximated with an exponential distribution having a rate of μ2tc: p ( x ) =μ2tce-xμ2tc . ( 1 ) If we knew which chromosome was the sister chromosome , we could compare them and identify the distance from the focal base to the nearest difference between the chromosomes ( i . e . x ) , and use this to estimate tc . This basic idea captures the underlying rationale of our approach . The final formulation takes into account the remaining issues: that we do not know which chromosome is the sister to the focal chromosome; that the focal chromosome may have multiple sisters; and that Eq ( 1 ) assumes no recombination ( see Materials and methods ) . Ultimately , an expression that resembles Eq ( 1 ) , but that differs in replacing x with the longest observed tract of identity between the focal chromosome and each of the other chromosomes in the sample , proves applicable . We call this quantity the maximum shared haplotype ( msh ) , and show that it arises on either branch A or S ( Fig 1 ) with high probability . We use t^c to denote the estimator , and because tc values range over several orders of magnitude and Poisson processes have variances proportional to their means , we focused primarily on the logarithm , log10 ( t^c ) . Performance was assessed in terms of the root mean squared error ( RMSE ) , bias ( mean of estimated minus true values ) , and correlation ( Pearson’s r for the true and estimated values of log10 ( tc ) ) for alleles at all frequencies in a series of large simulated data sets . We varied sample size and recombination rate , and considered three demographic histories that varied in terms of population sizes , exponential growth , historical bottlenecks , and intercontinental migration . We also considered samples of chromosomes with known and with estimated phase . All of these results are summarized in S1 and S2 Tables . For low and intermediate recombination , over a wide range of circumstances , the estimator exhibits an RMSE of about 0 . 4 log-transformed generations , corresponding to estimates that are typically within a factor of 2 . 5 of the true value . Across the models , correlations of true and estimated values ranged from 0 . 4 to 0 . 95 with a mode of 0 . 9 . For recombination rates equal to or less than the mutation rate , bias varies from -0 . 4 to 0 . 1 with a mode of -0 . 2 , which corresponds to an average underestimate by a factor of 0 . 63 . Performance was consistent across the spectrum of allele frequencies , and with respect to particular independent variables , performance was better: when recombination was low; when population size was constant; when phase was known; and when sample sizes were larger . A key factor that determines how informative estimates are is having chromosomes that are much more closely related to their closest relatives than to unrelated chromosomes . Thus , strong recent growth can produce more star-like genealogies that reduces these differences and reduce the quality of the estimates , while larger sample sizes improve them . When recombination rate is appreciably higher than the mutation rate , such that typical msh values are small enough to be within the range of distances between average pairwise SNPs , estimates worsens and the bias shifts from negative to positive ( S2 Table ) . The amount of recombination that is too high relative to the mutation rate will depend on the length of the region of high recombination ( e . g . if it is associated with hotspots of short length ) , and on sample size and the demographic history of the sample . We observed that larger sample sizes exert a greater improvement on the quality of estimates in the case of high recombination than with low to intermediate recombination ( S1 and S2 Tables ) . The change in sign of the bias , with high recombination , and that the observed absolute value of the bias is lower with intermediate levels of recombination , suggests that there are multiple contributions to the bias . Fig 2 shows plots of estimated versus true values for different ranges of allele frequencies and for two different sample sizes ( box plots are shown in S1 Fig ) . Also shown in Fig 2 is an idealized estimator of log10 ( tc ) that is based on allele frequency and for which all alleles at a given frequency generate the same estimate–shown as a band of red points . We show both the msh-based and the idealized frequency-based estimators to highlight the contrast; the former does not make use of information about a variant’s frequency while the latter does not make use of any information about a variant’s msh value . Unlike the msh-based estimator , where each variant may have a unique msh from which to generate a unique tc estimate , no frequency-based estimator can distinguish among the potentially large number of variants occurring at identical frequencies within a sample . When considered over the full range of allele frequencies , the idealized estimator can explain nearly as much of the variation in log10 ( tc ) as the msh-based estimator ( Fig 2f ) . However , for rare alleles , the msh-based stimator retains strong performance , whereas the frequency-based estimator explains little to none of the variation in log10 ( tc ) . An important application is assessing upon which of the two chromosomes of an individual a singleton allele correctly resides . As shown in Fig 3 , the phasing accuracy for singleton variants in simulated data rises as the ratio of the genetic lengths of the alternative msh tracts diverge . For variants with msh tracts of similar length ( and thus high probability of misassigned phase ) , similar t^c values will be found regardless of how phase is assigned . For non-singleton variants , the estimator is expected to be relatively immune to switch errors introduced by haplotype phasing software . This is because switch errors typically involve low frequency variants among similar haplotypes [21 , 22] , and these do not typically affect the distribution of msh values which are often terminated by relatively common variants or recombination events between unrelated haplotypes . S2 Fig shows that results for statistically phased chromosomes are quite similar to those for the correct chromosomes ( r = 0 . 941 ) based on analyses of singleton alleles in male X-chromosome UK10K data . From S1 and S2 Tables we see that mean error is slightly increased when the data ( including singletons ) are phased statistically . The bias also becomes more negative by a small amount with phasing . Singletons are phased by assigning them to the chromosome that reveals the shorter msh and thus the longer t^c . The proximal effect of this will be to introduce a positive bias that applies in those instances when this phase assignment is not correct . However , the observation that bias becomes slightly more negative with phasing , including for singletons , suggests a greater effect in the other direction . It is possible that the phasing of singletons prior to determining msh values causes them to cluster on fewer chromosomes , thereby lengthening the msh values that are observed . Based on analysis of variation ( ANOVA , Table 1 , S6 Table ) , we learned that different populations show different distributions of log10 ( t^c ) values for singleton variants ( d . f . = 25; F = 19248; p < 1 × 10−128 ) . As shown in Fig 4 and S3 Table , populations in Africa have higher geometric means ( 6353 to 7178 generations ) than populations from regions that have not had substantial admixture from African populations ( East Asia , South Asia , and Europe ) , which have lower geometric means ( 2018 to 3882 generations ) . Admixed American populations spanned a wide range ( 5610 to 7194 ) , with values near those of the African populations . The finding of younger ages for singletons from non-African old-world populations is expected under a general Out-of-Africa model in which those populations have passed through a bottleneck and have had an overall lower effective population size and thus shorter coalescent times . The 1KGP data allow us to compare the age of rare variants that are found only in a single population to the age of variants that have the same low frequency within that population , but that also are found in one or more other populations . For all of the 1KGP populations , private singletons had distributions that were shifted to the left ( younger ) relative to singletons that were shared . Fig 5 shows these distributions for a single population that is representative of those observed from each the five super-populations , each of which showed a characteristic distribution ( S9–S13 Figs ) . In every population , private singleton alleles are younger than singleton alleles that are also shared with other populations ( d . f . = 1; F = 896298; p = 0 , Table 1 ) , with private singleton variants being 70% younger than comparable shared variants . However , the size of the reduction varies considerably by population ( d . f . = 25; F = 3437; p = 0 , Table 1 ) , with the largest reduction observed in admixed American populations ( 82% to 92% ) . With respect to the functional impact of rare alleles , we expect that the rarest variants in a sample will be enriched ( relative to more common variants ) for alleles that have an impact on fitness , and therefore that rare alleles will be targeted more by natural selection . This is because such alleles pass through frequency space more quickly than neutral alleles and , if not lost from the population , will reach a given frequency more quickly than a neutral allele [2] . Consistent with this prediction we observed in the 1KGP data that , conditional on population and geographic spread , singleton variants that alter proteins are 3 . 6% younger than those that do not ( d . f . = 1; F = 24; p = 1 × 10−6 , Table 1 ) . There is no significant difference to the effect of protein-changing status in individual populations ( d . f . = 25; F = 0 . 58; p = 0 . 95 , Table 1 ) , and there is no significant interaction between protein-changing status and a variant being private to its population ( d . f . = 1; F = 2 . 86; p = < 9 × 10−2 , Table 1 ) . The theory that says that alleles that affect fitness will be younger applies to both harmful and beneficial alleles [2] . However , if it is the case that harmful rare alleles are much more common than beneficial rare alleles , then the age difference between functional alleles and neutral alleles should be greater the rarer are the alleles under comparison . This is because most harmful alleles are rapidly removed from a population and are more likely to be observed at the lowest frequencies in larger samples . We therefore predict that we should observe a greater difference in ages between alleles that change proteins and those that do not in the larger UK10K dataset than in the 1KGP data . In fact , the singletons in the 7 , 242 samples of UK10k data showed a considerably larger effect of protein-changing status than those identified as singletons in the 100-sample populations of the 1KGP data . The geometric mean t^c value for the 105 protein-changing singleton variants was 12% lower than it was for the 1 . 5 × 107 non-protein-changing variants ( 305 generations vs 347 generations; Wilcoxon-Mann-Whitney p = 0 ) .
Every mutation can be envisioned as occurring on a branch of the genealogy or gene tree for a sample of genomes at the locus where the mutation occurred . Previous estimators of allele age have focused on the time point at which different copies of an allele coalesce with each other , i . e . the time of the top of the gene tree edge upon which the mutation occurred [8–12] . Here we have taken a different approach and focused on the time at the bottom of that same edge , the time of first coalescence for an edge carrying a mutation . These first coalescent times have direct connections to msh values , which are easily measured from aligned genomes . The resulting estimator is not without noise , but estimates covary roughly linearly with true values , with moderate error and bias over a wide range of circumstances . Our estimator also meets a variety of desired criteria established at the outset . It is not a function of allele frequency , and thus can be used to study how an allele came to reach its observed frequency . It is only very weakly a function of demographic history , and thus it can be used to compare the ages of alleles of different populations that have unknown or widely varying histories . It can be applied to alleles that occur only once in a sample , and to genomic data with very large sample sizes . It can be applied individually to each copy of an allele , and thus can be used in cases when an allele has arisen by multiple mutations . And it suffers little degradation in performance when run on statistically phased chromosomes . The estimator is not expected to be highly sensitive to some additional issues that can arise with population genomic data , including sequencing errors . The UK10K data for example are low coverage ( 7x ) , however , because new singleton variants are only called when the data are strongly supportive ( high false negative rate , low false positive rate ) only a small minority of the singletons in the data set are expected to be errors . In fact , the proportion of called variants in the case of the UK10K sample that are true variants was estimated to be quite high ( ~94% for singleton variants ) , based on measurements across monozygotic twins . This is shown in the first figure of the extended data for that paper ( panel k , row AC = 1 , MZ twins section , column "non-ref genotypes %" , divided by two and subtracted from 100 ) [16] . However the variants that are missing from low coverage data can affect the distribution of msh values in ways not accounted for by the method . For example , many of the mutations that terminate tracts of identify and determine msh are themselves singletons , and so a coverage bias against singletons will tend to lengthen msh values and reduce t^c values . Although the method can be used to study selection , implicit in the method is an assumption of neutrality , such that mutations on branches A and S ( Fig 1 ) do not affect the distribution of tc values . But of course a significant fraction of the mutations in evolutionarily constrained regions are not neutral , and the tc values in these regions are reduced , as we see in our ANOVA results . The question then is , how does this kind of variation in the non-deleterious mutation rate affect msh and t^c values ? A closely related question applies to factors such as variation in background selection and flucations in rates of gene flow or admixture , that cause polymorphism levels to wax and wane across chromosomes , and that alter the distribution of msh values that terminate due to recombination events . The basic expectation is that these kinds of variations will have a greater effect on long tc values , when flucations in the detminants of what should be short msh values can have a greater affect . However short tc values are associated with very long msh values that span large portions of chromosomes and can be expected to be more immune to local flucations in the factors that terminate msh tracts . Another potential difficulty is that as sample sizes grow very large , some rare alleles will have been caused by multiple mutations [14] . While this is not a concern for singletons , it is possible that , for example , a doubleton will actually represent two mutations . In these cases , the likelihood estimator will simply generate a composite of two different singleton coalescent times . Unlike methods that estimate the time of most recent common ancestry among individuals carrying a rare variant , t^c is the estimated time since each copy of the rare variant last shared a common ancestor with an individual not carrying the variant . If two copies of an allele are actually the result of independent mutations , the composite t^c estimation will produce an intermediate value and not an aberrantly large one , as would occur with a method based on intra-allelic differences . S3 Fig illustrates the impact of analyzing two independently derived minor variants as copies of a single mutation . Some additional and important benefits of the estimator described here emerged during the course of this study . Estimated tc values , or msh values , can be used to phase singleton alleles ( i . e . estimate the correct chromosome for placement in diploid heterozygous individuals ) . Another benefit is that the method can be applied relatively quickly to very large data sets with the benefit of the PBWT algorithm [23] . Computing time increase linearly with both sample size and numbers of variants , and all the singleton variants for the full UK10K panel for chromosome 22 can be analyzed in under 30 minutes , for example ( S4 Table ) . A third likely benefit , that has not been explored here , is that it should be possible to work with genomic data in which only dispersed portions of the genome have been sequenced . In particular , msh tracts for rare alleles from large samples are typically much longer than the distances between genes , and consequently it should be possible to apply the method to data sets of aligned exome sequences . Our analysis of variants of 1% frequency among 26 populations of the 1KGP data , revealed considerable systematic variation in allele ages depending on the variant’s function , location , and geographic distribution . Different human populations have variants at 1% frequency with very different distributions of ages . Population-specific demographic histories , including population bottlenecks , expansion , and admixture events , have likely all contributed to these broad differences , and therefore changing , in effect , what it means to be a 1% frequency variant on a population-by-population basis . We also observed that the ages of variants found at 1% frequency in one population depend greatly on whether or not they are also found in other populations . To be found in multiple populations , a variant that arose by a single mutation must have either: 1 ) originated prior to the divergence of those populations and risen to sufficient frequency that it has not been lost in either; or 2 ) persisted in its population of origin long enough and risen to sufficient frequency that migrants have had an opportunity to bring it to at least 1% frequency in another population . Both of these phenomena are reflected in substantial differences of ages of variants found in a population at 1% frequency depending on their total geographic range . The survey of singleton t^c distributions for 1KGP revealed bimodal distributions in each of the non-African populations ( S9–S13 Figs ) . All of them exhibit a large peak at less than a thousand generations , and another smaller one at more than ten thousand generations , a pattern that is readily interpreted in terms of an older bottleneck associated with the out-of-Africa history of these populations . S14 Fig shows these same patterns arise for simulated data generated with parameter estimates for an Out-of-Africa model [24] . The younger larger peak is characteristic of variants that arose after the population’s ancestors had emigrated from Africa . These are relatively new variants that have not yet had an opportunity to rise to high frequency or spread to other populations . The older peak consists of variants that arose within an ancestral African population and predate the modern human expansion into Eurasia . Although they are identified as singletons in individual populations , these variants are often shared across multiple populations , and they typically occur at frequencies greater than 1% in some populations . In the populations where they are ascertained as singletons , founder events , genetic drift , and natural selection have made these older alleles rare , or even eliminated them entirely before re-introduction by migration from other populations . Independently of geography and demographics , protein-changing variants in the 1KGP populations that are found at 1% frequency are younger on average than comparable variants that do not change proteins , consistent with previous reports [25] . These are expected to be mostly deleterious variants that have not yet been removed from the population , but they may also include some beneficial variants that have not yet been pushed to higher frequencies . At the 1% frequency level , the impact of protein-changing status on the distribution of allele ages is statistically significant , but considerably smaller than the systematic differences introduced by demography and geography . Among the singleton protein-changing variants ascertained in the UK10k data , however , we find a larger difference with respect to variants that do not change proteins . The contrast with the 1KGP data is consistent with the younger , rarer , and more numerous variants of the UK10K data are subject to greater selective forces than the variants at 1% frequency in the 1KGP populations . Our ANOVA did not reveal evidence that local adaptation contributes to the variance in ages of alleles at 1% frequency . If selectively important alleles were disproportionately prevented from migrating between populations or remaining in multiple populations , we would have observed the pool of shared protein-changing variants to be enriched for protein-changing variants without selective function . There would have been less differentiation between non-shared and shared , non-protein-changing variants , and there would have been a significant ( negative ) interaction term between private status and protein-changing status , which we did not observe . We also did not detect an interaction between populations and protein-changing status . If different populations were broadly experiencing greater or lesser amounts of directional selection on rare variants , or if there were substantial differences between populations in the ability of natural selection to remove harmful variants or raise the frequency of beneficial variants ( such as due to variation in effective population size ) , we would expect to have seen a significant interaction between populations and protein-changing status . However , we did not; and while both of these phenomena may be taking place , they are not major drivers of the distribution of ages of variants found at 1% frequency , in contrast to the contributions of that demography , geographic spread , and global properties of natural selection . These population genetic results are all presented as conditioning on alleles at 1% frequency that have been down-sampled to equal sizes . By considering only alleles of a particular frequency , we are able to draw meaningful comparisons and contrasts among populations . While the formula for the estimator itself is not a function of the frequency of the allele to which it is being applied , the distributions of t^c estimates will vary greatly as a function of the frequencies of the alleles being studied ( see e . g . S15 Fig ) . Indeed , it is because of these very different age distributions that alleles of different frequencies can have very different histories , including the action of natural selection and the amount of movement among populations . Any future work that would make comparisons between populations or estimate demographic models based on t^c estimates will need to explicitly condition on allele frequencies . With an estimate of an allele’s age , an investigator has an important new piece of information to bring to bear on the possible functional impact of a mutation . As shown here , for 1KGP data and even more strongly for the larger UK10K data , functional alleles are younger , and therefore , concomitantly , any allele that is discovered to be especially young , given its frequency in the sample , is a good candidate for having an effect of fitness . In this context , an increase in sample sizes will have multiple important effects . First , the rarest alleles in large samples will be rarer and younger , on average , than those found in smaller samples , and they will thereby be relatively enriched for more alleles of harmful effect and for alleles of more harmful effect ( these are the alleles that would not reach those higher frequencies observable with smaller sample sizes ) . Second , the numbers of alleles in the rarest class rises with sample size , and thus so does the number of very rare alleles observed for a given gene . For example , the average number of autosomal singletons for the 26 1KGP populations ( 100 genomes ) was 3 , 311 , 984 , while the count for the UK10K data ( 7242 genomes ) was approximately 6 times higher at 19 , 078 , 777 . Based on those values , and assuming the number of SNPs is a function of the log of the sample size [5] , a sample of 2N = 100 , 000 genomes would have 36 million singletons , and a sample of 1 million genomes would have 45 million singletons . The increasing density of very rare alleles , as sample sizes grow , opens the door to a kind of mapping of functional constraint across a gene that will become increasingly fine-grained . Third , as data sets get very large so grows the potential for studying the impact of selection on variation that has arisen at different times and it will become increasingly possible to assess whether the action of selection has been changing for different genes or regions of the genome . The estimator can also be used to study forces that shape the overall distribution of allele ages across the genome , particularly demographic forces . As shown in Fig 5 , the distributions of ages of singletons across 1KGP populations varies greatly , as do the differences between the ages of private and shared alleles . These distributions can be used in principle to develop models of demographic history , including in particular recent events that are only revealed by their impacts on the ages of very rare alleles [26] .
We applied the estimator of tc to low frequency alleles in data from 26 human populations , grouped into five super-populations , from the 1000 Genomes Project ( 1KGP ) data set [15] . We sub-sampled each population to achieve a uniform sample size of N = 50 diploid individuals . Chromosomes were taken as phased by the 1000 Genomes Consortium using SHAPEIT [30] with the exception that we re-phased singleton variants in each population based on relative t^c values . To do this , for each heterozygous singleton , we masked all other singleton variants , calculated t^c for both possible phases , and assigned the variant to the chromosome producing the larger t^c . tc values were estimated separately for each population , not collectively as a single pooled sample . We also estimated tc for singleton variants in the mapping sample of 3 , 621 individuals ( 7 , 242 genomes ) from the UK10k data set that has been filtered by the UK10k consortium to remove close relatives and individuals of recent non-European ancestry [16] . These include genomes from the ALSPAC cohort , which focused on the Avon region , and the TWINSUK cohort which includes samples from across the UK . Haplotype phase for variants found two or more times was inferred by the UK10k consortium using SHAPEIT . Haplotype phase for singleton variants was determined as for the 1KGP data . For all data we masked TpG/CpG transitions to minimize mutation rate variability . We compared results found using genetic maps based on both linkage disequilibrium [26] and pedigrees [31] , and found very little difference in tc estimates ( S8 Fig ) . Results presented here used the pedigree-based map . We assume the ( non-CpG ) mutation rate is constant and equal to 1 × 10−8 per base per generation [32–34] . We assume a demographic history of constant population size of N = 1 × 104 . Data were simulated using the msprime program [35] under three demographic models: a constant population of size N = 1 × 104; a population with recent exponential growth that started from an ancestral population size of Na = 1 × 104 to a population size of N = 5 × 105 over the last 200 generations prior to sampling; and a bottleneck model in which samples were drawn from a ‘European’ population included in an out-of-Africa model with exponential growth and migration using the parameter estimates of Gutenkunst et al . , [24] . We simulated samples of 1000 10-Megabases chromosomes from which we sub-sampled populations of 100 chromosomes for comparison with 1KGP data . We used a fixed mutation rate of μ = 1 × 10−8 per base per generation , as is typical for human populations [32–34] . For recombination rate we used a value of ρ = 1 × 10−8 that is typical of the mean rate per base pair in human populations [26 , 31] , as well as both a lower recombination rate ( ρ = 1 × 10−9 ) and a higher recombination rate ( ρ = 1 × 10−7 ) to assess performance when a different proportion of msh tracts end in a variant that has been introduced to the focal chromosome or a sister chromosome by recombination , and not directly by mutation . We assess error as the root mean squared error of log10 transformed tc values , and bias as the average signed error of the log10 transformed tc values . To assess the impact of phase uncertainty we randomly paired chromosomes and phased the data using SHAPEIT with default parameters . Singletons were phased as for the 1KGP and UK10K data . We used SNPEff [36] to identify variants annotated as missense , stop gained , stop lost , start lost , splice acceptor , or splice donor as ‘protein changing’ . For singleton variants in UK10K populations , a variant is labeled as ‘private’ when it is found in only one population , and ‘shared’ when it is also found in other populations ( at any frequency ) . Singleton variant ages in the 1KGP data were analyzed with a type-II ANOVA that examined the effect of three variables , including: ( 1 ) the population in which the variant was observed; ( 2 ) whether or not a variant is private to a single populations; and ( 3 ) whether or not a variant effects a protein sequence . The model has the form log10 ( tc ) ~ Intercept + PC + Private + Population + ( PC × Population ) + ( Private × Population ) + ( PC × Private ) , where PC is a categorical variable indicating protein-changing status , Private is a categorical variable indicating the restriction of a variant to a single population , and Population is a categorical variable indicating the population in which a variant is observed . The only human data used in this study are genome sequences that are publicly available . Two sources of human data were used . The 1000 Genomes data were downloaded from the data server at http://www . internationalgenome . org/data . The UK10K data are made available to researchers as described ( https://www . uk10k . org/data_access . html ) and were made available to Dr . Hey following completion of the UK10K Project Data Access Agreement ( signed 14 Oct 2014 ) . These data included dataset IDS EGAS00001000090 ( UK10K COHORT ALSPAC ) and EGAS00001000108 ( UK10K COHORT TWINSUK ) . These data include no information such that the original subjects could be identified . Because all of the data is publicly available , and because none of the data can be used to identify the original subjects , the study falls under NIH Human Subjects Research Exemption 4 . For these same reasons the research was determined to not involve Human Subjects by Temple University IRB .
The program , entitled “runtc” , that implements the estimator for VSF files is available at https://github . com/jaredgk/msh-python/tree/master/msh_est . The simulation results are available at https://bio . cst . temple . edu/~hey/nolinks/Platt_etal_SimulationsAndAnalyses . zip . | We developed a way to estimate the time when a copy of a gene most recently shared ancestry with other copies of that gene . This is also an estimate of the upper bound of when a mutation has arisen , and it can be used to study the ages of alleles that are found in a population . The method can be applied to the very rarest alleles found only once in a sample , even in studies of many thousands of genomes . We tested the method extensively , found it performs well , and can be used under a wide variety of conditions . We applied it to 1000 Genomes project data ( 26 populations ) and the UK10K data ( over 7000 genomes ) and found clear evidence that alleles that change proteins are younger than alleles that do not , as expected . We also observed wide variation in the ages of alleles at low frequency among the 1000 Genome project populations , indicating that our method could be used to study the demographic history of human populations . Going forward , the estimator should be useful for many kinds of questions in population genomics , particularly as sample sizes continue to grow . | [
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| 2019 | An estimator of first coalescent time reveals selection on young variants and large heterogeneity in rare allele ages among human populations |
Circumstances that compromise efficient DNA replication , such as disruptions to replication fork progression , cause a state known as DNA replication stress ( RS ) . Whereas normally proliferating cells experience low levels of RS , excessive RS from intrinsic or extrinsic sources can trigger cell cycle arrest and senescence . Here , we report that a key driver of RS-induced senescence is active downregulation of the Minichromosome Maintenance 2–7 ( MCM2-7 ) factors that are essential for replication origin licensing and which constitute the replicative helicase core . Proliferating cells produce high levels of MCM2-7 that enable formation of dormant origins that can be activated in response to acute , experimentally-induced RS . However , little is known about how physiological RS levels impact MCM2-7 regulation . We found that chronic exposure of primary mouse embryonic fibroblasts ( MEFs ) to either genetically-encoded or environmentally-induced RS triggered gradual MCM2-7 repression , followed by inhibition of replication and senescence that could be accelerated by MCM hemizygosity . The MCM2-7 reduction in response to RS is TRP53-dependent , and involves a group of Trp53-dependent miRNAs , including the miR-34 family , that repress MCM expression in replication-stressed cells before they undergo terminal cell cycle arrest . miR-34 ablation partially rescued MCM2-7 downregulation and genomic instability in mice with endogenous RS . Together , these data demonstrate that active MCM2-7 repression is a physiologically important mechanism for RS-induced cell cycle arrest and genome maintenance on an organismal level .
In preparation for DNA replication , “licensing” of replication origins occurs during late M to early G1 phase [1 , 2] . These replication origins are selected and bound by the origin recognition complex ( ORC ) [3] . ORCs further recruit CDC6 and CDT1 to eventually load the MCM2-7 heterohexameric complex onto replication origins , thus forming pre-replication complexes ( pre-RCs ) [4] . Pre-RC formation is tightly regulated so origin licensing can only occur before , and not during , S phase to prevent re-replication of genomic regions [5] . Chromatin becomes replication-competent after MCM2-7 loading . Later , during S phase , replication machinery assembly is initiated at selected licensed origins with the formation of Cdc45/MCM2-7/GINS ( CMG ) replicative helicase complex , of which MCM2-7 is the catalytic core [6 , 7] . Stable MCM2-7 chromatin association is required for uninterrupted replication fork progression and restart after stalling [8–10] . MCM2-7 is the sole complex present in both the pre-RCs and the active replisome , making it a nexus of DNA replication control . The genome is vulnerable to exogenous and endogenous genotoxic stresses during DNA replication , which can lead to replication fork stalling [11] . Stalled replisomes must be stabilized to enable restart or displacement by converging replication forks to ensure complete and faithful DNA replication . Otherwise , mutations , genomic instability , and ultimately neoplasia can occur [12] . Numerous mechanisms exist to promote error-free replication under stressful conditions [13] . One of the mechanisms is utilization of dormant replication origins [11] . Most growing cells produce abundant amounts of MCM2-7 proteins that license large numbers of replication origins , but only a small proportion of these are used and they are sufficient to accomplish whole genome replication . This role of dormant origins in responding to RS was revealed in experiments where licensing was severely inhibited in cultured cancer cells via knockdown of MCM levels . While such cells can sustain limited proliferation under unchallenged conditions , the reduction of dormant origins renders them sensitive to additional RS [14–17] . Thus , abundant MCM production ensures adequate licensing of the dormant replication origins that serves as ‘backups’ and can be activated in response to stalled or collapsed replication forks and ensures completeness of DNA replication [18] . Inhibition of licensing in primary cells causes cell cycle arrest in G1 phase , leading to the proposed existence of a “licensing checkpoint” that prevents DNA replication under sub-optimal conditions [19 , 20] . Thus , the physiological relevance of severe experimental conditions in transformed cell lines is unclear , and more importantly , little is known about endogenous MCM2-7 regulation in response to RS . Another major mechanism that protects the genome during replication is the DNA damage response ( DDR ) , components of which detect replication-associated lesions or cellular conditions that impair DNA replication . In addition to directly interacting with MCM2-7 subunits to stabilizing stalled replisomes , the DDR regulates cell cycle progression in response to RS , such as inducing senescence [21–23] . Central to this mechanism is the tumor suppressor gene Trp53 ( also called Tp53 or p53 ) . Genotoxic stress such as RS stabilizes TRP53 , which then serves as transcriptional regulator of many downstream genes , including microRNAs ( miRNAs ) [24–26] . Through their complementary binding to one or more miRNA recognition elements ( MREs ) within the 3’ untranslated region ( UTR ) of a target protein-coding mRNA , miRNA-mRNA duplexes , together with the RNA-induced silencing complex ( RISC ) , can negatively regulate gene expression , usually in a moderate manner [27 , 28] that is sometimes reversible [29 , 30] . Cells can exploit this mechanism to tune responses to genotoxic stresses , without committing to terminal cellular decisions such as apoptosis and senescence . As mentioned earlier , most studies of how RS impacts cell growth and the DDR involve treatment of cell lines with exogenous agents that hinder DNA replication . One model of intrinsic RS is the Mcm4Chaos3 mutation in mice . This allele encodes a single amino acid change ( Phe345Ile ) that causes high levels of genomic instability and cancer susceptibility [31] . The "Chaos3" mutation destabilizes the MCM2-7 heterohexamer by disrupting MCM4-MCM6 interaction in vitro and is accompanied by a 40~60% decrease in MCM2-7 levels , leading to the conclusion that the associated phenotypes were primarily attributable to insufficient licensing of dormant replication origins [31–34] . This view is supported by similar cancer predisposition phenoptypes in mice that are hypomorphic for Mcm2 , and which also show premature aging and stem cell defects in certain cell lineages [35 , 36] . Here , we compare the consequence of low level endogenous RS in Chaos3 cells to chemically-induced RS in WT cells . Our results indicate that RS induces TRP53-dependent MCM2-7 downregulation , which coincide with loss of DNA replication potential in primary cells that eventually becomes senescent . We also identified a group of Trp53-responsive miRNAs that regulates MCM expression in response to RS . Modulation of miRNA expression partially rescued RS-induced cellular defects , including MCM repression and genomic instability . Our observations reveal that active MCM2-7 regulation is a key aspect of senescence induction when cells are exposed to chronic low level RS , and is important for safeguarding organisms from cells that undergo potentially deleterious RS-induced genomic alterations .
Early passage Mcm4Chaos3/Chaos3 ( "Chaos3" ) MEFs have proliferation defects in the presence of aphidicolin , a DNA polymerase inhibitor [31] . To determine if this reflects a predisposition to senescence , we monitored the growth of freshly isolated wild-type ( WT ) and Chaos3 MEFs for several passages , under typical culture conditions ( 20% O2 ) . The Chaos3 cultures exhibited reduced growth and eventual arrest at earlier passages than WT cultures ( Fig 1A ) . Furthermore , approximately twice as many cells in Chaos3 MEF cultures were positive for senescence-associated β-galactosidase ( SA-β-gal ) expression than in WT cultures ( Fig 1B ) . Oxygen sensitivity and DNA replication stress are the two major causes of natural senescence in cultured primary mouse cells [37] . Low oxygen conditions ( 5% O2 ) resulted in faster growth of both WT and Chaos3 MEFs compared to standard conditions ( 20% O2; Fig 1A ) . Whereas WT MEFs continued proliferating in 5% O2 , this condition only delayed the onset of senescence of Chaos3 MEFs before they stopped growing at P6-P7 ( Fig 1A ) . The growth defect of Chaos3 MEFs under normal and low oxygen conditions is similar to that of MEFs defective in the non-homologous end joining ( NHEJ ) pathway of DNA double strand break ( DSB ) repair [37] . In aggregate , these observations suggest that intrinsic RS caused by the Chaos3 mutation , not hypersensitivity to oxidative stress , triggers premature senescence . Chaos3 MEFs were reported to have 40–60% less MCM2-7 protein compared to WT cells [32 , 34] . The difference is also reflected at the mRNA level and is largely specific to the MCMs , not other DNA replication and cell cycle related genes [33] . Our observations that Chaos3 cells senesce prematurely in culture prompted us to investigate the cause and effect relationships between RS , MCM2-7 regulation , and senescence . To determine if lower MCM2-7 in Chaos3 MEFs is a constitutive feature of these cells or related to senescence , we tested whether WT primary MEF cultures exhibited MCM downregulation levels during passaging . The mRNA and protein levels of each MCM declined as a function of time in culture ( Fig 1C and 1D ) . Levels of PCNA , another essential DNA replication protein , also declined albeit less dramatically than MCMs ( Fig 1C and 1D , and S1 Fig ) . Decreased MCM levels have also been observed in older mouse hematopoietic stem cells ( HSCs ) , which have increased RS [38] . We further measured MCM2-7 mRNA and protein in primary Chaos3 and WT MEFs at both early ( P2 ) and later ( P4 ) passages . Consistent with the aforementioned published reports [32 , 34 , 39] , we observed 40% less MCM2-7 in P4 Chaos3 MEFs . However , there was little reduction of MCM2-7 mRNA or protein in P2 Chaos3 MEFs compared to WT littermate MEFs ( Fig 1E and 1F ) . These results suggest that MCM2-7 levels decrease roughly in parallel with the progression of RS-associated cellular senescence , and is either a cause or consequence of senescence . Furthermore , the results indicate that MCM2-7 pan-reduction in Chaos3 cells is not an incipient property , but like in WT MEFs , is acquired and likely a consequence of RS from culture conditions , which is exacerbated or accelerated by the defective MCM4Chaos3 protein . To determine the primary source of RS in the Chaos3 mutant , we investigated the biochemical nature of this mutation . The Chaos3 Phe345Ile change disrupts MCM2-7 heterohexamer complex stability [33 , 34] without reducing mutant helicase activity in vitro [34] . However , the Chaos3 mutation causes spontaneous replication fork stalling in vivo , and some of these stalled forks go unprocessed and persist into M phase [34] . Since MCM2-7 complex integrity at replication forks is essential for replisome progression and stalled fork recovery/restart [8 , 9] , we hypothesized that the replication defect in the Chaos3 cells is due to MCM2-7 helicase instability that compromises its association with replication forks . To test this , we studied MCM2-7 association at active and stalled replication sites using DNA-mediated chromatin pull-down ( Dm-ChP ) [40] . This technique isolates proteins bound to nascent DNA ( labeled with 5-ethynyl-2-deoxyuridine , EdU ) . SV40 large T antigen-immortalized WT and Chaos3 primary MEFs ( isolated from littermates ) were first subjected to stable isotope labeling of amino acid in culture ( SILAC ) to enable quantitative mass spectrometry ( MS ) analysis . Interestingly , total MCM2-7 levels in the SV40-immortalized Chaos3 cells were not decreased as in primary Chaos3 MEFs ( Fig 2A ) . However , MCM2-7 association with nascent DNA was consistently reduced in the Chaos3 samples ( ~50% reduction from two independent experiments; Fig 2B ) . Many other known replication proteins were also identified in this MS analysis , and their levels were similar in the WT and Chaos3 samples . These observations indicate that the Chaos3 mutation compromises MCM2-7 heterohexamer association with active replication forks in vivo . Next , we assessed MCM2-7 retention at stalled replication forks by first labeling ongoing forks in immortalized MEFs with an EdU pulse , then adding a high concentration of the ribonucleotide reductase inhibitor hydroxyurea ( HU ) for 30 minutes to stall replication forks [41] , followed by protein isolation using Dm-ChP . In both WT and Chaos3 cells , HU caused an increase in γH2AX and decrease of PCNA , consistent with replication fork stalling [41] . About 25% of MCM7 dissociated from stalled replication forks in WT cells ( Fig 2C; Dm-ChP “-” vs . “+” HU lanes ) , consistent with results using the similar iPOND method [42] , compared to a 47% MCM7 loss in Chaos3 cells ( Fig 2C ) . This loss from stalled forks is in addition to ~3 fold decreased MCM7 association with unchallenged replication forks ( Fig 2C; WT vs . Chaos3 Dm-ChP lanes , —HU ) . As expected , disengagement of MCM from stalled replication forks in the Chaos3 cells disrupted helicase function , as visualized by immunofluorescence analysis of cells that were pulse-chased with BrdU . DNA polymerase stalling ( by HU ) leads to their uncoupling from the helicase , which normally continues to unwind genomic double-stranded DNA ( dsDNA ) , thus exposing extensive amounts of single-stranded DNA ( ssDNA ) in front of the fork [43] . Since the anti-BrdU antibody only recognizes BrdU in ssDNA , immunolabeling of cells under non-denaturing conditions reveals the degree of helicase activity [44] . Unlike WT cells , a significant fraction of the replicating Chaos3 cells failed to display detectable ssDNA accumulation after HU-induced polymerase stalling ( Fig 2D and S2 Fig ) . Taken together , our observations suggest that the Chaos3 mutation causes RS by disrupting MCM2-7 complex stability , thus compromising helicase association and function during normal and challenged DNA replication . Given our data indicating that Chaos3 helicase instability induces RS , and MCM2-7 downregulation accompanies senescence in Chaos3 cells , we postulated that MCM2-7 pan-reduction is an authentic cellular response to RS in general ( not unique to Chaos3 ) , and that it contributes to the RS-induced senescence . To test this , we treated early passage ( P2 ) WT MEFs with a low concentration of HU as a means of imposing chronic RS . Long term HU treatment inhibited cell growth ( S3A Fig ) and caused an increase of cells in S phase ( 2–4 fold increase over untreated after 24-72h; Fig 3A ) . Despite this increase , DNA replication overall was inhibited due to HU treatment , as indicated by a relative lack ( vs untreated ) of EdU incorporation immediately after HU removal ( S3B Fig ) . However , mRNA levels of genes governing S phase entry and progression were actually upregulated ( cyclins Ccne1 , Ccne2 and Ccna2 ) or unchanged ( cyclin dependent kinase 2 , Cdk2 ) after 48h of HU treatment , while MCM mRNA levels had already declined by >20% ( Fig 3B and S3C Fig ) . It wasn’t until more prolonged treatment ( 72h ) that levels of the cyclins and Cdk2 declined dramatically along with MCMs ( Fig 3B ) , although expression of the essential replication gene Pcna and the licensing factor Cdc6 were not affected ( Fig 3C ) . This exclusivity of Mcm2-7 downregulation with respect to other replication/licensing genes mirrors that of untreated Chaos3 MEFs [33] . The MCM2-7 pan-reduction was dependent upon duration and dosage of HU treatment . Longer exposure ( S3C Fig ) or increased concentration of HU ( Fig 3D and 3E ) caused greater MCM2-7 reduction . These results suggested that the degree of Mcm2-7 downregulation is directly related to cellular responses to RS , which coincides with loss of DNA replication in primary cells . We next evaluated if HU treatment ( 72h ) induced premature senescence of primary WT MEFs . This triggered a 2–3 fold increase of the p16Ink4a and p19ARF tumor suppressors ( Fig 4A ) which are associated with senescence and can be induced in response to persistent RS [45] , and also a ~5–7 fold increase in the percentage SA-β-gal positive cells . The latter became evident after 48hrs of treatment ( Fig 4B ) , concurrent with MCM reductions . These combined data indicate that chronic RS triggers a decrease in MCM levels and an increase in senescence markers before downregulating cell cycle regulatory genes or other DNA replication licensing and replication-related genes . However , these experiments did not reveal the order or causality of MCM downregulation vs senescence onset . The results presented thus far show that MCM2-7 levels are reduced in response to intrinsic or chemically-induced RS , ultimately leading to reduced or abolished proliferation . Previous reports have indicated that severe reduction of an individual MCM sensitizes cancer cell lines to acute RS and impaired proliferation [15–17] . However , with respect to senescence , immortalized cells cannot address the relative contributions of exogenous RS vs . RS from physiologically-relevant MCM decreases . To test this , we examined proliferation and senescence of primary MEFs heterozygous for Mcm2 ( "M2" ) cultured with or without chemically-induced RS . M2 cells proliferated identically to WT littermate cultures under standard culture conditions , and did not senesce prematurely ( S4 Fig ) . However , HU treatment triggered more severe senescence-associated phenotypes in M2 than WT primary MEFs , both in terms of markedly higher p16Ink4a/p19ARF induction ( Fig 4A ) and a ~2 fold increase in cells positive for SA-β-gal ( Fig 4B ) . Interestingly , M2 primary MEFs expressed slightly higher basal levels of senescence markers than WT MEFs ( Fig 4A and 4B ) . Genetic reduction of MCM2 alone also causes a moderate MCM2-7 pan-reduction [32 , 35 , 36] , supporting the idea that MCM reduction itself sensitizes cells to senescence . The DNA damage response ( DDR ) is essential for senescence induction in MEFs [37] . Since our data show that MCM2-7 repression is an authentic cellular response to RS and contributes to senescence induction , it is possible that MCM regulation is controlled by the DDR . Indeed , accumulation of TRP53 was observed in the unchallenged Chaos3 cells , while Trp53 deletion in this mutant rescues MCM2-7 levels in MEFs [39] , consistent with our observation that SV40 T antigen , which inhibits Trp53 , rescued overall MCM2-7 expression ( Fig 2A ) [46] . We postulated that if the intrinsic replication stress caused by the Chaos3 mutation is what triggers TRP53-mediated MCM2-7 pan-reduction and premature senescence in Chaos3 MEFs , then TRP53 would also mediate senescence induction and MCM2-7 repression in WT MEFs subjected to exogenous RS . Similar to WT primary MEFs ( Fig 4A ) , HU treatment also stimulated p19ARF expression in Trp53-null primary MEFs ( Fig 5A ) . However , as ARF functions upstream of TRP53 to induce senescence in primary mouse cells [22] , the terminal senescence phenotype is bypassed in Trp53-null MEFs , as indicated by the barely detectable SA-β-gal expression after RS induction ( Fig 5B ) . Surprisingly , whereas HU treatment for 72 hrs caused a decrease in MCM mRNA in WT MEFs ( Fig 3E ) , the identical treatment caused a dose-dependent 50–100% increase of Mcm2 , 3 and 4 mRNA in Trp53-deficient cells treated with increasing concentrations of HU ( Fig 5C ) . These increases were also reflected at the protein level ( Fig 5D ) , unlike WT . Interestingly , HU-treated Trp53-null MEFs exhibited a drastic increase in H2AX Ser139 phosphorylation ( γH2AX ) compared to WT cells receiving the same treatment ( Fig 5D ) , indicating greatly elevated DNA damage and/or replication fork errors , and likely reflecting a failure of cells that have accumulated such defects to undergo senescence/apoptosis . Together , these results suggest that TRP53 repression of MCM2-7 in damaged or stressed primary cells is important for an organism to minimize the persistence of cells with excessive genomic instability that could predispose to neoplasia . We previously reported that MCM2-7 repression in Chaos3 cells occurs at the post-transcriptional level , is dependent upon Drosha and Dicer , and is paralleled by an increased levels of the miR-34 family of microRNAs [33] . Those data , considered in conjunction with the results presented thus far , suggest that miRNA-related silencing plays a role in repressing MCM2-7 in response to RS . To identify the culprit miRNAs , we performed small RNA sequencing on RNA samples isolated from WT primary MEFs after HU treatment , and on RNA samples isolated from WT and Chaos3 primary MEFs at each passage ( P2~P5 ) . Among the miRNAs that are significantly upregulated by endogenous and exogenous RS , we found miR-10b , 27b , 181a and all the members of the miR-34 family miRNAs . We confirmed the sequencing results using miRNA qRT-PCR . Interestingly , Trp53 deletion abolished the HU induced miRNA upregulation in the WT primary MEFs ( Fig 6A ) , confirming these candidate miRNAs are Trp53-dependent [25] . miRNAs usually interact with miRNA recognition elements ( MREs ) in the 3’UTR of the target mRNAs to repress their expression . Full-length 3’UTRs of each Mcm2-7 gene were cloned into a dual-luciferase reporter plasmid . Overexpression of all the aforementioned miRNAs except mir-10b repressed luciferase activity regulated by the corresponding MCM 3’UTR , confirming in silico predictions ( Fig 6B and 6C; see Methods ) . We also found potential targeting of the Mcm7 3’UTR by miR-34 , despite the lack of in silico-predicted binding sites ( Fig 6B ) . These results indicate that the candidate miRNAs target MCMs transcripts to reduce their expression , consistent with a subset of observations in human cells [47] . We also evaluated Mcm2-7 mRNA and protein levels after miRNA over-expression . Mcm2-7 mRNA levels were not reduced after miR-10b , 181a or 27b over-expression ( S5B Fig ) , while miR-27b and 181a repressed MCM4 but not other MCMs studied ( Fig 6F ) . Although miR-34 mainly repressed MCM5 as indicated by the luciferase assay ( Fig 6B ) , supporting the finding that it is a direct target of miR-34a in the context of the RISC [47] , overexpression of the miR-34s individually diminished MCM2-7 mRNA and protein ( Fig 6D and 6E ) . Interestingly , siRNA knockdown of MCM5 also caused MCM2-7 pan-reduction ( S5C Fig ) . In sum , we identified a group of Trp53-dependent miRNAs that can regulate MCM2-7 expression directly or indirectly in response to RS . Since MCM dosage impacts RS and Trp53-dependent RS-responsive miRNAs can regulate MCM expression , we tested whether modulating miRNA expression affects cellular responses to RS . Among the RS responsive miRNAs we studied , only the miR-34 family miRNAs caused MCM2-7 pan-reduction upon ectopic expression ( Fig 6D and 6E ) , a scenario similar to MCM2-7 repression after RS induction . Furthermore , overexpression of the miR-34 miRNAs , but not other miRNAs , significantly inhibited DNA replication ( Fig 7A ) . Numerous reports demonstrated that miR-34 miRNAs impact cell cycle progression partly by targeting DNA replication genes , including MCMs [25 , 47 , 48] . To determine if these miRNAs impact cellular response to RS in vivo , we generated miR-34abc triple knockout ( 34TKO ) mice , with and without the Chaos3 mutation , and examined genomic instability and MCM levels in these mice and cells derived from them . miR-34 deletion partially rescued MCM2-7 pan-reduction in HU-treated primary WT MEFs ( Fig 7B ) , complementing the previous experiments in which overexpression of miR-34s decreased MCM levels . To determine if miR-34abc deficiency could also rescue RS phenotypes in vivo , we measured MCM protein levels in various tissues . Chaos3 mice had dramatically lower MCMs in multiple tissues compared to WT , similar to primary MEFs at later passages ( Fig 7C; liver is shown ) . This observation indicates that the MCM2-7 pan-reduction in MEFs is not a culture artifact . MCM expression in both WT and Chaos3 mice was increased by 34TKO ( Fig 7C ) . These results indicate that miR-34 expression contributes to both endogenous and exogenous RS-induced MCM2-7 repression in vivo and in vitro . A hallmark of the Chaos3 mutation is highly elevated micronuclei ( MN ) , an indicator of genomic instability ( GIN ) , in reticulocytes and erythrocytes [31] . The 34TKO reduced MN levels in Chaos3 mice by ~20% ( Fig 7D ) , and this reduction in MN was sensitive to miR-34 genetic dosage ( S6 Fig ) . However , Chaos3 34TKO females still succumbed to cancers with a latency similar to Chaos3 single mutants ( Fig 7E ) . These data , in conjunction with the results showing that miR-34abc ablation increased MCM levels in Chaos3 mouse tissues , indicates that at least part of the genomic instability in Chaos3 mice is related to decreased replication origin licensing orchestrated by TRP53 induction of MCM-targeting miRNAs . However , MCM reduction in the Chaos3 mutant has a very minor impact on genomic instability and tumorigenesis , supporting our hypothesis that another character of the Chaos3 mutation , probably the helicase instability , is what stimulates secondary mutations ( namely deletions ) that drive tumors in Chaos3 mice [49] .
In this report , we studied cellular responses to both intrinsic ( Chaos3 mutation ) and environmentally-induced ( HU treatment ) RS in primary cultured mouse cells . Whereas normal cells maintain high MCM2-7 levels to sufficiently license dormant origins to cope with short-term RS , we found that chronic RS gradually decreases MCM2-7 expression over multiple generations in a Trp53-dependent manner . A group of Trp53-responsive miRNAs was identified which target MCMs directly to repress their expression in the presence of RS . Eventually , MCM levels become drastically lower , and proliferation ceases as senescence occurs . We postulate this is a consequence of failure to satisfy the hypothesized "licensing checkpoint , " which requires a minimal level of licensed replication origins to enter S phase [20] . Mcm2-7 are essential for DNA replication and their expression is highly correlated with the proliferation status of cells . High MCM expression is observed in proliferative cells , including tumor cells , but not in quiescent or terminally differentiated cells [50–52] . Severe knockdown of individual MCMs impairs proliferation of cultured cancer cells , indicating that the high levels are required for rapid proliferation [15–17] . Interestingly , hematopoietic stem cells ( HSCs ) isolated from old mice exhibit heightened RS attributable to selective down-regulation of MCM2-7 expression , and MCM knockdown in young HSCs impairs their expansion when transplanted into mice [38] . Genetically reducing Mcm2 expression to about 1/3 of normal levels causes stem cell defects and cancer predisposition in mice [35 , 36] . Furthermore , studies of mice in which MCM levels were reduced by combinatorial Mcm mutations led to the proposal of an MCM threshold , below which cell proliferation was compromised to a degree that developmental syndromes or embryonic lethality would occur [32] . Hypomorphic MCM4 mutations causing reduced levels of WT transcripts have been discovered in humans and cause various developmental defects and genomic instability [53 , 54] . Conversely , high level MCM expression is predictive of the oncogenic potential of pre-cancerous tissue , and is also used as an immunocytological marker to determine tumor grade and prognosis [19 , 50 , 51] . The prevailing model for why proliferating cells contain levels of MCM2-7 that exceed the amount required to license all primary origins and replicate the genome ( also known as "MCM paradox" ) is that cells license dormant origins that can be activated to replicate sequences in which an adjacent replication fork has stalled or collapsed [55] . This model arose from studies in which severe MCM reduction was induced ( for example by siRNA ) in conjunction with intense environmentally-induced RS , demonstrating that dormant origins are especially important for maintaining genomic integrity under conditions of RS [15–17] . However , since these studies were performed on transformed cell lines lacking a normal DDR and cell cycle control , and involved extreme degrees of MCM and licensing perturbation , the relevance to normal physiological situations is unclear . Consistent with previous reports that also used primary cell cultures , we found that moderate MCM reduction neither affects unchallenged cell proliferation , nor triggers a significant replication-related DDR [34 , 35] . However , our data showed that MCM reduction sensitizes the cells to additional RS as did Kunnev et al [35] , and in primary cells , senescence is the terminal consequence of RS-induced cell cycle arrest . Importantly , we showed that moderate MCM reduction induces expression of senescence markers at a low level that is compatible with continued cell growth [37] , but poises the cells to undergo full-scale senescence upon additional RS . Our findings underscore the importance of the DDR and Trp53 in response to RS , especially through their interaction with MCM2-7 . Activated components of the DDR interact with MCM proteins to stabilize stalled replication forks and regulate dormant origin firing during periods of short-term RS [10 , 56–58] . DDR-mediated activation of Trp53 to arrest cell cycle progression and trigger senescence has been well studied [59] . TRP53 is also required for delaying premature S-phase entry when replication licensing is inhibited in normal primary cells [60] . However , this licensing checkpoint delay is bypassed in cancer cell lines that lack a normal DDR and/or Trp53 function , leading to DNA damage accumulation from unregulated cell cycle progression and apoptosis [20 , 60] . TRP53 function during the licensing checkpoint is thus indicated , and our findings support the role of TRP53-dependent regulation of licensing factors under RS . The regulatory function of TRP53 on MCM2-7 expression was first implicated by the observation that Trp53 deletion rescued MCM2-7 levels in Chaos3 MEFs [39] . We found that TRP53 represses MCM2-7 expression in cells subjected to HU-induced RS . Trp53-null cells continued proliferating under RS , presumably because senescence cannot be sufficiently induced . We also identified a group of miRNAs that can be induced by RS , which also depends on normal Trp53 function . Ectopic expression of the identified miRNAs suppressed MCM expression direct or indirectly . miRNA-related expression regulation is typically moderate [27 , 28] , and sometimes reversible [29 , 30] . We found that primary MEFs exposed to low level RS took several generations to develop terminal phenotypes such as senescence . During this time , MCMs became gradually downregulated , consistent with the pattern of miRNA-related gene silencing . The miR-34 family targets MCM5 directly , and overexpression of these miRNAs causes downregulation of other MCMs and DNA replication genes to negatively regulation cell cycle progression [25 , 33 , 47 , 48] . Consistent with other studies showing that miR-34s function redundantly in the Trp53 pathway [61 , 62] , we observed no apparent genomic instability defects in miR-34abc TKO mutants . Furthermore , miR-34abc deletion only partially rescued MCM expression , which in turn led to a partial decrease in genomic instability in Chaos3 mice . These observations may have the following implications . First , reduced MCM2-7 expression in the Chaos3 mutant may only contribute in a minor way to the highly elevated level of genomic instability in these cells/mice , whereas helicase instability is the major culprit . Second , other miRNAs may also repress MCM expression , as we showed was the case for miR-27b and 181a . It is very likely that other mechanisms contribute to MCM regulation during RS . These observations lead us to propose a model by which RS-triggered Trp53 activation/stabilization lowers replication licensing factors to eventually arrest cell proliferation . Though moderate MCM reduction is tolerated in normal cells , chronic exposure to low-level RS can decrease MCM2-7 levels , and the degree of this downregulation is related to duration and intensity of the RS . An initial MCM reduction may not affect DNA replication and cell cycle progression , however , prolonged RS will continue to decrease MCM2-7 expression , partially through increased miRNA expression . Once MCMs decline below a threshold needed for licensing sufficient replication origins , the licensing checkpoint is implemented and cell proliferation is terminally arrested through senescence induction . This model provides an additional rationale for the high MCM2-7 expression in normal cells: that it is important for RS tolerance . This may have physiological relevance for stem cell pools . As mentioned before , severe MCM2 loss can lead to stem cell deficiency and arrested growth in mice [36] . The gradual loss of MCM2-7 expression in proliferative stem cells due to enduring prolonged and/or intense RS may dictate their replicative lifespans , thus these cells can be eliminated and the entire mechanism serving as a barrier to malignant transformation . To our knowledge , this is the first report to demonstrate active DNA replication control in terms of MCM regulation in response to RS in mammalian cells . The Chaos3 mutant is an unique model of intrinsic RS . Because MEFs homozygous for Chaos3 exhibited ~40% MCM2-7 pan-reduction and reduced numbers of dormant origins , and since studies in other systems showed that reducing dormant origins causes genomic instability that is also characteristic of Chaos3 mice and cells , we and others attributed the phenotypes to the shortage of dormant origins [31 , 32 , 34 , 39] . Nevertheless , the trigger for MCM reduction is likely MCM2-7 heterohexamer destabilization; the F345I change in MCM4Chaos3 disrupts MCM4-MCM6 interaction in vitro and in vivo [33 , 34] . Although this mutation does not affect helicase activity on naked DNA in vitro [34] , it is likely to impact unwinding of certain chromatin structures in vivo; indeed , yeast bearing the Chaos3 mutation showed chromosome breakpoints and rearrangements that were exclusively associated with Ty elements or solo long terminal repeat ( LTR ) elements [63] . Thus , we hypothesized that the genomic instability in Chaos3 mice and cells might be due to helicase instability primarily , and that RS caused by the defective helicase triggered MCM2-7 pan-reduction , which in turn likely adds to genomic instability and increases RS . This hypothesis was supported by the results of Dm-ChP experiments , a method for detecting protein dynamics at active and/or stalled replication forks [40 , 41 , 64] . These experiments revealed that Chaos3 cells normally have no significant alterations in the levels of proteins associated with stalled replication forks , consistent with observations that markers of fork stalling and DNA damage ( RPA32 , pRAD17 , γH2AX ) are only moderately increased in Chaos3 cells during unchallenged S-phase [34] . However , the accumulation of such proteins can still be induced upon RS . We also observed additional loss of MCMs at stalled replication forks in Chaos3 cells . Normally , MCMs are retained at stalled forks to enable restart after repair [8 , 10] . Given that additional MCM chromatin loading is prohibited once S-phase is initiated , the loss of MCM protein from stalled forks likely explains the unresolved replication intermediates interconnecting sister chromatids that persist into M phase in Chaos3 cells [34] . In sum , we have found that RS-mediated downregulation of MCM2-7 levels , which occurs over a period of time that we surmise is related to providing cells with an opportunity to overcome RS , is a key mechanism for eventually inducing senescence in WT cells . This is likely an important way to prevent transformation of cells experiencing a certain threshold of genomic instability . However , a conundrum is why Chaos3 mice or mice deficient for Mcm2 [36] are cancer prone [31 , 36] , since they would be expected to be more susceptible to undergoing senescence . We conjecture that since there is a threshold level for MCMs below which causes lethality and severe developmental defects [32] , that these mice are above that level and the cells that become transformed have acquired resistance to the senescence pathway , possibly via mutation or epigenetic alterations caused by intrinsic RS .
The use of animals in this study was performed under a protocol ( 2004–0038 ) approved by Cornell’s IACUC . Mice were euthanized via CO2 asphyxiation according to IACUC-approved conditions . Primary MEFs were isolated from 12 . 5~14 . 5 dpc strain C3HeB/FeJ ( C3H ) embryos in which organs were removed and the remainder was lightly homogenized to make a cell suspension . Cells were cultured in DMEM with 10% FBS and 100 units/ml penicillin-streptomycin . The initial plating of embryonic cells is designated passage 0 ( P0 ) . For cell proliferation assays and general MEF maintenance , 1 x 106 cells were seeded in 100 mm tissue culture dishes and maintained under either standard conditions ( 37°C , 5% CO2 and atmospheric O2 ) or low oxygen culture ( 37°C , 5% CO2 and 5% O2 level ) in parallel for 3~4 days between passages . Upon trypsinization for passage , cell numbers were counted . SA-β-gal staining of cultured cells was performed as described [65] . To facilitate the counting of SA-β-gal positive cells , nuclei were counterstained with Hoechst 33342 before mounting the coverslips . The slides were examined using light and fluorescent microscopy . Total RNA was extracted from cultured cells using the EZNA total RNA kit ( Omega ) . cDNA was synthesized from 1ug of total RNA using the iScript cDNA synthesis kit ( Bio-Rad ) and the supplied oligo-dT primer . qPCR reactions were performed as described [32] . PCR amplification and real-time detection was performed with a Bio-Rad CFX96 Real-Time system and data analysis was performed with the Bio-Rad CFX Manager software ( Bio-Rad ) . Relative gene expression was calculated using the ΔΔCq method with β-actin as endogenous control . A technical replicate was performed on each sample . Total RNA was extracted from cultured cells using miRNAeasy kit ( Qiagen ) . cDNA of small RNA was synthesized from 1ug of total RNA using qScript microRNA cDNA Synthesis Kit ( Quanta ) . PCR amplification and real-time detection was performed with a Bio-Rad CFX96 Real-Time system and data analysis was performed with the Bio-Rad CFX Manager software ( Bio-Rad ) . Relative gene expression was calculated using the ddCq method with RNU6 as endogenous control . A technical replicate was performed on each sample . The primers for microRNA amplification were purchased from PerfeCTa microRNA assays ( Quanta ) . Protein samples concentrations were determined with a BCA Protein Assay Kit ( Thermo Scientific ) . Equal amounts of protein were loaded onto SDS-PAGE gels . Western blot analysis was performed as previously described [32 , 33] . Proteins were electrotransferred onto PVDF membranes ( Millipore ) . Chemiluminescence was performed using the Luminata HRP substrate ( Millipore ) . Bands were detected either by exposure of the probed membranes to X-ray film or by scanning with a Bio-Rad Universal Hood II running Image Lab software ( Bio-Rad ) . Western blot quantification was performed using ImageJ software . Antibodies used were as follows: MCM2: ab108935 ( Abcam ) ; MCM3: 4012 ( Cell Signaling ) ; MCM6: sc-9843 ( Santa Cruz Biotechnology ) ; MCM7: ab2360 ( Abcam ) ; MCM7: 3735 ( Cell Signaling ) ; PCNA: P8825 ( Sigma ) ; total p53: 9282 ( Cell Signaling ) ; and β-actin: A1978 ( Sigma ) . Cells were trypsinized into a single cell suspension and fixed in 70% ice-cold ethanol overnight . They were stained for DNA content with 40μg/mL propidium iodide and 20μg/mL RNaseA for 30min at room temperature . Flow cytometry was performed on a BD Bioscience LSR II instrument . Stained cells were excited with a 488nm laser , and a 575/26 filter was applied for data collection . The percentages of cells in each cell cycle compartment was determined using ModFit LT software ( Verity Software House ) . Cells grown on coverslips were pulse labeled with 10μM EdU for 30min . Formaldehyde was added directly to the culture to a final concentration of 1% for 10min at room temperature ( RT ) . After 3 washes with PBS , cells were permeablized on ice with 0 . 3% Triton X-100 in PBS for 15 min . , followed by 3 washes in PBS containing 1% BSA . The ‘Click’ reaction staining was performed by placing the cells in 10mM ( + ) -sodium-L-ascorbate , 0 . 1mM 6-Caboxyfluorescein-TEG azide and 2mM CuSO4 cocktail for 30 min at RT . After PBS washes , nuclei were counterstained with Hoechst 33342 . Coverslips were mounted using ProLong Gold antifade reagent ( Invitrogen ) before examination by fluorescence microscopy . Whole cell protein extraction was performed in RIPA buffer . Protein extraction from tissues was performed using T-PER Tissue Protein Extraction Reagent ( Thermo Scientific ) . For fractionation , cultured cells were trypsinized and counted . After two PBS washes , cells were resuspended in Buffer A ( 10mM HEPES [pH7 . 9] , 10mM KCl , 1 . 5mM MgCl2 , 340mM sucrose , 10% glycerol , 1mM DTT ) with 0 . 1% Triton X-100 and incubated on ice for 5min . Low speed centrifugation ( 1 , 300g x 4min at 4°C ) was performed to separate the supernatant ( S1 ) and the pellet ( P1 ) . The pellet was washed once with Buffer A , then further extracted in Buffer B ( 3mM EDTA , 0 . 2mM EGTA , 1mM DTT ) on ice for 30min . After centrifugation ( 1 , 700g x 4min at 4°C ) , the supernatant contained the nuclear fraction ( S3 ) , and the pellet ( P3 ) containing the chromatin-bound proteins was washed once with Buffer B and then finally extracted in RIPA buffer . During the fractionations , cells were resuspended at 2 . 5 x 104 /μL at each step . Dm-ChP was performed essentially as described [40] with the following modifications . EdU pulse labeling was performed for 30 min . for all experiments . One mg of nuclear-enriched protein lysate was incubated with 100μL pre-blocked wet streptavidin agarose beads ( Novagen ) . Pull-down was performed at 4°C for 16~20h with constant rotation , then sequentially washed with RIPA , Wash Buffer 1 ( 10mM Tris [pH 8 . 0] , 200mM LiCl , 0 . 5% NP-40 , 0 . 5% sodium deoxycholate , 1mM EDTA , 360mM NaCl ) , Wash Buffer 2 ( Wash Buffer 1 without NaCl ) , and finally TE ( 10mM Tris pH = 7 . 6 , 1mM EDTA ) . All washing was performed at 4°C for 10 min . with constant rotation . Each washing step was performed twice with the volume of the washing buffer at 10 times the volume of the dried beads . After the final wash , equal amount of 2X Laemmli sample buffer was added to the dried beads and boiled for 10min to elute the EdU-bound fraction for western blot analysis . For mass spectrometry , beads were eluted in 100mM Tris [pH 8 . 0] , 1% SDS , 10mM DTT by boiling at 95° for 10 min . A schematic of this assay and examples of primary data are shown in S2 Fig . Cells were split and plated on two separate coverslips and cultured in the presence of 10μM BrdU . After 72h , the BrdU was removed . One of the coverslips ( "EdU" ) was incubated in media containing 10μM EdU for 30min to label the replicating cells , while the other ( "HU" ) was incubated in 3mM HU for 30min to induce replication fork stalling and allow the helicase to dissociate from the replisome and expose ssDNA in front of stalled forks . Then the EdU coverslip was stained for EdU ( see section above on EdU incorporation section ) , while the HU coverslip was stained for BrdU under "native" conditions ( no HCl denaturation of DNA , S2B Fig bottom panels ) . Positive controls for both parallel conditions were performed in which BrdU staining was performed following HCl denaturation of DNA , which demonstrates labeling of all cells due to the initial culturing in BrdU for 72 hours ( S2B Fig top panels ) . Nuclei were counterstained with Hoechst 33342 . Mounted coverslips were then examined by fluorescence microscopy . The percentage of BrdU-positive cells on the HU coverslip corresponds to the percentage of cells with sufficient helicase unwinding activity following fork arrest to expose ssDNA to a degree that allows detection ( BrdU foci ) over background . Comparison of this fraction to that of EdU-positive cells on the EdU coverslip reveals the percentage of replicating cells with active helicase unwinding activity . An SV40 large T antigen-encoding construct ( pBABE-puro SV40 LT ) was packaged into lentivirus particles and used to infect primary MEFs at early passages . Cells were then selected and maintained in media containing 1 . 25μg/mL puromycin . Four pairs of littermate MEFs were transformed with this method and used for Dm-ChP . Total RNA including small RNA was extracted using an miRNAeasy kit ( Qiagen ) . One μg of total RNA from each sample was used to prepare small RNA sequencing libraries using the TruSeq small RNA sample preparation kit ( index set 1–12 , Illumina ) according to the manufacturer’s instructions . Prepared libraries were sequenced on the Illumina HiSeq platform using single-end High Output mode . Reads were aligned to miRBase database v19 to generate read counts for each miRNA . Normalized miRNA reads for each sample were used as input for data analysis using the DESeq package [66] . Results are presented as supplementary information ( S1 Dataset ) . To determine the potential miRNAs that target at Mcm2-7 mRNAs , we used a combination of miRmap ( http://mirmap . ezlab . org/ ) and miRanda ( http://www . microrna . org/ ) software . Primary WT MEFs were transfected with 50nM of miRNA mimic ( Dharmacon ) using DharmaFECT 1 transfection reagent per manufacturer's instructions . Control cells were transfected in parallel with negative control miRNA mimics ( based on cel-miR-67 ) , which has minimal sequence similarity with miRNAs in mice . 48h after transfection , cells were harvested for RNA and protein analysis . Schematic of luciferase assay is shown in S5A Fig . In general , HeLa cells were cotransfected using Lipofectamine 2000 in a 96 well format with 50nM of miRNA mimic and 100ng of pmirGLO luciferase construct with or without a 3’UTR insert . The 3’UTRs of Mcm2-7 were cloned into NheI + SbfI digested pmirGLO vector . Empty vector was used as control . 24h after transfection , cells were changed to fresh media . After 48h of transfection luciferase activities were measured using the Dual Luciferase Assay System ( Promega ) and Synergy 2 Multi-Mode Reader ( BioTek ) per manufacturer's instructions . Renilla luciferase activity was normalized to Firefly luciferase activity in each well . The miR-34abc knockout alleles were acquired from Dr . A . Nikitin [61] . They were crossed to the Chaos3 mutant ( C3Heb/FeJ background ) for at least 5 generations . Male breeders from each generation were selected based on congenic status as evaluated by the DartMouse speed congenic service . A list of genotyping primers is presented in S1 Table . | Duplication of the genome by DNA replication is essential for cell proliferation . DNA replication is initiated from many sites ( “origins” ) along chromosomes that are bound by replication licensing proteins , including MCM2-7 . They are also core components of the replication helicase complex that unwinds double stranded DNA to expose single stranded DNA that is the template for DNA polymerase . Eukaryotic DNA replication machinery faces many challenges to duplicate the complex and massive genome . Circumstances that inhibit progression of the replication machinery cause “replication stress” ( RS ) . Cells can counteract RS by utilizing “dormant” or “backup” origins . Abundant MCM2-7 expression sufficiently licenses dormant origins , but reducing MCMs compromises cellular responses to RS . We show that MCM2-7 expression is downregulated in cells experiencing chronic RS , and this depends on the TRP53 tumor suppressor and microRNAs it regulates . Extended RS eventually reduces MCMs to a point that terminal cell cycle arrest occurs . We propose that this mechanism is a crucial protection against neoplasia . | [
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| 2016 | Chronic DNA Replication Stress Reduces Replicative Lifespan of Cells by TRP53-Dependent, microRNA-Assisted MCM2-7 Downregulation |
Biological signaling processes may be mediated by complex networks in which network components and network sectors interact with each other in complex ways . Studies of complex networks benefit from approaches in which the roles of individual components are considered in the context of the network . The plant immune signaling network , which controls inducible responses to pathogen attack , is such a complex network . We studied the Arabidopsis immune signaling network upon challenge with a strain of the bacterial pathogen Pseudomonas syringae expressing the effector protein AvrRpt2 ( Pto DC3000 AvrRpt2 ) . This bacterial strain feeds multiple inputs into the signaling network , allowing many parts of the network to be activated at once . mRNA profiles for 571 immune response genes of 22 Arabidopsis immunity mutants and wild type were collected 6 hours after inoculation with Pto DC3000 AvrRpt2 . The mRNA profiles were analyzed as detailed descriptions of changes in the network state resulting from the genetic perturbations . Regulatory relationships among the genes corresponding to the mutations were inferred by recursively applying a non-linear dimensionality reduction procedure to the mRNA profile data . The resulting static network model accurately predicted 23 of 25 regulatory relationships reported in the literature , suggesting that predictions of novel regulatory relationships are also accurate . The network model revealed two striking features: ( i ) the components of the network are highly interconnected; and ( ii ) negative regulatory relationships are common between signaling sectors . Complex regulatory relationships , including a novel negative regulatory relationship between the early microbe-associated molecular pattern-triggered signaling sectors and the salicylic acid sector , were further validated . We propose that prevalent negative regulatory relationships among the signaling sectors make the plant immune signaling network a “sector-switching” network , which effectively balances two apparently conflicting demands , robustness against pathogenic perturbations and moderation of negative impacts of immune responses on plant fitness .
To understand the regulation of a particular biological process , it is important to elucidate what structural features of the signaling network regulating the process govern the behavior of the signaling network as a whole [1] , [2] . With a complex signaling network , in which components are highly interconnected , this is a challenging task . One problem is that the function of a sector of the network can be compensated by some other sector , and , consequently , functional identification of these sectors by knocking out each of the sectors is difficult . In this example of network compensation , it is assumed that these network sectors are functionally redundant but mechanistically distinct: they are not composed of homologous molecular components . General strategies to efficiently elucidate the structure of a complex signaling network are in demand . The plant immune signaling network , which regulates defense triggered upon pathogen attack , is such a complex network . Two modes of plant immunity , pattern- and effector-triggered immunity ( PTI and ETI ) have been characterized in resistance against biotrophic and hemi-biotrophic pathogens [3] . PTI is initiated by recognition of a microbe-associated molecular pattern ( MAMP ) by the corresponding pattern recognition receptor ( PRR ) , which is typically integrated in the plasma membrane . For example , a fragment of bacterial flagellin , flg22 , is a MAMP , and is recognized by the FLS2 receptor-like kinase PRR in Arabidopsis [4] . Pathogens adapted to a particular plant host deliver effectors which interfere with PTI [5] . Countering pathogen effectors , plants have acquired another class of receptors , resistance ( R ) proteins , that specifically recognize particular effectors , leading to induction of ETI . For example , the Arabidopsis R protein RPS2 indirectly recognizes the Pseudomonas syringae effector AvrRpt2 [6] , [7] . Although the way pathogen attack is recognized is distinct between PTI and ETI , they are not separate , but rather form an integrated immune system . The intimate relationships between PTI and ETI have been suggested by the facts that many downstream events are shared . For example , in Arabidopsis , MAP kinases 3 and 6 are rapidly and transiently activated in PTI and activated for an extended period in ETI [8] . Reactive oxygen species ( ROS ) production in PTI is absolutely dependent on the NADPH oxidase RBOHD , and ROS production in ETI is largely dependent on RBOHD [9] , [10] . The nitric oxide ( NO ) signaling sector comprised of NO-associated 1 ( NOA1 ) protein and NIA1 and NIA2 nitrate reductases is also involved in both PTI and ETI [11] , [12] . Furthermore , similarities in the PTI and ETI transcriptome responses have been pointed out [13] . The signaling sectors defined by the phytohormones , salicylic acid ( SA ) , jasmonic acid ( JA ) , and ethylene ( ET ) , are important in plant immunity: generally the SA sector for immunity against biotrophic and hemi-biotrophic pathogens and the JA and ET sectors for immunity against necrotrophic pathogens [14] , [15] , [16] . The iso-chorismate synthase SID2 ( ICS1 ) [17] and the MATE-type transporter EDS5 [18] are required for SA synthesis in response to pathogen attack . NPR1 [19] is a major positive regulator of SA responses . The regulators EDS1 and PAD4 are important for SA accumulation as well as SA-independent signaling functions [20] , [21] , [22] . The JA sector contains the JAR1 enzyme that produces the JA-Ile conjugate , which is the active form of JA [23] , the F-box protein COI1 , which responds to JA-Ile by targeting the JAZ transcription repressors for degradation [24] , and the JIN1 Myc transcription activator [25] . The metal-ion transporter EIN2 is required for most ET responses [26] , and the EIN3 transcription activator positively regulates some ET responses [27] . Other phytohormones , such as abscisic acid , auxin , brassinosteroids , and gibberellins , are also involved in plant immune signaling [28] . Although the phytohormone levels change during PTI and ETI , the specific effects of the phytohormone sectors in PTI and ETI had been considered to be limited or unclear [3] , [29] , [30] . Recently , we demonstrated that both flg22-triggered PTI ( flg22-PTI ) and AvrRpt2-triggered ETI ( AvrRpt2-ETI ) are mostly dependent on the signaling network defined by the SA , JA , ET and PAD4 sectors [31] . Therefore , the signaling machinery is extensively shared between flg22-PTI and AvrRpt2-ETI . A main difference between PTI and ETI appears to reside in how the sectors in the common network interact one another . If this is true , then to further our understanding of the integrated plant immune signaling network , it is important to elucidate the global regulatory relationships among the network components . One major use of mRNA profiles is as detailed descriptions of biological states , because an mRNA profile data set is a massive phenotypic data set . This use was pioneered by the “compendium” approach , in which mutations and chemicals that cause similar changes in mRNA profiles are hypothesized to be involved in the same biological processes [32] . In our earlier studies , we implemented non-linear dimensionality reduction [33] in combination with graphical representation to reveal multi-dimensional relationships with locally variable dimensionalities among the mRNA profiles [34] , [35] , [36] . In this way , information about the nature of similarities between mRNA profiles was obtained in addition to the scalar similarities , and novel relationships among Arabidopsis mutants and accessions were discovered . Here , we report an integrated regulatory relationship model comprised of 22 components including most of the genetically-defined major regulators of immunity in Arabidopsis . The network structure was inferred based on mRNA profiles for 571 immune response genes of Arabidopsis mutants with defects in immune regulatory genes . The mRNA profiles were collected at a single time point six hours post inoculation ( hpi ) with the bacterial strain P . syringae pv . tomato DC3000 expressing the effector AvrRpt2 ( Pto DC3000 AvrRpt2 ) . This strain feeds multiple inputs to the network . The regulatory relationships were inferred by recursively applying a non-linear dimensionality reduction procedure , which allowed detection of many weak relationships . The model correctly predicted 23 out of 25 previously known relationships , suggesting the accuracy of newly predicted relationships . Two features of the network model were readily evident: the network components were highly interconnected; and negative regulatory relationships between signaling sectors were very common . We confirmed the latter point in one case by demonstrating a mutual inhibition between the SA and early MAMP-triggered ( EMT ) signaling sectors . Based on the prevalent negative regulatory relationships , we propose “sector-switching” as an important property of the plant immune signaling network .
With the above procedure , we obtained a regulatory relationship model for 22 genes corresponding to the mutations with 67 undirected links , which we refer to as our network model ( Figure 2 ) . Our network model has a form of an undirected graph since a single time-point data set does not allow inference of the direction of relationships without an additional assumption . Forty-eight and 19 links represented positive and negative regulatory relationships , respectively ( Figure 2A and 2B , Figure S2 , Table S2 ) . To evaluate the accuracy of the predicted regulatory relationships , the published literature was surveyed for supporting experimental data ( Table S3 ) . Twenty-five pairwise regulatory relationships between genes used in this study , that included information about the sign of the relationships , were found in published literature . Our network model correctly predicted 23 out of the 25 known regulatory relationships . One of the relationships not correctly inferred was the JIN1-MPK6 relationship: MPK6 was described as a negative regulator of JIN1 [47] whereas our model predicts a positive relationship between them . The other was that the model did not predict a direct relationship corresponding to negative regulation of SID2 by EIN3 , described in Chen et al . [48] . However , when JAR1 , which was connected positively and negatively with EIN3 and SID2 , respectively , was removed from the input data set , the negative regulatory relationship between EIN3 and SID2 was inferred ( Table S4 ) . Under our experimental conditions , JA signaling could be strong due to coronatine and may have masked the effect of EIN3 , which mediates ET signaling . Note that the known links were established with data from diverse experiments conducted using various Arabidopsis-pathogen interactions , performed by many different research groups . While such studies helped us to select useful mutants for our study , our network model was built based solely on mRNA profile data collected using a single experimental setup with a single time point . This fact demonstrates the richness of information in descriptions of the network state consisting of mRNA profiles and the high efficiency of network inference using mRNA profiles as detailed descriptions of network states . The high accuracy in prediction of previously known regulatory relationships suggests the accuracy of newly predicted regulatory relationships . The specificities of links can also be examined by removing the data for one mutant from the data set . For instance , a positive link between EIN3 and MPK6 was predicted in the model . This is consistent with the observation in Yoo et al . [49] that EIN3 is phosphorylated and activated by MPK6 . The direction of this regulatory relationship is from MPK6 to EIN3 but not from EIN3 in the ET sector to MPK6 [49]: in other words , this link is specific to EIN3 but not for the ET sector in general . Therefore , a link between EIN2 in the ET sector and MPK6 should not be made if EIN3 is removed from the model ( i . e . , if the model is made using the data set with the ein3 difference profile removed ) . In the resulting model with EIN3 removed , the link between MPK6 and the other ET signaling component EIN2 was not generated ( Figure S3A and A′ , Table S5 ) . Thus , the specificity of the biochemical regulation was captured in our network model . It should be noted that each link may not represent a simple logical relationship . For example , the link between vertices A and B may represent expression changes in one subset of genes profiled and the link between vertices B and C may represent expression changes in a different subset of genes profiled . Therefore , among three vertices a circular link of positive , positive , and negative ( e . g . , links among MPK3 , MPK6 , and NHO1 ) does not necessarily present logical conflicts . As expected , genes assigned to the same signaling sectors were predicted to have positive regulatory relationships except for the ROS sector ( Figure 2A ) . Although RBOHD and RBOHF , the two respiratory burst oxidase homologues , were assigned to the ROS sector , it is known that single rbohD and rbohF mutants have different pathogen-responsive ROS accumulation and HR cell death phenotypes [9] , [10] . Consistently , difference profiles of the two mutants were uncorrelated ( uncentered Pearson correlation coefficient between the expression changes from wild type: 0 . 043 ) . Thus , it is reasonable that no positive link was predicted between the two RBOH genes . Positive regulatory relationships between signaling sectors were also predicted . Among them , positive regulatory relationships between the NO and SA sectors were of particular interest . In our network model , NOA1 had links with NPR1 and PAD4 . Indeed , the noa1 difference profile had higher correlation with the pad4 and npr1 difference profiles than the nia2 difference profile ( uncentered Pearson correlation coefficients of 0 . 876 , 0 . 831 , and 0 . 714 with the pad4 , npr1 , and nia2 difference profiles , respectively ) . In the model made without NOA1 , NIA2 replaced NOA1 in the links with the two SA sector components , PAD4 and NPR1 ( Figure S3B and B′ , Table S6 ) . Therefore , the positive regulatory relationships between NOA1 and the SA sector components are not specific to NOA1 , but they indicate positive regulatory relationships between the NO and SA sectors in general . On the other hand , the fact that the predicted regulatory relationships between NOA1 and the SA sector are stronger than those between NIA2 and the SA sector is consistent with the observation that NOA1 , not NIA1/NIA2 , is responsible for SA-induced NO accumulation [50] . Negative regulatory relationships are very common between signaling sectors in our network model while negative regulatory relationships within each signaling sector are absent . The NO sector was an exception as it does not have any negative links with other sectors . The JA sector had negative relationships with most of the other signaling sectors tested . The SA sector was negatively linked with PMR4 , MPK3/6 , and the ET and JA sectors . Prevalent negative regulatory relationships between sectors strongly suggest that a limited number of signaling sectors are highly activated at a given time as the active sectors suppress the other sectors . Both the EMT and the SA sectors positively contribute to defense against the virulent strain Pto DC3000 [43] , [45] , [51] . Figure 3A illustrates a subnetwork of our network model featuring the EMT and SA sectors . We consider that RBOHD , PMR4 , MPK3/6 , and the ET sector comprise the EMT sectors because RBOHD-dependent ROS production [10] , PMR4-dependent callose deposition [52] , MPK3/6 activation [43] , and ET accumulation [44] are early MAMP responses . Note that although we designate them as the EMT sectors , RBOHD-dependent ROS production and MPK3/6 activation also occur for extended periods during ETI [8] , [9] . We previously reported that MAMPs can trigger accumulation of SA and thereby activate SA signaling [30] , i . e . , the EMT sectors positively regulate the SA sector . However , our network model contains negative links as well as positive ones between the sectors , suggesting that the regulatory relationships between the sectors can be positive or negative , depending on the context . In the following sections , we closely investigate this subnetwork of the EMT and SA sectors . Callose deposition is a cell wall-based defense following recognition of pathogens [53] . PMR4 is the callose synthase responsible for callose deposition upon infection with pathogens or treatment with elicitors [52] , [53] , [54] . Our model predicted a negative relationship between PMR4 and SID2 ( Figure 3A ) . It was previously reported that SA-mediated signaling is up-regulated in pmr4-1 plants [52] , i . e . , PMR4 negatively regulates the SA sector , which can explain the predicted negative regulatory relationship between PMR4 and SID2 . Can SA signaling also affect callose deposition ? We quantified callose deposition in the SA sector mutants , npr1-1 and sid2-2 , after flg22 treatment according to the method described in Denoux et al . [55] . Together with the SA sector mutants , pbs2-1 ( a mutant with a RAR1 deletion ) was included as a mutant with potentially enhanced callose deposition . RAR1 has a negative link with PMR4 in our network model , and different RAR1 alleles rar1-20 and rar1-29 were reported to have enhanced callose deposition phenotypes [56] . The callose deposition level in cotyledons of 10 day-old seedlings grown in liquid culture was measured at 6 and 16 hours post treatment ( hpt ) with 1µM flg22 ( Figure 3B ) . Consistent with a previous report [45] , no significant difference in the flg22-triggered callose deposition level was observed at 16 hpt between Col-0 wild type and the SA sector mutants . However , the callose deposition levels at 6 hpt in the SA sector mutants were significantly higher than in Col-0 . At 6 hpt , the callose deposition level in Col-0 was not significantly different from the flg22-receptor mutant fls2C , so the Col-0 level was the background noise level . These results indicate that flg22-triggered callose deposition is enhanced in the SA sector mutants at an early time point: the SA sector negatively regulates the PMR4 sector . Thus , negative regulatory relationships between PMR4 and the SA sector are mutual . There is also a positive relationship between PMR4 and NPR1 . It has been reported that pretreatment with SA can compensate loss of the flg22-triggered callose deposition caused by a pen2 mutation [45] . The positive PMR4-NPR1 link may correspond to this SA-enhanced callose deposition in pen2 plants . Such context-dependent regulatory relationships involving PMR4 were anticipated as PMR4 has a higher number of links compared with other genes in our network model . ROS production minutes after treatment with flg22 is one of the very early MAMP-triggered responses . RBOHD is required for flg22-triggered ROS production [10] . A positive regulatory relationship between the SA sector and ROS production was predicted as RBOHD has a positive link with NPR1 in our network model ( Figure 3A ) . We tested whether pretreatment with SA and/or a mutation in NPR1 affect flg22-triggered ROS production . Pretreatment of plant tissues with SA rather than co-treatment with SA and flg22 was chosen since flg22-triggered ROS production starts within a few minutes after addition of flg22 . Col-0 and npr1-1 were pretreated with 5µM SA or water for 3 hours before they were treated with 1µM flg22 or water . Pretreatment with SA enhanced ROS production in Col-0 wild type during the period between 3 and 12 minutes after treatment with flg22 ( Table S7 ) . This enhanced ROS production was abolished in npr1-1 , which indicates that SA positively regulates ROS production in an NPR1-dependent manner . This observation is consistent with the model prediction of an NPR1-RBOHD positive regulatory relationship ( Figure 2A ) . To further examine regulatory relationships between the EMT and the SA sectors , effects of SA and flg22 on the EMT and SA sectors , respectively , were examined using the mRNA level of a marker gene as a proxy for activity of each sector . Wild-type seedlings grown in liquid culture were treated with flg22 and/or SA . The mRNA levels of a putative chitinase ( At3g43620 ) [30] and the PR-1 ( At2g14610 ) genes were quantified for the EMT and SA sector activities , respectively ( Figure 4 ) . Induction of SA accumulation by flg22 was not significant at 3 hpt [30] . We measured the marker gene mRNA levels up to 3 hpt , so SA accumulation caused by flg22 treatment was negligible . Treatment with 500 or 5 µM SA induced PR-1 mRNA accumulation by 3 hpt . An inhibitory effect of 1 µM flg22 on PR-1 mRNA induction was observed with 5 µM SA at 3 hpt but not with 500 µM SA . An inhibitory effect of 500 µM but not 5 µM SA on induction of the chitinase mRNA accumulation by 1 µM flg22 was observed at 3 hpt . Significant inhibitory effects of 1 µM flg22 and 500 or 5µM SA were not observed 1 or 2 hpt ( Figure S4 ) . Thus , the EMT and SA sectors have mutual inhibitory effects in a dose-dependent manner .
We used mRNA profiles of mutant plants for inference of regulatory relationships among the genes corresponding to the mutations . This use of mRNA profiles was pioneered by the “compendium” approach [32] , and further developed , for example , to the “connectivity map” approach [57] . However , these approaches focused on the most prominent similarities in the global space and did not intend to dissect combinations of similarities to reveal multi-dimensional similarity relationships among mRNA profiles . In our earlier work , we combined the LLE algorithm [33] and graphical representation to visualize differences among similarities in mRNA profiles of Arabidopsis mutants with variable local dimensionalities to reveal different mechanisms used in plant immunity [34] . However , the analysis in our earlier work was limited to the local space defined by the global distance . In the current study we used RepEdLEGG , in which LLE was recursively applied to the residual of the first round of LLE . This approach enabled us to detect weak regulatory relationships and to reveal a highly interconnected network structure . A limitation of using mRNA profiles as descriptions of the network state is that the resolution of the network is determined by the number of network states measured – e . g . , in our study , the number of Arabidopsis mutants profiled . It should be noted that in our network model , when the genes corresponding to the mutations were linked , the link means that the genes or some other network components near the genes in the actual signaling network have regulatory relationships . On the other hand , an advantage of this approach is that the regulatory mode of the gene defined by a mutation does not have to be transcriptional although mRNA profiles are used for network inference . For example , we detected the regulatory relationship in the MPK6-EIN3 link even though MPK6 does not affect EIN3 expression , but rather its phosphorylation . Because the plant immune signaling network contains many major non-transcriptional regulatory components [9] , [24] , [43] , [58] , this advantage of the approach was essential for us to obtain a global network model using a single methodology . Using the predicted relationships between the EMT and SA sectors as examples , we have demonstrated that the resulting undirected regulatory relationships are highly informative in generation of hypotheses to guide intensive studies in focused parts of the network . We built this highly informative model in a cost-effective manner: mRNA profiling using a small-scale array at a single time point under a single experimental condition . Therefore , applications of this approach should be beneficial in studies of complex signaling networks in any genetically tractable organisms . Complex regulatory relationships among the network components strongly suggest that many relationships are dependent on context , such as the quantities and the states of other network components . To deepen our understanding of the signaling network , it will be important to elucidate the dynamic relationships among the network components . As the cost of mRNA profiling is rapidly decreasing , it will soon be practical to collect mRNA profiles of wild-type and many mutant plants at many time points . Such time-series mRNA profile data will enable extension of our network model to include information about network dynamics . Furthermore , cost reduction in mRNA profiling will improve applications of the approach used in this study . First , it could allow a symmetric and highly-overlapping experiment group design , which would reduce potential biases in the data set . Second , it could allow inclusion of mRNA profiles from uninfected plants of all the genotypes . Inclusion of such profiles would enable separating the genotype effect and the genotype∶infection interaction for each profiled gene , which we cannot do with the current data set that only includes infected plants . However , expression level information from many genes is combined as the network state description in our approach . Different genes have different ratios between the genotype effect and the genotype∶infection interaction . A data set that includes information from such genes allows incorporation of information about the genotype effect and the genotype∶infection interaction in the network inference . This may have contributed to the success of our approach in the absence of mRNA profiles from uninfected plants . Third , cost reduction could allow profiling of many more genes . If many more genes are profiled , some aspects of the network states that evaded detection in mRNA profiles of a limited number of genes ( 571 genes in this study ) may be detected , which could lead to discovery of additional weak regulatory relationships among the network components . Implementation of RepEdLEGG was a key to building the highly interconnected network model . Thirty-two out of 67 links predicted were obtained in the second round of LLE using the residuals from the first round of LLE as the response . Eight out of the 32 links found in the second round were supported by previous evidence . These links found in the second round connect vertices whose global distances are not particularly small and represent weak regulatory relationships . The validities of many links found in the second round of LLE indicate that common multivariate analysis methods that depend solely on the global distance are not ideal for inference of a highly interconnected network . Among existing methods , partial correlation is a method that can detect weak regulatory relationships [59] , like RepEdLEGG . The partial correlation between vertices X and Y is defined , when all the other vertices are Z1 , … , Zn , as the correlation between the residual of the linear regression of X with Z1 , … , Zn and the residual of the linear regression of Y with Z1 , … , Zn . When the results of RepEdLEGG and the partial correlation were compared using the data set used in this study ( q<0 . 01 ) , 51 links were predicted in common ( Figure S5 ) . There were 16 and 5 links unique to RepEdLEGG and the partial correlation , respectively . Whereas 7 out of the 16 links uniquely predicted by RepEdLEGG had supporting literature evidence , none of the links unique to the partial correlation did . This result suggests a higher accuracy of inference by RepEdLEGG than by partial correlation . We speculate that the difference between the two methods resulted from a difference in the size of the space that is considered linear for each vertex . While RepEdLEGG constrains the linear space to that delimited by the neighboring vertices found in the first and second rounds of LLE , partial correlation assumes that the entire global space is linear . Although RepEdLEGG is hampered by the arbitrariness in determining the size of the linear space ( i . e . , determining the number of neighbor vertices ) , the superior performance of RepEdLEGG over the partial correlation suggests that constraining the size of the linear space is important in modeling of a complex regulatory network . Guided by our network model , we have demonstrated that the EMT and SA sectors can antagonize each other . Such mutual inhibition is not intuitive since both sectors positively contribute to resistance against Pto DC3000 [30] , [45] . In addition , it appears to contradict our previous report that MAMPs trigger SA accumulation [30] , which is equivalent to positive regulation of the SA sector by the EMT sectors . It should be noted that two important aspects , kinetic and quantitative effects , are overlooked in these simplified arguments . The induction of SA accumulation by flg22 clearly takes longer than 3 hpt [30] while the mutual inhibition between the EMT and the SA sectors was evident at 3 hpt ( Figure 4 ) . In addition , we observed dose dependence in the mutual inhibition: inhibition of the SA sector by flg22 was effective only when SA signaling was weak while inhibition of the EMT sectors by SA was effective only when SA signaling was strong ( Figure 4 ) . We think that such kinetic and quantitative effects play important roles in coordinating positive and negative regulatory relationships between these sectors . The plant immune system must be robust against various perturbations caused by pathogens , which typically evolve much faster than plants . At the same time , not only are immune responses energy-expensive [60] but at least some are also detrimental to the plant fitness [61] , [62] , [63] . Therefore , ideally immune responses should be contained at the minimally necessary level . We speculate that to balance these apparently conflicting selection pressures , the EMT and SA sectors adjust the level of immune responses according to demand through the positive and negative regulatory relationships between them ( Figure 5 ) . When the plant is attacked by a pathogen , the EMT sectors are activated based on recognition of MAMPs . While the activation of the EMT sectors starts the activation of the SA sector with a delay , the SA sector does not become highly activated due to suppression by the strongly-activated EMT sectors . This is probably because detrimental effects of defense components controlled by the EMT sectors are less severe than those of the SA sector: if defense components controlled by the SA sector are not necessary , it is better not to activate them . The delay in activation of the SA sector by the EMT sectors is important in buying time for evaluation of the effect of the EMT sector-mediated defense . However , if the pathogen is to some extent adapted to the plant host and its effectors interfere with the EMT sectors , the resulting weakened activity of the EMT sectors could release the SA sector from suppression . In fact , several P . syringae effectors , such as HopAI1 [10] , target components of the EMT sectors . Using the SA sector-controlled defense components against more virulent pathogens is reasonable , as the SA sector-controlled defenses are known to be potent in defense against biotrophic and hemi-biotrophic pathogens [39] . Thus , an elaborate combination of positive and negative regulatory relationships between the EMT and the SA sectors may enable shifting the balance between the EMT sectors for defense against less virulent pathogens to keep negative impacts of the immune response on plant fitness low and to reserve the SA sector for defense against more virulent biotrophic and hemi-biotrophic pathogens . In our network model there are many inter-sector regulatory relationships . Such a high connectivity suggests a democratic network , in which each component of the network has a relatively small contribution to the function of the network and the level of contribution from each component is similar . We recently demonstrated that the AvrRpt2-ETI is robust against network perturbations because of positive contributions from each sector to immunity and compensatory interactions among them [31] . So , the network for AvrRpt2-ETI signaling appeared to be democratic . However , our current study showed that negative regulatory relationships are very common between different signaling sectors , such as between the EMT and the SA sectors . We speculate that the EMT and SA sectors are not exactly democratic: one of them is more active under a particular condition , and the other is suppressed by the active one; if the active sector is inhibited , the other sector gets activated to compensate . So , the apparent redundancy in immune signaling does not result from simple functional redundancy but from switching between the sectors . The prevalence of inter-sector negative regulatory relationships suggests that such sector-switching is common at the whole network level , not just between the EMT and SA sectors . In fact , an antagonistic relationship between the SA and JA sectors is well documented [64] . We propose to call this property of the signaling network “sector-switching” . If robustness of the immune system against fast-evolving pathogens had been the only driver in evolution , the signaling network could have evolved to be a simple redundant , democratic network . However , immune responses are generally deleterious to the host , and they impose fitness costs when the pressure from particular pathogens is not high [63] . Together with the demand to minimize negative impacts of immune response , we speculate that the signaling network has evolved to have a sector-switching property , so that the activities of the signaling sectors are switched in response to inputs to the network , such as inputs for induction of PTI and ETI , and to external perturbations , such as perturbations by pathogen effectors , to balance the performance and the negative impacts of the integrated immune system .
All Arabidopsis plants , wild type and mutants , used in the study had the genetic background of accession Col-0 . For mRNA profiling and ROS production assays , plants were grown in a controlled environment chamber at 22°C with 75% relative humidity and a 12h/12h light/dark cycle . For the assays using seedlings in liquid culture , seedlings were prepared essentially as described in Denoux et al . [55] with the following modifications: 0 . 25g/L as the concentration of sucrose in the culture medium , and the culture was incubated at 22°C . Pseudomonas syringae pv . tomato DC3000 carrying pLAFR3-avrRpt2 ( Pto DC3000 AvrRpt2 ) [65] was used for inoculation of plants subjected to mRNA profiling . Pto DC3000 AvrRpt2 was cultured in King's B medium at room temperature ( ∼22°C ) overnight and inocula were prepared at an OD600 of 0 . 05 in water . Leaves were infiltrated using a needle-less syringe as described in [66] . The flg22 peptide ( QRLSTGSRINSAKDDAAGLQIA ) was synthesized by EzBiolab Inc . ( IN , USA ) and was used at indicated concentrations . Sodium salicylate ( Fisher Scientific , PA , USA ) was used to prepare SA solutions at 5 or 500 µM . For treatment of seedlings , plates were centrifuged at 500 rpm for 10 seconds to remove condensation 1 day before treatment . Twenty-two mutants were divided into five experiment groups , and three biological replicates were made for each group , except for one ( group 00 ) with two biological replicates . The data collection for the biological replicates was conducted at least one week apart . Each experiment group consisted of Col-0 in addition to seven mutants . Detailed information about grouping is provided in Table S1 . The eight plants were grown at the outside positions of a 3×3 grid pattern in a 6″×6″ pot , and an additional Col-0 plant , which was not used for data collection , was grown in the center position of the grid pattern . The positions of the eight plants in each pot were randomly assigned . Some mutants in these experiments were irrelevant to this study and were excluded from analyses following normalization of mRNA profiles . The 5th experiment group ( group 00 ) consisting of one or two mutants used in each of three experiment groups ( groups 01 , 02 , and 03 ) and Col-0 was included to reduce potential bias associated with the experiment groups , e . g . , biases associated with particular dates when experiments were conducted or particular combinations of genotypes tested together . Two fully-developed leaves of each 4 week-old plant were inoculated with Pto DC3000 AvrRpt2 . For each mRNA profile , inoculated leaves were harvested from three plants of the same genotype from three different pots at 6 hpi and pooled . Procedures from target preparation to microarray data collection were performed as described in Sato et al . [37] . Raw expression data were normalized using the stable gene-based quantile normalization ( SBQ ) method [37] . For comparison of profiles among different plant genotypes tested in different experiment groups , it was necessary to compensate for potential bias caused by separating genotypes to different groups . A 2-stage mixed effect linear model was fitted to the data from each experiment group separately:where Y , G , T , R , γ , and ε are log2-transformed expression level value , gene , genotype , replicate , residual of the 1st model , and residual of the 2nd model . G and T are fixed effects , and R , γ , and ε are random effects . The second model was fitted for each gene separately . Using the G:T values for the genotypes common between pairs of the experiment groups , calibration values among the experiment groups were calculated for each gene . The values in the initial SBQ-normalized data set containing all the experiment groups were corrected using the calibration values and were used to fit another 2-stage model:where Y , G , T , E , R , γ , and ε are log2-transformed expression level value , gene , genotype , experiment group , replicate , residual of the 1st model , and residual of the 2nd model . G and T are fixed effects , and E , R , γ , and ε are random effects . The second model was fitted for each gene separately . The contrasts in the model were made to obtain the difference value between each mutant and Col-0 in each Tt + G:Tgt . A data set with 480 genes each of which had at least one mutant genotype with the significant log2-transformed ratio value ( q<0 . 05 ) were used to compare mRNA profiles of the genotypes ( 480 genes × 22 genotypes ) . The log2-transformed ratio values were not centered but scaled across the genes for each genotype ( difference profiles ) . In this way , the order of the pairwise distances of the genotype difference profiles is invariant when either the uncentered Pearson correlation coefficient or the Euclidean distance is used . EdLEGG was modified from LEGG [36] to use the Euclidean distance instead of the uncentered Pearson correlation , so that multiple regression can be used for the calculation . Briefly , in a data set of n genes × m genotypes , the difference profile of genotype i is denoted as a vector in an n-dimensional space . For the vector of each genotype i , k closest neighboring genotype vectors were identified using the uncentered Pearson correlation coefficient . Pi is the set of such j ( ) . The value k defines the size of the local space . Then the following multiple regression was fitted by minimizing the residual vector size :In this first round of EdLEGG , the condition , , was applied to allow only positive regulatory relationships for the identification of major components illustrated in Figure S1B . k = 6 was used in this study as this made some of aij for most i insignificant , which suggests that each local space was sufficiently sampled . In RepEdLEGG , each residual vector was subjected to a second round of EdLEGG . For each , l closest neighboring genotype vectors were identified using the absolute value of the uncentered Pearson correlation coefficient . Qi is the set of such j ( ) . In this way , the genotype vectors that are negatively correlated as well as positively correlated can be identified as neighbors , which allows detection of both negative and positive regulatory relationships . The following multiple regression was fitted by minimizing the residual vector size :In this second round , the coefficients bij were allowed to take positive or negative values to include negative regulatory relationships . l = 5 was used in this study as this made some of bij for most i insignificant , which suggests that each local space was sufficiently sampled . The p-value associated with each of the coefficients aij and bij , obtained from multiple regression , was corrected using the Benjamini-Hochberg False Discovery Rate ( FDR ) [67] to obtain the q-value , and the neighboring genotype vectors with coefficients significant for the indicated q-value threshold , , were identified for each genotype i ( ) . The output of RepEdLEGG was further evaluated using a leave-one-out ( LOO ) cross-validation . In each case , the profile for one of the 22 mutants was removed from the data set , and this LOO data set was subjected to RepEdLEGG analysis . Links that were found in at least 18 LOO cross-validation cases were considered significant . Note that for a particular link , 20 LOO data sets have both the genotypes flanking the link . Then , all the LOO-filtered neighboring genotype vectors from both rounds were subjected to multiple regression together to obtain the final coefficients cij , which could be positive or negative , and their associated p-values by minimizing the residual vector size , :The obtained p-value was FDR-corrected to obtain the q-value . When two significant coefficients were found for a single link ( aij and aji ) , the coefficient with the smaller q-value was selected . In the model , the significant links between the mutant genotypes are represented as the links between the genes corresponding to the mutations . The links are color-coded in Figure 2 according to their associated coefficient values . To collect experimentally validated regulatory relationships , a systematic search of literature describing the 22 genes in our network model was conducted . “LocusPublished . 20091204 . txt” in TAIR ( ftp://ftp . arabidopsis . org/home/tair/User_Requests/LocusPublished . 20091204 . txt ) was used to map genes to literature . A custom Perl script was used to parse information about each gene of interest in the file to identify publications , each of which was simultaneously mapped to any pair of the 22 genes , and to generate hyperlinks to the PubMed records ( http://www . ncbi . nlm . nih . gov/pubmed/ ) for the identified publications . Next , the contents of the identified publications were inspected for appropriateness . This relatively unbiased procedure identified 22 known regulatory relationships . Three more known regulatory relationships were added based on publications that were not included in “LocusPublished . 20091204 . txt” but that we knew . To our knowledge , these 25 relationships are the only relationships known for the 22 genes . Ten day-old Col-0 seedlings grown in liquid culture were incubated with 1 µM flg22 for 6 or 16 hours . Cotyledons were harvested for staining with aniline blue . Staining and visualization procedures were described in Wang et al . [68] . One image was obtained from each cotyledon . Stained callose deposits were counted using a custom macro combined with a custom plug-in for Image J ( http:// rsb . info . nih . gov/ij/ ) . The macro performs noise reduction , binarizing images , and counting objects with filtering for a particular size range . Six week-old adult plants grown under the conditions described above were used . Eight leaf discs with a diameter of 4 mm were prepared and incubated for ∼15 hours in sterilized water in 24-well flat-bottom cell culture plates ( Corning , Inc . , MA , USA ) before pre-treatments with water or 5 µM SA . Leaf discs for mock and SA pre-treatments were collected from each half of the same leaves . Eight leaf discs were used for a single sample , and four replicated samples were made using different individual plants for each combination of genotype and treatment . Leaf disks pre-treated for 3 hours were then treated with 1 µM flg22 or water . These were considered to be four conditions: 2 pre-treatments×2 treatments . The ROS production level was measured as the relative luminescence value as described in Trujiro et al . [69] . The results were analyzed by fitting a polynomial linear model through the ROS production curves of individual measurements and using a mixed-effect linear model on the coefficients of these curves [36]:where F , G , T , Tm , S , R , and ε are measured ROS production value , genotype , condition , time , sample , replicate , and residual , respectively . G , T , and Tm are fixed effects , and S , R , and ε are random effects . To avoid convergence problems , the coefficients of the ( 1+Tm+Tm2+Tm3+Tm4 ) |Sijk random effect were assumed to be independent and time was centered and scaled to range from −1 to 1 . Ten-day old Col-0 seedlings were treated with SA at an indicated concentration and/or 1 µM flg22 , or water for 3 hours and harvested for RNA extraction . RNA extraction and quantitative RT-PCR were performed as described in Tsuda et al . [31] . The Ct values of a putative chitinase ( At3g43620 ) and PR-1 relative to Actin2 ( At2g18780 ) were fitted to a mixed linear model:where C , G:T , R , and ε are relative Ct value , gene∶treatment interaction , replicate effect and residual , respectively . G and T are fixed effects , and R and ε are random effects . The mean estimate of the gene∶treatment interaction was used as the modeled Ct value . For the t-tests , the standard error appropriate for each comparison was calculated using the variance and covariance values obtained from the model fitting . The Gene Expression Omnibus ( GEO ) ( http://www . ncbi . nlm . nih . gov/geo ) accession numbers for data discussed in this paper are GSE19663 and GSM490922 to GSM490978 . | When a plant detects pathogen attack , this information is conveyed through a molecular signaling network to turn on a large variety of immune responses . We investigated how this plant immune signaling network was organized using the model plant Arabidopsis . Wild type and mutant plants with defects in immune signaling were challenged with a pathogen . Then , expression levels of many genes were measured using microarrays . Detailed analysis of the mutation effects on gene expression allowed us to build a signaling network model composed of the genes corresponding to the mutations . This model predicted that the network components are highly interconnected and that it is very common for network components that mediate different signaling events to inhibit each other . The prevalent signaling inhibitions in the network suggest that only part of the signaling network is usually used but that if this part is attacked by pathogens , other parts kick in and back up the function of the attacked part . We speculate that plant immune signaling is highly tolerant to pathogen attack due to this backup mechanism . We also speculate use of only part of the network at any one time helps minimize negative impacts of the immune response on plant fitness . | [
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| 2010 | Network Modeling Reveals Prevalent Negative Regulatory Relationships between Signaling Sectors in Arabidopsis Immune Signaling |
Chloroquine ( CQ ) and other quinoline-containing antimalarials are important drugs with many therapeutic benefits as well as adverse effects . However , the molecular targets underlying most such effects are largely unknown . By taking a novel functional genomics strategy , which employs a unique combination of genome-wide drug-gene synthetic lethality ( DGSL ) , gene-gene synthetic lethality ( GGSL ) , and dosage suppression ( DS ) screens in the model organism Saccharomyces cerevisiae and is thus termed SL/DS for simplicity , we found that CQ inhibits the thiamine transporters Thi7 , Nrt1 , and Thi72 in yeast . We first discovered a thi3Δ mutant as hypersensitive to CQ using a genome-wide DGSL analysis . Using genome-wide GGSL and DS screens , we then found that a thi7Δ mutation confers severe growth defect in the thi3Δ mutant and that THI7 overexpression suppresses CQ-hypersensitivity of this mutant . We subsequently showed that CQ inhibits the functions of Thi7 and its homologues Nrt1 and Thi72 . In particular , the transporter activity of wild-type Thi7 but not a CQ-resistant mutant ( Thi7T287N ) was completely inhibited by the drug . Similar effects were also observed with other quinoline-containing antimalarials . In addition , CQ completely inhibited a human thiamine transporter ( SLC19A3 ) expressed in yeast and significantly inhibited thiamine uptake in cultured human cell lines . Therefore , inhibition of thiamine uptake is a conserved mechanism of action of CQ . This study also demonstrated SL/DS as a uniquely effective methodology for discovering drug targets .
Chloroquine ( CQ ) and other quinoline-containing compounds have been major antimalarial drugs for many decades . They are also effective treatments for systematic lupus erythematosus , rheumatoid arthritis , and many other rheumatic and skin diseases [1] . In recent years , their effects in treating viral , bacterial , and fungal infections and cancer have also been explored [2] , [3] . Despite being relatively safe , these drugs can cause severe adverse side effects , including retinopathy , myopathy , cardiopathy , peripheral neuropathy , and others [4] , [5] , [6] . In many cases , the underlying molecular mechanisms of the therapeutic and deleterious effects are not well understood . The model organism yeast Saccharomyces cerevisiae is an excellent system for discovering conserved targets of bioactive compounds [7] . In this study , we took a novel functional genomics approach in yeast to explore the mechanism ( s ) of action ( MOA ) of CQ . By first performing a genome-wide drug-gene synthetic lethality ( DGSL ) screen , we identified 95 CQ-hypersensitive deletion mutants , including those involved in vacuole functions ( e . g . , mon2Δ , vma4Δ , and vma8Δ ) [8] , [9] , iron homeostasis ( e . g . , fet3Δ ) [10] , and thiamine metabolism ( e . g . , thi3Δ ) [11] . By centering on the thi3Δ mutation , we next performed genome-wide gene-gene synthetic lethality ( GGSL ) and dosage suppression ( DS ) screens and discovered the high affinity thiamine transporter Thi7 [12] as a candidate target of CQ . For simplicity , this unique combination of DGSL , GGSL , and DS screens was termed SL/DS . We subsequently showed that CQ inhibits Thi7-related functions , particularly Thi7-dependent uptake of thiamine . We also showed that CQ likely inhibits the low affinity thiamine transporters Nrt1 and Thi72 [13] in yeast . This MOA is also shared by other quinoline-containing antimalarials . Moreover , we demonstrated that CQ completely inactivates a human thiamine transporter ( SLC19A3 ) [14] , [15] expressed in yeast cells and significantly inhibited thiamine uptake in HeLa and HT1080 cells , suggesting that such a MOA is conserved across species . This study also demonstrated that SL/DS is an effective strategy for drug target identification , especially for discovering non-essential genes as drug targets .
To discover the in vivo target ( s ) of CQ that might mediate its effects in a eukaryote , we first explored haploinsufficiency [16] by screening a yeast genome-wide heterozygous diploid deletion library for hypersensitive mutants . This identified six mutants as CQ-hypersensitive , with the NEO1/neo1Δ mutant exhibiting the highest sensitivity ( Figure S1 ) . The defect of this mutant was complemented with expressing NEO1 from a plasmid ( Figure S1 ) . NEO1 encodes an essential aminophospholipid flippase involved in endocytosis and vacuolar biogenesis [17] . It is also required for resistance to other compounds [18] . Possibly , Neo1 is generally involved in regulating the accumulation of many different compounds in vacuoles . It is thus not further explored as a target of CQ in this study . With a genome-wide DGSL screen , we next identified and validated 95 CQ-hypersensitive haploid deletion mutants ( Table S1 ) . Gene Ontology enrichment analysis revealed that these mostly affected vacuole functions , steroid biosynthesis , endocytosis , iron homeostasis , and post-Golgi transport ( Figure S2 ) . However , instead of relying on such an enrichment analysis , we took a novel approach by emphasizing individual mutants exhibiting the highest levels of sensitivity to CQ . We reasoned that such a mutant would most likely affect a function closely related to a drug target . In support of this hypothesis , mutants defective in yeast vacuolar functions ( e . g . , vma4Δ and mon2Δ ) [8] , [9] and iron metabolism ( e . g . , fet3Δ ) [10] were among those exhibiting the highest levels of sensitivity ( Figure 1A ) , consistent with the general idea that CQ concentrates in vacuoles or lysosomes [19] and , as previously observed , inhibits iron uptake in budding yeast [20] . In addition , we found that a thi3Δ mutant affecting thiamine biosynthesis [11] also exhibited comparably high levels of CQ-hypersensitivity ( Figure 1A ) , and this defect was complemented by expressing THI3 from a plasmid . Furthermore , a fet3Δ thi3Δ double mutant was not apparently more sensitive to CQ than either single mutant ( Figure S3 ) , suggesting that the two mutations affect either the same or completely unrelated pathways . Consistent with the latter possibility , CQ-hypersensitive phenotype of the fet3Δ mutant was suppressed by exogenously supplied excess amount of iron but not thiamine , whereas that of the thi3Δ mutant was suppressed by thiamine but not iron ( Figure 1B ) . Similarly , a NEO1/neo1Δ thi3Δ/thi3Δ mutant was no more sensitive than the thi3Δ/thi3Δ mutant to CQ ( Figure S4 ) . These results together suggested that CQ likely inhibits at least three independent biological processes in yeast: vacuolar functions , iron homeostasis , and thiamine metabolism or thiamine-dependent functions . To further elucidate the MOA ( s ) of CQ that underlie the hypersensitivity of the thi3Δ mutant , we performed a genome-wide GGSL screen . We reasoned that a thi3Δ mutant is hypersensitive to CQ because Thi3 is required to functionally compensate for inactivation of the drug target . A genome-wide GGSL screen with thi3Δ , which discovers functional compensation between genes in an unbiased manner , could reveal such a target or components of a target pathway . Among 5 genes discovered ( Table S2 ) , deleting the high affinity thiamine transporter Thi7 [12] caused severe growth defects , although not lethality , in the thi3Δ mutant ( Figure 1C ) , possibly as a consequence of reduction in both thiamine synthesis and uptake in the double mutant . This double mutant was still viable , likely due to the expression of two low-affinity thiamine transporters Nrt1 and Thi72 [13] . Although a thi7Δ mutant was no more sensitive to CQ than a wild-type strain , a thi3Δ thi7Δ double mutant was much more sensitive than the thi3Δ single mutant ( Figure 1D ) . In addition , thiamine suppressed CQ-hypersensitivity of this thi3Δ thi7Δ mutant , and the amount of thiamine needed for such suppression roughly correlated with the amount of CQ in the media ( Figure 1E ) . Significantly , the CQ concentration ( i . e . , 20 µM ) needed to completely inhibit growth of the thi3Δ thi7Δ double mutant was also achievable in human patients , animal studies or human cell culture experiments , suggesting that the underlying MOA , if conserved , are likely medically relevant . In parallel , we performed a dosage suppression ( DS ) screen for genes that would suppress the CQ-hypersensitivity of the thi3Δ mutant . Such a screen could also discover a drug target or component of a target pathway . In order to increase specificity , we performed the screen first in the thi3Δ thi7Δ double mutant , which is much more sensitive to CQ than the thi3Δ single mutant , and subsequently tested candidate suppressors in the single mutant . We thought that screening in the double mutant would permit the use of a relatively low dose of CQ ( i . e . , 20 µM ) and potentially minimize inhibition of other pathways . The screen identified THI3 , THI7 , THI20 , and PDC2 ( Figure 1F and data not shown ) . We subsequently showed that overexpression of THI7 and PDC2 also suppressed CQ-hypersensitivity of the thi3Δ single mutant at a higher CQ concentration ( Figure 1G ) . However , the effect of PDC2 overexpression under this condition largely depended on THI7 ( Figure 1F and 1G ) , consistent with a previous report that PDC2 controls expression of THI7 and thiamine biosynthesis genes [13] . Taken together , both the GGSL and DS screens in the thi3Δ mutant background discovered Thi7 , suggesting that it might be a target of CQ . To investigate such a possibility , we tested whether CQ affects other phenotypes controlled by Thi7 . Thi7 was previously shown to be required for the uptake and toxicity of pyrithiamine in yeast [12] , and as expected , a thi7Δ mutant was resistant to pyrithiamine ( Figure 2A ) . Consistent with our model , CQ also partly suppressed the toxic effect of pyrithiamine in a wild-type strain ( Figure 2A ) . Furthermore , thiamine deprivation due to thi7Δ mutation or the lack of thiamine in growth medium was previously shown to induce expression of thiamine biosynthesis genes in a Thi3-dependent manner [13] . We found that CQ treatment induces Thi3-dependent expression of thiamine biosynthesis genes THI6 and THI11 ( Figure 2B and data not shown ) , indicating that it causes thiamine deficiency . These results were consistent with the model that CQ inhibits Thi7 . However , the thi7Δ mutation further enhanced the effect of CQ treatment on THI6 expression ( Figure 2B ) , suggesting that CQ likely also inhibit additional targets to augment thiamine deficiency . In addition to Thi7 , the yeast genome encodes two low affinity thiamine transporters Nrt1 and Thi72 , which share high sequence identity ( >84% ) with Thi7 [13] . Possibly , CQ also inhibits these two thiamine transporters , a model consistent with the observation that the thi3Δ thi7Δ double mutant is viable but much more sensitive to CQ than the thi3Δ single mutant ( Figure 1D ) . Presumably , it takes much less CQ to inhibit thiamine uptake through these low affinity transporters . This model was further supported by the observation that both Nrt1 and Thi72 confer CQ-resistance in the thi3Δ thi7Δ double mutant when overexpressed from a high copy plasmid under control of the THI7 promoter ( Figure 2C ) . To further corroborate this model , we took advantage of the fact that Nrt1 is also a high affinity transporter for nicotinamide riboside ( NR ) [21] , and tested if growth of the thi3Δ thi7Δ mutant is also inhibited by NR . Similar to CQ , 10 µM of NR impaired growth of the thi3Δ thi7Δ double mutant in the presence of 1 µM of thiamine ( Figure 2D ) , indicating that Nrt1 is at least partly responsible for thiamine uptake in this strain . We next tested whether CQ impairs the function of Nrt1 . By taking advantage of the observation that a qns1Δ mutant requires Nrt1-dependent uptake of exogenously supplied NR for survival [21] , [22] , we found that CQ inhibited NR-dependent growth of such a qns1Δ mutant , and that the amount of CQ needed for growth inhibition roughly correlated with the amount of NR present in the medium ( Figure 2E ) . These results together strongly suggested that CQ inhibits both the high- and low-affinity thiamine transporters in yeast . We next directly tested the model that CQ inhibits thiamine transporters using a well-defined uptake assay [23] . As expected , thiamine uptake in the thi3Δ thi7Δ mutant was undetectable , but this was restored with expression of wild-type THI7 from a plasmid ( Figure 3A ) . CQ blocked thiamine uptake mediated by the wild-type Thi7 transporter in a dose-dependent manner ( Figure 3A and 3B ) . In contrast , it completely failed to inhibit thiamine uptake mediated by a CQ-resistant Thi7 allele ( Thi7R9G T287N E573G ) ( Figure 3A ) isolated from screening a THI7 random mutagenesis library expressed in the thi3Δ thi7Δ double mutant . This allele conferred higher levels of CQ-resistance as compared to wild-type THI7 when expressed in the thi3Δ thi7Δ double mutant ( Figure S5 ) . We subsequently found that the T287N substitution was largely responsible for the resistance phenotype of this mutant ( Figure 3A and Figure S5 ) . These results together indicated that CQ directly inhibits the thiamine transporter activity of yeast Thi7 . To gain further insights into how CQ might inhibit thiamine uptake through Thi7 , we performed 3D- homology modeling of Thi7 using the crystal structure of the substrate bound benzyl-hydantoin transporter Mhp1 from Microbacterium liquefaciens [24] as a template . A model of correct topology and close structural homology was obtained as judged by confidence ( C ) and template modeling ( TM ) scores of 0 . 87 and 0 . 83 , respectively . Analogous to the Mhp1 structure [24] , the transmembrane ( TM ) helices 1 , 2 , 6 , and 7 of the Thi7 model form a four-helix bundle that harbors a putative substrate-binding site . The CQ-resistance T287N mutation was mapped to TM7 ( Figure 3C ) . Importantly , both thiamine and CQ could be docked into the substrate-binding site of Thi7 with high affinity ( Figure 3C and Table S3 ) . We thus tentatively conclude that CQ might compete with thiamine for binding to the transporter . Such a model is also consistent with the observation that excess amount of thiamine suppresses the inhibitory effect of CQ on cellular growth ( Figure 1B and 1E ) . Based on the docking results , the CQ-resistant T287N mutation is located closer to CQ than to thiamine ( Figure 3C ) . However , at a distance of about 6 . 1 Å , a close interaction between this residue and CQ does not seem possible . In addition , mutating this Thr287 residue to Ala , Asp , Gln , and Ile did not confer CQ-resistance ( data not shown ) . It is possible that the T287N mutation affects the conformation of the substrate-binding site of the transporter , a hypothesis also consistent with the observation of partial reduction in the thiamine uptake activity of the mutant even in the absence of CQ ( Figure 3A ) . However , understanding how the T287N mutation completely abolishes the inhibitory effect of CQ on thiamine uptake will likely require a crystal structure of Thi7 . We next investigated whether CQ inhibition of thiamine transporters is conserved . Human cells express two thiamine transporters SLC19A2 and SLC19A3 that are ∼70% identical in amino acid sequences [14] , [15] . We found that expression of human SLC19A3 , which share ∼15% sequence identity with Thi7 , partly restored thiamine uptake in the thi3Δ thi7Δ double mutant ( Figure 3D ) . Importantly , CQ almost completely inactivated such an activity ( Figure 3D ) . Moreover , CQ significantly inhibited thiamine uptake in two human cell lines tested ( Figure 3E and 3F ) . These results together demonstrated that inhibition of thiamine transporters and reduction in thiamine uptake is a conserved MOA of CQ in both yeast and human . We next asked whether other quinoline-containing antimalarials also inhibit thiamine uptake through the transporters . When applied at 0 . 2 mM , amodiaquine , quinacrine , mefloquine , primaquine , quinine , and quinidine all inhibited growth of a thi3Δ thi7Δ mutant ( Figure 4A and Figure S6 ) . Some of them also inhibited growth of the thi3Δ single mutant ( Figure 4A ) . Similar to CQ , the inhibitory effects of these other antimalarials on cellular growth were suppressed by excess amount of thiamine in the medium ( Figure 4B and data not shown ) . Most of these other antimalarials also inhibited thiamine uptake mediated by the wild-type Thi7 , with amodiaquine having the strongest effect ( Figure 4C ) . Similar to CQ , amodiaquine completely failed to inhibit thiamine uptake mediated by the Thi7R9G T287N E573G mutant ( Figure 4D ) . These results together suggested that inhibition of thiamine uptake is a conserved mechanism among quinoline-containing antimalarials .
In this study , we demonstrated that CQ and other quinoline-containing antimalarials inhibit thiamine transporters in yeast . We also showed that such a MOA is conserved between yeast and humans . In particular , the human thiamine transporter SLC19A3 was completely inhibited by CQ when expressed in yeast cells ( Figure 3D ) . This MOA is likely medically relevant . First , much like the thi3Δ yeast mutant , human cells completely depend on exogenously supplied thiamine for survival , and the thiamine transporters play essential roles in this process . Second , at 20 µM , a concentration achievable in human patients , CQ completely inhibited growth of the thi3Δ thi7Δ double mutant ( Figure 2B ) . The concentration need to significantly inhibit the thi3Δ single mutant was about 10 times higher , but yeast cells are generally known to be more resistant to many drugs than mammalian cells due to the presence of cell wall and potent drug pumps . Third , the concentration of thiamine in human serum is in the 10–20 nM range [25] , [26] , [27] , more than two-magnitude lower than those used in this study . The putatively competitive relationship between CQ and thiamine suggests that inhibition of thiamine uptake in human body is achievable using CQ concentrations much lower than those used in this study . Fourth , CQ accumulates in certain tissues ( e . g . the retina ) at high concentrations , an observation particularly relevant to retinopathy caused by CQ-based medications [4] , [28] , [29] . In this regard , there is already a connection between thiamine deficiency and retinopathy in diabetic patients [30] , and diabetic retinopathy can be prevented with thiamine supplementation in a rodent animal model [31] . In addition , thiamine deficiency and CQ treatment both lead to neurological and cardiovascular disorders [5] , [6] , [32] , [33] . Based on these , it will be interesting to investigate whether thiamine deficiency might underlie some of the CQ-induced adverse effects and whether these can be prevented with concomitant thiamine supplementation . This study also demonstrated SL/DS as a novel and effective functional genomics strategy for discovering drug targets . This strategy starts with identifying mutants that are hypersensitive specifically to a drug treatment with a genome-wide DGSL screen ( Figure 5A ) . Such a drug-hypersensitive mutant ( e . g , thi3Δ ) is then used as a key to directly discover drug target ( s ) with a genome-wide GGSL or DS screen , or both ( Figure 5A ) . Discovering a drug target with a subsequent GGSL screen is based on the premise that genetic and pharmacological inactivation of a drug target produce similar effects ( e . g . , fitness defect in the hypersensitive mutant ) ( Figure 5B ) . Discovering a drug target with a subsequent DS screen is based on the principle that overexpressing a drug target confers drug resistance [34] , in this case , in a hypersensitive mutant ( Figure 5B ) . That both GGSL and DS screens identified Thi7 greatly simplified its selection as a high likelihood candidate CQ target for validation . We note that the particular DS screen reported in this study was performed in the thi3Δ thi7Δ double mutant , with an intention of using a low dose of CQ to potentially minimize inhibition of additional targets to increase pathway specificity . Such a DS screen would probably have also succeeded if a thi3Δ single mutant had been used . Most existing in vivo target identification methods such as haploinsufficiency profiling [16] , outright dosage suppression [35] , and discovering resistance mutations with genome-sequencing or high throughput complementation [36] , [37] typically rely on a drug's ability to completely or severely inhibit growth of wild-type cells . In contrast , SL/DS does not have such a requirement and thus can be used to discover non-essential genes as drug targets , as shown with Thi7 in this study . This feature is very significant considering that >80% of all proteins encoded by the yeast genome are non-essential . As a result , this method will offer much broader opportunity than the existing methods for discovering drug targets , especially with drugs that inhibit the growth of certain mutants but not wild-type cells . SL/DS should also be useful in discovering essential proteins as drug targets . In this regard , it may not be as straightforward as the other methods . However , we have found that the existing methods fail to discover targets of many cytotoxic drugs ( unpublished ) . A possible reason for that is that some drugs simultaneously inhibit multiple targets and that , consequentially , overexpressing or mutating any single target gene does not confer drug resistance in an otherwise wild-type strain background . In such a case , SL/DS could be effective because it is always possible to first discover drug-hypersensitive deletion mutants using a DGSL screen and subsequently identify the drug targets using GGSL and DS screens in these mutant backgrounds . The DS screen in a hypersensitive mutant could work because a lower drug dose can be used to minimize inhibition of other target pathways . The SL/DS methodology seems to be similar to but is distinctly different from a previously described compendium approach , where targets of novel drugs are inferred from comparing a large compendium of genome-wide DGSL profiles of old drug treatments and GGSL profiles of genetic perturbation for similarities [38] . Like SL/DS , this compendium approach could identify both essential and non-essential proteins as drug targets using the DGSL and GGSL profiles [38] . However , it does not directly identify drug targets but instead infers candidate targets from profile similarity . A potential limitation is that perturbations in potentially many components of a given drug target pathway typically produce similar profiles , making it difficult to determine the actual drug target . Its discovery scope is also limited to the available DGSL or GGSL reference profiles , which are very difficult to generate at a large scale in higher eukaryotes . In contrast , SL/DS directly identifies a drug's target with only three genome-wide screens: DGSL followed with GGSL and DS . It does not rely on DGSL profiles of other drugs or GGSL profiles of other genetic perturbation as references . A similar SL/DS strategy will likely also be useful for drug target identification in human cells , where genome-wide DGSL , GGSL , and DS screens are now possible [39] , [40] , [41] , [42] .
Yeast strains used in this study include the wild-type strains BY4741a ( MATa his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0 ) and BY4743a/α ( MATa/α his3Δ1/his3Δ1 leu2Δ0/leu2Δ0 LYS2/lys2Δ0 met15Δ0/MET15 ura3Δ0/ura3Δ0 ) [43] and isogenic mutants derived from a genome-wide deletion library [44] . The haploid-convertible heterozygous diploid deletion library used in screening for CQ hypersensitive mutants was previously described [44] . The Thi6-TAP and Thi11-TAP strains were obtained from Open Biosystems and the derivative thi7Δ::kanMX and thi3Δ::natMX mutants were constructed by deleting the THI7 and THI3 genes , respectively in these strain backgrounds . Bacterial strain DH5α was used as the host during molecular cloning . Plasmids used in this study are listed in Table S4 . The vectors used are pRS416 [43] and YEplac195 [45] . Yeast media used in this study include a haploid selection synthetic complete medium SC−Leu−His−Arg+G418+canavanine [46] and a synthetic complete ( SC ) medium that either contained or lacked uracil ( SC-Ura ) or leucine ( SC-Leu ) . Amodiaquine dihydrochloride , Chloroquine diphosphate , mefloquine hydrochloride , primaquine bisphosphate , pyrithiamine hydrobromide , quinacrine dihydrochloride , quinine , quinidine , thiamine hydrochloride , and ferric chloride were all purchased from Sigma . Nicotinamide riboside ( NR ) was freshly derived from enzymatic hydrolysis of NMN ( Sigma ) and quantified as previously described [22] . Stock solutions were made in ddH2O with the help of adjusting pH when necessary , filter sterilized , and stored at −20°C or directly used . Genome-wide DGSL screens and subsequent individual validation were carried out as previously described [46] , [47] . 5 mM of CQ was used in the screen . Concentrations of 0 . 5 mM , 1 mM , 2 mM , 3 mM , 4 mM , and 5 mM were used in validation assays to further distinguish among the sensitivities of different mutants . Genome-wide GGSL screen with a thi3Δ::URA3 query construct was carried out as previously described [48] , [49] . Candidate hits were validated with tetrad dissection . Briefly , the heterozygous diploid deletion mutant of each candidate gene was transformed with a thi3Δ::URA3 query construct to disrupt one copy of THI3 . The resultant heterozygous diploid double mutant was sporulated and the spores were dissected under a microscope on a YPD plate . The plate was incubated at 30°C for 2 days and photographed and the genotype of the dissected spores were determined by their growth on SC-Ura and YPD+G418 plates . Genome-wide DS screen in the thi3Δ thi7Δ double mutant was performed using a genome-wide tiling library containing ∼95% of the yeast genomic sequences [50] . This library was en masse transformed into XPY1263a ( MATa thi3Δ::natMX thi7Δ::kanMX ) . An aliquot of ∼105 cells of the transformed pool was subsequently plated on a solid synthetic complete medium that lacked leucine ( SC-Leu ) but contained 20 µM CQ to select for resistant colonies . Plasmids were recovered from 96 representative colonies and sequenced at one end to identify the responsible genes . Candidate genes were then individually validated on YEplac195 for their ability to confer CQ-resistance in the thi3Δ thi7Δ double and thi3Δ single mutants . A yeast strain expressing Thi6-TAP or Thi11-TAP from the endogenous locus in a wild-type , thi3Δ , or thi7Δ background was grown in 5 ml of regular liquid SC at 30°C for an overnight . Cells were harvested , washed with 5 ml of sterile water , and inoculated into 5 ml of liquid SC that lacked thiamine at an starting cell density of ∼0 . 15 OD600 nm/ml . CQ and thiamine were added at the indicated final concentrations . The cultures were incubated at 30°C for 4 hr with shaking . About 1 . 0 OD600 nm cells were collected for each sample , directly lysed with boiling in 1× SDS buffer , and analyzed with western blot using an anti-TAP antibody ( Open biosystems ) and an anti-Tub2 antibody . Thiamine uptake in yeast cells was carried out as described [23] with minor modifications . Yeast cells of XPY1263a harboring an empty vector or expressing wild-type THI7 , thi7R9G T287N E573G , thi7T287N , or SLC19A3 were grown in 3 ml liquid SC-Ura containing 100 uM at 30°C for overnight . 1 ml of each overnight culture was inoculated into 50 ml of fresh SC-Ura liquid and incubated at 30°C for 4 . 5 hrs . Cells were harvested , washed twice each with 10 ml of ddH2O , and suspended in citric acid/phosphate buffer ( pH 4 . 5 ) containing 1% D-glucose at a density of 2 . 0–2 . 5 OD600 nm/ml . For each uptake experiment , 500 µl of cells were pre-warmed at 30°C for 3 min in a microcentrifuge tube in the presence or absence of CQ or another antimalarial at indicated concentrations . [H3]-Thiamine ( American Radiochemical Company ) was added at a final concentration of 2 µM and a specificity of 0 . 2 Ci/mmol and immediately mixed on a Mixmate at 30°C . 100 µl of each sample was taken at indicated time points ( 1 min , 3 min , and 5 min ) and transferred to a microcentrifuge tube that contains 900 µl of ice-cold 1 mM thiamine in citric acid/phosphate buffer ( pH 4 . 5 ) to terminate uptake of H3]-Thiamine . Cells were collected by filtering and washed with 10 ml of ddH2O . Radioactivity associated with each filter was measured with a Beckman scintillation counter and used to calculate thiamine uptake activity as pmol/OD600 nm cells . Three independent repeats were performed for each time point and the results were averaged . Thiamine uptake in human cells was carried out as described in another previous study [51] with minor modifications . HeLa and HT1080 cells were grown in DMEM medium until confluent monolayers in 12-well plates , with ∼5 . 0×105 cells in each well . Medium was aspirated 4 days following confluence , and each culture was washed twice with the uptake buffer ( NaCl , 125 mM; KCl , 4 . 8 mM; KH2PO4 , 1 . 2 mM; MgSO4 , 1 . 2 mM; CaCl2 , 1 . 2 mM; Glucose , 5 mM; Glutamine , 5 mM; HEPES-NaOH , 12 . 5 mM; MES , 12 . 5 mM; pH 8 . 0 ) that had been pre-warmed at 37°C . Cell monolayers were then pre-incubated in 0 . 2 ml uptake buffer that either contained or lacked CQ at 0 . 25 mM at 37°C for 10 min . H3]-Thiamine was subsequently added at a final concentration of 5 µM and a specificity of 1 Ci/mmol . [H3]-Thiamine uptake was terminated at 10- or 20-min time point by addition of 1 ml of ice-cold buffer that contained 1 mM of unlabeled thiamine into each well . Buffer was immediately aspirated . Cells from each well were rinsed twice with 1 ml of ice-cold buffer containing unlabeled thiamine , digested with 0 . 25 ml of 1 N NaOH for 2 hours , and neutralized with 0 . 25 ml of 1 N HCl . Cell lysates ( ∼0 . 5 ml each ) were transferred into scintillation vials . Residual lysate in each well was washed with 0 . 3 ml of stoppage buffer and also transferred to the same scintillation vials . Radioactivity of each sample was measured with a Beckman scintillation counter and used to calculate thiamine uptake activity as pmol/106 cells . Three independent repeats were performed for each time point and the results were averaged . Coordinates of the substrate bound form of the benzyl-hydantoin transporter Mhp1 structure from Microbacterium liquefaciens , ( PDB code :2JLO ) [24] . was used as a starting template to obtain a structural model of the yeast Thi7 through the online server I-TASSER ( http://zhanglab . ccmb . med . umich . edu/I-TASSER/ ) [52] . Thi7 has 21% sequence identity and 35% similarity with the benzyl-hydantoin transporter Mhp1 from Microbacterium liquefaciens . The Thi7 residues from A22 to E537 were used as the input sequence based on a BLAST sequence analysis with the Mhp1 sequence . Sequence template alignments were generated using the program MUSTER , which is built into I-TASSER . The quality of the generated model was assessed in I-TASSER based on two major criteria , the C- and the TM-scores . Thiamine ( Pubchem ID: 1130 ) and chloroquine ( Pubchem ID: 2719 ) were processed for docking using ADT tools . Addition of hydrogen atoms and setting of rotatable bonds for these substrates were carried out in ADT tools ( Molecular graphics lab of the Scripps research institute ) . The docking of substrates to the Thi7 model was performed using the AutoDockVina software [53] . A grid box with a dimension of 15×15×15 points was used . | By using a novel SL/DS methodology in the model organism yeast , we discovered that the antimalarial drug CQ inhibits thiamine transporters and consequently causes thiamine ( vitamin B1 ) deficiency and growth defects . This mechanism of action ( MOA ) is conserved in human cells and possibly also in other organisms . Given that both thiamine deficiency and treatment with CQ cause retinal , neurological , and cardiovascular disorders in humans , our results suggest that thiamine deficiency might be a root cause of some of CQ's adverse effects , which might be preventable with concomitant dietary thiamine supplementation . Such a MOA by CQ could also be responsible for its therapeutic effects against malarial parasites , which need exogenous thiamine for survival . Such a possibility needs to be investigated before dietary thiamine supplementation can be used to prevent CQ's adverse effects . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
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| [
"medicine",
"biology"
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| 2012 | Discovering Thiamine Transporters as Targets of Chloroquine Using a Novel Functional Genomics Strategy |
Eosinophils are effectors in immunity to tissue helminths but also induce allergic immunopathology . Mechanisms of eosinophilia in non-mucosal tissues during infection remain unresolved . Here we identify a pivotal function of tissue macrophages ( Mϕ ) in eosinophil anti-helminth immunity using a BALB/c mouse intra-peritoneal Brugia malayi filarial infection model . Eosinophilia , via C-C motif chemokine receptor ( CCR ) 3 , was necessary for immunity as CCR3 and eosinophil impairments rendered mice susceptible to chronic filarial infection . Post-infection , peritoneal Mϕ populations proliferated and became alternatively-activated ( AAMϕ ) . Filarial AAMϕ development required adaptive immunity and interleukin-4 receptor-alpha . Depletion of Mϕ prior to infection suppressed eosinophilia and facilitated worm survival . Add back of filarial AAMϕ in Mϕ-depleted mice recapitulated a vigorous eosinophilia . Transfer of filarial AAMϕ into Severe-Combined Immune Deficient mice mediated immunological resistance in an eosinophil-dependent manner . Exogenous IL-4 delivery recapitulated tissue AAMϕ expansions , sustained eosinophilia and mediated immunological resistance in Mϕ-intact SCID mice . Co-culturing Brugia with filarial AAMϕ and/or filarial-recruited eosinophils confirmed eosinophils as the larvicidal cell type . Our data demonstrates that IL-4/IL-4Rα activated AAMϕ orchestrate eosinophil immunity to filarial tissue helminth infection .
Infections by helminth parasites are frequently accompanied by overt eosinophilia at parasitized tissue niches[1] . In animal models of infection , eosinophils are functionally important in the immune effector response directed at tissue-invading helminths[2–8] but can also drive pathology[2] and are implicated in immune regulation potentially via the provision of T-cell polarizing signals[9 , 10] . Antibody-dependent cellular cytotoxicity ( ADCC ) and granule-released products have been implicated as the mechanism by which eosinophils mediate parasite helminth larval attrition both in vitro[11 , 12] and in vivo[4 , 7 , 8] . Corroborating eosinophilic immunity demonstrable in rodent models , clinical studies have identified that interleukin-5 , a growth factor supporting eosinophilia , is a correlate of resistance to helminth re-infection[13 , 14] . Also , tissue IL-5 and eosinophilia at the site of larval establishment have been demonstrated in experimental human challenge models[15 , 16] . Whilst the importance of eosinophils in immunity to tissue-invading helminth parasites is well-defined , much less is understood about the cellular mechanism by which a tissue eosinophilia in parasitized tissues is coordinated and maintained . Macrophages ( Mϕ ) , polarised to non-classical ‘alternatively activated’ ( AAMϕ ) phenotypes , are an additional cellular hallmark of helminth infection[17] . However , unlike the immune-effector activity of eosinophils , AAMϕ differentiated from recruited blood monocytes have been identified as mediators of host-protective , wound-healing T helper 2 ( Th2 ) responses to rapidly repair lesions caused by helminth larvae as they migrate through barrier sites ( the skin , lungs and gut ) [18–21] . An associated AAMϕ function of promoting immunoregulation , including during chronic helminth infection , has been demonstrated[9 , 20 , 22–25] . Thus , a paradigm of AAMϕ function is to regulate Th2 inflammation and initiate wound healing during parasitological assault . AAMϕ are also generated at non-barrier , ‘sterile’ sites of infection by tissue helminths , such as filarial nematodes , where they proliferate from resident Mϕ in response to interleukin ( IL ) 4 / IL-13 signals[26 , 27] . Therefore , at sterile sites of infection , tissue-proliferating AAMϕ may have distinct immune functions other than wound healing and immunoregulation , during an initial response to helminth infection . In this investigation , we delineate the functions of eosinophils and local AAMϕ populations in immunity against Brugia malayi larvae in a murine , Th2-adaptive immune peritoneal infection model . We determine that IL-4-dependent alternative activation and expansion of Mϕ are essential to regulate eosinophil-dependent immunity to filarial helminth infection via amplifying and sustaining CCR3-dependent tissue eosinophilia .
Previous studies have highlighted a role of tissue eosinophilia as an important factor in immunity to chronic filarial infections[3 , 5] . We examined the eosinophil dependency of immune control of B . malayi infections in non-permissive BALB/c mice . In this model , ~90% of infectious larvae do not survive to develop into adult nematodes ( +35dpi ) and sterile cure is apparent in most mice before fecund infections establish ( +84dpi , at a time point when female B . malayi are releasing microfilariae; mf ) . Utilizing mice with disrupted regulation of the GATA-1 gene ( ΔdblGATA-/- ) , essential for the development of eosinophils from bone marrow precursors[28] , the impact of eosinophil deficiency could be evaluated . Confirming deficiency , SigLecF+ tissue eosinophilia was absent in ΔdblGATA-/- mice , +14dpi , compared with WT mice ( Fig 1A & 1B ) . The impact of ablating tissue eosinophilia in ΔdblGATA-/- mice was an increased susceptibility to developing , immature larvae B . malayi infection , +14dpi , and permissiveness to chronic adult B . malayi infections , +84dpi ( Fig 1C ) . Murine circulating eosinophils express the chemokine receptor CCR3 and respond to CCR3-specific chemokines to migrate to tissue sites of inflammation . We utilized a CCR3 neutralising antibody [29] to temporarily deplete CCR3+ cells in WT mice prior to infection . Tissue eosinophilia and B . malayi development was tracked over the first 35 days of infection . A single treatment of αCCR3 was sufficient to reduce >95% infection-site tissue eosinophilia ( Fig 1D & 1E ) and this was concomitant with increased Brugia survival +6dpi ( Fig 1F ) . By +14dpi eosinophilia had resumed comparable to IgG control treated WT mice ( Fig 1D & 1E ) . The resumption of eosinophilia was associated with rapid decline in susceptibility , where levels of B . malayi larvae were not different from untreated , infected WT mice ( Fig 1F ) . We further addressed CCR3-dependency of tissue eosinophilia and impact on immunity to B . malayi by using CCR3 deficient mice where steady state eosinophils in peripheral circulation are maintained but their CCR3-dependent tissue recruitment is ablated[30] . CCR3 deficiency rendered a profound , sustained impairment in tissue eosinophilia throughout the course of B . malayi infection ( Fig 1G & 1H ) . CCR3 deficiency rendered mice susceptible to the development of chronic B . malayi adult infections ( Fig 1I ) , including permissiveness to fecund infections able to complete the filarial parasite life cycle +84dpi ( S1 Fig ) . Expansion of Mϕ has been described at serous cavities of filarial nematode infection , in a mechanism of in situ proliferation[26 , 27] . At the infection site , time-dependent expansions of Mϕ were evident from +6–14 dpi ( Fig 2A ) . We examined proliferation and activation status of infection-site Mϕ . By Ki67 intracellular staining we determined the majority of Mϕ expanded +6dpi were in an active proliferation cycle ( median 70 . 4% , range 62–84% ) ( Fig 2B & 2C ) . By measuring the AAMϕ product , arginase , we defined that arg1 transcripts and enzymatic activity within peritoneal cells ( PC ) from B . malayi ( Bm ) L3 primary infections were significantly enhanced compared with naïve mice ( Fig 2D & 2E ) . Elevated Mϕ-specific arg1 transcripts during infection were confirmed following purification from PC by FACS ( S2 Fig ) . By intracellular staining for resistin-like molecule-alpha ( RELMα ) , a helminth-activated Mϕ product[9 , 26] , we discerned high levels of RELMα protein expression in the expanded pool of peritoneal Mϕ +14d following BmL3 infection ( Fig 2F & 2G ) . Interleukin ( IL ) -4 and IL-13 can induce alternative activation of Mϕ populations in diverse tissue sites during helminth infections via the IL-4 receptor ( IL-4R ) [9 , 20 , 26 , 27] . Intra-peritoneal infections with Brugia larvae induce polarized Th2 responses[31] and we recorded increased splenic Th2 immune responses +6 dpi with BmL3 ( S3 Fig ) . However , because IL-4R-independent AAMϕ differentiation has also been demonstrated in helminth infections[26 , 32] , we examined Mϕ development in either Severe-combined ( SCID; no functional T or B cells ) or IL-4Rα deficient ( IL-4/IL-13 non-responsive ) BALB/c mice . Compared with WT mice , Mϕ expansions and Mϕ arginase expression , arginase activity and RELMα production was significantly hindered from SCID or IL-4Rα-/- mice +14-35dpi ( Fig 3A–3E ) . Both severe-combined and IL-4Rα-specific deficiencies rendered mice susceptible to chronic B . malayi adult-stage infections at +35dpi with significant differences apparent in the control of larval establishment from +14dpi ( Fig 3F ) . We delivered exogenous murine recombinant ( r ) IL-4 , as a long-acting formulation ( complexed to rat anti-IL-4 ) into the peritonea of BALB/c SCID mice and determined that rIL-4 delivery +BmL3 infection was sufficient to recapitulate Mϕ expansions and elevate arginase production in severe-combined immunodeficiency ( Fig 3G–3I and S4 Fig ) . Combined , this data indicates that provision of an adaptive immune IL-4:IL-4Rα ligating signal transduced either directly within peritoneal Mϕ or via non-lymphocyte lineages intact in SCID mice , is sufficient to support the development of the AAMϕ phenotype induced by B . malayi infection . Eosinophils have diverse immune-regulatory functions and can also influence AAMϕ activation , potentially by provision of IL-4/IL-13 cytokine delivery[9 , 33–35] . We assessed whether deficiency in tissue eosinophilia affected the development of AAMϕ post-BmL3 infection . The impaired eosinophilia evident at the infection site using either eosinophil-lineage depleted or CCR3-/- mice did not impinge on Mϕ expansions post-infection ( Fig 4A & 4B ) . Further , CCR3-deficiency did not affect initial Mϕ expansions post-infection or their chronic maintenance +35dpi to +84dpi ( Fig 4B ) . Temporary antibody depletion of CCR3 cells similarly did not impact on initial peritoneal Mϕ expansions +6dpi ( Fig 4C & 4D ) . Arginase production within the infection-expanded Mϕ pool was not significantly different in tissue BALB/c eosinophilia-deficient mice compared with WT , adjudged by arginase activity or Mϕ-specific arg1 transcripts ( Fig 4E & 4F ) . Infection of CCR3-/- mice also induced a high-level induction of RELMα expression in expanded peritoneal Mϕ ( Fig 4G & 4H ) . However , the expression levels of RELMα were subtly , yet significantly , modified compared with WT mice , indicating a degree of eosinophil ‘help’ in the full induction of RELMα within AAMϕ post-BmL3 infection ( Fig 4G & 4H ) . These data indicate that whilst adaptive immune provision of an IL-4Rα ligating signal is critical for AAMϕ development during B . malayi infection , eosinophilia is not essential for arginase production or AAMϕ expansion . We addressed the functional relevance of the expanded pool of tissue AAMϕ post-BmL3 infection , subsequently termed , “BmL3AAMϕ” , in the immune response to B . malayi by ablating resident phagocytes by ip administration of clodronate liposomes ( CL ) , prior to infection . Success of resident Mϕ ablations were confirmed by observing apoptotic Mϕ cells in cytospin preparations and >90% reductions in peritoneal F4/80+ Mϕ numbers in infected WT mice , three days after injection of CL and +2dpi ( Fig 5A & 5B ) . CL administration suppressed the initial expansion of BmL3AAMϕ , with Mϕ numbers remaining <90% of infection controls at +6dpi before recovering to 30–40% of WT controls by +14dpi ( Fig 5B & 5C ) . The impact of CL treatment and concomitant temporal depletion of AAMϕ was a significant increase in B . malayi larval survival ( Fig 5D ) . CL treatment did not modify immune priming of the larvicidal Th2 adaptive immune response , as post-CL Th2 splenocyte responses to larval antigen remained intact ( S5 Fig ) . However , peritoneal eosinophilia was temporarily , yet significantly , impacted by CL treatment at +6 dpi ( approx . 90% reduction in eosinophilia; Fig 5E ) . In follow up assessments , as well as the temporal detrimental impact on Mϕ and eosinophilia , we discerned that the ip administration route of CL also impacted both on circulating monocytes in WT naïve BALB/c mice ( S6 Fig ) , as well as partial increases in numbers of neutrophils and partial decreases in peritoneal B cells at the infection site in WT mice at +6dpi ( S6 Fig ) . Because of the pleiotropic effects of CL administration on multiple cell types both local and distal to the site of infection , we sought to isolate the relative roles of BmL3AAMϕ and eosinophilia in mediating immunity to B . malayi . To directly test the relative requirements of peritoneal eosinophils recruited by BmL3 infection or BmL3-activated AAMϕ , we performed in vitro motility assays whereby groups of 10 BmL3 were co-cultured with either 106 purified peritoneal recruited eosinophils , 106 BmL3AAMϕ or combination of both cell types , sourced from B . malayi WT infections by FACS ( Fig 6A & 6B ) . After tracking motility +7d , peritoneal eosinophil cultures contained 10% motile larvae compared with 60% in serum-only cultures ( Fig 5C ) . This reduction in motility in the presence of eosinophils was manifest with or without co-culture with BmL3AAMϕ . Surprisingly , BmL3AAMϕ-only cultures potentiated the motile phenotype of BmL3 +7d compared with serum only cultures ( 90% vs 60% motile BmL3 ) , indicating that fully polarised , WT BmL3AAMϕ , producing high levels of arginase and RELMα protein are not directly larvicidal in vitro . We next examined whether BmL3AAMϕ were necessary in CCR3-dependent tissue eosinophilia during infection . We added back 0 . 75x106 purified BmL3AAMϕ from BALB/c WT infections , +3d following CL-treatment and at the point of infection in BALB/c WT mice . Establishment of adoptively transferred BmL3AAMϕ was confirmed by increased F4/80+ Mϕ numbers compared with CL treated controls ( Fig 6D & S7 Fig ) . Restoration of BmL3AAMϕ coincided with a vigorous eosinophilia , comparable to infected WT controls ( Fig 6D & 6E ) . To measure subsequent impact on larval survival , we utilised BALB/c SCID mice in which AAMϕ fail to develop and chronic adult infections establish [36] . We observed a transient spike in tissue eosinophilia in BALB/c SCID mice at +6dpi where peritoneal eosinophils had dissipated by +14dpi ( Fig 6F ) . However , following adoptive transfer of BmL3AAMϕ , tissue eosinophilia was sustained at a density comparable to WT infections in SCID recipients at +14dpi ( Fig 6G & 6H ) . Engraftment of transferred BmL3AAMϕ was confirmed both by increased Mϕ number and increased arginase activity in SCID recipients ( S7 Fig ) . Adoptive transfer of BmL3AAMϕ rendered SCID mice resistant to B . malayi infection and was dependent on CCR3+ cell recruitment in SCID recipients because αCCR3 treatment effectively nullified the sustained eosinophilia in BmL3AAMϕ SCID recipients and reversed the resistant phenotype in controlling larval establishment ( Fig 6G–6I ) . Because rIL-4 , in combination with B . malayi infection , could recapitulate the WT BmL3AAMϕ phenotype in SCID mice ( Fig 2 ) , we examined the impact of exogenous rIL-4 treatment on tissue eosinophilia in SCID deficiency ( Fig 7A ) . We determined eosinophilia was dependent on dose of rIL-4 delivered , with low but not high levels of rIL-4 mediating elevated peritoneal eosinophils in isolation ( S4 Fig , Fig 7B , 7C and 7D ) . Tissue eosinophilia was significantly bolstered following infection coincident with rIL4 treatment ( Fig 7B , 7C and 7D ) . Using an oral CCR3 inhibitor[37] , tissue eosinophilia could be blocked in the face of rIL-4 treatments and BmL3 infection ( Fig 7B & 7D ) . Together these data indicate that ligation of IL-4Rα and subsequent BmL3AAMϕ development augments tissue eosinophilia via CCR3 chemotaxis during the adaptive immune response to infection . In support of this , via transcript analysis of peritoneal cells we identified a significant reduction in CCL11 ( eotaxin 1 ) expression in IL-4Rα deficient mice 6 days after infection with BmL3 ( S8 Fig ) . Because rIL-4 delivery can induce pleiotropic effects on IL-4 responsive cell types , which could influence tissue eosinophilia , we addressed the specificity of BmL3AAMϕ by ablating Mϕ prior to rIL-4 delivery and infection . Following depletions of peritoneal Mϕ mediated by CL , tissue eosinophilia was not significantly elevated +6dpi in rIL-4 treated SCID mice ( Fig 7B & 7D ) . The parasitological outcome of IL-4/IL-4Rα activation of BmL3AAMϕ and CCR3-dependent tissue eosinophilia was a significant reduction in B . malayi larvae in SCID mice +14dpi ( Fig 7E ) . However , temporal ablations of peritoneal Mϕ or CCR3+ eosinophils ( by CL or αCCR3 , respectively ) nullified the effect of rIL-4 in larval killing ( Fig 7E ) . These data define a role for Th2 adaptive immune induced AAMϕ as important regulators of filaricidal tissue eosinophilia via CCR3-mediated chemotaxis .
Our data demarcates the relative contributions of the hallmark Th2-associated cell types , eosinophils and AAMϕ , in filarial helminth immunity . Our data reveals a mechanism whereby eosinophil-dependent immunity to the filarial helminth , B . malayi , is locally coordinated by an in situ proliferating pool of Mϕ , activated by combination of ligation of IL-4Rα and parasite infection . Mϕ alternative activation and polarisation is a consistent feature of helminth infection[17] , yet a defined role of this cell phenotype in immunity to worm infection has remained elusive . AAMϕ-mediated immunity has been demonstrated in situations of Th2 memory and parasite-specific antibody leading to control of gut nematode larvae during secondary infections . In these challenge infection experiments , larval trapping of H . polygyrus bakeri within the gut mucosa[32 , 38] or N . brasiliensis within skin[39] is impaired if inflammatory AAMϕ recruitment to infection sites are blocked . A direct mechanism of worm attrition by AAMϕ-released factors within mucosal larval granulomas , including arginase , has been identified , following FcR-antibody-dependent alternative activation[32] [40] . Further in vitro evidence supports corroboration between AAMϕ and neutrophil granulocytes in larvicidal activity against the human gut nematode , Strongyloides stercoralis [41] . Our data demonstrates a unique mode of action of AAMϕ-orchestrated , eosinophilic immunity to filarial nematodes at a non-barrier site of infection . Firstly , we define that a B . malayi larvicidal response can be induced by targeting IL-4R in antibody-deficient mice , suggesting ADCC is not an absolute requirement for filarial larval killing . However , parasite-specific antibody may bolster worm killing following FcR engagement on Mϕ , as we observed more profound larvicidal effects upon transfer of +14 day BmL3AAMϕ generated from WT infection ( where anti-parasite antibody would presumably be bound to Mϕ FcR ) compared with in vivo IL-4R ligation and BmL3AAMϕ development within SCID mice . Secondly , we demonstrate conservation of arginase production in AAMϕ during eosinophil deficiency , which are yet insufficient to prevent the establishment of chronic adult filarial infection . Thirdly , in vitro co-cultures show no deleterious effect of BmL3AAMϕ in isolation on BmL3 motility . These differences may highlight fundamental distinctions in immune-effector processes during primary infection between AAMϕ subsets proliferating from local Mϕ populations in the serous cavities and those recruited from inflammatory blood monocytes via CCR2 at barrier sites of challenge infection[39 , 42] . Potentially , it may also indicate inherent differences in susceptibility of filarial vs gut nematode larvae to Mϕ-specific secreted products such as arginase . We demonstrate that optimum peritoneal Mϕ expansion and alternative activation is IL-4Rα dependent during B . malayi larval infection and further show that this phenotype can develop in the absence of functional adaptive lymphocyte lineages via exogenous delivery of IL-4 . One obvious mechanism for this polarization and proliferation is direct ligation of resident peritoneal macrophage IL-4Rα by IL-4/13 in combination with the complement factor C1q[27 , 43] . However , because Mϕ alternative activation can occur independently of IL4R via FcR ligation [32] or other polarising signals such as IL-33 [35] , we do not rule out a role for Mϕ alternative activation signals being triggered by non-lymphocyte , IL-4 responsive cell types in our infection system . Cross-talk between granulocyte populations and AAMϕ mediates diverse functional outcomes , including immunity[40] , [39] , immunomodulation[9] , and maintenance of glucose homeostasis[33 , 44] . In certain situations , granulocytes are important cellular sources of polarising signals instructing macrophage alternative-activation . Beyond arginase , RELMα and Ym-1 are abundantly expressed molecules in helminth-activated Mϕ[17] . We detected a subtle impact of deficiency in tissue eosinophilia in modifying the level of RELMα expression within AAMϕ , supporting earlier work in L . sigmodontis infected eosinophil deficient mice[35] . Further , Ym-1 production is demonstrably impaired in AAMϕ in response to L . sigmodontis in the absence of eosinophils[35] . Our in vitro assays indicate that arginase- and RELMα-producing WT BmL3AAMϕ do not affect larval viability in isolation and our adoptive transfer experiments into SCID recipients further indicate that arginase- and RELMα-producing WT BmL3AAMϕ do not affect B . malayi larval survival if CCR3 expressing cells and eosinophilia is effectively ablated . Therefore , we conclude that whilst eosinophil ‘help’ may contribute to the IL-4Rα-dependent polarisation of BmL3AAMϕ , we find no evidence from these experiments supporting a direct larvicidal mode of action of AAMϕ in vitro or in vivo against B . malayi , using the BALB/c ip infection model . GATA deficiency has latterly defined to disrupt basophil haematopoesis as well as ablating mature eoinophils [45] whilst mast cells are unaffected in ΔdblGATA1-/- mice [46] and neither is their recruitment to inflammed tissue compromised in CCR3 deficiency [30] . Murine basophils are recruited to tissue niches in a CCR3-independent mechanism and do not express CCR3 [47 , 48] . Thus , we carefully selected complementary systems ( ΔdblGATA deficiency , CCR3 deficiency and CCR3 depleting antibody ) to selectively target eosinophils whilst controlling for potential ‘off-target’ impact on basophilia or mastocytosis during peritoneal Brugia malayi larval infection . Recent studies in our laboratories have defined that origin of local tissue macrophage populations varies with age , gender , strain and infection status . Whilst embryonic self renewing macrophages predominate in young mice , in aged mice , bone marrow derived monocyte precursors continually seed the peritoneum during steady state to establish into long-lived self-renewing macrophages of similar tissue phenotype[49] . Interestingly , during filarial infection of the pleural cavity of BALB/c mice , the relative proportions CCR2-monocyte recruited macrophages increases relative to resident proliferating populations as chronicity of infection progresses[50] . Therefore , an increasing heterogeneity in local macrophage populations during infection may influence magnitude of eosinophil granulocyte influx . In the absence of adaptive IL-4/IL-13 signalling , a transient spike in innate immune tissue eosinophilia is apparent during initial B . malayi infection , at day 6 , which dissipates on or before day 14 . This kinetic has also been observed in experimental Brugia infections using SCID mice on a C57Bl/6 background[51] . Our data indicates that expansion and alternative activation of Mϕ populations within the serous cavity from 6 days post-infection is critical to amplify tissue eosinophilia to drive immunological resistance during filarial infection . Previous studies have demonstrated a role for IL-4 responsive AAMϕ in positively regulating eosinophil trafficking during situations of Th2 inflammation in the lung or gut[47 , 52] . In our B . malayi BALB/c infection model , CCR3-mediated chemotaxis was fundamental in the AAMϕ-dependent eosinophilia during Brugia larval infection as blocking CCR3 signalling ablated eosinophil recruitment to the peritoneum . Post-infection , the CCR3 ligand , CCL11 , was upregulated at the transcript level in peritoneal cells and relative transcripts were significantly impaired in BmL3-infected IL-4Rα-/- mice . In previous RNA-seq analysis of AAMϕ polarised by Brugia adult implantations into BALB/c mice , the CCR3 ligands , CCL8 and CCL24 have been identified as upregulated transcripts[53] . It is therefore likely that a repertoire of CCR3 ligands are produced by the resident pool of Mϕ , possibly with distinct kinetic expression profiles , as they undergo proliferation and alternative activation during the first two weeks of infection . Because , as well as eosinophils , Mϕ comprise a major cell type in granulomas formed around entrapped filarial larvae[54] , we suggest that AAMϕ may focally recruit eosinophils to the nematode cuticle and orchestrate eosinophilic larvicidal granuloma formation in vivo . Medically and veterinary important filarial parasites establish in diverse , non-barrier tissues including the peritoneum . Thus , local Mϕ Th2-induced proliferation and alternative-activation at these sites of infection may orchestrate diverse eosinophil-associated outcomes in filariasis , including sterilising immunity , immune control of circulating mf and acute immunopathologies induced following the death of filariae in parasitized tissues .
IL-4Rα-/- , CCR3-/- or dblΔGATA-/- mice ( BALB/c ) were purchased from Jax Labs USA . WT and SCID BALB/c mice were purchased from Harlan UK . Rodents were maintained in SPF conditions at the University of Liverpool Biological Services Unit . Infectious stage B . malayi L3 were propagated as previously described[36] . Male mice 6–10 weeks of age were infected with 50 BmL3 i . p . and infections maintained between +6-84d . Motile B . malayi parasites and exudate cells were recovered by peritoneal lavage at necropsy and enumerated by microscopy . All experiments on animals were approved by the ethical committees of the University of Liverpool and LSTM , and were conducted according to Home Office Legislation and ARRIVE guidelines . Single cell suspensions were prepared in FACS buffer ( PBS+0 . 5%BSA+2mMEDTA ) . Fc receptors were blocked with αCD16/32 ( eBioscience ) . Live/dead cell differentiation was undertaken with fixable viability dye efluor 450 as per manufacturer’s instructions ( eBioscience ) . Cell staining was undertaken utilising specific labelled anti-mouse antibodies or their matched isotype controls using a fluorescence-minus-one method . Intracellular staining was done following permeabilisation buffer treatment ( eBioscience ) . using a zenon Alexa Fluor 488 Rabbit IgG labelling kit as per manufacturer’s instructions ( Invitrogen ) . All multi-labelled cell samples were subsequently acquired using a BD LSR II flow cytometer ( BD Bioscience ) and analysed on FloJo Software ( S9–S11 Figs; also see supplementary methods ) . OneComp eBeads were used to optimise antibody staining panels and apply compensation . For compensation controls , we applied optimal PMT voltages for the positive signal to be detected within 10^4 and 10^5 whereas negative signal set to be below 10^2 . Compensation matrices were applied in which there was <40% overlap in any signal combination . Viable , Anti-F4/80 APC labelled Mϕ or anti-SigLecF+ PE labelled eosinophils , +14d following BmL3 infection , were sorted to >95% purity using a FACS AriaIII Cell Sorter ( BD Bioscience , Technology Directorate , UoL ) . Cell suspensions were washed in Hank’s Balanced Salt Solution ( HBSS ) before being resuspended to a density of 1x106 in HBSS+30% FCS . A volume of 0 . 1ml was placed in cytospin chambers ( Shandon ) with poly-l-lysin slides and centrifuged at 450 rpm in a Shandon cytospinner . After air drying , slides were stained with DiffQuick ( Shandon ) as per manufacturer’s instructions . Cellular arginase activities were measured as previously described[55] with the following modifications: 0 . 25x106 cell suspensions were determined following lysis and protein extraction by enzymatic conversion of arginine to urea , quantified by photometric assay at 570nm ( VarioSkan , Bio-Rad ) . Arg1 expression levels were determined by RNA extraction of 0 . 1x106 cell suspensions , reverse transcription and cDNA qPCR transcript analysis using murine TaqMan primers ( Applied Biosystems ) . Data was normalised to β-act by the ΔΔCt method . Clodronate liposome suspension ( 5mg/ml ) was diluted 1:5 in PBS and administered 100μl ip 1–3 days prior to infection . αCCR3 was purified from hybridoma supernatant by protein G affinity chromatography ( GE Healthcare ) and administered at 0 . 5mg/mouse ip . IL-4c was prepared as previously described [26] and administered at dosages of 1μg rIL-4 ip ( unless otherwise stated ) at +0 , +2 & +4 dpi . CCR3 inhibitor SB328437 ( R&D Systems , UK ) was administered p . o . at 10 mg/kg qd in 1% DMSO PBS between -1-+6dpi . BmL3 were washed in RPMI wash medium containing 1x penicillin , streptomycin and amphotericin B ( Life Technologies , UK ) , before being transferred in batches of 10 BmL3 to 96-well culture plate wells containing RPMI wash + 10% foetal calf serum and 1% normal mouse serum . 1x106 purified eosinophils , Mϕ or eosinophils + Mϕ were added to a total volume of 0 . 2ml . Cultures were incubated for +7d and motility assessed daily by microscopy . Significant differences between groups evaluated by Mann-Whitney or Kruskal-Wallis with Dunn’s post-hoc tests ( >2 groups ) . Significance is indicated P<0 . 05* P<0 . 01** P<0 . 001*** . | Helminths parasitize approximately one quarter of the global population . Medically-important helminths , including filariae responsible for elephantiasis and river blindness , are targeted for elimination as a public health problem . Currently there are no vaccines or immunotherapeutics available for filarial worms or other human helminth pathogens . Here we define a cellular mechanism whereby the interlukin-4 dependent activation of tissue macrophages are essential to sustain the recruitment of larvicidal eosinophil granulocytes , leading to immunity against filarial infection at a sterile tissue site of parasitism . This work delineates the relative non-redundant functional roles of both myeloid cell types in ‘type-2’ immunity to helminth infection . The study represents a mechanistic advance in our understanding of how immunity operates against metazoan macroparasites invading sterile tissues and may be used in the rational design of new therapeutics to limit helminth disease . | [
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| 2018 | Interleukin-4 activated macrophages mediate immunity to filarial helminth infection by sustaining CCR3-dependent eosinophilia |
Epstein-Barr virus ( EBV ) is a γ-herpesvirus that may cause infectious mononucleosis in young adults . In addition , epidemiological and molecular evidence links EBV to the pathogenesis of lymphoid and epithelial malignancies . EBV has the unique ability to transform resting B cells into permanently proliferating , latently infected lymphoblastoid cell lines . Epstein-Barr virus nuclear antigen 2 ( EBNA-2 ) is a key regulator of viral and cellular gene expression for this transformation process . The N-terminal region of EBNA-2 comprising residues 1-58 appears to mediate multiple molecular functions including self-association and transactivation . However , it remains to be determined if the N-terminus of EBNA-2 directly provides these functions or if these activities merely depend on the dimerization involving the N-terminal domain . To address this issue , we determined the three-dimensional structure of the EBNA-2 N-terminal dimerization ( END ) domain by heteronuclear NMR-spectroscopy . The END domain monomer comprises a small fold of four β-strands and an α-helix which form a parallel dimer by interaction of two β-strands from each protomer . A structure-guided mutational analysis showed that hydrophobic residues in the dimer interface are required for self-association in vitro . Importantly , these interface mutants also displayed severely impaired self-association and transactivation in vivo . Moreover , mutations of solvent-exposed residues or deletion of the α-helix do not impair dimerization but strongly affect the functional activity , suggesting that the EBNA-2 dimer presents a surface that mediates functionally important intra- and/or intermolecular interactions . Our study shows that the END domain is a novel dimerization fold that is essential for functional activity . Since this specific fold is a unique feature of EBNA-2 it might provide a novel target for anti-viral therapeutics .
Epstein-Barr virus ( EBV ) is a γ-herpesvirus that establishes a lifelong asymptomatic infection in the majority of human adults . EBV infection or reactivation can cause significant morbidity and mortality in immunocompromised transplant recipients of allogeneic hematopoietic stem cells or solid organs [1 , 2] . EBV has the unique ability to transform resting human B cells into permanently proliferating latently infected lymphoblastoid cell lines . This process is controlled by the concerted action of six latent EBV nuclear antigens ( EBNAs ) and three latent membrane proteins ( LMPs ) , which mimic cellular functions required for B cell proliferation and differentiation . EBNA-2 is a key viral factor in the initiation of the transformation process . The protein controls a specific transcription program that is associated with proliferation of the infected B cells and that closely resembles transcript patterns of EBV infected B cells described in post-transplant lymphoproliferative disorders ( PTLD ) of immunosuppressed patients [3] . Thus , EBNA2 could potentially serve as a target to develop therapeutic strategies which interfere with the proliferation of EBV positive PTLD originating from B cells . Structural information on EBNA2 could guide the development of new antivirals in the future . EBV belongs to the genus of lymphocryptoviruses ( LCV ) and is the only LCV species that infects humans . Mainly based on the sequence diversity of the EBNA-2 alleles EBV can be categorized in two individual strains called type 1 and 2 . Type 1 and 2 EBV strains differ in their capacity to immortalize primary B cells [4 , 5] which is predominantly determined by sequence variation in the C-terminus of EBNA-2 [6 , 7] . Most researchers in the field use the laboratory EBV strain B95-8 ( type 1 ) which encodes a 487 amino acid EBNA-2 protein [8 , 9] . Lymphocryptoviruses have also been isolated from baboon and macaque . While the EBNA-2 orthologs of baboon and macaque LCV show significant amino acid similarity with EBNA-2 encoded by the B95-8 strain [10 , 11] , similarity with the positional EBNA-2 homolog of marmoset LCV is below 20% ( reviewed in [12] ) . The transactivator EBNA-2 does not bind to DNA directly but uses cellular DNA binding proteins like CBF1/CSL as adapters to gain access to enhancer and promoter sites in the viral and cellular genome ( reviewed in [13] ) . Two transactivation domains have been mapped within the primary structure of the EBNA-2 protein by tethering EBNA-2 fragments fused to the yeast GAL4 DNA binding domain to GAL4 dependent reporter genes ( Fig 1A ) . The C-terminal acidic transactivation ( C-TAD , aa 448–471 ) domain can recruit components of the basic transcriptional machinery like TFIIE via p100 , TFIIB , TAF40 , to the TFB1/p62 subunit of the TFIIH complex , RBP70 [14–18] and chromatin modifiers like p300/ CBP and PCAF [19] and might directly bind to the viral co-activator EBNA-LP [20] . The EBNA-2 C-TAD is intrinsically unstructured as shown by NMR . However , the C-TAD forms a 9-residue amphipathic α-helix when bound to the pleckstrin homology ( PH ) domain of the yeast homolog of fragments of the TFB1/p62 subunit of the TFIIH complex . Three hydrophobic residues ( Trp458 , Ile461 , and Phe462 ) of this α-helix directly contact the TFB1 PH domain . The same EBNA-2 residues are critical for the interaction with CBP/p300 [21] . A second transactivation domain has been mapped to the N-terminus ( N-TAD , aa 1–58 ) of the EBNA-2 protein [22] . The molecular mechanism by which this second EBNA-2 transactivation domain acts has not yet been elucidated . Like the C-TAD its activity can be enhanced by EBNA-LP although it does not bind directly to EBNA-LP [22–24] . When GAL4 DNA binding domain fusion proteins of the N- or C-TAD are compared directly , they score equally well in transient transactivation assays [22] . Deletion of the N-terminus causes a severe loss of activity , while deletion of the C-TAD completely abolishes transactivation of target genes indicating that the function of the two transactivation domains are neither equivalent nor redundant [15 , 25] . The relevance of the N-terminus of EBNA-2 for the growth transformation process has been studied in two independent cellular systems . The results of both studies suggested that the N-terminus of EBNA-2 is of major importance for the transformation efficiency of the virus and the survival of EBV infected B cells [24 , 26] . Two N-terminal regions separated by a poly-proline stretch have been proposed to mediate homotypic self-association of EBNA-2 . The first , consisting of amino acid 1–58 coincides with the N-terminal transactivation domain [22 , 23] . A second self-associating region is composed of amino acid 97–121 [23] . An additional self-associating domain has been mapped to a non-conserved region which is flanked by the second dimerization and the adapter region [27] . The N-terminal region of EBNA-2 comprising residues 1–58 appears to mediate multiple molecular functions including self-association , transactivation and functional cooperation with EBNA-LP . Similar functions have also been assigned to other parts of the protein . So far it is unknown if the N-terminus of EBNA-2 directly provides all these functions or if these activities merely depend on the dimerization involving the N-terminal domain . Thus , the molecular basis and functional importance of the dimerization regions are poorly understood since three-dimensional structural data for the entire EBNA-2 protein have not been reported . Here , we present the three-dimensional structure of the EBNA-2 N-terminus which forms a compact parallel homodimer that is stabilized by a hydrophobic interface between the two monomers . The dimer interface involves two β-strands of each protomer that pack against each other in an anti-parallel manner . Based on this structural information we generated site-directed mutants which target either the hydrophobic dimer interface or solvent-exposed residues . We show that interface mutations abolish self-association of EBNA-2 and severely impair its transactivation function . Notably , surface mutants do not impair self-association . However , specific point mutations or deletion of a protruding α-helix on the surface of the END domain cause a major loss of biological activity . These data suggest that the EBNA-2 dimer provides a surface that is critical for its transactivation function .
Structure predictions for the full-length EBNA-2 amino acid sequence suggest that this viral protein does not form a globular three-dimensional fold , consistent with the presence of extended poly-proline or poly-glycine-arginine regions , and with a total proline content of 28% . The EBNA-2 protein thus appears to comprise intrinsically unstructured regions , which require interaction partners for proper folding . However , in silico analysis of the primary structure using PSIPRED [28] , predicts that the N-terminal region comprises β-strands and thus might represent a small globular domain ( Fig A in S1 Text ) . To characterize biochemical and structural details of this region of EBNA-2 , an N-terminal fragment comprising residues 1–58 was expressed in E . coli and purified with or without Z-tag under native conditions . The oligomerization status of the recombinant proteins was analyzed by analytical size exclusion chromatography ( SEC ) and static light scattering ( SLS ) ( Table 1 ) . The EBNA-2 N-terminal fragment lacking a Z-tag forms a single molecular species with a molecular mass of 13 . 1 kDa as expected for a dimer ( 2x6 . 7 kDa ) . Similarly , the EBNA-2 Z-tag fusion protein eluted as a single peak with a molecular mass of 46 . 3 kDa close to the theoretical molecular mass of a dimer ( 2x23 . 4 kDa ) . We next determined the three-dimensional structure of this N-terminal fragment by heteronuclear nuclear magnetic resonance ( NMR ) spectroscopy . The solution structure of the N-terminal domain is well-defined by the NMR data and based on more than 1250 nuclear Overhauser effect ( NOE ) -derived distance restraints per monomer and 205 inter-monomer NOEs ( Table 2 ) . The structure reveals a parallel homodimeric arrangement of monomers each comprising four β-strands ( β1-β4 ) and a short exposed α-helix ( α1 ) remote from the dimer interface ( Fig 1B and 1C ) . The central portion of the dimer is assembled by two curved anti-parallel β-sheets with an anti-parallel arrangement of β1-β4 with β4’-β1’ and β3-β2 with β2’-β3’ ( un/primed secondary structures refer to the individual monomers ) . The dimer interface is constituted by anti-parallel interactions of β4-β4’ and β2-β2’ , respectively ( Fig 1B and 1C , right panel ) . The secondary structure observed in the structure is consistent with NMR secondary chemical shifts ( Fig 2A ) . Structural similarity searches in the Protein Data Bank ( PDB ) using DALI and PDBeFold did not identify any structures with a similar fold ( see Experimental Methods for details ) . Thus , the N-terminal domain of EBNA-2 represents a novel dimerization fold , which we propose to name “END” ( EBNA-2 N-terminal Dimerization ) domain . The END domain is highly stable with a melting point of approximately 70°C ( determined by thermal denaturation [29] ) . A strong interaction between the monomers is also consistent with a large buried surface area ( 1165 Å2 , corresponding to one quarter of the total surface area per monomer ) [30] . NMR relaxation data show that the folded region of the END domain between β1-β4 is highly rigid , while C-terminal residues ( beyond Asn55 ) are flexible and exhibit internal dynamics at sub-nanosecond timescales ( Fig A in S1 Text ) . The END homodimer is stabilized by the formation of a hydrophobic core involving numerous residues from each monomer ( Fig 1C ) . While some of these residues mainly stabilize interactions within each monomer , the dimer interface is formed by hydrophobic interactions of the side chains of Leu8 , Tyr14 , Leu16 , Val18 , Ile46 , Leu48 , Ile50 , and Val52 . Also , stacking of the solvent exposed side chains of His15 and Phe51 from both monomers contributes to the dimer interface . In addition to the hydrophobic interactions , hydrogen bonds between the peptide backbone of β2 and β2’ , as well as β4 and β4’ are formed . These backbone interactions are supported by NMR-detected hydrogen-to-deuterium ( H/D ) exchange measurements , which indicate that most of the backbone amide protons that participate in intra-monomer or inter-monomer hydrogen bonds are protected against solvent exchange ( Fig 2B and Fig B in S1 Text ) . Taken together our structural and biophysical data shows that the recombinant wild-type END domain folds independently into a very stable dimer . Thus , we expect that the determined protein structure indicates a native assembly of the EBNA-2 protein and decided to further characterize and validate the dimer structure and its function using site-directed mutational analysis in vitro and in vivo . The primary sequences of the END domain from type 1 EBV strains are highly homologous ( >96% identity to B95-8 ) . AG876 , a type 2 strain , exhibits slightly lower sequence identity ( 79% ) , while the sequence identity of baboon and macaque LCV is significantly lower ( 41–50% ) . Interestingly , all hydrophobic amino acids which are an integral part of the dimeric interface are highly conserved between man and monkey viruses . Out of the eight residues , six are identical and two are highly similar ( Fig 2B and 2C ) . This suggests that the dimer interface of the END domain is conserved in the EBNA-2 proteins of EBV and baboon and macaque LCV , and thus may play an important functional role . In addition , the program PSIPRED predicts 4 β-strands in similar positions for EBV and LCV END domains proposing that the dimer fold might be a conserved motif across species ( Fig A in S1 Text ) . To determine the contribution of particular residues for END domain dimer formation , we designed mutations to disrupt specific interactions in the dimerization interface ( interface mutants , Fig 3A ) . We replaced Leu16 and Ile50 by either alanine or aspartate as both residues are positioned directly at the interface and interact with the same residue in the other monomer . Replacement by aspartate was considered to introduce charge repulsion in the dimer interface and thus expected to strongly impair dimerization . Leu16 and Ile50 mediate important hydrophobic interactions and are completely conserved in all human and monkey sequences ( Fig 2C ) . In a second set of mutations , we altered solvent-exposed residues at the surface of the END structure ( surface mutants , Fig 3A ) , such as His15 and Phe51 . We also studied an END domain variant where helix α1 , residues 35–39 , had been deleted ( Δα1 ) . These surface residues and helix α1 are not expected to be essential for dimerization but could mediate molecular interactions that might be required for functional activity . The dimerization properties and structural integrity of the mutant END domains were characterized by SEC/SLS and NMR spectroscopy ( Table 1 and Figs C and D in S1 Text ) . The interface mutants were more difficult to purify than the wild-type protein and are prone to aggregation as judged by SLS analysis . Due to the low solubility of mutant END domains , SLS was also performed on Z-tag fusion proteins to enhance solubility of the fusion proteins . The L16A mutant exists in equilibrium between an unfolded monomeric and folded dimeric state . The L16D , I50A , and I50D mutants are greatly destabilized leading to high molecular weight aggregates ( SLS , Fig D in S1 Text ) and could not be analyzed by NMR . The data suggest that interface mutations destabilize the dimerization interface and thus promote aggregation of monomeric END domains , as monomers would expose hydrophobic residues . H15A yields homogeneous protein samples and is a dimer as indicated by SLS analysis ( Fig D in S1 Text ) and a well-dispersed NMR spectrum ( Fig D in S1 Text ) . SLS data for F51A and Δα1 mutant END domains indicate the presence of dimer populations but also some aggregated species . This is further confirmed by NMR spectra , which are recorded at higher concentration and show the presence of dimeric and aggregated species in solution for these mutants ( Fig D in S1 Text ) . Residue F51 is located at the surface of the END domain but contributes to the dimerization interface . Mutation to alanine may thus destabilize the dimer and lead to aggregation due to solvent exposure of the hydrophobic dimerization interface . Similarly , although removal of helix α1 does not globally disturb the fold and dimerization it may enhance aggregation at the concentrations used in NMR and SLS . NMR spectra clearly indicate the presence of folded dimer species for all surface mutants , i . e . H15A , F51A and Δα1 . To further characterize these mutations , we analyzed their effect on dimerization of the full-length protein in cells ( see below ) . As EBNA-2 has been reported to carry at least two domains implicated in dimerization ( residues 1–58 , i . e . the END domain , and residues 96–210 ) , we tested whether mutants that abolish self-association of the END domain in vitro would also impair self-association of the full-length EBNA-2 protein [23 , 24] . We expressed wild-type , deletion , surface and interface mutants as full-length EBNA-2 HA-tagged proteins and performed co-immunoprecipitation experiments in EBV negative DG75 cells [31] ( Fig 3B–3E ) . For comparison we included HA-tagged mutants of EBNA-2 lacking amino acids 3–30 or 3–52 in our analyses ( Δ3–30 and Δ3–52 , respectively ) . All EBNA-2 mutants were expressed well and could be co-expressed with a FLAG-tagged EBNA-2 fragment encompassing amino acid 1–199 ( F199 ) . Co-immunoprecipitation studies using HA-specific antibodies indicated that all EBNA-2 mutants efficiently bound to endogenous CBF1 . Both EBNA-2N-terminal deletion mutants were significantly impaired for self-association as has been reported previously ( Fig 3D ) [24] . The residual binding of Δ3–30 and Δ3–52 to F199 might be supported by the second self-association domain , comprising residues 96–210 , which is still present in the F199 protein [23] . The self-association domain of a non-conserved region [27] is not present in F199 and thus cannot account for residual dimerization . Next , we tested whether the interface mutants L16A , L16D , I50A and I50D can still mediate self-association with the EBNA-2 F199 fragment , which also harbors the END domain ( Fig 3C , middle and right panel ) . While substitution of the Leu16 or Ile50 by alanine did not significantly affect F199 association , introduction of a negative charge by aspartic acid prevented self-association . These results confirmed the structural data indicating that hydrophobic residues facing each other across the dimer interface of the END domain are essential for EBNA-2 self-association . Surprisingly , Δ3–30 and Δ3–52 appeared to be less impaired than L16D and I50D . In order to further validate the structural integrity of the END domain in the context of the complete EBNA-2 protein we tested the surface mutants H15A , Δα1 and F51A for association with F199 ( Fig 3E ) . Consistent with the structural and biophysical data all surface mutants retained the capacity to self-associate , confirming that these residues are not essential for the dimerization of EBNA-2 . Nuclear localization and formation of nuclear speckles is a typical feature of EBNA-2 [32] . In order to analyze whether the END domain mutants had retained these features all EBNA-2 mutants were expressed in HeLa cells and the subcellular distribution of the EBNA-2 proteins was analyzed by confocal microscopy ( Fig E in S1 Text ) . All mutants still showed strict nuclear localization , which typically excludes the nucleoli . Moreover , all mutants formed granular speckles , which are characteristic of wild-type EBNA-2 protein . Based on previous work , EBNA-2 mutants impaired for dimerization were also severely impaired for activation of the viral target gene LMP1 [24] . In order to analyze the capacity of the EBNA-2 surface and interface mutants to activate the viral LMP genes we expressed EBNA-2 mutants in the EBV positive Burkitt's lymphoma cell line Eli-BL [33] . This B cell line exhibits a specific viral gene expression program where neither EBNA-2 , nor EBNA-LP nor LMP proteins are expressed . By transient transfection of EBNA-2 expression constructs , endogenous LMP1 protein expression can be induced . We used this cellular system to measure the biological activity of EBNA-2 amino acid substitution mutants compared to N-terminal deletion mutants . The N-terminal deletion mutants , Δ3–30 and Δ3–52 , which are expected to disrupt the END domain fold or delete it , are severely impaired for LMP1 activation , while the biological activity of the interface mutants L16A and I50A , which still self-associate , is comparable to wild-type EBNA-2 ( Fig 4A and 4B ) . However , the functionality of the interface mutants L16D and I50D , which do not retain dimerization , is strongly attenuated . Notably , activation of LMP1 by the surface mutants H15A , F51A , and Δα1 is differentially affected . While F51A is unaffected , the activity of H15A and Δα1mutants is severely reduced ( Fig 4C ) . EBNA-2 and all EBNA-2 mutants were expressed well in Eli-BL . Thus , the distinct biological activity of the mutant EBNA-2 proteins is not due to differential expression levels ( Fig 4D ) . In order to analyze the capacity of all EBNA-2 mutants to induce endogenous transcripts we selected two viral , LMP1 and LMP2A , and two cellular target genes , CCL3 and CD23 , for quantitative RT-PCR analyses in Eli-BL ( Fig 5 ) . These four genes all carry functional CBF1/CSL binding sites in their promoter region within less than 500 base pairs upstream of the transcription start site [34 , 35] . The LMP1 promoter is controlled by a complex network of transcription factors that includes CBF1/CSL . However , although CBF1/CSL enhances transactivation by EBNA-2 , the LMP1 promoter is unique since it can still be activated by EBNA-2 to up to 50% in the absence of CBF1/CSL [36] ( and our unpublished data ) . In contrast , the LMP2A promoter carries two adjacent CBF1/CSL sites which are essential for EBNA-2 transactivation . Activation of the two cellular genes CCL3 and CD23 is strictly CBF1/CSL dependent [37–39] . Compared to wild-type EBNA-2 , all END domain mutants , even those that still dimerize in cells , showed some loss of activity indicating that the integrity of this domain is critical for EBNA-2 function . The surface mutant F51A appears to be affected the least . Neither Δ3–30 nor Δ3–52 could efficiently activate any of the four genes . LMP1 induction was impaired the most , while activation of LMP2A is the least sensitive . In parallel we studied the activity of the viral C promoter and the endogenous EBNA-2 transcript levels after transfection . C promoter transcript levels were close to detection limits and were not modulated by either EBNA-2 or EBNA-2 mutants . Endogenous EBNA-2 transcript levels were undetectable and could also not be induced . Thus , we can exclude that endogenous EBNA-2 in Eli-BL interferes with our assay in Eli-BL ( Fig F in S1 Text ) . It appears that the END domain is critical not only for LMP1 transactivation but rather is required in a universal manner for transactivation of unrelated genes although to different extent . In order to prove that the END domain surface has a general impact on the transactivation capacity of the EBNA-2 protein we performed promoter reporter luciferase assays using Gal4 DNA-binding domain fusion proteins and two distinct promoter reporter constructs which either carried 10 GAL4 binding sites or 12 CBF1 binding sites to recruit GAL4 EBNA-2 ( Fig 6 ) . GAL4 EBNA-2 was efficiently recruited to both promoters and activated luciferase expression . The GAL4 EBNA-2 H15A mutant had lost more than 50% of its transactivation capacity on both luciferase constructs . The biological activity of GAL4 EBNA-2 Δα1 was almost completely abolished . Again the surface F51A mutant was affected the least . In summary , EBNA-2 END domain mutations that do not affect dimerization are severely impaired for transactivation of endogenous target genes as well as artificial promoter reporter constructs . Loss of function was most pronounced for END domain deletion mutants and was almost as strongly observed with the surface mutants H15A and Δα1 . The dramatic loss of function seen in mutants – that still dimerize , properly localize to the nucleus , and bind to CBF1 – suggests that the END domain not only promotes dimerization of EBNA-2 but conveys additional critical functions .
Here , we report the first three-dimensional structure information for the EBNA-2 protein . The N-terminal region of EBNA-2 represents a specific dimerization domain designated END ( EBNA-2 N-terminal Dimerization ) domain . The dimer is stabilized by anti-parallel interactions of β4-β4’ and β2-β2’ , which generate a strong hydrophobic interface which stabilizes the dimer . In fact , dimerization via hydrophobic interfaces of diverse structures is a frequent feature of small dimers ( <100 aa per monomer ) [40] . However , to our knowledge the specific fold of the END domain dimer is novel . Notably , the hydrophobic residues which form the dimerization interface are completely conserved in EBV and rhesus LCV sequences . We thus expect that the dimerization by the END domain is conserved in all EBV sequences and most likely also in macaque and baboon EBNA-2 orthologs . To probe the dimerization interface we generated END domain mutants which affect residues in the dimer interface . Mutation of these interface residues were indeed found to disrupt the fold of the END domain and/or lead to aggregation of recombinant protein . For further analysis , all END domain mutants were expressed as full length EBNA-2 protein in human B cells and tested for self-association and transactivation of endogenous target genes . While self-association of the EBNA-2 L16A and I50A interface mutants was marginally impaired , self-association of L16D and I50D was close to or below detection levels . Surprisingly , even the N-terminal deletion mutants ( Δ3–30 and Δ3–52 ) exhibited residual binding activity stronger than L16D and I50D . Potentially the second dimerization domain ( Dim2 , Fig 1A ) could be unmasked in the absence of the END domain . Or , single amino acid substitutions in the hydrophobic core may cause non-physiological aggregation-states of EBNA-2 and impair protein function even stronger than loss of the END domain . Our data provide convincing evidence that the END domain is a conserved dimerization motif for the full-length EBNA-2 protein . As the END domain is separated from the rest of the EBNA-2 protein by an extended poly-proline hinge region , we suggest that the END domain acts as an independent module that mediates self-association of the entire protein . EBNA-2 is recruited to DNA by adapters like CBF1/CSL but might require at least two factors to which it binds simultaneously to activate viral target genes . So , the viral LMP2A promoter carries two functional CBF1/CSL binding sites , while the LMP1 promoter requires PU . 1 and CBF1/CSL for efficient activation by EBNA-2 [36 , 41 , 42] . By using CBF1/CSL as a DNA adapter , EBNA-2 mimics the activated Notch receptor which also is recruited to DNA by CBF1/CSL . Interestingly , Notch dimers frequently use paired CBF1/CSL1 binding sites in the cellular genome [43] which might also be used by EBNA-2 dimers . In the cellular genome , EBNA-2 binds preferentially to enhancers which can be located remote from the promoter of the regulated genes [44] . Thus , it may be proposed that dimerization promotes higher order protein complex assembly that bridges promoter and enhancer regions . According to the NMR and SEC/SLS analyses all surface mutants of the END domain are folded and comprise dimeric species , although F51A and Δα1 have a tendency to aggregate . In B cells , the full-length surface mutants EBNA-2 H15A , Δα1 , and F51A mutants self-associate , further corroborating the in vitro data . Notably , transactivation of target genes by the surface mutants H15A and Δα1 was severely reduced to similar levels observed for aspartic acid interface mutants , which abolish self-association . This indicates that the effects onto the functional activity are not due to impaired dimerization but suggest that these residues may be involved in additional intra- or intermolecular molecular interactions . We directly compared the different END domain EBNA-2 mutants for their capacity to induce either LMP1 protein expression or endogenous LMP1 , LMP2A , CCL3 , or CD23 transcript levels in Eli-BL cells . These four genes share functional CBF1 binding motifs but rely on these motifs to varying degrees . Importantly , all END domain mutants retain the capacity to bind to CBF1 . We find that the residual self-association of the two N-terminal deletion mutants ( Fig 3D ) is not sufficient to restore the biological activity of the mutants to wild-type levels ( Figs 4 and 5 ) . Although LMP1 and CCL3 induction are affected the most , all mutants produce similar patterns of loss of activity for all genes we have tested . Since we did not observe a gene specific phenotype for any of the mutants , a single so far unknown factor could interact with the END domain of EBNA-2 and be required for the activation of each of the four target genes . In EBV infected B cells , the EBNA-LP co-activator of EBNA-2 could be a candidate factor to play this role . However , since EBNA-LP is not expressed in EBV negative DG75 cells and neither expressed nor induced by EBNA-2 in Eli-BL cells [45] , EBNA-LP can be excluded in our setting . At this point of our studies we speculate that basic mechanisms of transcriptional activation by EBNA-2 are impaired in the surface mutants H15A and Δα1 . In the past , multiple transactivation domains ( TADs ) have been defined by generating chimeras of protein fragments of interest and an unrelated DNA binding domain . These chimeras were tested for their activity to induce artificial promoters recruited by the DNA binding domain [46] . Most of the TADs , which scored positive in these assays , were enriched for hydrophobic or acidic amino acids or a 9aa TAD sequence motif [47] . In retrospect it was found that TADs not only bind to general factors of the transcription machinery , but also confer contact to components of the mediator , the SAGA complex or the chromatin remodeling machinery . Most TADs appear to be intrinsically unstructured . However , in complex with their cognate binding partners they may fold into specific structures which mediate protein-protein interactions ( reviewed in [48] ) . In contrast to the acidic C-TAD of EBNA-2 , which is intrinsically unstructured and attains a stable secondary structure only upon complex formation with cellular proteins [21] , the END domain appears to be a non-typical TAD . In the absence of any cognate cellular binding partner the END domain folds into a well-defined rigid dimeric globular structure . Taken together our structural and mutational analysis suggests that the dimerization by the END domain provides a surface that is critical for transactivation of target genes , for example , by exposing His15 and the α1-helix . Since all loss-of-function mutants interfere with activation of all genes that were tested , the END domain is likely to interact with candidate proteins which could be critical for transactivation at multiple steps . EBNA-2 expression is a hallmark of B cell lymphomas arising in immunocompromised patients and considered to drive the proliferation of these cells . The END domain has a strong impact on the biological activity of EBNA-2 and thus it should be considered as a potential drug target for small molecules [24 , 26] . The END domain forms a novel , highly stable parallel dimeric fold , which is stabilized by conserved hydrophobic interactions . Importantly , our in silico searches for cellular protein sequences or related folds similar to the END domain did not reveal any homologous cellular domains suggesting that the END domain is a unique structure that evolved in lymphocryptoviruses and thus is virus specific . Our future studies will focus on the identification of potential proteins which bind to the END domain and require His15 or the α-helix for protein interactions . The dimerization or the suggested binding surface of the END domain might be targeted by small molecules to impair EBNA-2 activity for potential therapeutic intervention .
The design of constructs for structural and biochemical studies was guided by secondary structure prediction ( PSIPRED ) [28] . Residues 1–58 of EBNA-2 ( Strain B95-8; Uniprot: P12978 ) were cloned into a modified pET-24d expression plasmid following standard restriction digest procedures . The vector contained a Z-tag , as well as a 6xHis-tag to facilitate purification . The Z-tag is a 125 amino acid protein tag based on protein A from Staphylococcus aureus and is known to enhance the solubility of fusion proteins [49] . Both of these N-terminal tags could be removed by proteolytic cleavage using tobacco etch virus ( TEV ) protease . For cloning purposes and efficient TEV protease cleavage the final protein construct contained four additional residues at the N-terminus ( Gly-Ala-Met-Glu ) . Mutations to study the functional importance of the END domain were introduced by overlap extension ( also known as two-step ) PCR . In brief , mutation primers were used in combination with the original forward or reverse primers in a first round of separate PCR experiments . The purified products were then combined and used as the template for a second round of PCR using only the original forward and reverse primers . Restriction digestion and ligation of the final product yielded expression plasmids in a similar way to the original construct . Mutant END domains were expressed and purified in similar fashion as the wild-type protein . For expression studies in mammalian cells all END domain mutant gene fragments were sub-cloned into pAG155 , to generate EBNA-2 carrying an HA tag at the C-terminus of full-length proteins by conventional cloning techniques [24] . In order to express GAL4 EBNA-2 fusion proteins the GAL4 DNA binding domain ( DBD ) gene fragment was added to the 5’ end of the EBNA-2-HA ORF . Luciferase promoter reporter gene assays were performed using the Promega dual luciferase assay system . The CBF1 reporter ( pGa981-6 ) carries 12 CBF1 binding sites [50] and the GAL4 ( Gal4 tk-Luc ) responsive reporter construct carries 10 GAL4 binding sites . For normalization the pRL-PGK Renilla Luciferase construct was used . The integrity of all expression plasmids was confirmed by sequencing . Recombinant proteins were expressed in Escherichia coli BL21 ( DE3 ) . Using kanamycin for selection , one colony was picked from a fresh transformation plate to inoculate a 5 mL pre-culture in lysogeny brothmedium . The pre-culture was used to start larger culture volumes of unlabeled LB , or minimal M9 media for expression of isotope-labeled proteins . For production of 13C and 15N-labeled protein samples [U-13C]-D-glucose and 15NH4Cl were included as the sole carbon and nitrogen sources , respectively . Cultures were grown at 37°C until the optical density reached 0 . 8 and then , after cooling to 20°C , induced overnight ( 16 h ) by addition of 0 . 5mM isopropyl β-D-1-thiogalactopyranoside . Cells were harvested by centrifugation ( 8000 g , 20min ) and disrupted by pulsed sonication ( 6 min , 30% power , large probe , Fisher Scientific model 550 ) in lysis buffer ( 20 mM TRIS pH 7 . 5 , 300 mM NaCl , 10 mM imidazole , and 0 . 02% NaN3 ) , containing protease inhibitors , DNase , lysozyme , and 0 . 2% IGEPAL . After centrifugation and filtering the lysate was passed three times over Ni-NTA agarose resin ( Qiagen ) in gravity-flow columns ( Bio-Rad ) . Bound protein was washed extensively with the lysis buffer , the lysis buffer containing no IGEPAL , and lysis buffer with high salt NaCl ( 1 M ) or imidazole ( 30 mM ) concentrations . The protein was eluted with the elution buffer ( 20 mM TRIS pH 7 . 5 , 300 mM NaCl , 300 mM imidazole , and 0 . 02% NaN3 ) . The eluted protein was buffer exchanged into TEV cleavage buffer ( 10 mM NaP pH 7 . 5 , 150 mM NaCl , 1 mM DTT , and 0 . 02% NaN3 ) . TEV protease was added to a molar ratio of 1:10 , protease to recombinant protein , and incubated overnight at 4°C . To efficiently remove TEV protease and the cleaved off solubility tag , the sample was passed over an ion-exchange column ( Resource Q , GE Healthcare ) which was equilibrated with the buffer ( 20 mM sodium phosphate , pH 6 . 9 , 20 mM NaCl , and 0 . 02% NaN3 ) . The protein was eluted from Resource Q column with a NaCl gradient ( 0–0 . 5M over 60 ml ) . Additionally , a last purification step was implemented and included size-exclusion chromatography ( HiLoad16/60 , Superdex 75 , GE Healthcare ) . The size-exclusion column was equilibrated and run in a buffer appropriate to subsequent studies . NMR experiments were performed on Bruker instruments operating at a field-strength corresponding to a proton resonance frequency of 500 , 600 , 750 , 800 , and 900 MHz equipped with pulsed field gradients and cryogenic probes ( except at 750 MHz ) . Spectra were generally recorded at 323K ( 50°C ) on protein samples ( 1 mM ) in20 mM sodium phosphate , pH 6 . 9 , 20 mM NaCl , and 0 . 02% NaN3 . Spectra were processed with NMRPipe [51] and analyzed in NMRView [52] and Sparky 3 . For assignment of backbone amides and side-chain signals the following multidimensional heteronuclear experiments were acquired [53]: 1H , 15N-HSQC , 1H , 13C-HSQC , HNCA , HNCACB , CBCA ( CO ) NH , ( H ) CC ( CO ) NH-TOCSY , H ( C ) CH-TOCSY , and HCC ( H ) -TOCSY . Assignment of aromatic protons was accomplished by two-dimensional ( HB ) CB ( CG , CD ) HD and ( HB ) CB ( CG , CD , CE ) HE spectra . Stereospecific assignment of the methyl groups in leucine and valine residues was achieved by partial 13C-labeling and by observing the presence or absence of a hydrogen-carbon J-coupling in a 2D 1H-13C HSQC [54] . Distance restraints were derived from three-dimensional NOESY experiments: 1H , 15N-HSQC-NOESY , 1H , 13C-HMQC-NOESY ( for both the aliphatic and the aromatic region ) , and 13C-edited-15N/13C-filtered NOESY ( aliphatic region ) . Denaturation and refolding of the END dimer was required for measurement of the intermolecular NOEs . This was accomplished by taking equimolar amounts of unlabeled and double labeled ( 15N , 13C ) protein and adding 8M urea . The mixture was heated to 80°C for 10 min and then dialyzed twice against NMR buffer at 4°C . Importantly , appropriate samples were lyophilized and dissolved in pure D2O to increase sensitivity of several experiments , and to simplify spectral analysis . Automated NOESY assignment and derivation of distance restraints was performed using CYANA v3 . 0 [55] . Dihedral restraints were obtained with TALOS+ [56] , using assigned chemical shifts as input , and inspected manually to remove less reliable predictions . The final structure calculations in ARIA v2 . 2 [57] included refinement in explicit water and activation of a non-crystallographic two-fold symmetry constraint . Out of one hundred calculated structures , ten models were selected as a representative ensemble based on low energy and restraint violations . Analysis of structure quality and restraint violations was performed with iCing including PROCHECK [58] and WHATCHECK [59] . Figures and structure ensemble alignment were prepared in Pymol v1 . 5 [60] . 1H , -15N heteronuclear NOEs were measured at 318K on a 500 μM 15N-labeled sample ( 750 MHz proton Larmor frequency ) as described previously [61] , and analyzed in NMRView . The secondary chemical shift analysis was also done in NMRView . Hydrogen-deuterium exchange experiments were performed by NMR to detect solvent protected backbone amide protons . A 1H , 15N-HSQC was recorded on a lyophilized protein sample 10 min after dissolving it in D2O , and compared to a reference spectra in H2O . Both spectra were recorded at 313K to reduce the amide proton exchange rates with the solvent . Any residual signals observed above noise were considered indicative of solvent protected amide protons . A BLAST sequence search of the Protein Data Bank ( PDB ) generated no hits with reasonable E-values ( < 1 ) or domains with structural similarities to the END domain . The fold of the END domain was further compared to previously determined protein structures deposited in the PDB using the DALI server as well as PDBeFold , available from EMBL/EBI . Interestingly , the DALI server only returned low-scoring hits for the complete dimer with relatively high RMSD values and low sequence identity . The structural superpositions of the END domain with the top twenty hits were manually examined , without the discovery of any similar folds . The most commonly matched structural feature of the END domain was the large anti-parallel beta-sheets ( β1-β4-β4’-β1’ ) , while the rest of the dimer and the ordering of the beta-strands , never exhibited an adequate fit . Likewise , PDBeFold produced no hits with reliable scores for the END monomer . Top hits only matched two out of the five secondary structure elements , and visual inspection confirmed lack of conserved structures . In conclusion this lack of similar structures strongly suggests that the END domain is of a novel fold and that this is the first structural determination of this viral dimerization motif . SLS was measured with a Malvern-Viscotekinstrument ( TDA 305 ) connected downstream to an Äkta Purifier equipped with an analytical size-exclusion column ( Superdex 75 10/300 GL , GE Healthcare ) . Samples were run at a concentration between 150 and 400 μM in a running buffer containing 20 mM NaP pH 6 . 9 , 20 mM NaCl , and 0 . 02% NaN3 . Elution profiles were collected for 30 min with a flow rate of 1 mL/min . Data were collected using absorbance UV detection at 280 nm , right angle light scattering ( RALS ) and refractive index ( RI ) . The molar masses of separated elution peaks were calculated using OmniSEC software ( Malvern ) . As standard for calibration , 4 mg/mL Bovine Serum Albumin ( BSA ) was used prior to all experiments and the change in refractive index with respect to concentration ( dn/dc ) was set to 0 . 186 mL/g [62] . DG75 [31] , Eli-BL [33] , and 721 [63] cells were maintained in RPMI 1640 medium supplemented with 10% fetal calf serum , 100 U/mL penicillin , 100 μg/mL streptomycin and 4 mM glutamine at 37°C in a 6% CO2atmosphere . For transfection , 5x106DG75 or 2x107 Eli-BL cells were electroporated in 250 μL Optimem medium at 240 V and 975 μF using the Genepulser II ( Bio-Rad ) and allowed to recover in 10 mL of cell culture medium for 24 h . Luciferase promoter reporter gene assays were performed using the dual luciferase assay system ( Promega ) according to the manufacturer's instructions . Results obtained for firefly luciferase activity were normalized to Renilla luciferase activity . HeLa [64] cells were cultivated in DMEM supplemented with 10% fetal calf serum , 100 U/mL penicillin , 100 μg/mL streptomycin and 4 mM glutamine at 37°C in a 6% CO2 atmosphere . Cells were transfected with a mixture 1 . 5 μg of EBNA2 expression plasmids and 4 μg polyethylenimine ( Sigma ) in the presence of Optimem ( Gibco ) . After 4 h , the medium was replaced with cell culture medium and cells were allowed to recover for 24 h and subsequently cultured for 24 h on cover slips . The cells were fixed with 2% paraformaldehyde ( PFA ) at RT for 15 min and subsequently permeabilized with PBS/0 . 15% TritonX-100 3 for5 min at RT . All samples were blocked with 1% BSA/0 . 15% glycine 3x for 10 min and incubated with the EBNA-2 specific antibody ( R3 ) over night at 4°C . Cells were washed with PBS for 5 min , with PBS/0 . 15% TritonX-10 for 5 min , with PBS 5 min , blocked with PBS/1% BSA/0 . 15% glycine for 7 min and incubated with Cy3-conjugated goat anti-rat immunoglobulin ( Jackson Immuno Research ) in the dark for 45 min at RT . Cells were washed again with PBS/0 . 15% TritonX-100 , and with PBS and stained with 0 . 1μg/ml 4' , 6-diamidino-2-phenylindole ( DAPI ) ( Sigma ) for 90sec and washed with PBS . Samples were embedded in fluorescent mounting medium ( DakoCytomation ) . Confocal microscopy was performed on a Leica LSCM SP5 microscope equipped with 405 nm , 488 nm , 561 nm and 633 nm lasers . Images were taken with an objective HCX PL APO 63/1 . 4 objective and an electronic zoom of 3 . 6 . Laser line 405 nm ( DAPI ) and 561 nm ( Cy3 ) were used for image acquisition . Detection settings were carefully chosen to exclude spill-over of DAPI and Cy3 fluorescence . For immunoprecipitation studies DG75 cells were lysed in 1% NP-40 buffer ( 10 mM TRIS pH7 . 4 , 1 mM EDTA , 150 mM NaCl , 3% Glycerol , 1x complete protease inhibitor tablets ( Roche ) ) . The lysates were submitted to immunoprecipitation and total cell lysates and immunoprecipitates were analyzed by immunoblotting . For direct immunoblotting of Eli-BL cells they were lysed in RIPA buffer ( 50mM TRIS pH7 . 5 , 150mM NaCl , 1% Igepal , 0 . 1% SDS , 0 . 5% Na-deoxycholate , 1x complete protease inhibitor tablets ( Roche ) ) for 1 h and sonicated for 10 min ( 30s on , 30s off ) at 230 V using a Bioruptor ( Diagenode ) . Immunoblot assays were performed as described previously [38] . HA ( 3F10 , Roche ) and Flag ( M2 , Sigma ) specific antibodies were obtained from commercial sources . The EBNA-2 ( R3 ) [65] , the EBNA-1 ( 1H4 ) [66] and the LMP1 specific monoclonal antibodies ( S12 ) [67] are published . Chemilumiscence signals of immunoblots were quantified by digital imaging using the Fusion Fx7 . Total RNA was extracted from 1x107 transfected Eli-BL cells 24 h post-transfection using the Qiagen RNeasy Mini Kit and cDNA was synthesized from 2 μg of RNA using the High-Capacity cDNA Reverse Transcription kit ( Applied Biosystems ) according the manufacturer´s protocol . qPCR of the transcripts was performed on a LightCycler 480 SYBR Green I Master ( Roche ) and the data were processed with the LightCycler 480 software ( version 1 . 5 . 0 . 39 , Roche ) . A total of 1/80 of cDNA product was used for amplification of actin and 1/40 of cDNA for all other genes . Cycling conditions were 10 min at 95°C and 45 cycles of 3 s at 95°C , 10 s at 60 or 63°C , and 20 s at 72°C on a 96-well thermal block . PCR products were validated by melting curve analysis and agarose gel electrophoresis . Quantification was based on standard samples of known concentration and standard curves for each primer pair . Primer pairs for RT-PCR were selected by Primer3 software All pairs were chosen to support amplification across intron borders . Primers were GGTGTTCATCACTGTGTCGTTGTC and GCTACTGTTTTGGCTGTACATCGT for LMP1 [68] , ATGACTCATCTCAACACATA and CATGTTAGGCAAATTGCAAA for LMP2A [69] , CTGGGACACCACACAGAGTC and GACACCTGCAACTCCATCCT for CD23 , ATGCAGGTCTCCACTGCTG and TTTCTGGACCCACTCCTCAC for CCL3 , AGATCAGATGGCATAGAGAC and GACCGGTGCCTTCTTAGGAG for C promoter usage , GCTGCTACGCATTAGAGACC and TCCTGGTAGGGATTCGAGGG for EBNA-2 [70] , and GGCATCCTCACCCTGAAGTA and GGGGTGTTGAAGGTCTCAAA for actin . Atomic coordinates of the END domain have been deposited at the Protein Data Bank ( PDB ) with accession code 2N2J . Experimental NMR distance restraints have been deposited at the Biological Magnetic Resonance Bank ( BMRB ) with accession number 19390 . | Epstein-Barr virus is an oncogenic γ-herpesvirus that may cause infectious mononucleosis in young adults and fatal lymphoproliferative disorders in immunocompromised patients and is associated with the pathogenesis of Burkitt's lymphoma , nasopharyngeal and gastric carcinoma . Epstein-Barr virus nuclear antigen 2 ( EBNA-2 ) is a key regulator of viral and cellular gene expression which initiates and maintains a specific transcription program that promotes proliferation and differentiation of the infected B cell . EBNA-2 is a transcriptional activator that is recruited to DNA by cellular adaptor proteins , carries two transactivation domains , and has the capacity to form dimers or multimers . This study provides the first three-dimensional structure of the EBNA-2 N-terminal Dimerization ( END ) domain . Two END domain monomers , each consisting of four β-strands and a single α-helix , assemble into a dimer by interaction of two β-strands from each monomer in a parallel fashion . The dimer surface exposes residues that are critical for transactivation of target genes by EBNA-2 . The dimeric fold of the EBNA-2 END domain has not been observed for any cellular protein and thus could provide a novel target for anti-viral therapeutics . | [
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| 2015 | The EBNA-2 N-Terminal Transactivation Domain Folds into a Dimeric Structure Required for Target Gene Activation |
In mammals , the suprachiasmatic nucleus ( SCN ) of the hypothalamus constitutes the central circadian pacemaker . The SCN receives light signals from the retina and controls peripheral circadian clocks ( located in the cortex , the pineal gland , the liver , the kidney , the heart , etc . ) . This hierarchical organization of the circadian system ensures the proper timing of physiological processes . In each SCN neuron , interconnected transcriptional and translational feedback loops enable the circadian expression of the clock genes . Although all the neurons have the same genotype , the oscillations of individual cells are highly heterogeneous in dispersed cell culture: many cells present damped oscillations and the period of the oscillations varies from cell to cell . In addition , the neurotransmitters that ensure the intercellular coupling , and thereby the synchronization of the cellular rhythms , differ between the two main regions of the SCN . In this work , a mathematical model that accounts for this heterogeneous organization of the SCN is presented and used to study the implication of the SCN network topology on synchronization and entrainment properties . The results show that oscillations with larger amplitude can be obtained with scale-free networks , in contrast to random and local connections . Networks with the small-world property such as the scale-free networks used in this work can adapt faster to a delay or advance in the light/dark cycle ( jet lag ) . Interestingly a certain level of cellular heterogeneity is not detrimental to synchronization performances , but on the contrary helps resynchronization after jet lag . When coupling two networks with different topologies that mimic the two regions of the SCN , efficient filtering of pulse-like perturbations in the entrainment pattern is observed . These results suggest that the complex and heterogeneous architecture of the SCN decreases the sensitivity of the network to short entrainment perturbations while , at the same time , improving its adaptation abilities to long term changes .
In mammals , the suprachiasmatic nucleus ( SCN ) of the hypothalamus constitutes the central circadian pacemaker [1] , [2] . The SCN comprises about 20000 densely packed neurons organized into bilateral pairs of nuclei on each side of the third ventricle , above the optic chiasm [2] ( Fig . 1 ) . The cells receive light signals from the retina via the optic nerve . The SCN controls circadian rhythms in other parts of the brain including the cortex and the pineal gland , as well as in peripheral tissues such as the liver , kidney , and heart . This hierarchical organization of the circadian system ensures the proper timing of physiological processes and behavior [1] , [3] . In natural conditions , the organism is subject to the alternation of days and nights . In response and anticipation to this cycling environment , the circadian pacemaker adjusts the phase of clock-controlled processes with respect to the light-dark cycle . Each SCN neuron expresses clock genes . Interconnected transcriptional and translational feedback loops form the core circadian network allowing each cell to produce circadian oscillations [4] , [5] . Such oscillations still subsist in cultured cells . However , in dispersed culture , the oscillator population is highly heterogeneous: many cells present damped oscillations [6] and the period of the oscillations varies from cell to cell [7] . To produce a reliable global rhythm , the SCN cells must oscillate in synchrony . Synchronization is achieved via intercellular coupling mechanisms [8] , [9] . The SCN can thus be regarded as a network of coupled oscillators . Cells of the SCN can be roughly divided in two groups of neurons that differ by their light sensitivity , the neurostransmitters they produce , and consequently by their coupling properties [2] ( Fig . 1 ) . Besides GABA which is expressed by all SCN neurons [10] , several region-specific neurotransmitters have been identified . In the ventro-lateral region ( VL ) , the neurons mainly express vasoactive intestinal peptides ( VIP ) , whereas the neurons of the dorso-medial region ( DM ) express a different neural hormone , the arginine-vasopressin ( AVP ) . When the two regions are dissociated , the VL cells remain synchronized while the DM cells run out of phase [11] . Such results suggest that the two SCN regions differ by their intercellular coupling properties . Additionally , only the VL region is light-sensitive and just a distinct subset of VL neurons is directly influenced by the photic input [12] , [13] . Little is known about the connectivity and topological properties of the SCN cellular network . However the characterization of anatomical and functional connectivity in other regions of the brain ( e . g . cortex ) revealed small-world properties [14] , [15] , [16] . Small-world topology combines local and long-range connections , thereby decreasing the average path length between cells [17] . Such organization was shown to lead to more efficient synchronization at a lower energy cost ( because fewer connections are needed ) [18] , [19] , [20] , [21] , [22] . It is thus reasonable to assume that the SCN also exploits such network properties to efficiently synchronize neurons . In this paper we developed a multi-oscillator model for the SCN and investigated the implication of the network topology on synchronization and entrainment properties . The model studied here extends the work previously published by Bernard et al . [23] in three main directions: we introduced heterogeneity among the different SCN cells , we systematically compared generic network topologies , we proposed a model accounting for the distinction between two distinct subareas in the SCN , and investigated the possible role of this separation in the response of the SCN to light signals . The core cellular oscillator is a molecular model of intermediate complexity , which is based on interlocked feedback loops [24] . In the present work , we introduced cellular heterogeneity through variability in parameter values to mimic experimental observations and various topologies for the coupling of the oscillators: random , scale-free , and local networks . Long connections are present in the random and scale-free topologies forming a small-world network [25] , [26] . Scale-free networks are characterized by a skewed distribution of the connections where a few cells ( hubs ) are connected to a large number of cells while the rest have few outgoing edges . On the contrary in a local topology , cells are only connected to their close neighbors . We compared the dynamical properties of the different networks: resynchronization time after a temporary arrest of the oscillations or after a transient decoupling , the synchronization and entrainment performances , as well as the response of the system to jet lags . Finally , we proposed a coupled dual network as a model of the VL-DM organization of the SCN .
Several models have been proposed for the cellular mammalian circadian clock . Earlier models are mostly phenomenological and rely either on abstract equations [27] , [28] , or on simple biomolecular mechanisms [29] . More recently , detailed molecular models have been proposed [24] , [30] , [31] , [32] . For our purpose we have chosen the model of intermediary complexity proposed by Becker-Weimann et al . [24] . This models does not explicitly incorporate all clock components ( for example no distinction is done between Per1–3 and Cry1–2 , the Per/Cry complex is denoted by ) , but accounts for the core architecture of the circadian clock , involving interlocked positive and negative feedback loops ( Fig . 2 ) . To take into account coupling and light entrainment , the Becker-Weimann model was extended to include a neurotransmitter and a signaling cascade [23] . The coupling between the molecular oscillators is accomplished by a neurotransmitter , released upon Per/Cry complex activity in the upstream cell . The neurotransmitter triggers , in the target cell , a signaling cascade involving PKA and CREB that have been experimentally shown to activate Per/Cry transcription [33] , [34] . The resulting two-step cascade can be seen as a generic signaling pathway . In addition to a modulation by CREB , the production of Per/Cry mRNA ( ) is also increased by light in the light-sensitive cells . Overall , the model we used comprises ten state variables that represent different molecular species or complexes ( Fig . 2 ) . Reaction rates are modeled using mass-action kinetics , except for the regulated mRNA production rate where Hill-type functions are used ( see Eqs . ( 1 ) in Models ) . To mimic the experimentally observed population heterogeneity , we introduce variability in all model parameters except Hill coefficients . For each cell , the parameters ( see Eqs . ( 1 ) and Tab . S1 ) are uniformly distributed in the logarithmic space around the original parameter values where the variability is controlled by the heterogeneity parameter : with , being a uniform distribution in the range . All individual parameters are randomly chosen without any intra- or intercellular correlations . Examples of individual oscillators are shown in figure 3A . We tested different values of between 0 . 025 and 0 . 3 ( Fig . 3B–C ) and observed that small values of generate a population where about half of cells have limit-cycle oscillations ( Fig . 3B ) , but the pseudo-periods ( defined as the average duration between two peaks , see Models ) have little variability ( Fig . 3C ) . On the other hand , large values of lead to high heterogeneity where some cells are overdamped and the pseudo-periods are broadly distributed . In the intermediate regime ( ) , the results are not very sensitive to , therefore for the different simulations , we chose a value of . For this value , about 35–40% of the cells oscillate in isolation as observed experimentally [6] ( Fig . 3D ) . The distribution of the pseudo-period of oscillation is centered on a value of 21 . 2 hours with a standard deviation of 0 . 7 hour ( Fig . 3E ) which is in the range of experimental results [35] . To form the SCN network , we supposed that the cells are connected with directed ( unidirectional ) edges through the dendrites . The upstream cell produces a neurotransmitter acting on a signaling cascade in the downstream cell that increases expression , the coding mRNA ( Fig . 2 ) . The effect of the incoming signals from the different cells sums up until saturation ( see Eq . ( 3 ) in Models ) . The coupling parameter , that represents the strength of the effect of on PKA activation in the downstream cell , was set to a value of 0 . 5 for most simulations ( the effect of the value of will be discussed later ) . Note that , although is identical for all cells , intercellular heterogeneity causes variability in the connection strengths due to differences in the dynamics of the species involved in the cell-cell communication ( , PKA and CREB ) . In this analysis , we mainly focused on the effect of the network topology on the synchronization properties and ignore the effect of individual parameters . We selected three generic types of networks: random connections between cells ( ) , scale-free distribution of the outgoing edges ( ) , or local connections only ( ) ( see Models and Fig . 4 ) . Each type of network contains 200 cells and we tested different values of , the average number of edges per cell , ranging from 3 to 15 . For simulations with light , in agreement with the experimental observations [13] , we assumed that only 20% of cells , on average , are light-sensitive and the distribution of light-sensitive cells can be either random ( , or respectively , second column in Fig . 4 ) , biased to favor the cells with the highest outgoing degree ( or ) or spatially localized in the case of the local topology ( , third column in Fig . 4 ) . In the six topologies , the average degree and the fraction of light-sensitive cells are identical to allow a fair comparison ( see Tab . S2 ) . We first performed the following in silico experiments [9] , [11] , [36]: interruption of the protein production due to an administration of cycloheximide ( CHX ) or interruption of the cell-cell communication through exposure to tetrodotoxin ( TTX , see Models for implementation ) , both in a network without light entrainment . Our results are consistent with the experiments: cells stop oscillating upon exposure but quickly resynchronize after CHX ( Fig . 5A ) or TTX wash-out ( Fig . 5B ) . The phase of the individual oscillators ( measured at the stationary state , about 30 cycles after the perturbation ) is conserved after both CHX ( Pearson's , in Fig . 5C ) [11] and TTX ( Pearson's , in Fig . 5D ) perturbations [36] . Results in figure 5 were made using a scale-free network , but the other topologies tested in this work ( random and local ) display similar results . As previously reported [37] , the period of the individual oscillators is negatively correlated with the difference between the phase of the same oscillator and the phase of the network ( Pearson's , , Fig . S1 ) . To compare the different networks , we focused on the concentration of the PER/CRY complex ( variable ) averaged over all cells ( , see Eq . ( 7 ) in Models ) and evaluated its amplitude and period of oscillation ( see Fig . 6A and Models ) . As the networks are randomly generated , all results in figures 6 and 7 represent the mean of the value measured over 30 different networks . We also defined two different order parameters as described in the Models section , equations ( 8 ) and ( 9 ) : the state order parameter that measures how synchronized are the individual oscillators over the length of the simulation , and the phase order parameter , that measures how the individual oscillators are in phase at a given time ( note that this measure is independent of the magnitude of the amplitude ) . It is worth noting that the coupling function implies that a cell always acts on the dynamics of its downstream cells even if they are synchronized . This differs from a diffusive interaction ( e . g . Kuramoto oscillators [28] , [38] in which the coupling depends on the phase difference ) for which synchronized oscillators have no influence on each other . This property , along with cellular heterogeneity , prevent the application of theoretical results found in the literature [39] , [40] , and require a numerical analysis . In the following , we distinguish two different conditions of simulation: in constant dark ( DD , no entrainment , Fig . 6A ) or in a light-dark cycle ( LD , period of 24 hours with 12 hours of entrainment , Fig . 6B ) . The results of the mean amplitude and the oscillatory period for different network architectures ( average of 30 randomly chosen networks for each condition ) are shown in figure 6C–F . Considering the random networks , the amplitude of oscillations strongly depends on the average degree with the maximum value seen for an intermediate connectivity: 5 edges per cell for the case without entrainment ( Fig . 6C ) and 7 with entrainment ( Fig . 6D ) . This dependence on the number of edges is also reflected in the order parameters and whose values are maximal for an intermediate connectivity of ( Fig . S2 ) . The period in DD conditions is around 25 hours for low connectivity which is closer to experimental evidence [35] than the period of 22 hours found in highly connected networks . In LD conditions , all random networks have a 24-hour period , reflecting proper entrainment by light . For the scale-free networks , the amplitude the system exhibits in darkness is the largest of all topologies , also for very low connectivity ( Fig . 6C ) . It drops when increasing the number of edges and converges to the results of the other networks . The period of networks without entrainment is around 26 hours for and decreases down to 22 hours for higher connectivity as for the random networks . In light-entrained conditions , a significant difference can be noticed between the case where the light-sensitive cells are randomly distributed ( ) , and the case where the cells with high outgoing degree are light-sensitive ( ) . Although both network types show large amplitude , the networks do not systematically have the same period as the entrainment signal for because a significant fractions of the cells are not located downstream of a light-sensitive cell . On the other hand , networks have a period of 24 hours for all tested values which means that networks are more suitable to represent the SCN . For the local topology , we observed that , without entrainment ( left column in Fig . 6 ) , local networks have a low amplitude due to a lack of synchronization throughout the network ( see also Fig . S2A–B ) . Clearly , since the connections are only local ( Fig . 4 ) , the network does not have the small-world property [25] . On the other hand , with light entrainment , local networks with a random distribution ( ) of the light-sensitive cells have ample oscillations and a 24-hour period . In this specific case , due to the random distribution of light-sensitive cells , most of the cells are directly downstream of a cell entrained by light even for small ( Fig . S3 ) . In the case where the light-sensitive cells are closely localized ( ) , the entrainment efficiency is weak and the oscillation amplitude of is low . These results suggest that , in constant dark , the scale-free , and to a lesser extent , the random architectures with an intermediate connectivity ( 5–7 edges per cell on average ) seem to represent the experimental data best . In contrast , local architectures as defined in our work impede an efficient synchronization of the cells and therefore show small oscillations . In LD conditions , the distribution of the light-sensitive cells plays a significant role and the networks that have a smaller average distance to a light-sensitive cell ( Fig . S3 ) , i . e . the , , or networks , show a larger oscillatory amplitude ( Pearson's , over all networks types and average degrees ) . The relationship between the average number of degrees and the amplitude in both DD and LD conditions ( Fig . 6C–D ) suggests that a strong connectivity is detrimental for system performance . This raises the question of how the value of the coupling constant affects the network oscillations . While maintaining , a stronger coupling constant ( larger ) decreases the amplitude and the period of oscillations in DD conditions ( Fig . S4A , C ) . In light/dark conditions , the relation between and oscillation amplitude follows a bell-shaped curve , the maximum of which depends on the network type . For networks , a weak coupling ( ) is optimal , whereas an intermediate coupling ( ) favors networks and a strong coupling ( ) is preferred for random networks . Note that , for most of the values the performance ranking of the network types remains the same ( scale-free networks showing largest amplitude ) . In addition , although can be fine-tuned to increase the performance of a given network type , the results we obtained with are qualitatively similar to results with other values which is why will be used for further analyses . We then considered the case of a perturbation in the entrainment pattern of light/dark alternation . Since one of the goals of the circadian clock is to ensure the adaptation to the day-night cycle , an efficient clock should resynchronize rapidly after a jet lag . We chose the case of an 8-hour shift resulting in a long night of 20 hours , followed by the regular 12 h∶12 h LD cycle . As a measure of resynchronization , we considered the number of cycles until the system recovers , i . e . has a phase difference between the peak of and the beginning of the night similar to the one prior to the jet lag [41] . We also determined the maximal decrease of the phase order parameter after the jet lag as a measure of how the individual cells desynchronize as a consequence of the jet lag . As shown in figure 7A–C , the effect of a long night depends on the network type . In the case of an topology , the synchronization of the system is hardly perturbed ( blue line in Fig . 7A ) and the phase difference between the peak of and the beginning of the night recovers its value prior to the jet lag in about 3 cycles . On the contrary , the network needs about 6 cycles to regain the proper phase with a strong decrease of synchronization ( Fig . 7B ) . For the network , although the system experiences desynchronization , the phase difference is recovered in about 4 cycles ( Fig . 7C ) . A systematic analysis of the different network types shows that random networks ( and ) and scale-free networks with biased distribution of the light-sensitive cells ( ) undergo very little desynchronization ( Fig . 7D–E ) . Note that the results for the networks are less relevant because these networks display very low amplitude . In order to generalize the measured advantage of the , and network types for resynchronization after a jet lag , we tested 3 other types of 8-hour shifts: a short night , a long and a short days . The results ( summarized in Fig . S5 ) show that these three types of networks are also the best performers when experiencing other types of jet lags , but also that the long day or night ( delay shifts ) have less impact than the short day or night ( advance shifts ) . We further investigated this difference between delays and advances for and networks with . For different shifts ranging from 4 to 10 hours , long shifts induce longer resynchronization time ( Fig . 7F–G ) , but additionally , the network resynchronizes significantly faster after a delay than an advance of the same shift duration ( Wilcoxon's with n = 30 for all shifts and both networks , expect for with a 4-hour shift ) . Remarkably , this corresponds to experimental evidence on mice [42] and physiological observations showing that recovery from a jet lag due to westbound flights ( long day or night ) is easier than recovery from eastbound ones [43] . The next question we addressed concerns the separation of the SCN in two different regions , namely ventro-lateral ( VL ) and dorso-medial ( DM ) . Experimental observations have shown that the VL is entrained by light but oscillates with large amplitude even in dark conditions [11] , [12] . These properties closely correspond to networks with , or architectures . On the other hand , the current consensus for the DM , is an entrainment through the VL and not directly by light [11] . Additionally , when detached from the VL , the cells of the DM hardly oscillate and are not synchronized . When looking for these features in the network types studied above , a local network with random distribution of the entrained cells seems to best represent the DM . In terms of geometry , the VL forms a core surrounded by the DM which would lead to the hypothesis that connections between the VL and the DM regions occurs locally on the border between the two regions . A biased distribution of the light-sensitive cells in the VL is also plausible as the SCN is located above the optical chiasm ( Fig . 1 ) and thus the cells located in the lower part of the VL could be more sensitive to the light clues . Such configuration would allow a compact organization of the SCN without long neuronal connections ( Fig . 8A–B and S7 ) . To test this hypothesis , we performed simulations of a SCN composed of two regions with the following properties . The VL is modeled by an network composed of 200 cells and has an average value of edges . Random networks are also able to produce ample oscillations in the VL ( Fig . S6A , C ) , however the local networks are not plausible due to their low amplitude in DD conditions . For the DM , we chose a local network of 200 cells with surrounding the VL region as other topologies would require long connections across the VL . Cells of the VL and the DM are heterogeneous with parameters distributed as previously ( ) . Entrainment of the DM by the VL is made by local connections with an average outgoing degree of ( Fig . S7 ) . Note that this architecture implies that no DM cells can be upstream of a VL cell . The simulations of this system show good synchronization and entrainment of the DM part in both dark and light/dark conditions ( Fig . S8 ) . However we saw a delay of the DM phase in comparison to the VL ( Fig . S8B , D ) , which contradicts the experimental results [11] . To counter this problem , we used faster oscillating cells for the DM ( see Eq . ( 6 ) in Models ) as suggested by experimental data [44] . With this adjustment , the DM is not properly entrained by the VL because the free-running period of the whole DM is too short . This can be improved by decreasing to in the DM only ( Fig . 8 ) which results in oscillations with larger amplitude ( Fig . S4B ) in LD conditions , as well as an increase of the free-running period ( Fig . S4C ) . Additionally , reducing the coupling has also been suggested as a way of facilitating the entrainment [45] . With this configuration , the center of the DM is in phase with the VL and some exterior cells are in phase advance ( Fig . 8A , B ) . When isolated from the VL , the DM cells are not synchronized ( Fig . S9 ) which is in agreement with experimental observations [11] . Note that for a core formed of a random network , the DM is delayed in LD conditions despite these adjustments ( Fig . S6B , D ) . This suggests that a scale-free architecture is the most plausible topology for the VL region of the SCN . A possible advantage of a division of the SCN in two regions can be to filter disturbances of the entraining LD cycle . To test this hypothesis , we perturbed the light inputs in two different ways and measured the effect on in the VL and the DM regions of the SCN . The first perturbation is an interruption of 4 hours of the light cue during the day ( a pulse of light during the night has only a marginal effect and was therefore not studied further ) . In this case , ( Fig . 9A ) , the amplitude of the average concentration over the VL cells rises before dropping by about 20% . The initial value is recovered after about 10 cycles ( Fig . 9C ) . The phase is also affected , first delayed by about 1 . 5 hours and then advanced by the same value ( Fig . 9E ) . However , the amplitude of the DM part is hardly affected by the perturbation , although the maximal phase shift is similar . To quantify the effect of the perturbation , we defined as the average normalized difference between the peaks and the stationary peaks over 300 hours after the perturbation ( see Eq . ( 10 ) in Models ) . Averaged over 30 different networks , the effect of the 4 h light interruption on the VL is , which is 33% more ( Wilcoxon's ) than on the DM: . The second perturbation studied is , as previously , a jet lag of 8 hours occurring during the night ( resulting in a long night of 20 hours ) . The VL cell reacts strongly by increasing the peak value of oscillations by about 50% ( Fig . 9B , D ) . As already measured ( Fig . 7E ) , the phase of the VL adjusts precisely to the new entrainment pattern in about 4 cycles ( Fig . 9F ) . The phase of the DM follows the VL within one cycle reaching the correct phase in 5 cycles . Here also , we observed a strong difference between the VL and the DM parts of the SCN: whereas ( Wilcoxon's ) . These results suggest that the separation of the SCN in two parts with different topologies allows the DM region to have a lower sensitivity to short entrainment perturbations while at the same time better adapts to long term changes than a network formed of a unique topology such as the VL .
In this work , we addressed the question of the organization of the neuronal cells in the SCN by assessing the synchronization properties of different types of networks . In these networks , each cell is a circadian oscillator but the population shows heterogeneity in its oscillatory behavior as observed experimentally [7] , [6] . We found that , in general , the network is able to cope with cellular heterogeneity and the system oscillates with large amplitude and a period slightly longer than the individual period which is consistent with in vitro measures [46] . Our results show that the architecture of the network , independently of the number of cells in the network ( Fig . S10 ) , plays a significant role in the synchronization properties . In general , we observed that a strong connectivity , either due to a high number of connections or a strong value of the coupling constant , is detrimental for the amplitude of oscillations . The distribution of the edges also plays a critical role: Vasalou et al . [25] already observed that small-world networks are better synchronized than networks with local connections . Our results not only confirm that random networks better synchronize than our local networks , but also show that scale-free networks exhibit larger oscillations and better synchrony with fewer connections in DD conditions . In LD conditions , a strong correlation exists between the average distance to a light-sensitive cell and the performance of the network ( Fig . S3 ) . In our work , two types of networks result in a short average distance and therefore ample oscillations in LD conditions: ( 1 ) networks with a uniform degree distribution ( local or random ) and uniformly distributed light-sensitive cells , or ( 2 ) scale-free networks where the cells with high outgoing degree are light-sensitive . These results were obtained with a variability as the distribution of individual cells properties matched experimental data . We now briefly comment on the effect of the value for the different types of networks . To simplify the analysis , we varied only for networks with an average degree of as all types of networks show good performance for this value . In both DD and LD conditions , although the synchronization increases , oscillation amplitude remains similar for values of between 0 and 0 . 1 ( Fig . S11A–B ) , reflecting that the networks can efficiently cope with some cell-to-cell variability and that a tight tuning of individual oscillators is not necessary . This property holds for all types of networks . Cell heterogeneity also induces phase fluctuation [47] and we found a rather weak correlation ( Pearson correlation coefficient ) between individual phase differences and the period of the cellular oscillators ( Fig . S1 ) which is closer to experimental observations [47] than the high correlation reported for simpler models where heterogeneity was only introduced at the level of the period [37] . One of the properties of the circadian clock is adaptation to changes in the entrainment pattern for example after a jet lag or a long period of dark ( hibernation ) . Although circadian rhythms and chronotherapy play an important role in medicine , the specific case of jet lag has only been marginally discussed in the modeling literature [41] . Our contribution to this question shows that the network topologies are strongly related to the resetting of the SCN with an advantage for small-world networks ( such as random or scale-free networks with biased distribution of light-sensitive cells , ) with an intermediate connectivity of 5–7 edges per cell . When comparing our results to experiments [42] , [43] , we observed that the networks are closer to the experimental results where resynchronization is fast ( 2–3 cycles ) for delay , and slower ( 4–5 cycles ) for advance in the entrainment , confirming the observations that the circadian rhythm is more affected by eastbound than westbound-induced jet lags . It is also interesting to notice that a heterogeneous cell population seems to enhance resynchronization after a jet lag for the , and network types ( Fig . S11C–D ) . Remarkably , experimental observations already suggested that the SCN regional heterogeneity and the multiple phase relationships among SCN cells could contribute to the photoperiodic adaptation [48] . Alternatively , a different entrainment pattern with a shorter light exposure ( diurnal duration of 8 hours with a period of 24 hours ) , results in ampler oscillations than a system with a 16-hour light exposure especially for networks ( Fig . S12 ) , which is once again consistent with experimental observations [49] . Finally , the last and probably most ambitious part of this work consisted of coupling two networks with different properties to mimic the two regions of the SCN , namely the ventro-lateral and the dorso-medial parts . From our previous results , we selected a network combination that matched experimental facts: namely a core ( VL ) that is entrained by light and oscillates on its own , and a shell ( DM ) that can have sustained oscillations only while entrained by the VL . A scale-free network with biased distribution of the light-sensitive cells for the VL combined with a local network for the DM results in the desired properties with minimal connections . To more accurately match experimental data , we had to decrease the period of the cells in the DM as well as their coupling strength . With these adjustments , we obtained waves of expression through the SCN ( Fig . S13 ) as observed in cultured SCN slices [47] , [50] . Other combinations of parameters can possibly reproduce the properties of the VL and DM parts but our exploration of the different types of networks was not exhaustive , due to high number of possible combinations . We nevertheless tried different types of connectivity for the DM as well as different distribution of the edges ( allowing longer connections ) and eventually obtained a valid model of the SCN that can be used for further analysis . In this work , we found that combining two networks with different connectivity properties ( both in the topology , the strength of connections and the oscillation speed of the individual cells ) showed better results than a homogeneous network . These results may provide insight on why different neurotransmitters are found in the different regions of the SCN . Our results , proposing an optimal organization for the SCN , represent a step toward the understanding of the brain topology [51] . In practice , we can think that the neurons sensitive to light increase their number of connections to other cells in the SCN to form a scale-free network , an architecture already observed in C . elegans [52] . With such architecture , our model is able to reproduce many experimental results including the difference in recovery time between eastbound and westbound-induced jet lags , the larger amplitude for short days , and the distribution of the phase differences in the VL and DM regions of the SCN . The next stage in the SCN modeling would be to study how these topologies scale for a few thousands of cells in three dimensions [50]; indeed our hypotheses of a scale-free core with a surrounding shell should hold if the number of connections between the core and the shell remains sufficient . Further studies could take into account additional sources of noise such as the molecular noise due to the low number of molecules involved in the generation of circadian oscillation in a single cell [53] . This approach could help to determine whether circadian oscillations at the level of a single cell are noisy self-sustained oscillators or damped oscillators driven by noise as current single cell bioluminescence data are not sufficient to discriminate between the two hypotheses [54] . Other sources of variability such as differences in the light sensitivity , or in the cellular coupling [55] along with correlations in the parameter variability can impair or , on the contrary , contribute to the sustainability of the circadian oscillations [56] . Indeed , heterogeneity in the periods has also been shown to help the population of globally coupled Goodwin-like oscillators to respond in a more coherent way to the external light-dark cycle [57] . Future work could also include more details on the molecular mechanism involved in the signaling pathway to explicitly study the consequence of a loss of cAMP circadian production [24] . Another direction would be to analyze the role of the network topology on the robustness of the oscillations with respect to noise as well as other perturbations like mutations [58] . Finally , other oscillator models [30] , [32] should be tested and if our predictions ( high connectivity is detrimental , the DM is less perturbed than the VL ) are proven to be independent of the model , these results may have interesting medical applications and would be worth being studied experimentally in the context of circadian disturbances .
Using the generic parameters , the equations of the Becker-Weimann model [23] , [24] , extended to account for the receptor signaling cascade are ( 1 ) . Note that , in the network , each parameter of the cell has a specific values randomly drawn as described in equation ( 5 ) and Table S1 . ( 1 ) Note that no distinction between PER and CRY is made . Thus , denotes both Per mRNA and Cry mRNA , the PER/CRY cytosolic protein complex , and the PER/CRY nuclear protein complex . The regulated transcription rates of the Per/Cry and Bmal1 genes are modeled by the phenomenological functions and , respectively: ( 2 ) Parameter allows us to modulate the protein production rate . By default , is kept equal to 1 . In presence of cycloheximide ( CHX , a toxin used experimentally to decrease protein production ) , is decreased to 0 . 01 . The effect of light is expressed by the function which is a multiplicative smoothed square wave that oscillates between 1 and 2 , scaled by [23] , [59] , with a period simulating a 12 h∶12 h Light/Dark cycle:For parsimony , we assumed that the neurotransmitter is produced in a linear manner by the PER/CRY complex , but more complex rate functions such as Hill terms can produce equivalent results . The effect of cell-cell communication on the concentration of in the -th cell ( see Eq . ( 1 ) ) depends on the sum of the neurotransmitters from the upstream cells: ( 3 ) with , or when tetrodotoxin ( TTX , a neurotoxin that blocks cell-cell communication ) is added to the medium . is the concentration of in the -th cell , is the total number of cells , and is the adjacency matrix of the network . As self-loops induce strong self-sustained oscillations in individual cell , a behavior that contradicts our hypothesis about cells in isolation , we deliberately prevented self-loops ( i . e . ) . The topological characteristics of each set of 30 networks used for figure 5 are reported in Table S2 . The simulations of the system are made with an ordinary differential equation integrator in MATLAB . Parameters of the oscillator model are adjusted to obtain damped oscillations with an individual period around 21 hours and sustained oscillations when entrained by light or stimulated by another cell through intercellular communication . Although there is no direct biological evidence for the values of each individual parameter , these values are in their biological range and the model show results consistent with experimental evidence for individual cell behavior [24] , synchronization and entrainment . If we define the original parameter ( see description in Tab . S1 ) as ( 4 ) with and , the parameters of the -th cell are defined as: ( 5 ) where is the value in ( 4 ) , and is a uniformly distributed random number in the interval . The variability parameter represents the amplitude of the rescaling of the parameters in the model . For the model of the SCN composed of the VL and the DM regions , the cells in the DM are oscillating faster due to a rescaling of the kinetic constants in by a factor 1 . 15 prior to the draw of their parameters , i . e . ( 6 ) To calibrate ( Fig . 3 ) , the oscillatory behavior of the individual cells should be classified even for damped oscillators . To this purpose , we defined the ‘pseudo-cycle’ as the trajectory between two peaks of concentration . We considered that the cell stops oscillating ( are damped ) if the relative amplitude ( amplitude of a pseudo-cycle divided by the maximal value ) is lower than 0 . 1 . With this statement , we defined the ‘pseudo-period’ as the average duration of the pseudo-cycles until the cell is damped . Cells are called ‘Not Oscillating’ if they are overdamped ( i . e . no peak ) after the entrainment is released . A potentially important phenotype of the SCN is the average signal of the network . We considered the output to be the cell-average concentration of , as experimental studies usually measure the luminescence of a reporter linked to the PER gene [50] , [60]: ( 7 ) and we measured the mean amplitude and the mean period ( Fig . 6A ) over a time of 100 hours ( 300 hours for the VL-DM model ) after a relaxation time of 720 hours . We also defined two order parameters to quantify the synchronization of the cells in the network . The first one is the state order parameter based on defined as [23] , [61]: ( 8 ) where Var is the variance over time of . However informativeness-wise , this measure is not always appropriate as cells have different individual amplitude due to parameter variability . Moreover it is based on an average over time which implies that it cannot measure how cells are synchronized at a given time point . We therefore defined another order parameter based on the phase of the individual oscillators . If is the phase at time of the -th cell evaluated with the Hilbert transform ( see supplementary information of [62] ) and is the phase of the cell-average , the phase order parameter at each time point is: ( 9 ) For the results of the model composed of two regions , we measured how the extrema differ from their stationary values . In order to account for the stationary amplitude , we normalized the difference . If the ensemble of local minima for in the time interval is:the ensemble of local maxima:the cardinality of each ensemble and , and the absolute minimum and resp . maximum before the perturbation:the value calculates as: ( 10 ) This value is evaluated for the VL and the DM independently . | In order to adapt to their cycling environment , virtually all living organisms have developed an internal timer , the circadian clock . In mammals , the circadian pacemaker is composed of about 20 , 000 neurons , called the suprachiasmatic nucleus ( SCN ) located in the hypothalamus . The SCN receives light signals from the retina and controls peripheral circadian clocks to ensure the proper timing of physiological processes . In each SCN neuron , a genetic regulatory network enables the circadian expression of the clock genes , but individual dynamics are highly heterogeneous in dispersed cell culture: many cells present damped oscillations and the period of the oscillations varies from cell to cell . In addition , the neurotransmitters that ensure the intercellular coupling , and thereby the synchronization of the cellular rhythms , differ between the two main regions of the SCN . We present here a mathematical model that accounts for this heterogeneous organization of the SCN and study the implication of the network topology on synchronization and entrainment properties . Our results show that cellular heterogeneity may help the resynchronization after jet lag and suggest that the complex architecture of the SCN decreases the sensitivity of the network to short entrainment perturbations while , at the same time , improving its adaptation abilities to long term changes . | [
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| 2012 | Effect of Network Architecture on Synchronization and Entrainment Properties of the Circadian Oscillations in the Suprachiasmatic Nucleus |
The liver and pancreas originate from overlapping embryonic regions , and single-cell lineage tracing in zebrafish has shown that Bone morphogenetic protein 2b ( Bmp2b ) signaling is essential for determining the fate of bipotential hepatopancreatic progenitors towards the liver or pancreas . Despite its pivotal role , the gene regulatory networks functioning downstream of Bmp2b signaling in this process are poorly understood . We have identified four and a half LIM domains 1b ( fhl1b ) , which is primarily expressed in the prospective liver anlage , as a novel target of Bmp2b signaling . fhl1b depletion compromised liver specification and enhanced induction of pancreatic cells from endodermal progenitors . Conversely , overexpression of fhl1b favored liver specification and inhibited induction of pancreatic cells . By single-cell lineage tracing , we showed that fhl1b depletion led lateral endodermal cells , destined to become liver cells , to become pancreatic cells . Reversely , when fhl1b was overexpressed , medially located endodermal cells , fated to differentiate into pancreatic and intestinal cells , contributed to the liver by directly or indirectly modulating the discrete levels of pdx1 expression in endodermal progenitors . Moreover , loss of fhl1b increased the regenerative capacity of β-cells by increasing pdx1 and neurod expression in the hepatopancreatic ductal system . Altogether , these data reveal novel and critical functions of Fhl1b in the hepatic versus pancreatic fate decision and in β-cell regeneration .
Bone morphogenetic protein ( Bmp ) signaling plays an essential role in inducing the liver at the expense of Pdx1-expressing ventral pancreas and intestinal progenitors in different animal models [1–5] . In murine and zebrafish endodermal progenitors , Bmp signaling activates liver genes by affecting the expression levels of zinc finger transcription factor Gata4 [5 , 6] . In addition , Bmp signaling induces liver genes epigenetically by activating Smad4 , a common-mediator Smad , and recruiting a histone acetyltransferase , p300 [4] . This activation results in histone acetylation at the liver gene regulatory elements [4] . Meanwhile , several studies suggest that Bmp signaling may actively suppress the pancreas gene program . In mice , treating half-embryo cultures with Bmp4 at the 3–4 somite stage inhibited expression of Pdx1 [3] . Single-cell lineage tracing in zebrafish showed that lateral endodermal cells close to the Bmp2b signal keep pdx1 expression off , while medial cells distant from the Bmp2b signal turn on pdx1 , forming a medio-lateral pdx1 expression gradient [1] . The former differentiates into the liver and the latter gives rise to pdx1-positive tissues such as the ventral pancreas and intestine [1] . Consistently , inhibition of BMP signaling was critical for the induction of PDX1 at the expense of liver gene expression and the consequent generation of INSULIN-secreting β-cells in human embryonic stem cells ( hESCs ) and zebrafish [7–11] . Activation of Bmp signaling cell-autonomously blocked the induction of β-cells in zebrafish [7] . Nonetheless , the identity of downstream gene regulatory networks of Bmp signaling that specify the liver to the detriment of Pdx1-expressing cells remains to be further elucidated . Moreover , the key question of whether Bmp signaling suppresses Pdx1 expression keeping progenitors competent to differentiate into the liver or directly induces the liver gene program has not yet been answered . The hepatopancreatic ductal ( HPD ) system , which consists of the extrahepatic duct ( EHD ) , cystic duct ( CD ) , common bile duct ( CBD ) , and extrapancreatic duct ( EPD ) , connects the liver , gallbladder , and pancreas with the intestine . Amniotes and zebrafish have developmentally and structurally similar HPD systems , both originating from a specific domain within the foregut endoderm that lies between the emerging liver and pancreas [12] . Lineage tracing studies in mammals have revealed that the HPD system and the ventral pancreas , but not the liver , were derived from cells expressing both Pdx1 and Sox17 , a master regulator of the pancreaticobiliary ductal system [13] . These data are consistent with the pdx1 expression in zebrafish [14] . The existence of a progenitor cell population that can differentiate into liver or pancreas cells in the HPD system is supported by the wide spread misdifferentiation of hepatocyte-like and pancreatic-like cells in the HPD system of fgf10 and sox9 mutant zebrafish [12 , 15 , 16] . Notch signaling and pdx1 function have been further suggested to play essential roles in the induction of pancreatic endocrine cells from the progenitors in the HPD system and intrapancreatic ducts ( IPD ) of zebrafish [17] . Intriguingly , the expression of Inhibitor of DNA binding 2 ( Id2 ) protein , a cell-autonomous marker of Bmp signaling activity [18] , is excluded in the endocrine pancreas , HPD system , and intrapancreatic ducts [7] which are the tissues that retain the potential to form pancreatic endocrine cells . In a rat pancreatic epithelial cell line , Id2 has been implicated in repressing the function of key pancreatic endocrine transcription factor Neurod , which is essential for endocrine pancreas development [19] . In line with these data , suppression of Bmp signaling by dorsomorphin increased neogenesis of β-cells adjacent to the HPD system in zebrafish . Nonetheless , the underlying mechanisms of how Bmp signaling orchestrates the proper lineage choice of the progenitors in the HPD system await further investigation . β-cell regeneration can be promoted by either increasing residual β-cell proliferation [20] or stimulating neogenesis of new β-cells from non-β-cells . Non-β-cells include progenitors residing in the extra- and/or intra-pancreatic ductal structures [21] , other mature cell types including glucagon-expressing α-cells [22] , or digestive enzyme-secreting acinar cells [23] . Although the transcriptional network that regulates β-cell development has been well explored [24 , 25] , the signaling pathways that regulate β-cell regeneration remain largely unknown . Recently , the adenosine signaling pathway has been shown to increase β-cell proliferation during homeostatic control and regeneration of the β-cell mass in a zebrafish model of β-cell regeneration [26] . Nevertheless , compared to several studies that have discovered the origin of newly formed β-cells [27 , 28] , only a few studies have pinpointed extrinsic signaling pathways that can induce de novo formation of β-cells during regeneration . The LIM ( LIN-11 , ISL-1 , and MEC-3 ) domain is the key protein-protein interaction motif that integrates diverse cellular processes without intrinsic catalytic activity [29 , 30] . The LIM proteins often contribute to biological activity as molecular adaptors or scaffolds to support the assembly of multimeric protein complexes [31] . The four complete LIM domains with an N-terminal half LIM domain is characteristic of the four-and-a-half LIM ( FHL ) proteins [32] . These proteins are expressed in a cell- and tissue-specific manner to regulate cellular processes such as proliferation , differentiation , and adhesion/migration . However , little is known about their role in the cell fate choice between the liver and the pancreas and in β-cell regeneration . Here , by transcriptome analysis , we identified a novel Bmp2b target , four and a half LIM domains 1b ( fhl1b ) . fhl1b is primarily expressed in the prospective liver anlage . Loss- and gain-of-function as well as single-cell lineage tracing analyses indicate that Fhl1b inhibits specification of the pancreas and induces the liver . Moreover , Fhl1b regulates regeneration of insulin-secreting β-cells by directly or indirectly modulating pdx1 and neurod expression in the HPD system .
In order to uncover novel Bmp2b target genes essential for regulating the fate of bipotential hepatopancreatic progenitors , we performed the transcriptome analysis on endodermal tissues exposed to either increased or decreased levels of Bmp2b signaling ( S1A Fig ) . Total RNA from FACS-sorted Tg ( sox17:GFP ) s870-expressing endodermal cells [33] was used in gene expression profiling analysis . Known genes showing at least a 2-fold ( in the case of increased Bmp2b signaling ) or a 2 . 75-fold ( in the case of decreased Bmp2b signaling ) changes with p ≤ 0 . 05 were clustered by biological processes , which were derived from Gene Ontology analysis using PANTHER ( http://www . pantherdb . org/ ) ( S1B Fig ) . A total of 998 and 1261 genes showed changes in increased ( S1 Table ) and decreased ( S2 Table ) Bmp2b signaling , respectively . Among the genes that exhibited at least a 2-fold change in expression in both conditions ( S3 Table ) , four and a half LIM domains 1b ( fhl1b ) , which encodes a LIM domain only protein ( S2B Fig ) , had a prominent change in expression ( S1C Fig ) . The microarray results were confirmed by reverse transcription quantitative real-time polymerase chain reaction ( RT-qPCR ) analysis ( Fig 1A ) . The transcription of fhl1b , which was detected by RT-qPCR analysis of whole embryos , starts at the 12-somite stage after the endogenous bmp2b expression is initiated in the lateral plate mesoderm ( LPM ) ( Fig 1B; [1] ) . Double antibody and in situ hybridization staining in Tg ( sox17:GFP ) s870 embryos revealed that fhl1b is primarily expressed in the anterior part of the endoderm , which corresponds to the prospective liver anlage , at 22–24 hours-post-fertilization ( hpf ) ( Fig 1C , 1D , 1G and 1G” , black arrows; early-forming dorsal pancreatic bud , which gives rise exclusively to the principal islet , a cluster of endocrine cells , is marked by white and black dotted circles in G-G” ) . Additionally , fhl1b is expressed in the pronephric duct ( Fig 1C and 1E , blue arrowheads ) and heart ( Fig 1C , 1E and 1F , black arrowheads ) from 24 hpf onwards . Double antibody and in situ hybridization staining in TgBAC ( neurod:EGFP ) nl1 [34] embryos revealed that fhl1b continues to be highly expressed in the liver when the liver has started budding from the medially migrated endodermal rod [35] at 30 hpf ( Fig 1E , 1F and 1H , black arrows ) and 78 hpf ( Fig 1I , black arrow ) . At 78 hpf , levels of fhl1b are also high in patches of cells in the distal intestine ( Fig 1I ) , low in the HPD system ( Fig 1I , black bracket ) , and absent in most of the pancreatic cells except for a few cells in the periphery of the principal islet ( Fig 1I , yellow arrow; magnified images of fhl1b expression in the principal islet in Fig 1I’ and 1I” ) . To confirm that Bmp2b signaling regulates fhl1b , we examined fhl1b expression in the excess or absence of Bmp2b signaling . Compared to control embryos ( Fig 1J , black bracket , and Fig 1M ) , in embryos where bmp2b expression was induced at the 8-somite stage , which is before the initiation of endogenous bmp2b expression , fhl1b expression showed a significant anterior-posterior ( A-P ) and M-L expansion in the liver ( Fig 1K , black bracket , and Fig 1M ) . Furthermore , suppression of Bmp signaling with DMH1 ( a highly selective inhibitor of the BMP type I receptors Alk3 ( Bmpr1a ) and Alk8 ( Acvr1/Alk2 ) ) [36] led to a marked reduction of fhl1b expression in the liver at 30 hpf ( Fig 1L , black bracket , and Fig 1M ) . These data indicate that fhl1b is a target of Bmp2b signaling and it is primarily expressed in the prospective liver anlage . Based on the phylogenetic tree of zebrafish Fhl1b and the related proteins in mammals , Fhl1 was selected as the mouse ortholog of zebrafish Fhl1b ( S2A Fig ) . Fhl1 ablation exacerbated the cardiomyopathy in hypertrophic cardiomyopathy ( HCM ) mice [37] . Fhl1 shares 61% amino acid identity with Fhl1b ( S2B Fig ) . We examined mRNA and protein expression of Fhl1 in developing mouse embryos . At embryonic day 8 . 5 ( E8 . 5 ) - E9 . 5 , Fhl1 is detected in the foregut endoderm where the liver and the pancreas are derived [3] ( S2C Fig ) . From E10 . 5 onwards , Fhl1 is expressed in the liver ( S2C Fig ) . At E14 . 5 , Fhl1 proteins are highly expressed in the Prospero homeobox protein 1 ( Prox1 ) -positive liver cells ( S2D–S2D”‘ Fig ) , whereas their expression is weakly detected in the Prox1-positive pancreatic cells [38 , 39] ( S2E–S2E”‘ Fig ) . These findings suggest that similar to zebrafish Fhl1b , Fhl1 is expressed in the developing liver . Taken together , these results indicate that the hepatopancreatic expression of Fhl1b and its mouse ortholog Fhl1 is evolutionarily conserved . To elucidate the role of Fhl1b in regulating the fate choice of endodermal progenitors , we disrupted the function of fhl1b with morpholino oligonucleotides ( MOs ) either against the splice acceptor site of the second exon , which includes the start codon ( MO 1 ) , or against the splice donor site of the third exon ( MO 2 ) ( S3A Fig ) . At 30 hpf , either single MO 1- or MO 2- as well as a mixture of MO 1- and 2-injected embryos ( morphants ) showed a decrease of the hhex [40] expression domain in the liver ( Fig 2A and 2B , black arrows ) , whereas its expression appeared to be expanded in the early-forming dorsal pancreatic bud ( Fig 2A and 2B , white dotted circles ) . The pdx1 expression domain in morphants was also expanded in the dorsal pancreatic bud ( Fig 2C and 2D , white dotted circles ) , whereas its expression in the intestinal bulb primordium appeared to be reduced ( Fig 2C and 2D , black brackets ) . Immunostaining with the antibodies recognizing Pdx1 and the early liver marker Prospero homeobox protein 1 ( Prox1; [35] ) in Tg ( sox17:GFP ) s870 embryos [33] ( Figs 2E–2F’ and S4A–S4B’ ) as well as Islet and Prox1 in Tg ( ins:GFP ) zf5 embryos ( [41] , Fig 2G–2H’ ) , respectively , showed an evident reduction of the Prox1 expression domain in the liver ( Fig 2E–2H’ ) , an increase in the number of Tg ( ins:GFP ) zf5-expressing and Islet-positive pancreatic endocrine cells ( Fig 2G–2H’ , white dotted circles ) , and an expansion of the Pdx1-expressing cell population in the dorsal pancreatic bud in morphants at 30–36 hpf ( Figs 2E–2F’ and S4A , S4B , S4C and S4D , white dotted circles; 78 . 3±3 . 2 cells in controls vs . 101 . 6±4 . 1 cells in morphants; n = 5 per condition; P = 0 . 0009 ) . The Pdx1 expression domain in the intestinal primordium appeared to be decreased in morphants ( Fig 2E–2F’ , yellow brackets ) . At 55 hpf , morphants continuously exhibited an enlarged Insulin-expressing β-cell population ( Figs 2I–2K and S3C , S8I; 33 . 9±2 . 1 cells in controls vs . 57 . 8±3 . 6 cells in morphants; n = 5 per condition; P = 0 . 00003 ) with a reduced number of Prox1-positive cells in the liver ( Figs 2I–2K and S3C and S8I; 262 . 7±14 . 0 cells in controls vs . 148 . 0±15 . 2 cells in morphants; n = 5 per condition; P = 0 . 00003 ) . No TUNEL-positive liver cells were observed in fhl1b morphants at 48 hpf , suggesting that the small liver observed in morphants was not caused by enhanced cell death ( S5A and S5B Fig ) . In addition to the endodermal phenotypes , morphants displayed pericardial edema and a reduced heart rate from 30 hpf onwards ( S3H and S3I Fig , black arrowheads ) . To validate the specificities of fhl1b MOs , reverse transcription polymerase chain reaction ( RT-PCR ) was performed . MO 1 and 2 each blocked the endogenous splice sites of fhl1b and , as a result , either a deletion of exon 2 ( S3B Fig , MO 1 , white asterisk ) or a formation of a cryptic splice form of exon 3 ( S3B Fig , MO 2 , white asterisk ) occurred . A mixture of both MO 1 and 2 led to the deletion of both exon 2 and 3 ( S3B Fig , MO 1& 2 , white asterisk ) . MO-mediated knockdown can often induce apoptosis via aberrant p53 activation . Hence , we performed simultaneous knockdown of tp53 and fhl1b to ameliorate apoptosis induced by MO off-targeting [42] . Single fhl1b and double fhl1b/tp53 MO-injected embryos and larvae showed no difference in the phenotypes of small liver ( S3E and S3F , white circles , and S3K , S3M and S3N Fig ) , an increased Insulin-expressing β-cell population ( S3E , S3F and S3H–S3J Fig ) , and pericardial edema ( S3H–S3I , black arrowheads ) at 55 hpf and 5 days-post-fertilization ( dpf ) . These data indicate that fhl1b knockdown phenotypes in the endoderm and heart are independent of the p53 pathway . Throughout this study , fhl1b MOs were used as a mixture of MO 1 and 2 ( total 4 ng ) as each MO caused essentially the same phenotype ( S3C Fig ) , and standard control MO was used as a negative control . Furthermore , co-injection of fhl1b mRNA with a mixture of MO 1 and 2 partially rescued the effect of fhl1b MO knockdown ( S6A–S6D Fig ) . To complement fhl1b MO knockdown studies , knockout of fhl1b was performed by applying the CRISPR/Cas9 nuclease targeting system [43] , which has been shown to lead to highly efficient biallelic conversion in somatic cells in zebrafish [44] . We microinjected cas9 mRNA and two guide RNAs ( gRNAs ) , which were both designed to target overlapping regions in the exon 2 of fhl1b ( S7A Fig ) , into one-cell stage embryos . We found that 11 . 62% of Cas9/gRNA-treated embryos ( 38 out of 327 embryos ) showed an enlarged Insulin-expressing β-cell population ( 31 . 6±3 . 51 cells in controls vs . 45 . 3±6 . 0 cells in Cas9/gRNA-treated embryos; n = 5 per condition; P = 0 . 02 ) with a reduced number of Prox1-positive cells in the liver ( 265±18 . 6 cells in controls vs . 164±16 . 5 cells in Cas9/gRNA-treated embryos; n = 5 per condition; P = 0 . 002 ) as in fhl1b MO knockdown embryos at 55 hpf ( S7D–S7F Fig ) . We randomly selected 4 embryos with these phenotypes and confirmed to contain insertions/deletions ( indels ) with the T7 endonuclease I ( T7EI ) assay and Sanger sequencing . T7EI assay revealed that the percent gene modification in the 4 tested embryos was between 21 . 75% and 31 . 58% ( S7B Fig ) . Sanger sequencing of these 4 embryos ( 20–30 PCR amplicons were sequenced for each embryo ) confirmed site-specific insertions/deletions ( indels ) including 2–17 bp deletions or 2–11 bp insertions ( S7C Fig ) . Consistent with the report that Cas9 cuts the target DNA at six base pairs upstream of the protospacer adjacent motif ( PAM ) [45] , all mutations occurred at the 3′ end of the target sequence , further validating the sequence specificity of this targeting process . Taken together , these comparable MO knockdown and CRISPR/Cas9 knockout results suggest that Fhl1b is required for restraining endodermal progenitors from specifying to pancreatic endocrine cells and for the proper induction of the liver . To further analyze which pancreatic cell types are induced in fhl1b morphants , we first examined the expression of Tg ( P0-pax6b:GFP ) ulg515 , a pan-endocrine progenitor reporter [46] . The number of Tg ( P0-pax6b:GFP ) ulg515-expressing cells increased from 82 . 6±4 . 5 in controls to 103 . 2±2 . 0 in morphants at 30 hpf ( Figs 3A , 3B and 3I and S8G; n = 5 per condition; P = 0 . 0009 ) . Next , we investigated which endocrine subpopulation was expanded in the morphants . The number of Insulin-expressing β-cells was increased from 30 . 6±1 . 5 in controls to 44 . 6±2 . 0 in morphants at 30 hpf ( Figs 3C–3D and 3I and S8G; n = 5 per condition; P = 0 . 0004 ) . While the number of Somatostatin-expressing δ-cells was also increased in morphants ( Figs 3E , 3F and 3I and S8G; 20 . 7±0 . 8 cells in controls vs . 26 . 7±2 . 0 cells in morphants; n = 5 per condition; P = 0 . 0033 ) , the number of Glucagon-expressing α-cells appeared unaffected in morphants ( Figs 3G , 3H and 3I and S8G; 26 . 5±0 . 7 cells in controls vs . 24 . 6±1 . 5 cells in morphants; n = 5 per condition; P = 0 . 2 ) . As recently reported [47] , Insulin and Glucagon , but not Insulin and Somatostatin , are co-expressed in both control embryos and fhl1b morphants at 30 hpf ( S8G Fig ) . The number of these dual-hormone expressing cells was slightly increased in fhl1b morphants at 30 hpf ( S8G Fig; 8 . 0±1 . 0 cells in controls vs . 10 . 6±1 . 5 cells in morphants; n = 5 per condition; P = 0 . 03 ) . A previous report showed that cell-autonomous suppression of Bmp signaling is critical for the induction of endocrine cells derived not only from the early-forming dorsal bud but also from the late-forming ventral bud [7] . In zebrafish , the late-forming ventral pancreas , which mostly generates pancreatic exocrine cells ( acinar and duct cells ) and endocrine cells , subsequently encapsulates the early-forming , pdx1-positive , dorsal pancreas , which gives rise exclusively to the endocrine cells , thus establishing the mature pancreatic structure [14] . To test the role of Fhl1b in the induction of endocrine cells from the ventral bud specifically , we examined the numbers of the newly differentiated ventral bud-derived endocrine cells in Tg ( ins:GFP ) zf5;Tg ( ins:dsRed ) m1018 double transgenic embryos . As dsRed takes 18–22 hours longer than GFP to mature , we can distinguish GFP only ( ventral bud-derived ) from GFP/dsRed double-positive ( dorsal bud-derived ) β-cells until at least 60 hpf [48] . At 48 hpf , the number of GFP-only-positive β-cells increased in morphants compared to that of control embryos ( Figs 3J–3L and S8H; 5 . 0±0 . 7 cells in controls vs . 12 . 2±2 . 3 cells in morphants; n = 5 per condition; P = 0 . 0002 ) , suggesting an augmented induction of β-cells from the ventral bud . We further quantified total and subpopulations of pancreatic endocrine cells at 72 hpf . The number of Tg ( P0-pax6b:GFP ) ulg515- , Insulin- , and Somatostatin-expressing cells was increased from 98 . 6±3 . 0 , 32 . 3±2 . 0 , and 28 . 7±1 . 4 , respectively , in control embryos , to 132 . 0±5 . 2 , 54 . 8±3 . 5 , and 40 . 0±2 . 1 , respectively , in morphants ( S8A–S8G Fig; n = 5 per condition; P = 0 . 0003 , P = 0 . 0003 , P = 0 . 006 , respectively ) , while the number of Glucagon-expressing cells appeared unaffected ( S8C , S8F and S8G Fig; 28 . 6±1 . 1 cells in controls vs . 27 . 3±1 . 5 cells in morphants; n = 5 per condition; P = 0 . 2 ) . Altogether , these data suggest that Fhl1b is required for restricting the induction of pancreatic endocrine cells , specifically Insulin- and Somatostatin-expressing cells , from endodermal progenitors . In a converse experiment , we assessed the effects of ectopic expression of fhl1b on liver and pancreas induction . We overexpressed fhl1b using a heat-inducible transgene , Tg ( hsp:fhl1b; hsp:GFP ) gt3 . In response to heat shock , robust ectopic expression of GFP was observed in a variety of tissues throughout the embryos without any discernible body phenotype . Concurrent expression of fhl1b all over the embryos was confirmed with whole-mount in situ hybridization . When fhl1b expression was induced at the 8-somite stage , the initial time point of pdx1 expression in the pancreatic exocrine and intestinal progenitors and before the beginning of endogenous fhl1b expression , hhex expression domain was greatly expanded in the liver at 45 hpf ( Fig 4A and 4B , black arrows ) . In these embryos , pdx1 expression was significantly reduced in the intestinal bulb primordium and ventral pancreas , which gives rise mainly to the pancreatic exocrine cells , intestine cells , and a few endocrine cells ( Fig 4C and 4D , black brackets ) . pdx1 expression in the dorsal pancreatic bud appeared unaffected ( Fig 4C and 4D , white dotted circles ) , consistent with the previous data that the lineage of this bud is specified primarily during the gastrulation stage [1] . To determine whether specification of pancreatic exocrine cells is affected in fhl1b-overexpressing embryos , we examined the expression of Tg ( ptf1a:GFP ) jh1 [49] , which is largely restricted to the developing exocrine pancreas [50] , along with Prox1 , which is highly expressed in the liver and developing exocrine pancreas at 50 hpf . Compared to control embryos , we found that in the embryos where fhl1b expression was induced at the 8-somite stage , Tg ( ptf1a:GFP ) jh1expression was almost completely eliminated whereas the Prox1 expression domain was markedly expanded , suggesting that virtually all Prox1-expressing cells are liver cells ( Figs 4E–4F’ and S9A ) . Quantification showed that while 235 . 5±7 . 3 cells were Prox1-positive in control embryos , 306 . 5±12 . 6 cells expressed Prox1 in fhl1b-overexpressing embryos ( Figs 4G and S9B; n = 5 per condition; P = 0 . 000067 ) . In contrast , the number of Tg ( ptf1a:GFP ) jh1-expressing cells was decreased from 82 . 2±6 . 4 in controls to 16 . 0±5 . 2 in fhl1b-overexpressing embryos ( Figs 4H and S9B; n = 5 per condition; P = 0 . 000004 ) . These results suggest that Fhl1b is sufficient to inhibit specification of pancreatic exocrine cells and induce the liver . To determine the role of Fhl1b in the M-L patterning of endodermal progenitors , which is essential for the fate decision of liver versus pancreas [1] , we first examined the pdx1 gradient in the endodermal sheet of fhl1b-depleted embryos . From the 14-somite stage onwards , morphants showed a dramatic lateral expansion of the pdx1 expression domain ( Fig 5A and 5B ) . The expression domain of neurod , which marks pancreatic endocrine progenitor cells that express high levels of pdx1 ( corresponding to the cells with white asterisks in Fig 5A and 5B; [1 , 51] ) , was markedly expanded ( Fig 5C and 5D ) . Furthermore , multiple TgBAC ( neurod:EGFP ) nl1-expressing cells were found even in the lateral part of the endodermal sheet , which normally gives rise to the liver , exocrine pancreas , and intestine ( Fig 5E and 5F , white arrows ) [1] . Next , we performed single-cell lineage tracing experiments to examine possible cell fate changes caused by modulation of Fhl1b activity . Tg ( sox17:GFP ) s870 embryos were injected at the one-cell stage with the photoactivatable lineage tracer CMNB-caged fluorescein dextran conjugate , and single endodermal cells at 3 different M-L positions ( medial , lateral 1 , and lateral 2 ) at the level of somite 2 were uncaged using a 405nm laser at the 6–8 somite stage . In consistent with earlier data [1] , in control embryos , lateral 2 cells at the level of somite 2 predominantly gave rise to the exocrine pancreas , intestine , and liver , but rarely to the endocrine pancreas ( Figs 5G and S10C–S10E and S10F ( as L2 ) and S10H; in 1 out of 10 control embryos lateral 2 cells gave rise to the endocrine pancreas ) . In every fhl1b-depleted embryo , lateral 2 cells contributed to the pancreatic endocrine cells ( Figs 5H and S10D and S10F ( as fhl1b MO L2 ) and S10H; n = 10 ) . Assessment of exocrine pancreas development and differentiation by analyzing the expression of Tg ( ptf1a:GFP ) jh1 , which labels developing exocrine pancreatic cells , as well as that of Tg ( fabp10a:DsRed;ela3l:EGFP ) gz15 [52] , which marks differentiated hepatocytes and pancreatic acinar cells , showed a reduced number of pancreatic exocrine cells at 72 and 96 hpf ( S11A–S11D Fig ) . These data suggest that depletion of Fhl1b function results in the conversion from no/low to high pdx1-expressing cells , leading to a significant increase in the number of pancreatic endocrine cells along with a concomitant compromise of the development of liver and pancreatic exocrine cells , which are derivatives of no and low pdx1-expressing cells [1] . Conversely , we examined the pdx1 gradient in fhl1b-overexpressing embryos . In Tg ( hsp:fhl1b; hsp:GFP ) gt3 embryos in which fhl1b expression was induced at the 8-somite stage , medial cells , as their counterpart in control embryos , exhibited high levels of pdx1 ( Fig 5I–5J , white asterisks ) . Consistently , neurod expression appeared unaffected ( Fig 5K and 5L ) . In contrast , lateral cells exhibited greatly reduced levels of pdx1 compared to that of control embryos ( Fig 5I–5J , gray arrows ) , demonstrating that fhl1b overexpression during the post-gastrulation stage led to a decrease of pdx1 expression in the pancreatic exocrine and intestinal progenitors . Next , a single lateral 1 cell in Tg ( hsp:fhl1b; hsp:GFP ) gt3 embryos was heat- shocked and uncaged at the 6–8 somite stage . In every embryo where fhl1b expression was induced at the 6–8 somite stage , lateral 1 cells contributed to the liver ( Figs 5N and S10D and S10G ( as HS @ 8s L1 ) and S10H; n = 11 ) . However , in most control embryos lateral 1 cells only gave rise to the pancreas and intestine , but not to the liver ( Figs 5M and S10B and S10D and S10E and S10G ( as L1 ) and S10H; 1 out of 10 control embryos showed contribution of lateral 1 cells to the liver ) . These results indicate that augmentation of Fhl1b activity decreases pdx1 expression levels in pancreatic exocrine and intestinal progenitor cells , leading them to become liver cells . As previously reported [1] , the medial cells at the 6–8 somite stage , which express high levels of pdx1 , give rise mostly to pancreatic endocrine cells ( S10A and S10D and S10E and S10H Fig; n = 11 ) , indicating an early fate restriction of these cells primarily during the gastrulation stage . Intriguingly , forced induction of fhl1b during the gastrulation stage led to a significant reduction in the number of high and low pdx1-expressing cells resulting in a decrease in the number of Insulin-expressing cells and pancreatic exocrine cells ( S12A–S12G Fig ) . Taken together , these results suggest that Fhl1b plays an essential role in determining the precise patterning of medial and lateral endodermal progenitors by directly or indirectly modulating the levels of pdx1 expression for proper fate choice of liver versus pancreas . Our data indicate that Fhl1b is a novel physiological effector of Bmp2b signaling that regulates the adequate fate choice for liver and pancreas . To investigate the epistatic relationship between Bmp2b signaling and Fhl1b , we induced bmp2b expression at the 8-somite stage in the presence or absence of fhl1b . As previously reported [1] , in bmp2b-overexpressing embryos , the Prox1 expression domain in the liver was significantly expanded ( S13B Fig ) , whereas the number of Islet-positive pancreatic endocrine cells appeared unaffected ( S13B Fig; red; dorsal pancreatic bud is outlined by white dotted circle ) . We found that the majority of bmp2b-overexpressing fhl1b morphants exhibited an enlarged Islet-positive pancreatic endocrine cell population ( S13D Fig , 80%; red; dorsal pancreatic bud is outlined by white dotted circle ) with a reduced number of Prox1-positive cells in the liver ( S13D Fig , 80% ) as in fhl1b morphants ( S13C Fig ) , whereas a small portion of bmp2b-overexpressing fhl1b morphants restored the developmental defects of the liver and pancreatic endocrine formation ( S13D Fig , 20% ) . These results suggest that Fhl1b is a critical mediator of Bmp2b signaling in governing the liver versus pancreas fate decision . Furthermore , these data raise the possibility of other effector ( s ) of Bmp2b signaling that may act in concert with Fhl1b in this process . Hence , we analyzed the function of Id2 , which has been shown to suppress the function of Neurod [19] and an immediate target of Bmp signaling [18] . Zebrafish have two id2 genes: id2a and id2b [53] . Only id2a is expressed in the liver from 30 hpf onwards [53] . We conducted loss-of-function analyses using published id2a MO [54] . While id2a morphants showed a decrease of the hhex expression domain in the liver ( S13F Fig , black arrow ) , its expression in the dorsal pancreas appeared unaffected at 30 hpf ( S13F Fig , white dotted circle ) . Consistently , the pdx1 expression domain in id2a morphants was comparable to that of control embryos ( S13J Fig ) . Double id2a/fhl1b morphants exhibited synergistically more severe defects in liver formation ( S13H Fig , black arrow ) than that of single id2a ( S13F Fig , black arrow ) or single fhl1b morphants ( S13G Fig , black arrow ) , whereas the dorsal pancreas of double morphants ( S13H and S13L Fig , white dotted circles ) phenocopied that of single fhl1b morphants ( S13G and S13K Fig , white dotted circles ) . The expression of id2a in the liver biliary epithelial cells of morphants was comparable to that of control embryos at 72 hpf ( S13M and S13N Fig ) . These data suggest that Id2a is required for hepatic outgrowth , not for the fate decision of liver versus pancreas . Given our results showing incomplete penetrance of phenotype in fhl1b morphants ( S3C Fig ) and restoration of the liver and pancreatic endocrine formation defects in a small portion of bmp2b-overexpressing fhl1b morphants ( S13D Fig , 20% ) , these data indicate that other effector ( s ) of Bmp2b signaling may also function to regulate the liver versus pancreas fate decision at least in part ( S13O Fig ) , while Fhl1b plays a major role to govern this process . Given the critical role of Fhl1b in restricting the induction of pancreatic endocrine cells , we investigated whether altering Fhl1b activity changes β-cell regeneration efficiency . Using Tg ( ins:CFP-Eco . NfsB ) s892 ( abbreviated as Tg ( ins:CFP-NTR ) s892 ) [55] together with Tg ( ins:Kaede ) jh6 [56] , we compared the β-cell regeneration efficiency in control vs . fhl1b MO-injected larvae . We first converted the fluorescence of the Kaede protein from green to red by exposing the larvae to UV light . This conversion permanently marked all β-cells that were present before the ablation step . We then treated the larvae from 84−108 hpf with metronidazole ( MTZ ) to ablate the β-cells . In this set-up , newly formed β-cells express green-fluorescent Kaede only , whereas β-cells that survive the ablation co-express red- and green-fluorescent Kaede . We observed that a greater number of green-only β-cells regenerated in fhl1b MO-injected recovering larvae than in control recovering larvae ( Figs 6A–6C and S14A; 3 . 8±1 . 3 cells per islet in controls vs . 9 . 6±1 . 4 cells per islet in fhl1b MO-injected larvae; n = 10 per condition; P = 0 . 00000005 ) . Conversely , we overexpressed fhl1b using Tg ( hsp:fhl1b; hsp:GFP ) gt3 in conjunction with Tg ( ins:CFP-NTR ) s892 and Tg ( ins:Kaede ) jh6 to measure the regenerative efficiency of β-cells in control vs . fhl1b-overexpressing larvae . We found that the number of regenerated β-cells significantly decreased when fhl1b was induced at 50 hpf ( S14A Fig; 3 . 8±1 . 3 cells per islet in controls vs . 1 . 5±0 . 5 cells per islet in fhl1b-overexpressing larvae; n = 10 per each condition; P = 0 . 0008 ) . We further examined the underlying mechanism of how Fhl1b modulates the efficiency of β-cell regeneration . At 72 hpf , the number of Islet-positive cells in or adjacent to the HPD system dramatically decreased after inducing fhl1b at 50 hpf even in the presence of Fgf receptor inhibitor SU5402 , which induces ectopic Islet1-positive cells in the HPD system [7] ( S15A–S15C’ Fig ) . Conversely , at 72 hpf , fhl1b morphants showed a dramatic increase of pdx1 and neurod expression in the principal islet ( Fig 6D–6G , white dotted circles ) and in the HPD system ( Fig 6D–6G , white brackets ) . In line with these expression data , in recovering fhl1b MO-injected larvae , multiple regenerating β-cells were found at the junction between the pancreas and the HPD system marked with 2F11 [57] ( S14C–S14C” Fig , white arrows ) . Intriguingly , fhl1b and pdx1 exhibit a reciprocal expression pattern in control embryos at 3 dpf . The level of fhl1b expression is high in the liver ( Fig 6I , black arrow ) and in patches of cells in the distal intestine ( Fig 6I , white dotted lines ) , low in the HPD system ( Fig 6I , white bracket ) , and absent in most pancreatic cells except for a few cells in the principal islet ( Fig 6I , yellow arrow ) . Double antibody and in situ hybridization staining in Tg ( ins:GFP ) zf5 embryos at 3 dpf showed that in the principal islet , fhl1b expression is confined to the peripheral boundary and does not overlap with the centrally located β-cells ( S14D Fig , yellow arrow ) nor does with the δ-cells ( S14E Fig , black arrowheads ) but partially with a small number of α-cells ( S14F Fig , black arrowheads ) . The pdx1 level of expression is high in the proximal intestine ( Fig 6H , white dotted lines ) and in most pancreatic cells , moderate in the HPD system ( Fig 6H , white bracket ) , and absent in the liver ( Fig 6H ) . These results indicate that the antagonistic interplay between fhl1b and pdx1 may affect β-cell regeneration by directly or indirectly modulating pdx1 and neurod expression in the HPD system . Previous studies showed that glucose is crucial for β-cell differentiation and regeneration [47 , 58] and acts as a potent β-cell mitogen [59–61] . To test the possibility of whether Fhl1b regulates β-cell regeneration by affecting liver-derived glucose production , we measured free glucose levels . At 3 dpf , prior to MTZ treatment , there was no significant difference in free glucose levels between control/WT , fhl1b-MO injected , and fhl1b-overexpressing larvae ( S14G Fig ) . Free glucose levels were dramatically elevated after β-cell ablation , but were recovered to a great extent from 5–7 dpf in MTZ-treated , MTZ/fhl1b MO-injected , and MTZ/fhl1b-overexpressing larvae ( S14G Fig ) . Importantly , normal levels of free glucose were recovered significantly faster in the MTZ/fhl1b MO-injected larvae ( S14G Fig , green line ) than in the MTZ-treated ( S14G Fig , red line ) or MTZ/fhl1b-overexpressing larvae ( S14G Fig , purple line ) . Furthermore , MTZ/fhl1b-overexpressing larvae still had increased levels of free glucose at 7 dpf ( S14G Fig , purple line ) compared to MTZ-treated ( S14G Fig , red line ) or MTZ/fhl1b MO-injected larvae ( S14G Fig , green line ) . Taken together , these data suggest that the activity of Fhl1b on the HPD system , rather than the liver-derived glucose production , can modulate the efficiency in restoration of functional β-cells .
In this study , we analyzed the essential functions of a novel Bmp2b downstream effector Fhl1b in the hepatic versus pancreatic fate decision and in β-cell regeneration . In bipotential hepatopancreatic progenitors from the 12-somite stage onwards , Fhl1b regulates the proper cell fate choice of the liver over the pancreas by directly or indirectly modulating the discrete levels of pdx1 expression ( Fig 7A and 7B ) . fhl1b depletion compromised liver and exocrine pancreas specification and enhanced induction of pancreatic endocrine cells , causing a hepatic-to-pancreatic endocrine fate switch . Conversely , fhl1b overexpression at the 8-somite stage promoted liver specification and inhibited induction of pancreatic cells , redirecting pancreatic progenitors to become liver cells . In the progenitors residing in the HPD system at later stages , Fhl1b regulates induction of pancreatic endocrine cells and regeneration of β-cells ( Fig 7C ) . Suppression of fhl1b increased pdx1 and neurod expression in HPD progenitors , augmenting pancreatic endocrine cell formation and β-cell regeneration , whereas overexpression of fhl1b inhibited induction of pancreatic endocrine cells and β-cell regeneration . Previously , we showed that there is a medial-lateral pdx1 “gradient” in the endodermal sheet in zebrafish [1] . The most medial cells with high levels of pdx1 mainly gave rise to pancreatic endocrine cells , whereas lateral 1 cells with low levels of pdx1 gave rise to pancreatic exocrine cells and intestinal cells , as well as a few pancreatic endocrine cells . Some lateral 2 cells without pdx1 expression populate the liver . Consistently in mice , a hypomorphic allele with targeted deletion of a cis-regulatory region of Pdx1 in combination with a protein-null allele has demonstrated that the level of Pdx1 gene activity is differentially required for the proper development of the pancreas and subsequent lineage allocation of the pancreatic endocrine cells [62] . While homozygous mutants of the Pdx1 enhancer region deletion resulted in severe impairment of endocrine maturation , but normal formation of acinar tissue , heterozygous mice showed an islet size similar to that of wild type mice with abnormally more α and pancreatic polypeptide- producing PP cells , but fewer differentiated β-cells . These findings support the possibility that conversion of common endocrine precursors to β-cells relies on a high-level of Pdx1 expression . Our studies show that depletion of fhl1b resulted in the conversion from no/low to high pdx1-expressing cells , which is marked by neurod expression . This conversion led to a significant increase in the number of pancreatic endocrine cells , especially β-cells , and compromised the development of liver and pancreatic exocrine cells which are derivatives of no and low pdx1-expressing cells . In these embryos , lateral 2 cells contributed frequently to pancreatic endocrine cells . Conversely , fhl1b overexpression at the post-gastrulation stage ( i . e . 8-somite stage ) caused a decrease in the number of low pdx1-expressing cells , leading to the induction of the liver at the expense of the exocrine pancreas . In these embryos , lateral 1 cells contributed primarily to liver cells . When fhl1b was overexpressed during the gastrulation stage , it led to a decrease in the number of low and high pdx1-expressing cells , resulting in a subsequent reduction in the number of pancreatic exocrine cells and Insulin-expressing β-cells . These data confirm the critical role of Fhl1b in directly or indirectly modulating pdx1 levels to coordinate the medio-lateral patterning of the endodermal sheet for proper induction of the liver and pancreas . Intriguingly , the numbers of β- and δ-cells were increased , whereas the number of α-cells appeared unaffected in fhl1b morphants . These results are consistent with previous data that Bmp receptor alk8 MO-injected donor cells mainly gave rise to β- and δ-cells , but rarely to α-cells [7] . It has been shown that β/δ-cell versus α-cell fate is differentially regulated by Pax4 and Arx [63] . Moreover , overexpression of Pdx1 eliminated glucagon mRNA and protein and promoted the expression of β-cell specific genes , while induction of dominant-negative Pdx1 resulted in differentiation of β-cells into α-cells in the rat insulinoma cell line [64] . Hence it is plausible to speculate that Fhl1b is an essential mediator of Bmp signaling by directly or indirectly regulating the discrete levels of pdx1 expression for precise lineage allocation of the pancreatic endocrine progenitors . Interestingly , we found that in a portion of embryos from 30 hpf onwards , fhl1b is also expressed in the TgBAC ( neurod:EGFP ) nl1-expressing cells . Therefore , it is possible to hypothesize that after serving as an essential effector for the hepatic versus pancreatic fate decision , Fhl1b may function further to fine-tune the lineage allocation of the specified pancreatic endocrine cells . As LIM proteins often function as molecular adaptors or scaffolds to support the assembly of multimeric protein complexes [31] , it will be intriguing to determine whether Fhl1b directly modulates pdx1 expression by facilitating the formation of a novel protein complex that is involved in either mediator-or chromatin-mediated gene expression control . Previous studies have suggested the plasticity of cells in the HPD system , where differentiation into a specific lineage is suppressed by Fgf10 and Sox9b in zebrafish [12 , 15 , 16] . Furthermore , expression analysis of Id2 has shown that Bmp signaling is blocked and/or excluded in HPD and non-HPD tissues ( principal islets and intra-pancreatic ducts ) that retain the potential to form pancreatic endocrine cells [7] . Our data provide the intriguing evidence that Bmp2b signaling controls the induction of pancreatic endocrine cells from the HPD system by inhibiting pdx1 expression through its effector Fhl1b . The reciprocal expression pattern of fhl1b and pdx1 further supports the suppressive effect of Fhl1b on pdx1 expression . At 3 dpf , liver cells , which never express pdx1 in lineage tracing analyses in mice [62 , 65] and in zebrafish [1] , express high levels of fhl1b , while the HPD system expresses low levels of fhl1b . Consistently , the proximal intestine , which has been shown to have marked plasticity [12] , expresses low levels of fhl1b . Most pancreatic cells do not express fhl1b except for a few cells in the principal islet . Intriguingly , these few pancreatic cells are located in the peripheral boundary of the principal islet and partially overlap with a small number of α-cells , not with the core β-cells , which maintain a high-level of pdx1 expression . Manipulating this antagonistic interplay may direct a common endodermal progenitor pool towards pancreatic endocrine , specifically β-cell , fate by directly or indirectly modulating distinct levels of pdx1 expression . While the intrinsic transcriptional network that regulates β-cell development is well identified [24 , 25] , the extrinsic signaling pathways that control β-cell regeneration remain largely elusive . For the first time , our studies suggest that Bmp signaling plays an essential role in the regeneration of β-cells , in part by directly or indirectly modulating pdx1 and neurod expression in the HPD system through its regulator Fhl1b . Our loss-of-function analyses of Fhl1b during development imply that increased formation of endocrine progenitors may primarily lead to enhanced β-cell regeneration . In line with this hypothesis , in β-cell ablated fhl1b MO-injected larvae , multiple regenerated β-cells were found at the junction between the pancreas and the HPD system , specifically at the extrapancreatic duct ( EPD ) . However , because of the low expression levels of fhl1b in a small population of α-cells , we were not able to exclude the compelling possibility that fhl1b depletion lead to the occurrence of high pdx1+ α-cells , augmenting β-cell regeneration . In mouse and zebrafish models of β-cell regeneration , Pdx1 is detected in α-cells during α-to β-cell transdifferentiation [22 , 47 , 66] , contrary to its normal detection in β-cells [67] . In contrast to Bmp signaling , adenosine signaling , one of the few signals that has been shown to function during β-cell depletion in zebrafish [26] , plays a significant role in regulating β-cell mass during regeneration , but not under normal conditions . Careful dissection of extrinsic signals and intrinsic factors acting on a specific aspect of β-cell regeneration will allow us to perform individual or combinatorial therapies to pinpoint the most valid regeneration strategy . Our findings of Bmp2b regulation of Fhl1b suggest a new paradigm of how Bmp signaling regulates the cell fate choice of liver versus pancreas and β-cell mass . Despite the long-standing focus on the active role of Bmp signaling on the liver gene program through both genetic and epigenetic regulation [4–6] , the link between Fhl1b and pdx1 expression shown in this study suggests that Bmp may function actively to suppress the pancreas gene program to properly modulate liver induction , lineage allocation , and β-cell regeneration . Hence , our data elucidates why effective BMP suppression is critical for the induction of PDX1 and the subsequent generation of β-cells in human pluripotent stem cells ( hESCs ) [8–11] and zebrafish endodermal progenitors [7] . A comprehensive understanding of how lineage-specific multipotent progenitors make a developmental choice will shed light on the programming and reprogramming of stem/progenitor cells into specific cell lineages , enabling us to generate functionally relevant cells for clinical utility .
This study was approved by the Institutional Animal Care and Use Committee at Georgia Institute of Technology ( A13075 ) . All animal work was performed according to procedures approved by the Institutional Animal Care and Use Committee at Georgia Institute of Technology . This study was approved by the Institutional Animal Care and Use Committee at Georgia Institute of Technology ( A13075 ) . All animal work was performed according to procedures approved by the Institutional Animal Care and Use Committee at Georgia Institute of Technology . Adult fish and embryos were raised and maintained under standard laboratory conditions [68] . We used the following published zebrafish transgenic lines: Tg ( P0-pax6b:GFP ) ulg515 [46] , Tg ( ins:GFP ) zf5 [41] , Tg ( ins:dsRed ) m1018 ( from W . Driever , Freiburg ) , TgBAC ( neurod:EGFP ) nl1 [34] , Tg ( sox17:GFP ) s870 [33] , Tg ( hsp70l:bmp2b ) f13 [69] , Tg ( fabp10:dsRed , ela3l:GFP ) gz12 [52] , Tg ( ptf1a:GFP ) jh1 [49] , Tg ( ins:Kaede ) jh6 [56] , Tg ( ins:CFP-NTR ) s892 [55] , and Tg ( fabp10a:CFP-Eco . NfsB ) gt1 [70] . To generate the Tg ( hsp:fhl1b; hsp:GFP ) gt3 , fhl1b coding sequence was amplified ( forward: 5’-CCGGAATTCATGGCAAGCCGGTCCAACTG-3’ , reverse: 5’-CCGGAATTCTTACAGTTTCTTGGAGCAGTCG-3’ ) and cloned into a vector containing a multimerized minimal heat shock promoter , which drives gfp and fhl1b transcription bi-directionally in response to a heat shock [71] . Tol2-mediated transgenesis was achieved as described [72] . Tg ( sox17:GFP ) s870 embryos were either crossed with Tg ( hsp70l:bmp2b ) f13 to induce overexpression of bmp2b at the 8-somite stage or treated with 0 . 3 μM DMH1 . For each condition , 100 embryos were used . At 20 hpf , sox17:GFP-positive endodermal cells from dissected zebrafish trunks containing the organ-forming area were isolated by FACS and subjected to transcriptome profiling using the Zebrafish 44K gene expression microarray ( Agilent Technologies ) . Data with an average fold change of 2 ( bmp2b overexpressing ) or 2 . 75 ( DMH1-treated ) at p ≤ 0 . 05 were considered for GO analysis using PANTHER ( http://www . pantherdb . org/ ) . The phylogenetic tree of zebrafish Fhl1b ( NM_199217 ) was constructed using Phylogeny . fr [73] with mammalian homologous proteins sorted by performing alignment on UniProtKB/Swiss-Prot database . Total RNA was extracted using the Trizol Reagent ( Invitrogen ) . cDNA synthesis was performed using Superscript III First-strand Synthesis System ( Invitrogen ) . PCR was conducted using iTaq Universal SYBR Green Supermix in triplicate ( Bio-Rad ) . Optimized primers targeting each gene were designed using Primer3 [74] . The StepONE Plus PCR System ( Applied Biosystems ) was used to obtain the Ct value . The relative gene expression of each sample was determined using the comparative Ct method with β-actin as an internal control [75] . The following primers were used: fhl1b: forward 5’-GTGAGGAAAGACGAGAAACAAG-3’ , reverse 5’-GGCACATCGGAAACAATCAG-3’; β-actin: forward 5’-CGAGCTGTCTTCCCATCCA-3’ , reverse 5’-TCACCAACGTAGCTGTCTTTCTG-3’; mouse Fhl1: forward 5’- ATAAGGTGGGCACCATGTCGG-3’ , reverse 5’- GTGATTCCTCCAGATGTGATGG-3’ . Knockdown of fhl1b was performed via injection of individual fhl1b MO 1 ( 2 ng; 5’-CCCGCGAAAAGCTGTGAGAAATAAT-3’ ) or MO 2 ( 2 ng; 5’-ATAAATATCTGTCCCCTCACCTGGC-3’ ) or a combination of MO 1 and 2 ( 4 ng; Gene Tools , LLC ) . A standard control MO ( 4 ng; 5’-CCTCTTACCTCAGTTACAATTTATA-3’ ) targeting a human beta-globin intron mutation was used as a negative control ( Gene Tools , LLC ) . id2a MO ( 5’ -GCCTTCATGTTGACAGCAGGATTTC-3’ ) [54] and tp53 MO ( 5’- GACCTCCTCTCCACTAAACTACGAT-3’ ) [42]were purchased from Gene Tools , LLC . 4 ng of id2a MO or 2 ng of tp53 MO was used . The primers annealing to the first ( 5’- GCAAAACACTTTGCTGTGGC-3’ ) and the sixth ( 5’- GCCAGGTTGAGGGAGCATTT-3’ ) coding exons were used to confirm the specificity of fhl1b MOs . Sense-strand-capped fhl1b-P2A-mCherry mRNA was synthesized with mMESSAGE mMACHINE kit ( Ambion ) . For rescue experiments , embryos were injected with 200 pg of fhl1b-P2A-mCherry mRNA with a mixture of fhl1b MO 1 and MO 2 . Whole-mount in situ hybridization was performed as previously described [76] , using the following probes: pdx1 [77] , neurod [78] , hhex [79] , and fhl1b ( template for antisense RNA probe was amplified from embryonic cDNA with the following primers: forward: 5’-CCGCTCGAGATGGCAAGCCGGTCCAACTG-3’ , reverse: 5’-ACGGCTGGTCCTGGTAATTC-3’ ) . Immunohistochemistry on whole-mount zebrafish embryos was performed as previously described [12] using the following antibodies: mouse anti-Glucagon ( 1:100; Sigma ) , mouse anti-2F11 ( 1:200; Abcam ) , mouse anti-β-catenin ( 1:100; BD Transduction Laboratories ) , chicken anti-GFP ( 1:1000; Aves Labs ) , rabbit anti-Somatostatin ( 1:100; MP Biomedicals ) , mouse anti-Islet1/2 ( 1:10; Developmental Studies Hybridoma Bank ( DSHB ) , clone 39 . 4D5 ) , guinea pig anti-Insulin ( 1:100; Sigma ) , rabbit anti-Prox1 ( 1:100; Millipore ) , guinea pig anti-Pdx1 ( 1:200; gift from C . Wright ) , rabbit anti-pan-Cadherin ( 1:1000; Sigma ) , goat anti-Fluorescein ( 1:100; Molecular Probes ) , rabbit anti-Carboxypeptidase ( 1:100; Rockland ) , and fluorescently conjugated Alexa antibodies ( 1:200; Molecular Probes ) . Nuclei were visualized with TOPRO ( 1:10000; Molecular Probes ) . For the TUNEL assay , embryos were fixed in 3% formaldehyde , preincubated in PBST , and then labeled with the TUNEL kit ( Roche ) for 1 hour at 37°C . For coimmunostaining with Prox1 , sections were first incubated with primary antibodies , then with TUNEL solutions , and finally with secondary antibodies . For the detection of mouse Fhl1 protein , the entire gut , including the liver and pancreas of E14 . 5 mice , was isolated and fixed in 4% paraformaldehyde , then embedded in Tissue-Tek OCT compound ( Sakara Finetek ) . 8 μm cryostat sections were obtained by using a cryostat microtome ( Leica CM1520 ) . Immunohistochemistry was performed using the following antibodies: rabbit anti-FHL1 ( 1:200; Abcam ) , goat anti-Prox1 ( 1:20; R&D Systems ) , and fluorescently conjugated Alexa antibodies ( 1:200; Molecular Probes ) . Embryos and sections were mounted in Vectashield ( Vector Laboratories ) and imaged on a Zeiss LSM 510 VIS confocal microscope . The guide RNA ( gRNA ) targeting sites , which are downstream of the start codon ( gRNA 1: 5’- CTGTCGTGAGGACCTCAG-3’ , gRNA 2: 5’- AGTGGAAAGAAGTTCGTG-3’ ) , were selected using the online application available at crispr . mit . edu . Complementary oligonucleotides corresponding to the target sequences were annealed as previously described [44] . Annealed oligonucleotides ( 1 μl ) were mixed with 500 ng of the gRNA cloning vector pDR274 , 0 . 5 μl of BsaI-HF , 0 . 5 μl of T4 DNA ligase , 1 μl of 10× NEB buffer 2 , 1 μl of 10× T4 ligase buffer , and water for a total of 10 μl . Digestion and ligation were performed in a single step as previously described [44] . The gRNAs were transcribed using HindIII-digested expression vectors as templates and the MEGAshortscript T7 kit ( Life Technologies ) . The cas9 mRNA was transcribed using NotI-digested Cas9 expression vector and the mMESSAGE mMACHINE kit ( Ambion ) . The mixture of 1 nl of cas9 mRNA ( 300–450 ng/μl ) and an individual or a combination of gRNA 1 and 2 ( final concentration 12 . 5 ng/μl ) were injected into one-cell stage embryos . The genomic region flanking the target sites was amplified using PCR ( forward: 5’-ACTTACACATGAGGGGCTGTG-3’ , reverse: 5’- ATAGTCCTTAATGGAAAACATGCTG-3’ ) . A total of 200 ng of the purified PCR products was denatured and re-annealed , as previously described [44] to facilitate heteroduplex formation . The re-annealed products were digested with 10 units of T7 endonuclease I ( New England Biolabs ) at 37°C for 30 min . The reaction was stopped by adding 1 μl of 0 . 5 M EDTA . Samples were analyzed by 2% agarose gel . Band intensity was quantified using ImageJ software ( National Institutes of Health ) . Gene modification levels were estimated based on the following equation [80]: %gene modification=100× ( 1− ( 1−fraction cleaved ) 12 ) % . For sequencing the target region in injected embryos , the PCR products were cloned into pGEM T-easy vector ( Promega ) . Plasmid DNA was isolated from individual transformants and sequenced . Embryos were treated with 0 . 3 μM DMH1 ( EMD Chemicals ) from 12 hpf to 20 hpf or 3 μM SU 5402 ( Tocris Bioscience ) from 50 hpf to 72 hpf in egg water . To ablate β-cells , Tg ( ins:CFP-NTR ) s892 embryos were treated with freshly prepared 5 mM metronidazole ( MTZ ) ( Sigma ) from 84 hpf to 108 hpf in the dark , followed by 24–48 hours recovery . Before ablation , Tg ( ins:Kaede ) jh6-expressing β-cells were converted from green to red by exposing them to UV light . Control embryos from the same batch were treated with DMSO in egg water . Tg ( hsp:fhl1b; hsp:GFP ) gt3 and Tg ( hsp70l:bmp2b ) f13 embryos were heat shocked at various stages by transferring them into egg water pre-warmed at 40°C and 37°C , respectively . After a 30-minute heat shock , embryos were returned into a 28°C incubator and harvested at various stages . Caged fluorescein dextran was synthesized by coupling a dextran-spermine conjugate to 5-carboxymethoxy-2-nitrobenzyl ( CMNB ) -caged carboxyfluorescein succinimidyl ester . Dextran-spermine conjugate was produced from dextran ( MW 10 kDa , Molecular Probes ) . In the final product , 20% glucose units were bonded with spermine ( estimated by 1H NMR spectroscopy ) . A mixture of dextran-spermine conjugate ( 8 mg ) and CMNB-caged carboxyfluorescein succinimidyl ester ( 1 mg , Molecular Probes ) in 1 mL of borate buffer ( 0 . 1 M , pH 8 . 5 ) was added into a tinted tube and reacted on a vortexer at room temperature for 24 hours , protected from light . After the reaction , the solution was poured into a dialysis membrane ( 14000 cutoff cellulose membrane ) and dialyzed with deionized water at 4°C for 1 day . The dialysate was gravimetrically filtered to remove insoluble parts and lyophilized to dryness . A total of 4 . 6 mg were obtained ( yield 57% w/w ) . The average loading of CMNB-caged carboxyfluorescein on dextran was 3 . 2 dye molecules per dextran chain ( estimated from UV-Visible spectra ) . Light exposure at 360 nm removed the CMNB cage and rendered the free fluorescein modified dextran . Uncaging was followed in solution by the increase in absorbance in the region of 350–550 nm and the increase in fluorescence emission between 460–700 nm . Apparent quantum yield of uncaging is calculated to be 0 . 0051 from fluorescence spectra . Tg ( sox17:GFP ) s870 embryos were injected with 2 nl of 0 . 5% caged fluorescein dextran and allowed to develop until the 6-somite stage ( corresponding to 12 hours-post-fertilization ( hpf ) ) . After manual dechorionation , embryos were mounted ventrally in a mold filled with egg water . Using a Nikon Eclipse Ti confocal microscope , we visualized the endodermal sheet in live embryos at the 6–8 somite stage , and the A-P position of endodermal cells was determined by counting somites . Caged-fluorescein was activated in a single endodermal cell in each embryo with a 405 nm laser focused through a 40X objective lens . The uncaged embryos were fixed at various time points and stained with antibodies against GFP , uncaged-Fluorescein , and Islet1 or Prox1 . Glucose measurements were performed 3 times on 10 zebrafish larvae per condition using a fluorescence-based enzymatic detection kit ( Biovision Inc . ) [26] . The larvae were collected in 1 . 5 ml microcentrifuge tubes . Excess medium was removed and embryos were frozen on crushed dry ice . After thawing , 200 μl PBS was added and the larvae were homogenized using a hand-held mechanical homogenizer . Reactions were assembled on ice in black , flat bottom 96-well plates ( Costar ) . Standard curves were generated using glucose standard solution ( according to instructions ) and were included in each assay . To measure glucose in embryo extracts , 15 μl of sample were used . Control reactions without sample lysate were included in each row . Reactions were incubated for 30 minutes at 37°C in the dark . Fluorescence ( excitation 535 nm; emission , 590 nm ) was measured using a Safire II plate reader equipped with XFLUOR4 software ( v 4 . 51 ) . Fluorescence values were corrected by subtracting measurements from control reactions without sample . Glucose levels were interpolated from standard curves . Each sample was measured in triplicate and each experiment repeated three times . The p-values were calculated using an unpaired two-tailed Student t-test with Excel ( Microsoft , Redmond , WA ) . | Lineage-specific multipotent progenitors play crucial roles in embryonic development , regeneration in adult tissues , and diseases such as cancer . Bone morphogenetic protein ( Bmp ) signaling is critical for regulating the cell fate choice of liver versus pancreas , two essential organs of body metabolism . Through transcriptome profiling of endodermal tissues exposed to increased or decreased Bmp2b signaling , we have discovered the zebrafish gene four and a half LIM domains 1b ( fhl1b ) as a novel target of Bmp2b signaling . fhl1b is primarily expressed in the prospective liver anlage . Loss- and gain-of-function analyses indicate that Fhl1b suppresses specification of the pancreas and induces the liver . By single-cell lineage tracing , we showed that depletion of fhl1b caused a liver-to-pancreas fate switch , while fhl1b overexpression redirected pancreatic progenitors to become liver cells . At later stages , Fhl1b regulates regeneration of insulin-secreting β-cells by directly or indirectly modulating pdx1 and neurod expression in the hepatopancreatic ductal system . Therefore , our work provides a novel paradigm of how Bmp signaling regulates the hepatic versus pancreatic fate decision and β-cell regeneration through its novel target Fhl1b . | [
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| 2016 | Four and a Half LIM Domains 1b (Fhl1b) Is Essential for Regulating the Liver versus Pancreas Fate Decision and for β-Cell Regeneration |
lin-28 is a conserved regulator of cell fate succession in animals . In Caenorhabditis elegans , it is a component of the heterochronic gene pathway that governs larval developmental timing , while its vertebrate homologs promote pluripotency and control differentiation in diverse tissues . The RNA binding protein encoded by lin-28 can directly inhibit let-7 microRNA processing by a novel mechanism that is conserved from worms to humans . We found that C . elegans LIN-28 protein can interact with four distinct let-7 family pre-microRNAs , but in vivo inhibits the premature accumulation of only let-7 . Surprisingly , however , lin-28 does not require let-7 or its relatives for its characteristic promotion of second larval stage cell fates . In other words , we find that the premature accumulation of mature let-7 does not account for lin-28's precocious phenotype . To explain let-7's role in lin-28 activity , we provide evidence that lin-28 acts in two steps: first , the let-7–independent positive regulation of hbl-1 through its 3′UTR to control L2 stage-specific cell fates; and second , a let-7–dependent step that controls subsequent fates via repression of lin-41 . Our evidence also indicates that let-7 functions one stage earlier in C . elegans development than previously thought . Importantly , lin-28's two-step mechanism resembles that of the heterochronic gene lin-14 , and the overlap of their activities suggests a clockwork mechanism for developmental timing . Furthermore , this model explains the previous observation that mammalian Lin28 has two genetically separable activities . Thus , lin-28's two-step mechanism may be an essential feature of its evolutionarily conserved role in cell fate succession .
Tissue and organ formation in animals requires that diverse cell types arise in proper succession from a common pool of progenitors . Mutations in the heterochronic genes of the nematode Caenorhabditis elegans either skip or reiterate developmental events , indicating that they encode components of a cell fate succession mechanism . A lin-28 null mutant , for example , causes precocious development by skipping many second larval stage ( L2 ) cell fates [1] . A let-7 null mutant causes retarded development by reiterating larval fates and delaying differentiation [2] . Lin-28 encodes one of twelve proteins and let-7 one of five microRNAs known to act in the heterochronic pathway [3]–[5] . The complex dynamics of activation of the microRNAs and repression of particular proteins specifies stage-appropriate behavior in progressively differentiating lineages . Genetic and molecular analyses have revealed further complexity in the form of feedback loops , oscillating regulators , and microRNA redundancy [4] , [6]–[10] . Still , our knowledge of their relationships remains inadequate to explain how many of these components contribute to the cell fate succession mechanism . Vertebrate homologs of several heterochronic genes , including lin-28 , lin-41 , and let-7 , have developmental roles in a variety of contexts [11]–[16] . In particular , mammalian Lin28 is expressed in developing tissues of embryos and adults and is down-regulated as cells differentiate [17]–[22] . During neurogenesis for example , Lin28 can control cell fate succession like it does in C . elegans , suggesting that a similar developmental timing mechanism is at work [18] . Importantly , Lin28 is one of several factors that can participate in reprogramming mammalian somatic cells to pluripotent cells , and has been linked to regulatory processes in the germline , post-natal development , and cancer [17] , [23]–[25] . While investigating the mechanism by which accumulation of the mature let-7 microRNA is blocked in pluripotent cells , Viswanathan and colleagues discovered that mammalian LIN28 protein can bind the let-7 pre-microRNA and inhibit its processing [26] . The details of this mechanism have been elucidated and the phenomenon has been confirmed for the C . elegans ortholog [27]–[33] . Prior to this finding , the direct targets of LIN-28 protein in C . elegans were unknown . Mammalian LIN28 has been reported to act on mRNAs as well , but a specific regulatory mechanism has not yet been discovered [21] , [34]–[38] . Its inhibition of let-7 microRNA processing is a novel form of gene regulation and offers a molecular explanation for how lin-28 controls cell fate succession in C . elegans . Earlier studies of the C . elegans heterochronic pathway had not addressed the issue of whether lin-28 requires let-7 microRNAs for its function [2] , [29] , [39] . Like other animals , C . elegans possess multiple let-7 family members [40]–[44] . Significantly , Abbott and colleagues discovered that three let-7 relatives—miR-48 , miR-84 and miR-241—function redundantly to repress the transcription factor gene hbl-1 and cause the succession of L2 to L3 cell fates [6] . Because lin-28's primary role is to govern this same cell fate transition , it is reasonable to hypothesize that it acts via one or more of these let-7 relatives . let-7 itself has been believed to act much later in the heterochronic pathway , at the L4-to-adult transition . However , another possibility is that let-7 acts earlier together with its relatives in a previously unrecognized role , which would explain lin-28's action upon it . Our results show , however , that lin-28 does not act via any of these let-7 family members in its primary role in C . elegans development . To explain this discrepancy , we provide evidence that lin-28 acts in two-steps to control successive cell fates in a manner like that of lin-14 [45] . We speculate that the pairwise and overlapping activities of lin-14 and lin-28 reveal a “clockwork” logic underlying the pathway . The significance of our findings is that they explain two activities observed of mammalian Lin28 and thus may reveal an essential feature of lin-28's evolutionarily conserved role as a regulator of cell fate succession in animals .
To test whether let-7 microRNAs indeed mediate lin-28's developmental function we first examined its ability to interact with precursor forms of let-7 relatives . Seven C . elegans microRNAs—let-7 , miR-48 , miR-84 , miR-241 , miR-793 , miR-794 , and miR-795—belong to the let-7 family based on 5′-end sequence identity of the mature microRNAs [41]–[43] . Two others—miR-265 and miR-1821—are more distantly related [46] . We tested the precursor form of each for interaction with LIN-28 in a yeast three-hybrid assay [47] . C . elegans LIN-28 protein interacted with pre-let-7 , pre-miR-48 , pre-miR-84 and pre-miR-241 , but not with the other let-7 family pre-microRNA sequences ( Table 1; Figure S1 ) . LIN-28 also did not interact with pre-lin-4 , pre-miR-237 ( a lin-4 relative ) , pre-miR-1 ( an unrelated microRNA ) , or a control RNA , the Iron Response Element ( IRE ) . Additional interaction tests are shown in Table S2 . Thus , LIN-28 can specifically recognize the precursors of the four let-7 family members already known to function in the heterochronic pathway . The binding of mammalian LIN-28 to pre-let-7 leads to the degradation of the precursor and eventual loss of mature let-7 [27]–[32] . To determine whether C . elegans lin-28 prevents the developmental accumulation of the let-7 family microRNAs , quantitative RT-PCR assays were performed on wildtype and lin-28 mutant larvae . Relatively few worms ( ∼200 ) are required to perform this assay , allowing precise staging of worms at the lethargus period prior to each larval molt . As previously reported [2] , [6] , [48] , [49] , mature let-7 was very low or undetectable in wildtype larvae at the L1 and L2 molts , accumulated during the L3 stage , and reached its peak by L4 ( Figure 1A , grey bars ) . The miR-48 , -84 , and -241 levels were all relatively low but detectable at the L1 molt and peaked by the L2 molt ( Figure 1B–1D , grey bars ) . The absence of lin-28 caused substantial premature accumulation of let-7 in both the L1 and L2 stages , higher than its peak at the L4 molt in wild type ( Figure 1A , blue bars ) . The removal of lin-28 caused no change in the levels of mature miR-48 and -241 in the early stages ( Figure 1C and 1D , blue bars ) . Only miR-84 showed a significant difference between wild type and the lin-28 mutant at the L2 molt ( Figure 1B , blue bars ) , as has been reported by others [29] . These findings suggest that lin-28 does not alter the accumulation of miR-48 , miR-84 , and miR-241 to the extent that it affects let-7 , despite its ability to interact with them in the yeast three-hybrid assay . Importantly , only let-7 levels were altered at the L1 lethargus , the period immediately preceding the seam cell divisions of the L2 . To test whether let-7 family microRNAs are required for lin-28's developmental activity , we examined mutants lacking both lin-28 and let-7 family members . The lateral hypodermal seam cells normally divide at each larval stage and differentiate as the animal becomes adult . lin-28 null mutants have fewer seam cells than wild type because they skip the one symmetric division in the seam lineage during the L2 , and these cells differentiate at least one stage early , synthesizing adult cuticle alae precociously ( Table 2 , lines 1 and 2 ) [1] . let-7 null mutants show retarded adult alae synthesis , but produced the normal number of seam cells ( Table 2 , line 3 ) [2] . We observed that lin-28; let-7 animals had the reduced seam cell number characteristic of lin-28 mutants ( Table 2 , lines 2 and 4 ) , but as reported previously did not display precocious adult alae [2] . Thus , the let-7 null allele is epistatic to the lin-28 null allele only for the alae phenotype , not for the early seam cell division defect; the animals display both precocious and retarded characters . The three let-7 family members mir-48 , mir-84 , and mir-241 act redundantly to control seam cell fates: when they are deleted together , the L2-specific symmetric cell division is reiterated , resulting in supernumerary seam cells [6] . In addition , in these triple-mutant animals , seam cell differentiation fails and they form no adult alae . A lin-28 null mutation is entirely epistatic to this retarded phenotype , having a reduced seam cell number and precocious adult alae ( Table 2 , lines 5 and 6 ) [6] . Given that mir-48 , mir-84 , and mir-241 act redundantly and are related in sequence to let-7 , we first wished to test whether let-7 might also be redundant with them in controlling L2 seam cell behavior . We constructed a strain lacking all four genes and assessed its seam cell phenotypes: we observed that animals lacking all four let-7 family members had the same seam cell number as those lacking only three ( Table 2 , lines 5 and 7 ) . Surprisingly , a strain lacking lin-28 and all four let-7 genes had the reduced seam cell number of a lin-28 mutant ( Table 2 , line 8 ) . Thus , lin-28 requires none of these let-7 family members to control the L2 seam cell fates . However , this strain did not make precocious adult alae ( Table 2 , line 8 ) , indicating that let-7 is required by lin-28 after the L2 . We surmised that lin-28 might act on a microRNA unrelated to let-7 to control L2 events . To test this idea we constructed strains defective in a gene needed for general microRNA function: ain-1 [50] . Removing ain-1 alone causes a slight increase in seam cell number from wild type ( Table 2 , line 9 ) , as previously reported [50] . In contrast to removing let-7 , which had no effect , removing ain-1 from a strain lacking mir-48 , mir-84 , and mir-241 nearly doubled its seam cell nuclei number ( Table 2 , line 10 ) . This increase reflects a reiteration of the L2 seam cell fate , and indeed indicates additional microRNA regulation of the L2 seam cell fate . However , removing ain-1 in a strain lacking lin-28 and the three let-7 family members did not result in an increase in seam cell number ( Table 2 , line 11 ) . This result is consistent with previous studies showing a lin-28 mutation is epistatic to ain-1 and ain-2 mutants in seam cell development [50] , [51] . The ain-1 mutation did substantially suppress the precocious adult alae phenotype of a lin-28 mutant , as if let-7 was fully active , demonstrating that the ain-1 mutation was able to reduce although not eliminate microRNA function in seam cell development ( Table 2 , line 11 ) . To further test the idea that lin-28 inhibits accumulation of another microRNA , we performed a microarray analysis comparing wild type and lin-28; lin-46 double mutant animals staged during the L1 lethargus period ( GEO accession: GSE35634 ) . These double mutants develop like wild type [10] , thus reducing the potential for indirect effects on microRNA abundance . We chose the L1 molt period because the first observable defect in lin-28 ( null ) occurs shortly afterward . We observed that let-7 was up-regulated 42-fold in the absence of lin-28 , and that no other microRNA was affected more than 1 . 5-fold ( Table S3 ) . Therefore , because lin-28 regulates no other microRNA in the same manner it regulates let-7 , we conclude that it possesses a different molecular activity to control L2 cell fates . hbl-1 is believed to be the most direct regulator of L2 hypodermal fates [6] , [52] , [53] . We addressed whether lin-28 affects hbl-1 expression using a hbl-1::GFP::hbl-1 3′UTR reporter [54] . As previously observed , the reporter was high in hypodermal nuclei in the L1 , down-regulated through the L2 and L3 , and undetectable by the L4 stage ( Figure 2A , Table S4 ) [52]–[54] . Also as seen previously [6] , in a strain lacking mir-48 , mir-84 , and mir-241 , the reporter was constitutively expressed from L1 to L4 ( Figure 2B , Table S4 ) . We observed that when lin-28 was also mutant , the reporter was rapidly down-regulated after the L1 , earlier than it was in wild type , becoming undetectable by the L4 , despite the absence of the three microRNAs ( Figure 2C , Table S4 ) . This observation indicates that lin-28 is a positive regulator of hbl-1 expression that acts independently of the let-7 relatives . Similar results were obtained with animals lacking all four let-7 family members ( Figure S2 ) . When the analysis was performed with a companion reporter that substitutes the hbl-1 3′UTR with the unrelated unc-54 3′UTR , the reporter was continuously expressed despite the absence of lin-28 ( Figure 2D ) . This observation indicates that lin-28 acts via the 3′UTR of hbl-1 , possibly directly , to temporally support hbl-1 expression and thereby promote L2 cell fates . We were surprised that despite the evolutionary conservation of lin-28's ability to block let-7 accumulation , this activity is not required for its primary effect on C . elegans larval development , namely the normal execution of L2 cell fates . Previously , lin-28 was thought to specify L2 fates only , but the possibility that it has two activities was raised by these findings . In other words , to explain the relevance of let-7 to lin-28 function , we hypothesized that lin-28 acts in two mechanistically independent steps: first to control early fates and second to control later fates via direct action on pre-let-7 . Ambros and Horvitz documented that some seam cell lineages in lin-28 null mutants display precocious development that skips two larval stages [1] , [55] . In quantifying this phenotype , we found that in lin-28 null mutants 37% of seam cells differentiated at the L2 molt , two stages early ( Table 3; Figure 3 ) . Because lin-28 null mutants execute normal L1 cell lineages throughout the animal [1] , we concluded these lineages skipped the L2 stage and one subsequent stage ( Figure 3 ) . The other 63% of seam cells in these animals skipped only the L2 stage ( Table 2 and Table 3; Figure 3 ) . Although all animals contained both one-stage and two-stage precocious lineages , why some lineages skipped only the L2 fates , while others skipped two stages , is not clear . We addressed whether any aspect of lin-28's two-stage precocious phenotype depended on let-7 family members . Comparable to lin-28 null mutants alone , 21% of the seam cells in animals that also lack mir-48 , mir-84 , and mir-241 displayed adult alae at the L2 molt ( Table 3 ) . By contrast , none of the lin-28; let-7 animals displayed adult alae at the L2 molt ( Table 3 ) . These observations indicate that let-7 , and not its three relatives , is needed for the two-stage precocious phenotype of lin-28 null mutants . To further address whether lin-28 possesses two genetically separable activities , we performed RNAi using bacteria not induced with IPTG ( lin-28 ( lowRNAi ) ) , which we expected to produce a range of weaker precocious phenotypes . Many animals displayed the same precocious phenotype observed commonly in lin-28 null mutants ( Figure 3 ) . However , in 10% of the animals that had skipped L2 cell fates , all seam cell lineages terminally differentiated at the normal time ( Figure 3 ) . We interpret these seam cell lineages as having executed L3 fates precociously as well as L3 fates at the normal time . These abnormal lineages demonstrate that a precocious phenotype early does not necessitate a precocious phenotype later , suggesting the two are separately regulated by lin-28 . In characterizing the interactions between LIN-28 protein and let-7 precursor sequences , we observed that LIN-28 could interact with the loop portion of the C . elegans pre-let-7 but not with that of Drosophila pre-let-7 ( Table S2 ) . Thus we could construct a version of let-7 that encoded the loop sequence of Drosophila pre-let-7 and thereby was insensitive to LIN-28's inhibitory activity . We generated animals carrying either a wildtype let-7 genomic transgene or a chimeric worm/fly transgene . We found that at a given concentration of DNA injected , 22% of F1 animals with the wildtype construct displayed precocious adult alae ( n = 50 ) , whereas 46% of F1 animals with the chimeric construct displayed precocious alae ( n = 50 ) . Animals receiving either transgene had an average of 16 seam cells at the L4 stage , indicating no change in the early cell fate decision ( wildtype let-7 , n = 47; chimeric let-7 , n = 51 ) . We established stable lines carrying each construct and found that those with the chimeric pre-let-7 expressed higher mature let-7 in early larval development than those with the wildtype pre-let-7 ( Table S5 ) . Therefore , the inhibition of mature let-7 accumulation is likely the means by which lin-28 governs seam cell development after the L2 . let-7 is thought to act during the L4 stage to cause the L4-to-adult transition , including the terminal differentiation of seam cells [2] . We and others have observed that let-7 accumulates in the L3 stage in wild type , a stage earlier than originally reported ( Figure 1 ) [2] , [6] , [48] , [49] . Therefore , one possibility is that let-7 mutants reiterate L3 developmental events in the L4 stage . We therefore reconsidered when let-7 has its earliest role in larval development . We examined let-7 null mutant animals in the L4 stage to see whether any defects had already occurred by this time . A confounding issue in this analysis is that the hermaphrodite seam cell lineages display exactly the same division patterns in L3 and L4 stages , so that reiteration of L3 or L4 fates cannot not be distinguished ( see Figure 3 ) . One seam cell lineage that is different in this regard is the male V5 lineage [56] . We observed a cell division in the V5 lineage that normally occurs during the L3 lethargus to be reiterated at the end of the L4 stage: 100% of animals showed a V5 lineage division in let-7 males recurring 12–13 hours after the L3 molt , in the late L4 ( n = 10 ) . Another consistent defect observed in let-7 null males was a delay in tail tip retraction that normally occurs in male tail morphogenesis during the L4 ( Figure 4 ) [57] . All males examined displayed a marked failure of tip retraction by the mid-L4 stage ( n = 10 ) . These observations indicate that the earliest observable consequence of let-7 activity occurs long before the L4-to-adult transition , and suggest let-7 acts at the late L3 stage . The let-7 family microRNAs have two known targets in the heterochronic pathway: hbl-1 and lin-41 . We observed that lin-28 positively regulates expression of hbl-1 , a regulator of L2 seam cell fates ( Figure 2 ) [6] , [52] , whereas lin-41 is thought to act later to regulate the L4-to-adult transition [39] . We sought to clarify the roles of these two genes with respect to let-7 activity . In a wildtype background , reduction of hbl-1 by RNAi caused 80% of animals to display precocious adult alae formation , and reduction of lin-41 by RNAi caused 35% to have precocious alae ( Table 4 ) . In a let-7 null mutant background , seam cells divide at the L4 molt and synthesize adult alae one stage later [2] . We observed that the two let-7 target genes differed in their abilities to suppress this phenotype: penetrance of let-7's retarded defect was reduced from 100% to 80% by hbl-1 ( RNAi ) , whereas it was reduced to 6% by lin-41 ( RNAi ) ( Table 4 ) . These observations suggest that let-7 acts primarily through lin-41 to regulate seam cell differentiation . hbl-1 has been shown to be the primary target of let-7's relatives mir-48 , mir-84; and mir-241 [6] . How the microRNAs belonging to the same family act selectively on different targets is currently unknown .
lin-28 and let-7 had been thought to act at widely separated times in C . elegans larval development , with lin-28 controlling an early , proliferative fate of seam cells and let-7 controlling their terminal differentiation two larval stages later [3] , [58] . The serendipitous discovery that mammalian LIN28 binds to and inhibits let-7 precursor processing [26] , and the subsequent proof that this mechanism is evolutionarily conserved in C . elegans [29] , [31] , caused us to consider what their molecular interaction means for the regulation of cell fate succession in C . elegans . The progressively differentiating lateral hypodermal seam cells of C . elegans are often used to model cell fate succession in the analysis of heterochronic genes . These cells adopt three types of stage-appropriate fates: an asymmetric division producing one blast and one differentiated cell; a double division characteristic of the L2 stage producing two blasts and two differentiated cells; and terminal differentiation in which all cells fuse and secrete adult cuticular alae ( Figure 3 ) [56] . Based on their null allele phenotypes , lin-28 controls the characteristic L2 proliferative division and let-7 controls the terminal differentiation . Given the redundancy of the three let-7 paralogs mir-48 , mir-84 , and mir-241 in regulating L2 fates , two alternatives seem likely: either lin-28 inhibits the accumulation of multiple let-7 family members , including these three let-7s known to control the L2-to-L3 transition , or let-7 is at least partially redundant with its relatives in controlling this early fate transition . Surprisingly , we find that neither of these situations is the case . We demonstrate by using null alleles that lin-28 does not require let-7 , mir-48 , mir-84 , and mir-241 for its control of L2 cell fates ( Table 2 ) . It remains possible that other let-7 family members mediate lin-28's control of L2 fates , however , the LIN-28 protein interacts with none these ( Table 1 ) , and no microRNAs other than let-7 itself are dysregulated in a lin-28 null mutant ( Table S3 ) . Even in the absence of these microRNAs , we observe a marked positive effect of lin-28 on hbl-1 expression , supporting the model that lin-28 acts via hbl-1 to control the L2-to-L3 transition ( Figure 2; Figure S2 ) . Furthermore , this regulation depends on the hbl-1 3′ UTR , suggesting a post-transcriptional mechanism . Our findings using the ain-1 mutant suggest additional microRNA activity controlling L2 cell fates , but are inconsistent with microRNAs mediating lin-28's role in the L2 ( Table 2 and Table S3 ) . We therefore conclude that lin-28 acts to oppose hbl-1's repression , but does so without changing microRNA abundance . Given that the premature accumulation of mature let-7 does not account for lin-28's precocious phenotype , why then does LIN-28 inhibit let-7 ? Because heterochronic genes act in succession , the actions of early-acting genes necessarily have consequences later in life . For example , the microRNA lin-4 represses the expression of lin-14 , and when that repression fails , L1 cell fates are reiterated [59] , [60] . The fact that seam cell differentiation never occurs is not taken to mean that lin-4 directly controls that event . Rather , the reiteration of L1 fates—the direct consequence of loss of lin-4—leads to the permanent postponement of differentiation . Likewise , the precocious terminal differentiation of seam cells in a lin-28 mutant might simply be the consequence of skipping the L2 cell fates and everything else falling in line after that . In such a scenario , each factor has a single activity and an early defect leads to a cascade of wrong fate decisions directed by other factors . However , an alternate interpretation is possible . lin-14 , another heterochronic gene which controls primarily the L1 cell fates , was shown to possess two separable and sequential activities [45] . These activities are termed lin-14a and lin-14b , although they do not correspond to distinct gene products [61] . lin-14a controls the L1-to-L2 transition and lin-14b controls the L2-to-L3 transition . [45] . By analogy , lin-28 can be said to have two separable activities as well ( Figure 5 ) . The first of lin-28's activities governs the L2-to-L3 transition and is independent of let-7 and the second acts via let-7 to control the L3-to-L4 transition . Thus , a parsimonious explanation for lin-28's inhibition of let-7 in C . elegans is that it constitutes the second of two activities . However , this view requires adjustments to existing models of the heterochronic pathway . First , because LIN-28 protein is down-regulated by the L3 , we must consider the time of let-7 expression . Early reports showed mature let-7 rising in the L4 stage , however as microRNA detection methods have improved , expression of mature let-7 could be seen a full stage earlier [6] , [49] . Our quantitative RT-PCR data indicate that mature let-7 accumulates during the L3 ( Figure 1 ) , after LIN-28 has disappeared [62] . Second , although it is impossible at present to distinguish between L3 seam cell fates and L4 seam cell fates , we must reconsider the time of let-7's activity . Because mature let-7 levels are very low at the L2 molt and nearly at their peak by the end of the L3 , it is reasonable to assume that let-7 could act by the end of the L3 . Thus loss of let-7 might actually cause the reiteration of L3 fates , the consequence of which would be problems in the L4 . None of the previous data concerning let-7's role in seam cells decides whether it acts to control the L3-to-L4 transition or the L4-to-adult transition . However , we observed consistent abnormal cell division and morphogenesis events in the L4 male , which is in agreement with a reiteration of L3 cell fates in let-7 null mutants . Thus we propose that let-7 ( and possibly other regulators believed to control the L4-to-adult transition such as lin-41 ) act earlier than previously thought . Third , hbl-1 has been assigned to roles in both L2 seam cell fates and terminal differentiation [6] , [52] , [53] . Our comparison of the ability of hbl-1- and lin-41-knockdown to suppress a let-7 null mutation reveals that lin-41 has a more significant role downstream of let-7 . Therefore , we propose that hbl-1 is the most proximal regulator of L2 fates , being regulated by the three let-7 paralogs , and lin-41 is let-7's target for controlling later events ( Figure 5 ) . Thus , it is LIN-28's direct action on pre-let-7 that exerts influence on those later events via lin-41 . We note that although lin-14 and lin-28 each act twice to govern successive cell fate decisions , their functions overlap by one stage , with the second lin-14 activity coinciding with the first of lin-28's ( Figure 5 ) . We have previously proposed that the lin-14b activity is a consequence of a positive feedback loop between lin-14 and lin-28 [10] . Therefore , the second period of lin-14's action is tied to the first one for lin-28 . We speculate that the pairwise and overlapping activities of these two factors reveal an underlying “clockwork” mechanism for cell fate succession . Each of these regulators has its first role in determining the fates expressed in a particular stage , then a second role that is linked to the next regulator in sequence . In the case of lin-14 , it first determines what fates are expressed in the L1 , then by positive feedback on lin-28 , it governs what happens in the L2 [10] , [45] . Similarly , lin-28 first determines what events occur in the L2 , then by its positive regulation of lin-41 via let-7 , influences events of the L3 . By each factor having both a cell fate determining role and a link to the next stage through the next factor in the pathway , the proper succession of cell fates is achieved . This overlap of regulators resembles , at least superficially , the ABC model for floral organ identity [63] . In each case , four developmental distinctions are specified: larval stage-specific cell fates in C . elegans and whorl organ identities in plants . Because in C . elegans the overlap is temporal rather than spatial , the cell fates progress sequentially as successive regulators are repressed in turn . We also note that for each lin-14 and lin-28 , the earlier of its activities is more sensitive to reduction than the later activity ( Figure 3 ) [45] , which may be important for the order in which the two activities occur . Most significantly , lin-28's two-stage action in C . elegans explains a split function observed of mammalian Lin28 in neural development [18] . Lin28 activity can promote neuronal differentiation and inhibit astroglial differentiation . These two activities were found to be genetically separable: a mutant form of Lin28 can block gliogenesis without affecting the number of neurons . Furthermore , changes in let-7 levels do not fully account for Lin28's activity in this system . By finding that C . elegans lin-28 has two distinct activities , we surmise that the split phenotype in mammalian neurogenesis is a consequence of a similar two-step mechanism involving let-7-dependent and let-7-independent activities . Considering the long evolutionary association of lin-28 and let-7 with cell fate succession in diverse contexts , we propose that having two sequential , mechanistically distinct activities is critical to lin-28's role in governing successive developmental transitions .
Nematodes were grown under standard conditions at 20°C unless otherwise indicated [64] . Many strains carry the transgene wIs78 that contains a seam cell nuclei marker ( scm::GFP ) and a seam cell junction marker ( ajm::GFP ) to identify lateral hypodermal seam cells [65] . To construct mir-48 mir241; mir-84 let-7 quadruple mutants , animals of the genotype mir-48 mir-241; mir-84 unc-3 let-7/+ were cultured on hbl-1 ( low RNAi ) ( see below ) to suppress the lethality characteristic of these mutations . Unc animals examined were progeny of mothers transferred off hbl-1 ( lowRNAi ) at the L4 stage . Control experiments using the mir-48 mir-241; mir-241 mutant strain showed that this procedure caused no attenuation of the progeny's retarded phenotype . Strains used: N2 wild type ( Bristol ) , BW1891 ctIs37 [hbl-1::GFP::unc-54 3′UTR] , BW1932 ctIs39 [hbl-1::GFP::hbl-1 3′UTR] , RG733 wIs78 [ajm-1::gfp; scm-1::gfp; unc-119 ( + ) ; F58E10 ( + ) ] , ME200 lin-46 ( ma174 ) V; wIs78 , ME202 mir-48 mir-241 ( nDf51 ) V; mir-84 ( n4037 ) X; wIs78 , ME203 lin-28 ( n719 ) I; mir-48 mir241 ( nDf51 ) V; mir-84 ( n4037 ) X; wIs78 , ME204 lin-28 ( n719 ) ; wIs78 , ME212 lin-28 ( n719 ) I; mir-48 mir241 ( nDf51 ) V; mir-84 ( n4037 ) X; ctIs39 , ME213 mir-48 mir-241 ( nDf51 ) V; mir-84 ( n4037 ) X; ctIs39 , ME214 lin-28 ( n719 ) I; mir-48 mir-241 ( nDf51 ) V; mir-84 ( n4037 ) X; ctIs37 , ME283 mir-48 mir-241 ( nDf51 ) V; mir-84 ( n4037 ) ain-1 ( ku322 ) X; wIs78 , ME284 lin-28 ( n719 ) I; mir-48 mir-241 ( nDf51 ) V; mir-84 ( n4037 ) ain-1 ( ku322 ) X; wIs78 , ME286 mnDp1 ( X V ) /+ ;unc-3 ( e151 ) let-7 ( mn112 ) X; wIs78 , ME287 mir-84 ( n4037 ) unc-3 ( e151 ) let-7 ( mn112 ) /szT1 X; wIs78 , ME297 lin-28 ( n719 ) I; unc-3 ( e151 ) let-7 ( mn112 ) X; wIs78 , ME298 lin-28 ( n719 ) I; mir-48 mir-241 ( nDf51 ) V; mir-84 ( n4037 ) unc-3 ( e151 ) let-7 ( mn112 ) X; wIs78 , ME314 him-5 ( e1467 ) V; wIs78 , ME322 aeEx35 [let-7 ( + ) ; ttx-3::GFP; scm-1::gfp] , ME323 aeEx36 [Ce/Dmlet-7 ( + ) ; ttx-3::GFP; scm-1::gfp] , ME331 aeEx37 [pCR2 . 1-TOPO ( + ) ; ttx-3::GFP; scm-1::gfp] , ME332 aeEx38 [let-7 ( + ) ; ttx-3::GFP; scm-1::gfp] , ME333 aeEx39 [Ce/Dmlet-7 ( + ) ; ttx-3::GFP; scm-1::gfp] , MT1524 lin-28 ( n719 ) I , VT751 lin-28 ( n719 ) I; lin-46 ( ma164 ) V . Nomarski DIC and fluorescence microscopy were used to count seam cell nuclei . Developmental stage was assessed by the extent of gonad and germ line development . In some cases where seam cell division was ongoing or just completed , the two daughter nuclei were counted as one . All images were taken with a 100× objective on a Zeiss Axioplan2 imaging microscope equipped with a CCD camera . To analyze the V5 cell-lineage in let-7 mutant males , wIs78; him-5 ( e1467 ) males were crossed to wIs78; mnDp1 ( X:V ) /+;unc-3 ( e151 ) let-7 ( mn112 ) X hermaphrodites and Unc males among the cross progeny were examined for V5 seam cell divisions . Bacterially-mediated RNA-interference was performed as previously described [66] . The RNAi vectors contained a 3 . 5 kb region of hbl-1 genomic sequence or 740 bp of the lin-28 ORF . The I-4J11 bacterial strain from the Ahringer RNAi library that expresses lin-41 dsRNA was also used . dsRNA-expressing bacteria were induced in culture and seeded on NGM plates containing 1 mM IPTG , 50 µg/ml ampicillin and 12 . 5 µg/ml tetracycline . Empty vector was used as a negative control . RNAi for hbl-1 and lin-41 was done post-embryonically: gravid adults were dissected and embryos allowed to hatch on dsRNA expressing bacteria . For hbl-1 and lin-28 “low” RNAi , uninduced bacterial cultures were seeded on NGM plates without IPTG . Animals were propagated on lin-28 ( low RNAi ) for analysis . L4 animals grown on hbl-1 ( low RNAi ) were transferred to NGM plates seeded with normal food ( AMA1004 ) for analysis . Yeast three-hybrid assays were performed using the YBZ-1 strain as described previously [18] , [47] . The C . elegans lin-28 open reading frame was fused to the activation domain sequence in pACT2 , and experimental RNAs were fused to the MS2 stem loop sequence in pIIIA/MS2-2 . X-gal overlays were assessed after 6 hours and overnight . All RNAs that produced negative interactions were shown by RT-PCR to be expressed at a level comparable to those of RNAs that produced positive interactions . Sequences of selected RNAs tested in interaction assays are listed in Table S1 . For RNA isolation , 50–200 animals in the pre-molt lethargus were collected in M9 buffer . RNA was isolated using mirVana miRNA isolation kit ( Ambion ) following the manufacturer's instructions with an additional sonication step performed immediately after the addition of lysis/binding buffer . The quality and concentration of the RNA were determined using a Nanodrop 1000 spectrophotometer ( Thermo Scientific ) . The microRNA-qRT-PCR ( TaqMan assay , Applied Biosystems ) was performed using TaqMan probes for let-7 , miR-48 , miR-84 , miR-241 and small nucleolar RNA sn2841 according to the manufacturer's instructions . Reverse transcriptase-free controls confirmed amplification was dependent on input RNA . Samples were analyzed on an Applied Biosystems StepOne machine . Relative changes in the microRNA levels were determined by the ΔΔCt method using snoRNA sn2841 levels for normalization [67] . Gene copy number assessments were made using the SYBR Green assay ( Applied Biosystems ) and primers specific for ama-1 and let-7 on approximately 20 animals . Single amplicon SYBR Green products were confirmed by agarose gel electrophoresis . Dissociation/melting curves were determined after each run . Samples were analyzed on an Applied Biosystems 7500 machine . Triplicate technical replicates were performed with each sample . RNA was isolated from a synchronized population of late L1 wild type and lin-28 ( n719 ) ; lin-46 ( ma164 ) animals using the mirVana microRNA isolation kit ( Ambion ) . Global microRNA profiling was performed by Exiqon ( Vedbaek , Denmark ) using miRCURY LNA miRNA Arrays annotated to miRBase version 14 . 0 . A 2 . 5 kb let-7 genomic sequence identical to the rescuing fragment used previously [2] was cloned into pCR2 . 1-TOPO ( Invitrogen ) . A modified version of this sequence was made by replacing the C . elegans pre-microRNA loop sequence with that of Drosophila let-7 ( see Table S1 ) . These plasmids were injected into wild type with scm::GFP and ttx-3::GFP co-injection markers , each at a concentration of 50 ng/µL . F1 animals were scored for precocious alae at the L4 stage . Stable lines were generated and RNA was isolated from L1/L2 animals approximately 16 hours post hatching and mature let-7 levels were measured by TaqMan assay . Transgene copy number was assessed on stable lines . | As tissues form , different cell types are generated from a common pool of undifferentiated cells . The mechanisms that control this developmental timing are largely unknown . In the nematode Caenorhabditis elegans , the heterochronic genes control a succession of cell fates in progressively differentiating tissues of the larva . Two of these genes , lin-28 and let-7 , are evolutionarily conserved in animals where they have roles in pluripotency and differentiation . The LIN-28 protein is known to bind to and block the maturation of the small RNA encoded by let-7 . This mechanism would seem to explain lin-28's role in development . Here we show that lin-28's primary activity in C . elegans—the proper timing of second larval stage cell fates—does not require let-7 or related genes . In explaining this discrepancy , we provide evidence that lin-28 has two distinct activities controlling successive cell fates . This situation is remarkably like that of lin-14 , which acts one stage earlier . The overlap of their activities by one stage may reflect a fundamental feature of this cell fate succession mechanism . Furthermore , the two-step mechanism explains observations that mammalian Lin28 also has genetically separable activities . Therefore , lin-28's two successive activities may be essential to its evolutionarily conserved role in developmental timing . | [
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| 2012 | lin-28 Controls the Succession of Cell Fate Choices via Two Distinct Activities |
Cartilage and endochondral bone development require SOX9 activity to regulate chondrogenesis , chondrocyte proliferation , and transition to a non-mitotic hypertrophic state . The restricted and reciprocal expression of the collagen X gene , Col10a1 , in hypertrophic chondrocytes and Sox9 in immature chondrocytes epitomise the precise spatiotemporal control of gene expression as chondrocytes progress through phases of differentiation , but how this is achieved is not clear . Here , we have identified a regulatory element upstream of Col10a1 that enhances its expression in hypertrophic chondrocytes in vivo . In immature chondrocytes , where Col10a1 is not expressed , SOX9 interacts with a conserved sequence within this element that is analogous to that within the intronic enhancer of the collagen II gene Col2a1 , the known transactivation target of SOX9 . By analysing a series of Col10a1 reporter genes in transgenic mice , we show that the SOX9 binding consensus in this element is required to repress expression of the transgene in non-hypertrophic chondrocytes . Forced ectopic Sox9 expression in hypertrophic chondrocytes in vitro and in mice resulted in down-regulation of Col10a1 . Mutation of a binding consensus motif for GLI transcription factors , which are the effectors of Indian hedgehog signaling , close to the SOX9 site in the Col10a1 regulatory element , also derepressed transgene expression in non-hypertrophic chondrocytes . GLI2 and GLI3 bound to the Col10a1 regulatory element but not to the enhancer of Col2a1 . In addition to Col10a1 , paired SOX9–GLI binding motifs are present in the conserved non-coding regions of several genes that are preferentially expressed in hypertrophic chondrocytes and the occurrence of pairing is unlikely to be by chance . We propose a regulatory paradigm whereby direct concomitant positive and negative transcriptional control by SOX9 ensures differentiation phase-specific gene expression in chondrocytes . Discrimination between these opposing modes of transcriptional control by SOX9 may be mediated by cooperation with different partners such as GLI factors .
Chondrogenesis and the formation of bone by endochondral ossification depend on progressive steps of cell differentiation . Mesenchymal cells condense and differentiate into chondrocytes in a pattern that will define the eventual shape of the different skeletal elements . These chondrocytes proliferate , mature , exit the cell cycle and become prehypertrophic . The differentiation program culminates in the terminal differentiation and apoptosis of post-mitotic hypertrophic chondrocytes [1] . This differentiation program is controlled by members of the SOX and RUNX families of transcription factors and the integration of multiple signaling pathways mediated by Indian hedgehog ( Ihh ) , parathyroid hormone-related protein ( PTHrP ) , Wnts , BMPs , and Notch ( reviewed in [2] ) . PTHrP and Ihh are two important players which interact to form a feedback loop that controls the pace of the differentiation program [3] . Sox9 is essential for chondrogenesis and chondrocyte differentiation [4]–[6] . It is essential for mesenchymal condensation prior to chondrogenesis , and in its absence chondrocyte differentiation fails . Inactivation of Sox9 in chondrocytes at different stages of differentiation suggests that its expression is essential for the survival of chondrocytes so that they can progress to hypertrophy [5]–[7] . Mutations in SOX9 are associated with the human skeletal malformation syndrome , campomelic dysplasia , in which skeletal abnormalities can be attributed to the disruption of the chondrogenic differentiation program due to failure to express SOX9 target genes . Upon hypertrophy , chondrocytes down-regulate Sox9 expression [8] , [9] , which is believed to mark the end of SOX9 control in the growth plate . Despite the wealth of information about spatial and temporal gene expression patterns in the developing growth plate , it is not clear how transcriptional controls achieve appropriate and specific gene expression during chondrocyte differentiation . SOX9 activates many genes expressed in proliferating chondrocytes , including the extracellular matrix ( ECM ) genes Col2a1 , Col9a1 , Col11a2 , Acan ( aggrecan ) and Cd-rap/Mia1 [10]–[15] . For the Col2a1 gene , which is expressed most strongly in proliferating chondrocytes , SOX9 directly transactivates the gene in vivo via a conserved enhancer sequence within the first intron [10] , [11] . The collagen X gene , Col10a1 , is a hypertrophic chondrocyte specific marker . The specificity and reciprocity of Sox9 and Col10a1 expression epitomise the strict control of temporal and differentiation phase-specific gene expression in the growth plate . Col10a1 is ideal for studying transcriptional regulation because as well as its highly specific expression pattern , over-expression or loss-of-function does not disrupt chondrocyte differentiation . These properties simplify interpretation of changes in gene expression resulting from perturbing transcriptional control [16]–[18] . Here , we examined the transcriptional controls that restrict Col10a1 expression to hypertrophic chondrocytes . We found that SOX9 coordinates gene expression during chondrocyte differentiation through both transcriptional activation and repression . Discrimination between these opposing actions is probably achieved by cooperation between SOX9 and different partners such as GLI factors .
Previous cell transfection studies identified an enhancer element upstream of human COL10A1 [19] . This element is highly conserved in mammals and corresponds to a 640 bp region between −4 . 3 and −3 . 6 kb of the mouse Col10a1 gene ( designated element A ) ( Figure 1A and 1B ) . We used DNase I footprinting assays to test the configurations in which the element A sequences could be directly bound by nuclear factors derived from chondrocytes at different differentiation states ( Figure 2A ) . Extracts from hypertrophic chondrocytes MCTs , but not fibroblasts COS-1 or osteoblasts MC3T3-E1 , protected six blocks of sequence ( H1–H6 ) . Noticeably , four different blocks ( P1–P4 ) that partially overlap H1–H4 were protected by extracts from the proliferating chondrocyte/chondrosarcoma cell line CCL ( Figure 1C and Figure 2A ) . Since proliferating chondrocyte/chondrosarcoma cells do not express Col10a1 ( Figure S1A ) , these results suggest that in these cells , the proteins that bind to element A may contribute to the repression of Col10a1 . We and others previously showed that SOX9 regulates COL2A1/Col2a1 gene via a functional in vivo binding site in the intron 1 enhancer element [10] , [11] . We identified the same SOX9-binding sequence within the Col10a1 element A ( Figure 1C and Figure 2B ) . This site , COL2C1 , lies on a region in block P3 that is not protected in hypertrophic chondrocytes , and is adjacent to a stretch of thymidine/guanine-rich ( TG-rich ) sequence . Electromobility shift assays revealed that SOX9 bound to this SOX9/TG-rich motif with a similar affinity as to the COL2A1 enhancer element ( Figure S1C , S1E , S1F ) [10] and the interaction involved dimeric binding ( Figure 2C , cf . lane 3–4 ) . SOX9 also interacted with the TG-rich motif but with a lower affinity than with the consensus SOX9 site . Mutation of the TG-rich motif reduced the overall SOX9 binding to the P3 element ( Figure 2C , cf . lanes 6–10 ) . The TG-rich motif resembles a RUNX binding consensus sequence , but we found that RUNX2 did not interact with this motif effectively compared with its binding to the RUNX site in the Bglap ( osteocalcin ) enhancer ( Figure 2D ) . Chromatin immunoprecipitation ( ChIP ) assays using extracts from E13 . 5 mouse limb , a stage at which the cartilage anlagen is largely composed of immature chondrocytes , confirmed specific SOX9 binding to the Col10a1 element A and the Col2a1 enhancer in vivo ( Figure 2E ) . The paired SOX9 binding sequences in element A are separated by 4 bp , a distance similar to that between the paired SOX-like consensus sequences in Col2a1 , Col9a1 , and Acan that mediate transactivation of expression [15] . We tested the in vivo role of element A and the effects of SOX9/TG-rich motif mutations on the expression of Col10a1 mini-genes ( Figure 3A ) in transgenic mice . We have previously shown that a Flag-tagged Col10a1 vector Col10Flag ( formerly known as FColX ) is expressed in P10 hypertrophic chondrocytes [18] . Here , we show that in E15 . 5 humeri , the Col10Flag transgene was expressed in islands in prehypertrophic and hypertrophic chondrocytes in the upper hypertrophic zone ( Figure 3B , e ) . In two independent mouse lines , a transgene comprising element A fused to the Col10Flag ( Col10Flag-E ) was expressed in a similar pattern as Col10Flag , but was significantly more strongly expressed than Col10Flag in all hypertrophic chondrocytes ( Figure 3B , i ) , reflecting the enhancer activity of element A ( see also Figure S2 ) . However , mutation of the SOX9 site in element A ( Col10Flag-EΔ1 ) resulted in marked expansion of the expression domain of the transgene , extending from the hypertrophic zone to the proliferating zone in the majority of transgenic fetuses ( 71 . 4% ) ( compare Figure 4 , a with Figure 3B , i ) and in almost all the Sox9-expressing chondrocytes in the rest ( Figure 4 , e ) . Expansion of transgene expression in proliferating chondrocytes was also noted but was less marked when the TG-rich motif in element A was mutated ( Col10Flag-EΔ2 ) ( compare Figure 4 , m and i with a and e ) . Mutation of either SOX9 or the TG-rich motif did not abrogate transgene expression in hypertrophic chondrocytes . Together , these observations suggest that element A contains both positive and negative regulatory sequences , and that mutations in the SOX9/TG-rich motif in element A might disrupt SOX9-mediated repression in immature chondrocytes . To test whether SOX9 negatively regulates Col10a1 , we established a cell line from hypertrophic chondrocytes MCTs which expressed the Col10Flag-E transgene at the non-permissive ( growth-arrest ) temperature ( Figure 5A ) . Similar to previous findings for dedifferentiated chondrocytic cells MC615 , over-expression of SOX9 in MCTs did not transactivate endogenous Col2a1 [20] . However , SOX9 over-expression significantly down-regulated expression of both endogenous Col10a1 and the exogenous Col10Flag-E reporter ( Figure 5B ) supporting the notion that SOX9 is a negative regulator of Col10a1 . To study the regulation in vivo , SOX9 was ectopically expressed in hypertrophic chondrocytes in mice . Mice expressing Cre recombinase inserted into the endogenous Col10a1 gene ( Col10a1-Cre ) [21] were crossed with transgenic mice carrying a single copy of a Cre-inducible Sox9-IRES-EGFP expression construct ( Z/Sox9 ) [22] ( Figure 5C ) . In Col10a1-Cre;Z/Sox9 mice , Sox9 and the linked Egfp reporter gene were activated in the hypertrophic chondrocytes at E17 . 5 in the anterior ribs ( Figure 5D , h , i ) . These mice displayed an expanded hypertrophic zone and reduced Col10a1 expression ( Figure 5D , e , k ) . Interestingly , we also found that transcription of Cre from the Col10a1 locus was reduced in Col10a1-Cre;Z/Sox9 mice ( Figure 5D , a , g ) . Col2a1 expression in Col10a1-Cre;Z/Sox9 mice hypertrophic chondrocytes was not up-regulated suggesting the reduction of Col10a1 expression in these cells was not because of a reversion to a more immature state ( Figure 5D , d , j ) . In the ribs Runx2 was expressed predominantly in osteoblasts , the perichondrium flanking the hypertrophic chondrocytes , and in prehypertrophic chondrocytes ( Figure 5D , f ) . Expression was low in hypertrophic chondrocytes . There was no significant change in Runx2 expression in prehypertrophic and hypertrophic chondrocytes in Col10a1-Cre;Z/Sox9 mice ( Figure 5D , l ) . Collectively , these data suggest that SOX9 negatively regulates Col10a1 gene expression independent of Runx2 . The specificity of SOX protein action is known to be achieved through interaction with cell-specific partners [23] , [24] . We questioned whether concomitant transactivation of Col2a1 and repression of Col10a1 by SOX9 in proliferating chondrocytes could be mediated by different combinations of cofactors . ChIP assays in E13 . 5 mouse limb chondrocytes or CCL cells revealed similar interactions of TRAP230/MED12 , a mediator of SOX9 activity [25] , and of TRPS1 , a GLI3-interacting repressor [26] , [27] , with both the Col10a1 element A and the Col2a1 enhancer ( Figure 6A , upper panel ) . On the other hand , the transcriptional co-repressor , histone deacetylase HDAC4 [28] immunoprecipitated neither element . GLI1 , GLI2 and GLI3 are effectors of Hh signaling which controls chondrocyte proliferation and maturation [29] . GLI1 is a transactivator expressed in proliferating chondrocytes and perichondrial tissue flanking the prehypertrophic and hypertrophic zones [30] whereas GLI2 and GLI3 can act as repressors and are predominantly expressed in non-hypertrophic chondrocytes and are down-regulated in hypertrophic chondrocytes [29] , [31] . Since there is a conserved GLI-binding site near the SOX9/TG-rich motif in the same footprint block P3 ( Figure 1C ) , we examined whether GLI1 , GLI2 and GLI3 can interact with the element A . Strikingly , while SOX9 bound to both the Col10a1 element A and Col2a1 enhancer , GLI2 and GLI3 associated with only Col10a1 element A ( Figure 6A , lower panel ) . GLI3 interacted the most with element A , while GLI1 interaction was much less . Quantitative ChIP assays confirmed the preferential interaction ( Figure 6B ) . From these results we hypothesized that GLI proteins may repress Col10a1 expression . To test this in vivo , we examined the impact on transgene expression of mutating the GLI-binding site in element A ( Col10Flag-EΔ3 ) . Consistent with our hypothesis , the majority of fetuses ( 7 out of 10 ) expressing Col10Flag-EΔ3 showed distinct islands of transgene misexpression in non-hypertrophic chondrocytes ( Figure 6C , e , f ) . Mutating all three sites ( GLI , SOX9 , TG-rich ) in the transgene ( Col10Flag-EΔ4 ) did not restrict the expansion of the expression domain to proliferating chondrocytes in all the expressing transgenic fetuses obtained ( Figure 6C , i , j ) . Indeed in the majority of these expressing fetuses ( 3 out of 5 ) , transgene expression extended throughout the entire cartilage zones . Thus mutation of the GLI site alone had a similar derepressing effect as mutating the SOX9/TG-rich motif and mutating all the motifs did not restrict expression but resulted in more extensive mis-expression . This is consistent with a model whereby SOX9 and GLI act cooperatively to repress Col10a1 transcription . To assess whether the cooperation of SOX9 and GLI2/3 is a potential common mechanism for restricted or preferential gene expression in hypertrophic chondrocytes , we searched in silico for this configuration of binding sites in genes , other than Col10a1 , that have strong and specific up-regulation in hypertrophic chondrocytes ( HC genes ) in the growth plate . Six of 11 HC genes analyzed , namely Col10a1 , Bmp2 , Hdac4 , Mef2c , Runx2 , and Sox4 , possess the linked SOX9 and GLI sites ( <100 nt spacing ) in the inter- or intragenic conserved non-coding regions ( Figure 7A and Figure S3 ) . In contrast , these sites were absent from most of the genes tested ( 12 out of 14 ) that were expressed in proliferating but not ( or down-regulated ) in hypertrophic chondrocytes ( PC genes ) . These include known SOX9 targets: Col2a1 , Col9a1 , Col11a2 , Acan , and Mia1 ( see Figure 7A legend for all negative genes ) . The exceptions were Sox5 and Sox6 ( Figure 7A and Figure S3 ) . To investigate whether the over-representation of linked SOX9-GLI sites in the HC genes but not the PC genes occurs by chance , we performed a hypergeometric test to calculate the probability of finding 6 or more SOX9-GLI site-containing genes out of 11 genes randomly sampled from the mouse genome . For the HC genes , the results showed that the occurrence of 6 or more genes with associated conserved SOX9-GLI sites is unlikely to occur by chance ( p = 0 . 0000201 ) ( Figure 7B ) . For the PC genes , the p-value was 0 . 17 , which is comparable to random occurrence . Furthermore the frequency of the presence of SOX9-GLI sites for HC genes ( 6/11 ) was significantly higher than that for PC genes ( 2/14 ) ( Fisher's test p = 0 . 043 , one tailed ) . This suggests that the linked SOX9-GLI sites are preferentially associated with the HC genes .
The positive and negative mechanisms mediating the stage-specific transcription of genes within the growth plate are not well defined , partly because of the difficulty in distinguishing direct effects on transcription from the consequences of abnormal differentiation . In this study we have exploited the specificity of Col10a1 expression in hypertrophic chondrocytes and the fact that manipulating its expression in vivo has no overt effect on differentiation , to dissect these transcriptional controls . We provide new insight into how differentiation stage-specific gene expression is achieved in the growth plate , presenting in vitro and in vivo evidence that SOX9 , in addition to its known role as a transactivator of many genes preferentially expressed in non-hypertrophic chondrocytes , such as Col2a1 , directly represses expression of Col10a1 at a stage prior to the onset of hypertrophy and subsequently in proliferating chondrocytes . This discovery extends our understanding of the mechanisms by which SOX9 controls chondrocyte differentiation phase-specific gene expression . We have identified a conserved regulatory sequence , element A , that acts as an enhancer of Col10a1 expression in both cultured cells and in vivo . This element contains a SOX9 binding sequence that , when bound by SOX9 , represses Col10a1 expression in immature and proliferating chondrocytes . Since Sox9 is expressed in non-hypertrophic chondrocytes but not in hypertrophic chondrocytes , this repressive action of SOX9 restricts Col10a1 expression to hypertrophic chondrocytes . SOX9 has been proposed to direct chondrogenic fate in osteo-chondroprogenitor cells in part by interacting with RUNX2 [32] , [33] . SOX9 may inhibit chondrocyte hypertrophy in part via activation of Bapx1 which represses Runx2 [34] , [35] . Previous in vitro and in vivo studies suggest that Col10a1 expression is regulated positively by Mef2c , Runx2/Cbfa1 , and AP-1 members , which are expressed in hypertrophic chondrocytes [19] , [36]–[38] . RUNX2 has been shown to directly regulate the expression of Col10a1 [37] . The element A that we identified contains no conserved consensus RUNX site . The RUNX2 site revealed by Zheng et al . [37] is located within a poorly conserved region outside the element . Our data showed that the ectopic expression of Col10a1 transgene in non-hypertrophic chondrocytes does not require co-expression of Runx2 . In addition , RUNX2 is not expressed in the costal hypertrophic chondrocytes and cultured hypertrophic chondrocytes MCTs ( which is derived from costal cartilage ) , where Col10a1 expression is strong . Although real-time PCR showed levels of Col10a1 was markedly reduced in P1 Runx2-null mice [37] , hypertrophic chondrocytes with strong Col10a1 expression do develop in many cartilages in Runx2 null fetuses [39] , [40] . Collectively existing data suggest that RUNX2 together with other factors regulate Col10a1 in vivo via promoting chondrocyte hypertrophy or otherwise functions to initiate a cascade of regulatory pathways that sustain Col10a1 expression in hypertrophic chondrocytes . Previous in vitro studies in chicken have suggested that a combined action of positive and negative DNA elements may contribute to the hypertrophic chondrocyte-specific expression of Col10a1 [41] , [42]; however , these chick Col10a1 elements are not conserved in mammals . The enhancer element we identified is highly conserved in mammals , but not in chicken , which agrees with previous data [43] . This suggests that in both mammals and chicken , Col10a1 transcription is restricted to hypertrophic chondrocytes by repression , though by different cis-acting elements . In the chicken , this repression may extend to non-chondrogenic cell types [41] . We found no evidence to support such a mechanism in the mouse since when we abolished the interaction of SOX9 with the repressive element , we observed no ectopic Col10a1 expression in non-chondrogenic cells . Consistent with a role for SOX9 in repressing Col10a1 in vivo , we have shown in Col10a1-Cre;Z-Sox9 mice , that activation of Sox9 expression in hypertrophic chondrocytes in costal cartilage caused down-regulation of Col10a1 . It is also notable that in a recent report where Sox9 was over-expressed in hypertrophic chondrocytes in transgenic mice , expression of Col10a1 and the BAC-Col10-Sox9 transgene appeared reduced in the hypertrophic chondrocytes [44] . In the same report Sox9 knockdown in cultured chondrocytes did not affect Col10a1 expression [44] . By contrast Yamashita et al . showed that shRNA knockdown of Sox9 can up-regulate Col10a1 expression in primary costal chondrocyte culture , and that over-expression of Sox9 can down-regulate it [35] . These contradictory results may be related to the incomplete elimination of SOX9 protein and the known dosage dependent requirement for SOX9 action . A role for SOX9 as transcriptional repressor of Col10a1 in non-hypertrophic chondrocytes is consistent with the observation that COL10A1 expression was up-regulated in cartilage isolated from SOX9 haploinsufficient campomelic dysplasia patients [33] . How may SOX9 act both as a transactivator and a repressor in non-hypertrophic chondrocytes ? It has been proposed that interactions between specific partner factors stabilize SOX protein binding to DNA and hence regulate target selection [24] , thereby determining cell specification , as exemplified by SOX2 in embryonic stem cells and other systems [45] . Such selective cooperation of protein binding partners may mediate the concomitant positive and negative regulation of SOX9 target genes in the same cell that contributes to specification of the differentiation state ( Figure 8 ) . As illustrated in the schematic ( Figure 8 ) , we propose that SOX9 mediates repression of Col10a1 in proliferating chondrocytes by selective cooperation with GLI factors . Together with its role in activating Col2a1 and other matrix genes , SOX9 therefore plays an important role in maintaining chondrocytes in an immature non-hypertrophic state . RUNX2 by contrast promotes chondrocyte hypertrophy . SOX9 cannot regulate chondrocyte differentiation appropriately without sonic hedgehog ( Shh ) , which mediates the generation of chondrogenic precursor cells [46] , and Indian hedgehog ( Ihh ) , which regulates their proliferation and maturation [47] . GLI proteins are the effectors of Hh signaling . Double knockout mutants indicate that GLI2 has overlapping functions with GLI1 and GLI3 in skeletal and CNS development [48] , [49] . Binding of Hh to its receptor , Patched , blocks the proteolytic processing of the GLI transcription factors from active ( GLIA ) to repressive ( GLIR ) forms , and the balance between these forms modulates hedgehog target gene expression [50] . In the growth plate , GLI2A can positively regulate chondrocyte hypertrophy and control vascularization of the hypertrophic cartilage in endochondral ossification [29] , [51] . GLI3 , which acts mainly as a repressor , has been suggested to inhibit chondrocyte hypertrophy [29] , [52] and it is interesting that the highest interaction of element A was with GLI3 . Mau et al . reported that Gli2/3 null mutations altered the expression domain of collagen X , but it was not possible to distinguish whether this was due to a direct effect on Col10a1 transcription or more general perturbation of hypertrophy [29] . How the GLI factors interact with other regulatory factors or genes in the chondrocyte differentiation program is not clear . Our results are consistent with cooperation between SOX9 and the Hh signaling pathway and suggest that SOX9 acts in synergy with GLI2 and GLI3 , probably their repressive forms GLIR , to repress transcription in chondrocytes . Thus , reduced synergy between GLIR and SOX9 may explain the accelerated chondrocyte hypertrophy seen when Ptch1 is inactivated [53] . Over-representation of the SOX9-GLI paired consensus in a number of genes that are preferentially expressed in hypertrophic chondrocytes and not in proliferating chondrocytes , suggests that SOX9 may use this partnership to repress transcription of several genes in other chondrocyte types . However this partnership may not be the exclusive mechanism by which SOX9 acts to repress expression in chondrocytes . Hattori et al . have recently shown that SOX9 directly represses Vegfa in cultured primary chondrocytes [44] by interacting with the 5′ untranslated region of the gene . This agrees with our findings of a repressive role of SOX9 , however , we found no linked SOX9-GLI binding sites near the Vegfa gene and a recent SOX9 ChIP-on-chip study reported no in vivo interaction in Vegfa exon 1 [54] . A different mode by which SOX9 may repress gene expression in chondrocytes has been proposed by Huang et al [55] . In their model , SOX9 negatively regulates Ccn2 expression in non-hypertrophic chondrocytes via binding to overlapping binding sites for SOX and TCF/LEF , thereby interfering with binding of a TCF/LEF/β-catenin transactivation complex . Reduction of SOX9 upon hypertrophy allows this TCF/LEF/β-catenin complex to activate Ccn2 expression [55] . However , Ccn2 is also expressed in resting zone chondrocytes in the epiphyses of the growth plate and it is not clear why SOX9 does not repress the gene in these cells . We identified a conserved TCF consensus site in Col10a1 element A , but this is unlikely to interfere with SOX9 binding since it is located 59 bp downstream of the functional SOX9-GLI motif , unlike in Ccn2 where the SOX and TCF/LEF sites overlap ( Figure 1C ) . This suggests that the model proposed by Huang et al . does not apply to Col10a1 element A-mediated repression of transcription . It is also possible that SOX9 and GLI cooperate to activate or repress transcription depending on context . While our data implicate a cooperation of SOX9 with GLI factors in transcriptional repression , this association between SOX9 and GLI may not be restricted to negative regulation . Amano et al . have recently reported that GLI2 cooperates with SOX9 to transactivate the Pthlh gene ( also known as PTHrP ) in chondrocyte culture without direct binding to the gene [56] . However the expression patterns of Sox9 and Pthlh are mutually exclusive in the developing growth plate , Pthlh being expressed mainly in the perichondrium and only at extremely low levels in proliferating chondrocytes [57] , [58] . This contradiction may reflect differences between in vitro assays and regulation in vivo , and it is also unclear whether the expressed GLI2 was processed to a repressor form or not in these cells . The observed stimulation of Pthlh promoter activity in the cultured chondrocytes could therefore be attributable to over-expression of GLI2 which persisted largely as the activated form GLI2A . However this report does raise the possibility of a context dependent SOX9-GLI partnership that mediates either transactivation or repression . Sox9 has been suggested to act upstream of Sox5 and Sox6 in chondrogenesis [6] , [46] . The presence of conserved SOX9–GLI sites in the Sox5 and Sox6 genes suggests their expression in proliferating chondrocytes may be positively controlled via cooperation of SOX9 with GLIA or GLI1 , an activator that reinforces GLIA function . Hence , the roles played by SOX9 in transcriptional regulation may be determined by context—partnering with GLIA/GLI1 favours transactivation , with GLIR favours repression . Alternatively , as discussed above , the mode of regulation might depend on whether intermediate factors are present to interfere with the SOX9-GLI interaction . Interestingly , while there is a linked SOX9-GLI motif in Sox6 , a conserved TCF site occurs between the SOX9 and GLI sites ( Figure S3 ) . Cooperation between SOX9 and TCF/LEF/β-catenin might therefore abrogate cooperative repression by SOX9 and GLI and transactivate Sox6 in proliferating chondrocytes . Validation of these different modes of cooperative regulation by SOX9 and GLI factors in vivo would require the generation and analyses of compound null or conditional knockout mutants; however , the consequent dysregulation of chondrogenesis and impact on cell survival would make it impossible to distinguish changes in transcriptional control from effects on differentiation . For example , Sox9 is essential for chondrogenesis and Sox9 conditional null chondrocytes undergo apoptosis and as a consequence , hypertrophy with the characteristic activation of Col10a1 expression , fails to occur [4] , [7] , [59] . Because inactivation of Col10a1 does not disrupt the chondrogenic program , it provides an ideal system and tools to interrogate the transcriptional controls governing specificity of gene expression within the growth plate , independent of changes in chondrocyte differentiation . Important questions to be addressed in future are the identities and diversity of SOX9 partnerships and how the activity of SOX9 and its partners is modulated . In early myogenic differentiation , SOX9 and Smad3 have been reported to prevent premature expression of α-sarcoglycan gene expression by synergistic repression in a transforming growth factor β dependent manner [60] suggesting negative transcriptional regulation by SOX9 and its partners may be more common . The transactivation of Matn1 in chondrocytes by SOX9 can be modulated by combined action of L-SOX5 , SOX6 and NFI factors [61] . Whether control of transcription by the SOX9-GLI partnership can be modulated by additional factors is an important question to be addressed in future . In summary , our study implicates a complex regulatory function for SOX9 whereby it acts with different partners to orchestrate activation and repression of transcription in the chondrogenic differentiation pathway . Mutations in human SOX9 cause the skeletal malformation syndrome campomelic dysplasia which is attributed to the disruption of the chondrogenic differentiation program because of failure to express SOX9 target genes . This interpretation may need to be revised to include inappropriate expression of genes normally repressed by SOX9 .
COL10A1 sequences were aligned using LAGAN and PipMaker . The conserved SOX9 binding sites ( COL2C1 and COL2C2 ) [10] were identified by rVISTA . The UCSC Mouse 30-Way Multiz Alignment data and a custom Perl script were used to identify human-mouse perfectly conserved SOX9 , GLI , and TCF sites with a maximum spacing of 100 bp in the inter- and intra-genic non-coding regions . Conservation percentage of sequence spanning the SOX9 and GLI sites was calculated based on the number of perfectly matched nucleotides among all the aligned species ( mouse , human , chimpanzee , canine , bovine , and opossum ) . From the 32 , 120 genes in the mouse genome , the number of genes containing conserved linked SOX9-GLI sites was found and used as the reference frequency of such genes in the genome . Whether the frequency of the presence of conserved SOX9-GLI sites in HC or PC genes exceeded this reference frequency was assessed by the hypergeometric distribution . The difference in the frequency of the presence of SOX9-GLI sites in the HC and PC genes was assessed by the Fisher's exact test . Hypertrophic chondrocyte cell line MCTs ( gift of Véronique Lefebvre [62] ) was transfected with pCol10Flag-E , pSG-Sox9 ( gift of Peter Koopman ) , or pSG5 expression vector using Fugene 6 ( Roche ) . Expression of the exogenous collagen X from pCol10Flag-E in MCTs cells was examined by immunohistochemistry using anti-Flag M2 antibody ( Sigma ) . CCL ( gift of James Kimura [63] ) , MCT3T3-E1 , and COS-1 cells were cultured in DMEM ( Invitrogen ) containing 10% FCS ( Wisent ) at 37°C at 5% CO2 . MCTs cells were normally cultured at 32°C at 5% CO2 for expansion . Prior to assays , MCTs cells were cultured at 37°C for 1 day to induce growth arrest [62] . A 300 bp DNA fragment within element A , corresponding to −4240 to −3935 bp of the mouse Col10a1 , was used as probe . [γ-32P]ATP-labeled probes were incubated with nuclear extracts from CCL , MCTs , MCT3T3-E1 , or COS-1 cells in the presence of poly ( dA⋅dT ) at room temperature , followed by DNase I digestion and denaturing PAGE . COS-1 nuclear extracts over-expressing SOX9 and RUNX2 were pre-incubated with poly ( dI⋅dC ) or poly ( dG⋅dC ) at room temperature , followed by reaction with [γ-32P]ATP-labeled probes with or without the presence of nucleotide competitors , or antibodies for SOX9 ( gift of Peter Koopman [64] ) and OSF2/RUNX2 ( gift of Gerard Karsenty [65] ) , then subjected to non-denaturing PAGE at room temperature . The sequences of oligonucleotide COL2C1 and OSE2 were as previously described [10] , [65] . The mouse Col10a1 element A , a 640 bp-fragment located between −4 . 2 and −3 . 6 kb , was cloned at the 5′ end of the pCol10Flag , previously known as FColX [18] consisting of −2070 to +7176 bp mouse Col10a1 genomic sequence , to generate pCol10Flag-E . The SOX9 , TG-rich motif , and GLI binding sites in pCol10Flag-E were mutated to generate the single site mutants – respectively pCol10Flag-EΔ1 , pCol10Flag-EΔ2 , and pCol10Flag-EΔ3 . All of these 3 motifs in pCol10Flag-E were mutated to generate pCol10Flag-EΔ4 . A 4 . 8 kb fragment of mouse genomic DNA ( from 82 bp upstream of the start of transcription of Sox9 to 1119 bp downstream of the polyadenylation sequences ) , including the Sox9 coding region , its two introns and 1 . 1 kb of 3′ flanking DNA , together with an IRES2-EGFP ( Clontech ) sequence inserted between the Sox9 stop codon and the polyadenylation site ( at +3237 bp ) , was cloned downstream of the loxP-flanked βgeo/3xpA of the pCall2 vector ( gift of Andras Nagy [66] ) to create the pZ/Sox9 expression vector . pZ/Sox9 was transfected into 129/SvEv-derived L4 embryonic stem ( ES ) cells by electroporation and ES clones containing a single copy of the transgene were injected into blastocysts followed by crossing of chimeras with C57BL/6N mice to generate the mouse line Z/Sox9 . A mouse line carrying a single copy of pZ/Sox9 was generated which was then crossed with Col10a1-Cre mice [21] to obtain compound mutants . CCL cell lysates or 13 . 5 dpc mouse limb tissue lysates were cross-linked followed by lysis and sonication to yield 200–500 bp DNA fragments then immunoprecipitation with antibodies against acetylated histone H3/H4 ( Ac-H3 , Ac-H4 ) ( Upstate ) , HDAC4 ( Abcam ) , GLI1 , GLI2 , GLI3 ( all from Santa Cruz ) , SOX9 ( gift of Robin Lovell-Badge [67] ) , TRAP230/MED12 ( gift of Robert Roeder [25] ) , or TRPS1 ( gift of Yasuteru Muragaki [26] ) . The target elements in Col10a1 , Col2a1 , or Crygb genes were amplified by real-time or standard PCR . In-situ hybridization was performed as previously described [9] . The probes used were pRK26 for Col10a1 [68] , pWF21 for the Flag sequence [18] , p88 for full-length Sox9 ( gift of Peter Koopman ) , pBS-Cbfa1-Sh for full-length Runx2 ( gift of Gerard Karsenty ) , pWF98 for full-length Egfp , pBSII-Cre-frag for Cre ( gift of Andrew Groves ) , and pNJ61 for Col2a1 [9] . Gene expression in cell culture was analyzed by RT-PCR . For additional details of all experiments , see the Text S1 . | Chondrogenic differentiation is a key process in the formation of endochondral bone . Despite the wealth of information about gene expression patterns and signaling pathways important for this process , it is not clear how differentiation state-specificity of transcription is controlled . The transcription factor SOX9 regulates chondrocyte differentiation , proliferation , and entry into hypertrophy and is highly expressed in immature/proliferating chondrocytes . It directly transactivates Col2a1 , enhancing this gene's expression in immature/proliferating chondrocytes . The Col10a1 gene is specifically expressed in hypertrophic chondrocytes in which Sox9 is downregulated . How is differentiation phase-specific transcription of genes controlled in chondrocytes , particularly during hypertrophy ? We found that SOX9 directly represses Col10a1 expression in immature/proliferating chondrocytes of the growth plate , so that its expression is restricted to hypertrophic chondrocytes . Discrimination of this concomitant opposing transcriptional control may involve cooperation between SOX9 and different partners such as GLI factors ( effectors of hedgehog signaling ) . SOX9 control of chondrocyte maturation therefore may be integrated with hedgehog signaling . Mutations in human SOX9 cause the skeletal malformation syndrome campomelic dysplasia , which is attributed to the disruption of the chondrogenic differentiation program because of failure to express SOX9 target genes . This interpretation should be revised to include inappropriate expression of genes normally repressed by SOX9 . | [
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| 2011 | SOX9 Governs Differentiation Stage-Specific Gene Expression in Growth Plate Chondrocytes via Direct Concomitant Transactivation and Repression |
Diagnosis and treatment for visceral leishmaniasis ( VL ) is considered to be delayed amongst poor , rural women in highly endemic districts of Bihar and Jharkhand . The objective of this study was to assess and understand barriers to VL diagnosis and treatment for women in endemic districts with a high burden of VL . The study used a stratified and purposive sample of 33 female patients with VL , 11 health staff , 11 local ( unqualified ) health providers and 12 groups of community elders drawn from ten districts in Bihar and four in Jharkhand with high burdens of VL . The study was conducted within an exploratory and inductive framework , using semi-structured in-depth interviews and discussions . Women accessing treatment more quickly tended to move faster from treating their symptoms on their own to seeking care from local providers . Perception among female patients of the illness being not serious ( owing to initially non-specific and mild symptoms ) , lack of money , prioritisation of household chores over their need to seek care and the absence of a male guardian to accompany them in seeking care at facilities worked together to drive these choices . Most patients and their families did not suspect VL as the cause for their non-specific symptoms , but when VL was suspected , treatment shopping ended . Lack of prioritization of women’s health issues appears to be a pervasive underlying factor . Public health facilities were not an early treatment choice for the majority , but where it was , the diagnosis of VL was often not considered when presenting with under 2 weeks of symptoms , nor were appropriate follow-up plans instituted . The insidious presentation of VL and the low prioritisation of women’s health need to be jointly addressed through messages that emphasise the importance of early diagnosis and treatment of disease , which is low-cost in time and money when managed in public health facilities . Clear messages that project prioritising women’s care-seeking over household work as a smart choice and the need for rallying male support are needed . Additionally , efforts to reduce missed opportunities through early case suspicion and engaging private providers to better counsel women with suspected VL could close critical gaps in the continuum of care .
Visceral leishmaniasis ( VL ) or Kala azar , is a vector-borne disease caused by the protozoan Leishmania donovani and transmitted through the bite of the phlebotomine sand fly . Up to 100 , 000 cases are estimated to occur globally every year [1] , and the disease is normally fatal within two years if untreated [2] . The pathogenesis of VL is complex and clinical presentations vary from asymptomatic infection to fatal disease [3 , 4] . While VL is highly endemic in the Indian subcontinent and East Africa , India accounts for nearly half of all cases worldwide [1] . The epidemiological features of the disease in the Indian subcontinent together with the availability of effective diagnostic , treatment and vector control measures make it amenable to its elimination as a public health problem [5] . As such , since 2005 , India has been part of a regional initiative to achieve this elimination target [6] , defined as less than 1 case per 10 , 000 population at the sub-district level . In the Indian context , these units are called blocks , with populations ranging from 80 , 000 to 300 , 000 . As a result of the elimination efforts , and possibly also due to the cyclical epidemic nature of VL , there has been a steady decline in reported cases , with an 82% decline between 2011 and 2017 [7] . Despite the encouraging trend , much remains to be done . India remains the only country in the regional elimination initiative that has not yet reached the threshold target , with the disease continuing to be endemic in the states of Bihar , Jharkhand , West Bengal and Uttar Pradesh . By 2018 , 49 blocks in Bihar and 25 blocks in Jharkhand have not yet achieved the elimination threshold , with Bihar contributing to nearly 70% of national cases [7] . These highly endemic blocks are spread across 10 districts in Bihar and four districts in Jharkhand . Under the aegis of the National Roadmap for Kala azar Elimination and with support from a range of stakeholders , over recent years there has been an accelerated effort to control the disease , with rapid diagnostic tests made available at all , and treatment with single-dose Liposomal Amphotericin B at selected nodal public health care facilities in endemic blocks [8] . Kala azar technical supervisors ( KTS ) at each block oversee vector management and active surveillance , while frontline health workers called Accredited Social Health Activists ( ASHA ) support prevention and case detection activities at the community level . Development partners have also been conducting a wide range of community education efforts for VL . Timely detection and treatment of those with acute symptomatic VL is the mainstay of elimination efforts , as this results in the interruption and shortening of transmission from human hosts , which are currently the only known reservoir in the Indian subcontinent . Results from recent modelling studies suggested that decreasing the time between the onset of symptoms to treatment ( OT ) from 40 to 20 days could bring the elimination target forward by a year [9] , with an even larger potential in Bihar [10] . Women with VL appear to access care later than men . A retrospective study of VL patient data from 2012–13 in Bihar found significantly lower proportion of women among reported cases compared to the background population . The paper concluded that this is likely due to under-reporting ( as a result of poorer access to healthcare for women ) , and not necessarily due to lower incidence amongst women , as there was no corresponding difference between the sexes among those under 15 years of age [11] . This is supported by a 2014 study that showed a marked decrease in the proportion of female patients with rising age [12] , while an earlier 2006 study that showed that 60–80% of VL patients in facilities were men [13] . A 2003 study from Bangladesh reported that in one highly affected village , reproductive-age women were three times as likely to die from VL compared to men or children; where VL accounted for 23% of all deaths , 80% of these were adult women . Qualitative data from the same study suggest that women experienced substantial barriers to seeking care [14] . VL remains a disease of the poor , with 83% households belonged to the lowest two wealth quintiles in the state’s wealth distribution , while 70% live in mud adobe houses , consistent with the breeding preferences of the sand fly [15] . Caste is an important and reliable indicator of socio-economic status and is used in national surveys as a measure of economic inequality and access to services . It appears that those belonging to the caste categories of Scheduled Caste ( SC ) , and Scheduled Tribes ( ST ) are disproportionately impacted by VL: a 2012 study of VL patients in Bihar , found that patients from SC had twice the odds of presenting late at treatment centres than others [16] . The objective of this study was to assess and understand barriers to VL diagnosis and care for women in endemic districts with a high burden of VL .
The study was designed to be exploratory and inductive within the post-positivist paradigm , using qualitative inquiry techniques . A purposive and stratified sample was used , with the overall intent of providing a rich and contextualized understanding of care-seeking patterns and drivers of those choices of women patients , but also to serve as an ex-ante strategy for extrapolating results towards providing evidence for practice . The days between onset of symptoms to treatment ( OT ) was used to stratify the sample into: Other participants in the study were: In addition , a smaller sample of male patients with VL with OT of 28–50 days and > 50 days was selected and investigated as a form of quality control , to compare the findings related to female patients and thus remain open and alert to alternative explanations for themes that emerge for female patients . The total sample size was a trade-off between that which would sufficiently answer the research question and capture a range of experiences , without being too repetitive , and what was feasible . Sampling for patients with VL was done in three stages . In the first stage , 12 blocks were selected as study sites using the Kala Azar Management Information System ( KAMIS ) national surveillance data from 2015–17 , based on the relative distribution of the disease burden between Bihar and Jharkhand . Two blocks each from four of the 10 highly endemic districts in Bihar and one each from all four endemic districts in Jharkhand were selected , using the following criteria: Of the 12 blocks selected , all met the above criteria aside from one , which met the first three but did not have cases with OT>50 , which was selected for logistical reasons . S1 Table gives the details of selection criteria listed above for the blocks that were selected for the study . In the second stage , a list of patients with VL aged 18 years and above were obtained from the selected blocks and organized by age , caste and OT , and samples were chosen for each of the OT categories of female patients , ensuring those belonging to SC ( in Bihar ) and those belonging to ST ( in Jharkhand ) were included . Additional samples were selected as backup . One male patient–with either moderate or late access–was selected from nine of the twelve blocks chosen . In the third stage , field teams obtained identifying information of these pre-selected patients from the surveillance register maintained at the block level by the KTS , along with details of those with confirmed VL who had died before treatment . Where the pre-selected patients were not traced , new cases from September–December 2017 were included . The final sample included 45 patients: 10 women with early access , 10 women with moderate access , 13 women with late access , 3 men with moderate access , 6 men with late access and 3 who died from confirmed VL . The deceased include two men and one male child , and interviews were conducted out with the wives of the male patients and the mother of the child . In addition , the following non-patient participants were selected: 11 KTS , 11 local providers and groups of community members in 12 communities . Table 1 gives the distribution of the sample across study units: Field teams carried out in-depth interviews using field-tested semi-structured guides that probed for factors affecting care-seeking , circumstances around each decision and sought to construct a timeline for care-seeking . Similar tools were used to carry out in-depth interviews of the KTS , local providers and focus group discussions in communities to explore trends in care-seeking patterns and their perspectives on factors affecting decisions about care-seeking . Data collection teams consisted of an interviewer/facilitator , note-taker and a manager . Data was collected between December 2017 and January 2018 . Transcribed data and field notes were translated to English and coded in NViVo 8 , along with analytic reflective notes of the principal investigator , forming and refining categories as the coding progressed . Data was further compared and contrasted to discern conceptual similarities as well as outliers and refine the distinctions between categories until a theoretical framework emerged . The study was approved by the Ethics Review Committee of the Foundation for Research in Health Systems , Bangalore , India . Written informed consent was obtained from all participants in the study . Standard procedures for maintaining patient confidentiality and data privacy were followed . All human subjects were 18 years of age or older .
Of the 33 female patients sampled , 23 were from Bihar , with equal numbers having early , moderate and late access; while 10 were from Jharkhand , of which half were those with late access . Women from SC communities was predominantly from Bihar and those from ST communities from Jharkhand , in line with the overall distribution of communities in the two states . Seven out of ten women with late access were from ST communities while two of the three deceased were from ST communities and one from OBC community . Of the nine male patients sampled , four were from OBC communities , three from SC and two from ST communities . About a third of the female patients lived 15 km or farther from the nearest primary health centre ( PHC ) , while two-thirds of the female patients were either landless labourers or worked on non-irrigated land of their own . All women with early access , nine out of ten women with moderate access and nine out of thirteen women with late access lived in houses partly or fully made of mud . A conspicuous observation was the abject poverty of most of the participants and their living conditions , such as inadequate warm clothing to protect against the cold weather . Fig 1 gives the distribution of patients by OT . A striking finding in the study was that many patients and their family members did not consider VL as a possibility , even after weeks and months of having the symptoms , and after having rounds of unsuccessful treatment . This appeared to be despite having experienced a case of VL in the immediate or extended family , within the neighbourhood or even having been treated for VL themselves in the past . Others reported that they learnt about VL through awareness activities that were held in their neighbourhood , and could recite the salient features of the disease , and yet did not suspect VL in themselves . In an extreme instance , the husband and mother-in-law of a woman with late access had suffered from VL in the previous 2 years and yet she did not suspect VL in herself , but went from self-medicating to several private facilities and a traditional healer before ending up at the local government facility . Others who had prior knowledge of VL , including those who have had VL in the family or in their neighbourhood , stated that they would have gone to the government facility earlier and “not run here and there for treatment” , had they gotten a clear indication earlier that they were suffering from VL . Levels of awareness in communities ranged from comprehensive knowledge of causation , transmission , treatment and prognosis , to just knowing that VL is a killer disease . Misconceptions were few and not widely reported , and they include the belief that VL is caused by evil spirits and that it spreads through coughing . Several patient-participants stated that they had not heard about VL prior to falling ill , but picked up the basics from staff at the government facility during treatment . Several non-patient participants stressed the need for creating more awareness , while others felt that public awareness was already high . All patient-participants were confident to encourage others with similar symptoms to seek care at the government facility if symptoms persisted .
The overall care-seeking pattern shows similarities across male and female patients and those with early , moderate and late access: Initial self-medication , followed by repeated visits to or by the local provider , and visits to one or more private facilities , the last of which diagnosed VL and encouraged them to reach a government facility . Perceptions about the severity of the illness , lack of money , not having a male relative in the home and having the male of the household migrating for work drove early treatment choices . These factors also decided how long each of episode of care-seeking would last . This pattern appears to have been more drawn out for women and men with late access , but not for those with early and moderate access , depending on which of those factors were present . Some went to a government facility early on , but were not tested for VL . Fig 2 below depicts this typical pattern , along with some variations . The government facility is not typically an early choice , but those who did attend one within two weeks of the onset of symptoms were not tested or had a negative test . Without a clear follow-up plan , they went back to treatment shopping . It is unfortunate that patients that reach the public health system in highly endemic districts for VL are not offered advice about the possibility of persistent fever being VL and the important to return for follow up , and it represents significant missed opportunity . At least three options emerge as changeable drivers to expedite care seeking behaviour of women in the short to medium term , as indicated in Fig 4 below . The study has some important limitations that need to be considered . Primarily , the sampling frame was taken from the national VL surveillance register , which means that patients that were either not reported or not recorded in the register , such as those receiving diagnosis and treatment entirely in the private sector , were not considered in the sampling frame–although recent studies show that underreporting is low [2] . Secondly , the study was limited to high endemic blocks , where presumably the knowledge and awareness of VL is higher than in moderately and lower endemic area , where the results of this study cannot be extrapolated . Finally the small sample of male patients makes comparative analysis and thematic saturation for male patients difficult . | India bears the greatest burden of a fatal parasitic disease called visceral leishmaniasis ( VL ) , popularly known as Kala Azar . The disease is confined mostly to hot spots in Bihar and Jharkhand in the eastern part of the country . Amongst factors hampering efforts to eliminate VL are delays from the onset of symptoms to the time patients are diagnosed and treated , where disease transmission is thought to be greatest and longer delays result in poorer outcomes; this is considered to be a particular problem in poorer women living in rural areas . This study found patterns in careseeking: initial self-medication followed by multiple visits to local ( unqualified ) providers and visits to private facilities , before ultimately reaching the public health facilities where treatment was freely available . Those who did attend public health facilities early in their illness were not tested for VL nor followed up with a possible diagnosis of VL in mind . Female patients tended to under-estimate the severity of their illness , while social and economic reasons also influenced care-seeking behaviour–particularly the lack of a male relative in the house , and a reluctance to utilise already meagre resources . The conclusions of the study were the need to encourage women with persistent fever to seek care without delay , while ensuring that factors that serve as barriers , such as low prioritisation of women’s health by households ( and the women themselves ) is countered by messages emphasizing the danger of delayed treatment for VL and the low time-cost burden of availing care within the public health sector . Improving awareness of VL amongst informal and formal health providers remains key in this process . | [
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| 2019 | “It’s just a fever”: Gender based barriers to care-seeking for visceral leishmaniasis in highly endemic districts of India: A qualitative study |
N-terminal acetylation ( N-Ac ) is a highly abundant eukaryotic protein modification . Proteomics revealed a significant increase in the occurrence of N-Ac from lower to higher eukaryotes , but evidence explaining the underlying molecular mechanism ( s ) is currently lacking . We first analysed protein N-termini and their acetylation degrees , suggesting that evolution of substrates is not a major cause for the evolutionary shift in N-Ac . Further , we investigated the presence of putative N-terminal acetyltransferases ( NATs ) in higher eukaryotes . The purified recombinant human and Drosophila homologues of a novel NAT candidate was subjected to in vitro peptide library acetylation assays . This provided evidence for its NAT activity targeting Met-Lys- and other Met-starting protein N-termini , and the enzyme was termed Naa60p and its activity NatF . Its in vivo activity was investigated by ectopically expressing human Naa60p in yeast followed by N-terminal COFRADIC analyses . hNaa60p acetylated distinct Met-starting yeast protein N-termini and increased general acetylation levels , thereby altering yeast in vivo acetylation patterns towards those of higher eukaryotes . Further , its activity in human cells was verified by overexpression and knockdown of hNAA60 followed by N-terminal COFRADIC . NatF's cellular impact was demonstrated in Drosophila cells where NAA60 knockdown induced chromosomal segregation defects . In summary , our study revealed a novel major protein modifier contributing to the evolution of N-Ac , redundancy among NATs , and an essential regulator of normal chromosome segregation . With the characterization of NatF , the co-translational N-Ac machinery appears complete since all the major substrate groups in eukaryotes are accounted for .
N-terminal acetylation ( N-Ac ) is a common modification of proteins , but its general role has remained rather enigmatic . For specific proteins , N-Ac is recognized as an important regulator of function and localization [1]–[4] . Recently , it was suggested that it may act as a general destabilization signal for some yeast proteins , [5] while other reports imply that it might serve as a stabilizer , for instance by blocking N-terminal ubiquitination mediated degradation [6] . N-Ac in eukaryotes mainly occurs co-translationally when 25–50 amino acids protrude from the ribosome , by the action of ribosome associated N-terminal acetyltransferases ( NATs ) [7]–[12] . N-Ac may occur on the initiator Met ( iMet ) or on the first residue after iMet excision by methionine aminopeptidases ( MAPs ) [13] , [14] . Three major NAT complexes conserved from yeast to humans are thought to be responsible for the majority of N-terminal acetylation events: NatA , NatB and NatC [15] . Each complex is composed of specific catalytic and auxiliary subunits . NatA , the first NAT defined by Sternglanz and co-workers [16] , potentially acetylates Ser- , Ala- , Thr- , Val- , Gly- , and Cys- N-termini after iMet-cleavage [17]–[19] . NatB and NatC potentially acetylate Met- N-termini when the second residue is either acidic or hydrophobic respectively [19]–[21] . In yeast , NatD was described to acetylate the Ser- N-termini of histones 2A and 4 in vitro and in vivo [22] , while no such activity has yet been presented for higher eukaryotes . NatE is another NAT of which the in vitro activity was described for the human hNaa50p towards some Met-Leu- N-termini [23] , but direct evidence of in vivo activity is still lacking . Thus , each hitherto in vivo characterized NAT appears to acetylate a distinct subset of substrates defined by the very first N-terminal amino acids . Phenotypes induced by loss or reduction of NATs suggest that these enzymes , and thus probably N-Ac , are implicated in a number of cellular processes . In higher eukaryotes , depletion of NatA , NatB or NatC is associated with cell cycle arrest or apoptosis [20] , [21] , [24]–[28] while sister chromatid cohesion defects are observed upon NatE depletion [29]–[31] . N-Ac occurs on more than 50% and 80% of cytosolic yeast and human proteins , respectively [18] . The reason for the major difference in occurrence of N-Ac between yeast and humans to date is not known . Furthermore , the fact that specific subsets of protein N-termini , like those initiated by Met-Lys- , are often acetylated in humans and fruit fly while rarely being acetylated in yeast , is also an unsolved issue [18] , [32] . Further , such substrates do not match the predicted substrate specificity of any of the known NATs . Potential explanations for this evolutionary shift from lower to higher eukaryotes include: i ) evolution towards more acetylation-prone N-termini in higher eukaryotes , ii ) a shift in the substrate specificity between species-specific NATs , iii ) the presence of novel , yet uncharacterized NATs in higher eukaryotes , and iv ) the presence of species-specific co-factors or chaperones such as HYPK [33] . However , so far , no evidence for any of these hypotheses was presented . In the current investigation , we sought to elucidate the mechanistic explanations for the evolutionary shift in N-terminal acetylation from lower to higher eukaryotes . To this end we investigated the potential evolution of acetylation prone N-termini , but found this to be a trivial contributing factor . We further explored the presence of novel NATs in higher eukaryotes as a possible explanation . In silico analysis revealed the existence of an uncharacterized human protein with a significant sequence similarity to known catalytic NAT subunits . Indeed , multiple lines of in vitro and in vivo evidence clearly demonstrate that this candidate protein conserved among animals is a major NAT displaying distinct substrate specificity , denoted Naa60p ( NatF ) . Our data collectively suggest that Naa60p contributes to the increased occurrence of N-terminal acetylation in higher versus lower eukaryotes , and additionally revealed a novel regulator of chromosome segregation .
We first investigated whether an evolution towards more acetylation-prone N-termini in higher eukaryotes could help explain the higher acetylation levels observed . Upon comparing the yeast , fruit fly and human proteomes , it is evident that the general distribution of N-termini is largely unaltered between the different classes , ‘NatA’ , ‘NatB’ , ‘NatC’ and ‘Other’ ( Figure 1A ) . However , when considering all different subgroups based on the first two N-terminal amino acids , some significant alterations ( p<0 . 01 ) appeared . Besides the general difference of the amino acid usage in yeast versus human N-termini in agreement with recent observations [34] , the occurrence of ( Met- ) Ala- N-termini increased from 8% in yeast to 23% in humans , while Met-Glu- N-termini increased from 5% to 10% . On the other hand , ( Met- ) Ser- N-termini have decreased in occurrence from 23% in yeast to 11% in humans ( Figure 1B ) . Interestingly , for these major trends , the occurrences in fruit-fly are intermediate between yeast and humans , indicating that these might be characteristic of the evolution to multicellular and more complex organisms . The next question is thus whether these changes in N-terminal sequences are causing a shift in N-Ac . In the current work , we performed COFRADIC-based N-terminal acetylation analyses of yeast and HeLa proteomes and present datasets covering 868 and 1 , 497 unique yeast and human N-termini , respectively ( Tables S1 and S2 ) . An overview of the occurrence of N-Ac of the different classes of assigned N-termini in the yeast ( n = 648 ) and human ( n = 1345 ) control samples is presented in Table 1 . When relating the occurrence of N-Ac in yeast to the distribution of human N-termini and vice versa ( based on the first two amino acids of the identified N-termini ) , we found no overall significant changes in N-Ac levels ( Table S3 ) . Thus , alteration in usage of the first two N-terminal amino acids , which are the major determinants for N-Ac , is not a significant cause for the observed shift from lower to higher eukaryotes . Since it was shown that amino acid usage at protein N-termini differs significantly from what is expected [34] , and differences in dipeptide composition have been used to predict protein expression levels [35] , thermostability [36] and subcellular localization [37] , we further characterized the residue contact order at protein N-terminal parts by studying dipeptide frequencies in the theoretical proteomes of Homo sapiens , Drosophila melanogaster and Saccharomyces cerevisiae ( UniProt/SwissProt entries ( version 2011-05 ) ) . Therefore , the occurrence of the 400 possible dipeptides from the 20 amino acids in all proteins was estimated for randomly selected human dipeptides and N-terminal ( amino acids 2–11 ) dipeptides by Monte-Carlo sampling . Further , a z-score was applied to correct for differences in database size . Contacting residues in a random , non-N-terminal set correlate well with the expected theoretical contact order ( data not shown ) . In sharp contrast , the overall dipeptide composition deviates significantly for database-annotated N-termini . A heatmap visualization centered and scaled by species mean and standard deviation for Homo sapiens , Drosophila melanogaster and Saccharomyces cerevisiae is shown for the ten dipeptides with the highest and lowest z-scores ( union of n = 49 ) ( Figure 1C ) . Overall , these data strengthen the observation that N-terminal sequences not only display altered patterns of amino acid frequencies but deviate extensively in their residue contact order in a species-specific manner , which might additionally impose yet undetected constraints in determining N-Ac . When considering each type of N-terminus , it is evident that several of these are more acetylated in humans while some are mainly unchanged , but none are less N-Ac . The major groups of protein N-termini with an increase in N-Ac in humans as compared to yeast include ( Met- ) Ala- , ( Met- ) Val- , and Met-Lys- N-termini and thus represent major contributors to the overall evolutionary shift ( Table 1 and Table S3 ) . Another potential cause for the evolution towards the higher level of N-Ac is a shift in the substrate specificity between species-specific NATs . For NatA , which is responsible for N-Ac of two of the important N-terminal types mentioned above , ( Met- ) Ala- and ( Met- ) Val- this seemed not to be the case as both human NatA and yeast NatA acetylated the very same subset of N-termini in yeast [18] . For the final group , Met-Lys- N-termini , no information is available since such N-termini have not been linked to any of the NAT classes previously characterized . In search of novel human NATs , we used the sequences of known human NATs in NCBI BLAST queries ( search set: Swiss-Prot database restricted to human proteins ) . We identified one protein with a significant similarity to several of the known NATs , namely NAT15/Q9H7X0/Naa60p ( Figure 2 ) . NAT15/Naa60p is highly conserved among animals ( Figure 2B ) and homologues are also potentially present in plants ( for instance At5g16800 ) . In order to assess whether NAT15 was an N-terminal acetyltransferase , the NAT15 ORF was recombinantly expressed and purified from Escherichia coli and applied to a newly developed in vitro proteome-derived peptide library N-terminal acetylation assay [38] . In brief , natural proteomes are used to generate Nα-free peptide substrate pools ( libraries ) by enrichment with strong cation exchange ( SCX ) . When such a peptide library is incubated with a NAT enzyme , the newly Nα-acetylated peptides are enriched by a second SCX fractionation step , resulting in a positive selection of NAT-specific peptide substrates . Subsequently , the NAT-oligopeptide substrates are identified by LC-MS/MS , and the in vitro substrate specificity profile of the NAT in question is analyzed using IceLogo [39] , an analytical tool that uses probability theory to visualize significant conserved sequence patterns in multiple peptide sequence alignments by comparing against a chosen background ( reference ) sequence set . Using this proteome-derived peptide assay , NAT15 Nα-acetylated numerous peptides in vitro and displayed a distinct substrate specificity profile ( Figure 3A ) . Thus , according to the revised NAT-nomenclature system [15] , we named this protein Naa60p and its activity NatF . Remarkably , the preferred N-termini included Met-Lys- , Met-Ala- , Met-Val- , and Met-Met- , categories for which there are currently no known N-terminal acetyltransferase ( s ) . Of particular interest , recent data revealed that several Met-Lys- N-termini were acetylated in humans and fruit fly while no such N-Ac events of Met-Lys- N-termini were found in yeast , pointing to the presence of ( a ) NAT ( s ) specific for higher eukaryotes or an altered specificity profile of ( a ) higher eukaryotic NAT ( s ) as compared to yeast NAT ( s ) [18] , [32] . To expand these observations to higher eukaryotes in general , we purified the predicted fruit fly homologue dNaa60p ( CG18177 ) and confirmed this protein to be a NAT with a nearly indistinguishable specificity profile as compared to hNaa60p ( Figure 3B ) . As deduced from the in vitro specificity profile , besides Met-; Leu- was also preferred at the first position , which , as we described previously [38] , is expected since both Met and Leu share similar physiochemical characteristics [40] , [41] . However , for co-translational Nα-acetylation , Leu at the first position appears physiologically irrelevant as it is not expected as the first amino acid , since when it follows the initiator methionine , its size precludes the removal of this initiator methionine by MAPs [14] . When only including Met residues at the first position , the specificity profile remains largely unchanged ( Figure 3C , 3D and Figure S1 ) . Given its in vitro specificity , we considered Naa60p a qualified candidate for the Met-Lys- acetylation activities observed in higher eukaryotes . In order to assess whether hNaa60p represents a NAT in vivo and to address its potential role in the evolutionary N-Ac shift , we generated a yeast strain expressing hNaa60p . We were not able to observe any differences in growth rates or plating efficiencies between yeast control strains and yeast strains expressing hNaa60p ( data not shown ) . Since yeast does not have an obvious homolog of hNaa60p , ectopic expression was expected to reveal whether hNaa60p endows yeast with a greater acetylating potential . Indeed , when comparing N-terminal acetylation in the proteome of control yeast ( yeast control ) to the yeast expressing hNaa60p ( yeast+NatF ) , significant alterations in the Nα-acetylome were observed ( Figure 4 ) . For example the Smr domain-containing protein YPL199C and uncharacterized protein YGR130C , with respectively Met-Lys- and Met-Leu- N-termini , were unacetylated in control yeast while 82% and 48% acetylated in the strain expressing hNaa60p/NatF ( Figure 5 ) . In total , for 464 of the 544 ( or 85% ) unique N-termini identified in both proteomes , the N-acetylation status could univocally be determined . Of these , 72 N-termini were more acetylated in the hNaa60p expressing strain , while none were less acetylated , indicating that at least 16% of the identified yeast proteome was acetylated by hNaa60p ( Figure 4 and Table S4 ) . 44 of the 72 hNaa60p acetylated N-termini were completely unacetylated in control yeast , while 28 were partially acetylated . For the latter group , hNaa60p increased the degree of acetylation with at least 10% . It should be noted that this may represent an underestimation of hNaa60p's capacity since fully acetylated N-termini ( 53% ) in the control strain may also represent targets , which would be masked by redundancy with the yeast NAT-machinery . The hNaa60p yeast substrates identified in vivo were in agreement with the in vitro determined substrate specificities . The most common in vivo substrate classes were Met-Lys- ( n = 14 ) , Met-Ser- ( n = 9 ) , Met-Val- ( n = 8 ) , Met-Leu- ( n = 8 ) , Met-Gln- ( n = 6 ) , Met-Ile- ( n = 5 ) , Met-Tyr- ( n = 5 ) , and Met-Thr- ( n = 5 ) ( Table 2 ) . Among those acetylated by hNaa60p were proteins with Met-Lys- starting N-termini , which are of particular interest because these are acetylated in humans by an unknown NAT , while only rarely acetylated in yeast [18] . When considering the yeast control dataset , only 13% of the Met-Lys- N-termini are fully or partially acetylated , while the corresponding number for the yeast+NatF strain increases to 48% . In striking resemblance , 40% to 70% of Met-Lys- N-termini are N-terminally acetylated in human cell lines as respectively demonstrated previously [18] and in the current dataset ( Table 1 ) . Met-Leu- , Met-Ile- , and Met-Phe- starting N-termini , a class of N-termini considered NatC substrates , are other types of N-termini frequently found to be acetylated by hNaa60p . Finally , many substrate N-termini without a proper NAT-classification ( including initiator Met-retaining N-termini of which the iMet is only partially removed ) were acetylated: Met-Ser- , Met-Val- , Met-Thr- and Met-Met- , and Met-Gln- . Thus , hNaa60p acetylates both N-free besides partially acetylated protein N-termini in yeast , some without any known corresponding yeast NAT , as well as N-termini for which there is a putative NAT ( NatC ) . This indicates that Naa60p may mediate a significant part of the shift in N-terminal acetylation from lower to higher eukaryotes . Furthermore , in contrast to the current opinion , this also strongly suggests redundancy in the Nα-acetylation system , meaning that different NATs may have ( partially ) overlapping substrates . The effect of hNaa60p on overall N-terminal acetylation in yeast is shown in Figure 5C . Overall , the expression of hNaa60p increased the fraction of Nα-acetylated yeast proteins from 68% to 78% , in particular affecting the groups ‘yNatC’ and ‘Other’ ( Figure 4 and Figure 5 ) . Overexpression or knockdown of hNAA60 in HeLa cells was found to increase or decrease , respectively , the N-terminal acetylation of proteins matching the above defined in vitro and in vivo substrate specificity of hNaa60p ( Table S5 ) . Examples include the proteins STIP1 homology and U-box containing protein1 ( 1MKGKEEKEGGAR12 ) and mediator of RNA polymerase II transcription subunit 25 ( 1MVPGSEGPAR10 ) where the Nα-acetylation status is shifted as a consequence of hNAA60 overexpression ( from 18% to 32% acetylation ) or knockdown ( from 26% to 17% acetylation ) , respectively ( Figure 6 ) . These data strongly point to the fact that hNaa60p in human cells can act on the classes of N-termini deduced from the in vitro and in vivo yeast analyses described above ( Table 2 ) . Obviously , overexpression analysis will be limited by the redundancy among NATs and by the fact that naturally hNaa60p-acetylated N-termini may be fully acetylated and as such do not appear as substrates for the overexpressed hNaa60p . Furthermore and in line with previous knockdown analyses of NatA in HeLa cells , the semi-effective nature of siRNA-mediated knockdown as well as the long time period needed for a clear effect on N-terminal acetylations to occur , make such analyses indicative rather that providing the full picture of acetylation events mediated via a specific NAT and as shown previously , primarily affects the least efficiently acetylated N-termini [18] . Thus , the real number of Naa60p substrates in human cells is likely to be significantly higher as compared to the substrates identified in these particular analyses . Finally , two of the acetylated N-termini of the predicted NatF class picked up from the HeLa dataset ( Table S2 ) were tested by a direct in vitro approach . Synthetic peptides derived from the Met-Lys- and Met-Ala- N-termini of Septin 9 and Protein phosphatase 6 , respectively , were subjected to an in vitro acetylation assay with purified hNaa60p followed by an HPLC-based analysis of acetylated and unacetylated peptides . In agreement with the human and yeast in vivo data and in vitro substrate profiles obtained above , hNaa60p acetylated both these peptides , as well as representatives of NatC and NatE class substrates ( Figure 7 ) . Thus , we confirmed the N-terminal acetylation of human substrates as well as the potential redundancy with NatC and NatE enzyme classes ( Table 2 ) . In order to assess the cellular function of dNaa60p , its expression was knocked down in Drosophila Dmel2 cells by RNAi . Similarly to dNAA50-depleted cells [30] , [31] ( data not shown ) , dNAA60-depleted cells showed chromosomal segregation defects during anaphase ( Figure 8A–C , 8F , 8G , 8J , 8K ) . However , while dNAA50-depleted cells exhibit abnormal metaphases with an obvious mitotic arrest , control and dNAA60-depleted cells exhibited normal metaphases , with all chromosomes perfectly aligned within the spindle equator and without any mitotic arrest ( Figure 8D , 8E , 8H , 8I and Figure S2 ) . In contrast , during anaphase we consistently observed chromosome segregation defects in dNAA60-depleted cells , which included lagging chromosomes ( Figure 8K , highlighted by asterisk ) and chromosomal bridges ( Figure 8B , 8G , highlighted by asterisk; quantification of abnormal anaphases is shown in Figure 8C ) . Chromosome lagging and bridging in dNAA60-depleted cells may be explained by kinetochore abnormalities; however we failed to detect any obvious defect in the localization of the Centromere identifier protein ( Cid ) during metaphase or anaphase ( Figure 8D–G ) . We also failed to detect any obvious cohesion defect since the distance between kinetochores during metaphase was normal according to Cid localization ( Figure 8D , 8E ) . Chromosome lagging could also be explained by centrosome/mitotic spindle defects . Yet , we did not detect any obvious defect in the localization of Centrosomin ( Cnn ) , and the mitotic spindle was bipolar and correctly attached to chromosomes and centrosomes ( Figure 8D–G ) . Furthermore , dNAA60-depleted cells showed no obvious defects in the actin and microtubule cytoskeleton in both mitotic and interphase cells ( Figure 8H–M ) . Since dNAA60-depleted cells were otherwise normal , our data suggest that dNaa60p is required for chromosome segregation during anaphase . Naa60p-dependent N-terminal acetylation of one or more substrates is therefore likely to be required for chromosome segregation in vivo .
The basic co-translational machinery performing N-Ac in eukaryotes was believed to be fully identified and mostly characterized , with five NATs , NatA-NatE , each of which composed of specific subunits and acetylating its own subset of substrates [15] . However , the significant shift in occurrence of N-Ac from lower to higher eukaryotes , clearly points to the fact that species-specific factors are major determinants for N-Ac . Indeed , in the current study we revealed that higher eukaryotes express NatF/Naa60p , a unique NAT responsible for N-Ac of a large subset of eukaryotic proteins . These N-termini include Met-Lys- , Met-Met- , Met-Val- and Met-Ser- to which so far no NAT has been assigned . Also N-termini like Met-Leu- and Met-Ile- , previously believed to be solely NatC substrates , may be acetylated by NatF . Thus , the previous clear-cut classification between Nat substrate classes based on the N-terminal sequences should be re-evaluated when in vivo datasets are considered . The current knowledge on the NATs of higher eukaryotes and their corresponding substrates is presented in Figure 9 . In contrast to the N-termini acetylated by NatF , for the increased N-Ac of the processed ( Met- ) Ala- and ( Met- ) Val- N-termini there is presently no explanation . The intrinsic enzymatic properties of human and yeast NatA appeared to be very similar when expressed in yeast [18] . Co-determining factors that should be elaborated upon concerning the NatA substrates are interaction partners specific for NatA of higher eukaryotes , like HYPK which was demonstrated to modulate N-terminal acetylation [33] . Notwithstanding the generally lower expression levels , the existence of higher eukaryotic paralogues of Naa15p and Naa10p , being Naa16p and Naa11p respectively [42] , [43] , might additionally account for modulators of the observed Nα-acetylome . However , information regarding their potential proteome-wide contribution to N-Ac is currently lacking . We found that evolution of N-Ac prone N-termini most likely contributes only to a very small degree to the overall evolutionary shift in the occurrence of N-Ac . Furthermore , there might be a shift in the substrate specificity between species-specific NATs , for instance for the NatB , NatC and NatE activities , requiring further experimental validation . However viewing their strict evolutionary conservation , this may be rather unlikely . The current data are more comprehensive as compared to previous analyses [18] , and overall the 648 unique yeast and 1345 unique human N-termini identified were analysed for their acetylation status ( Table 1 , Tables S1 and S2 ) . 68% of the yeast N-termini and 85% of the human N-termini are partially or fully N-terminally acetylated . Previously , we determined that 57% of yeast proteins and 84% of human proteins were N-terminally acetylated , thus implicating some shift in the N-Ac of the yeast N-termini between experiments . We believe the current dataset likely holds a more representative picture since more N-termini were sampled and since yeast was grown under slightly different deprivating ( SILAC ) conditions in the previous setup . Nevertheless , still a significant difference between yeast ( 68% ) and humans ( 85% ) can be observed and as demonstrated , this difference is significantly diminished in yeast expressing NatF ( 78% ) ( Figure 4 and Figure 5 ) . The current study provides to the best of our knowledge , the first evidence shedding light on the molecular basis of the evolutionary shift in the Nα-acetylome from lower to higher eukaryotes . With the presence of NatF , higher eukaryotes are enforced in their capacity to acetylate Met-Lys- , Met-Leu- and other Met- starting N-termini , thus explaining in part the increased occurrence of N-Ac . This additional NAT may have evolved to meet the increased demands of more complex proteomes with a higher level of regulation . In light of the recent suggestion that N-Ac generates degrons and thus acts as a destabilizer [5] , these issues will be of particular importance . Our results suggested that dNaa60 activity is likely to be specifically required for chromosome segregation during anaphase , as cells depleted for dNaa60 showed normal alignment of chromosomes during metaphase plates and progressed normally through mitosis , without any obvious cell cycle arrest ( Figure 8 and Figure S2 ) . With an increasing support for N-Ac in controlling protein stability , function and subcellular localization , it is very likely that Naa60p will emerge as a key regulator for several proteins . Future investigations will aim at elucidating these specific Naa60p substrates .
The random dipeptide frequencies ( n = 400 ) were estimated by Monte Carlo sampling of one randomly selected decapeptide per protein in the databases of; Homo sapiens , Drosophila melanogaster and Saccharomyces cerevisiae ( UniProt/SwissProt entries ( version 2011-05 ) ) . After 100 sampling rounds , the mean and standard deviation for each dipeptide were estimated . Thereafter , the N-terminal dipeptide frequency of all decapeptides from position 2 to 11 were calculated , and the obtained frequencies compared with the random frequencies . The corresponding species-specific z-score , reflecting the amino acid dipetide frequency differences between the protein N-terminal and overall protein sequence were calculated as follows: Sequences of the known human catalytic NAT units/subunits , hNaa10p ( P41227 ) , hNaa11p ( Q9BSU3 ) , hNaa20p ( P61599 ) , hNaa30p ( Q147X3 ) and hNaa50p ( Q9GZZ1 ) , were used in the search of novel human NATs by making use of NCBI BLAST ( blastp ) queries ( search set: ‘Swiss-Prot protein sequences’ restricted to organism: ‘Homo sapiens’ and otherwise the predefined parameters ) . Besides the known human NATs , there was in particular one significant hit , the uncharacterized NAT15 ( Q9H7X0 ) , which held sequence similarity to all query NATs with E-values between 3×10−6 and 0 . 24 . NAT15 is an automatically annotated name due to the presence of a N-acetyltransferase domain ( pfam00583 ) in the protein sequence . When using hNaa30p and hNaa50p as query sequences , NAT15 scored even better than some of the known human NATs ( hNaa20p and hNaa10p/hNaa11p/hNaa20p , respectively ) . When using hNaa30p as query sequence , some other human proteins scored equally well as NAT15: NAT8 ( Q9UHE5 ) , NAT8B ( Q9UHF3 ) , NAT8L ( Q8N9F0 ) and ATAC2/CRP2BP ( Q9H8E8 ) with E-values ranging from 7×10−5 to 0 . 034 . However , all these candidates were biochemically characterized as members of the GNAT family ( pfam00583 ) with functions distinct from protein N-terminal acetylation . NAT8 is a cysteinyl-S-conjugate N-acetyltransferase catalyzing the last step of mercapturic acid formation while NAT8B is a likely pseudogene of NAT8 [44] . NAT8L catalyses the synthesis of N-acetylaspartate [45] and ATAC2 catalyses lysine acetylation on histone H4 [46] . Thus , NAT15 was the only uncharacterized protein with a significant similarity to the known human NATs ( Figure 2 ) and was therefore further pursued . Plasmid encoding V5-tagged NAT15/hNaa60p ( Gene ID: 79903 ) used for mammalian expression was constructed from human HEK293 cDNA by use of Transcriptor Reverse Transcriptase ( Roche ) . The PCR product containing the CDS plus four 5′ nucleotides ( gaga ) was inserted into the TOPO TA vector pcDNA 3 . 1/V5-His TOPO Invitrogen ) according to the instruction manual . An E . coli expression vector encoding MBP-His-tagged hNaa60p was constructed by subcloning hNAA60 from phNAA60-V5 to the pETM-41 vector using the Acc65I and NcoI sites . pETM-41-dNAA60 , encoding the predicted fruit fly Naa60p was made by subcloning the CG18177 CDS from pOT2-CG18177 ( clone LD27619 from the Drosophila Genomics Resource Centre , Indiana University ) to pETM-41 . pETM-41 was generously provided by G . Stier , EMBL , Heidelberg . A yeast expression vector , pBEVY-U-hNAA60 encoding untagged hNaa60p was constructed by subcloning hNAA60 from phNAA60-V5 to the pBEVY-U vector [47] using the BamHI and SalI sites . The plasmid pETM-41-hNAA60 or pETM-41-dNAA60 was transformed into E . coli BL21 Star™ ( DE3 ) cells ( Invitrogen ) by heat shock . A 200 ml cell culture was grown in LB ( Luria Bertani ) medium to an OD600 nm of 0 . 6 at 37°C and subsequently transferred to 20°C . After 30 min of incubation , protein expression was induced by IPTG ( 1 mM ) . After 17 h of incubation , the cultures were harvested by centrifugation and the pellets stored at −20°C . E . coli pellets containing recombinant proteins were thawed at 4°C and the cells lysed by sonication in lysis buffer ( 1 mM DTT , 50 mM Tris-HCl ( pH 7 . 5 or 8 . 3 for MBP-dNAA60p and MBP-hNAA60p , respectively ) , 300 mM NaCl , 1 tablet EDTA-free protease Inhibitor cocktail per 50 ml ( Roche ) ) . Following centrifugation , the cell extracts were applied on a metal affinity FPLC column ( HisTrap HP , GE Healthcare , Uppsala , Sweden ) . MBP-hNaa60p and MBP-dNaa60p were eluted with 300 mM Imidazole in 50 mM Tris ( pH 7 . 5 or 8 . 3 for MBP-dNAA60 or MBP-hNAA60 , respectively ) , 300 mM NaCl and 1 mM DTT . Fractions containing recombinant protein were pooled and further purified using size exclusion chromatography ( Superdex™ 75 , GE Healthcare ) until apparent purity as analysed by Coomassie stained SDS-PAGE gels . The protein concentrations were determined by OD280 nm measurements . Preparation of proteome derived peptide libraries . Proteome-derived peptide libraries were generated from human K-562 cells . Cells were repeatedly ( 3× ) washed in D-PBS and then re-suspended at 7×106 cells per ml in lysis buffer ( 50 mM sodium phosphate buffer pH 7 . 5 , 100 mM NaCl , 1% CHAPS and 0 . 5 mM EDTA ) in the presence of protease inhibitors ( Complete protease inhibitor cocktail tablet ( Roche Diagnostics , Mannheim , Germany ) ) . After lysis for 10 min on ice , the lysate was cleared by centrifugation for 10 min at 16 , 000× g and solid guanidinum hydrochloride was added to the supernatant to a final concentration of 4 M . The protein samples were reduced and S-alkylated , followed by tri-deuteroacetylation of primary amines and digestion with trypsin as described previously [48] , [49] . The resulting peptide mixtures were vacuum dried . The dried peptides were re-dissolved in 500 µl 50% acetonitrile . The sample was acidified to pH 3 . 0 using a stock solution of 1% TFA in 50% acetonitrile and further diluted with 10 mM sodium phosphate in 50% acetonitrile to a final volume of 1 ml . This peptide mixture was then loaded onto an AccuBONDII SCX SPE cartridge ( Agilent Technologies , Waldbronn , Germany ) and SCX separation ( SCX fractionation 1 ) of Nα-blocked N-terminal peptides ( and C-terminal peptides ) from Nα-free peptides was performed as described previously [48] , [50] . The flow-through containing the Nα-blocked N-terminal peptides and C-terminal peptides was discarded and the SCX-bound fraction ( containing the Nα-free peptides ) was collected by elution with 4 ml of 400 mM NaCl and 10 mM sodium phosphate in 40% of acetonitrile ( pH 3 . 0 ) . Eluted peptides were vacuum dried and re-dissolved in 1 ml of HPLC solvent A ( 10 mM ammonium acetate in 2% acetonitrile , pH 5 . 5 ) . C18 solid-phase extraction ( SPE desalting step ) of the Nα-free peptides was performed by loading the peptide mixture onto a AccuBONDII ODS-C18 SPE cartridge ( 1 ml tube , 100 mg , Agilent Technologies ) . This cartridge has a binding capacity of 1 mg of peptides and thus for each mg of material , a separate cartridge was used . Prior to sample loading , the cartridges were washed with 2 ml of 50% acetonitrile and then washed with 5 ml of HPLC solvent A . Sample loading was followed by washing the C18 cartridge with 5 ml of 2% acetonitrile . Peptides were eluted with 3 ml of 70% acetonitrile and subsequently vacuum dried . In vitro peptide library-based NAT assay . 100 nmol of the desalted Nα-free peptide pool was reconstituted in acetylation buffer ( 50 mM Tris-HCl ( pH 8 . 5 ) , 1 mM DTT , 800 µM EDTA , 10% glycerol ) together with equimolar amounts of a stable isotope encoded variant of acetyl-CoA , 13C2-acetyl CoA , ( 99% 13C2-acetyl CoA , ISOTEC-Sigma ( lithium salt ) ) and 1 nmol of enzyme ( i . e . recombinant hNaa60p or dNaa60p ) was added to a final reaction volume of 1 ml . The reaction was allowed to proceed for 1 h at 37°C and stopped by addition of acetic acid to a 5% final concentration . SPE was then performed as described above . NAT oligopeptide-substrate recovery and RP-HPLC based separation . Peptides starting with pyroglutamate were unblocked prior to the second SCX fractionation step . Here , 25 µl of pGAPase ( 25 U/ml ) ( TAGZyme kit , Qiagen , Hilden , Germany ) was activated for 10 min at 37°C by addition of 1 µl of 50 mM EDTA ( pH 8 . 0 ) , 1 µl of 800 mM NaCl , and 11 µl of freshly prepared 50 mM cysteamine-HCl . 25 µl of Qcyclase ( 50 U/ml , TAGZyme ) was then added to the pGAPase solution . The dried peptides were re-dissolved in 212 µl of buffer containing 16 mM NaCl , 0 . 5 mM EDTA , 3 mM cysteamine , and 50 µM aprotinin . The activated pGAPase and Q-cyclase mixture was added to the peptide sample and the mixture ( 275 µl total volume ) was incubated for 60 min at 37°C . 275 µl acetonitrile was then added and the sample was acidified to pH 3 using a 1% TFA stock solution in 50% acetonitrile . The sample was further diluted with 10 mM sodium phosphate in 50% acetonitrile to a final volume of 1 ml . SCX enrichment of Nα-blocked N-terminal peptides was performed as described [48] ( SCX fractionation 2 ) . The SCX fraction containing the newly blocked N-terminal peptides was vacuum dried and re-dissolved in 100 µl of HPLC solvent A . To prevent oxidation of methionine between the primary and secondary RP-HPLC separations ( and thus unwanted segregation of methionyl peptides [51] , methionines were uniformly oxidized to sulfoxides prior to the primary RP-HPLC run by adding 2 µl of 30% ( w/v ) H2O2 ( final concentration of 0 . 06% ) for 30 min at 30°C . This peptide mixture was injected onto a RP-column ( Zorbax 300SB-C18 Narrowbore , 2 . 1 mm ( internal diameter ) ×150 mm length , 5 µm particles , Agilent Technologies ) and the RP-HPLC separation was performed as described previously [48] . Fractions of 30 s wide were collected from 20 to 80 min after sample injection ( 120 fractions ) . To reduce LC-MS/MS analysis time , fractions eluting 12 min apart were pooled , vacuum dried and re-dissolved in 40 µl of 2% acetonitrile . In total , 24 pooled fractions per setup were subjected to LC-MS/MS analysis ( see below ) . LC-MS/MS analysis . LC-MS/MS analysis was performed using an Ultimate 3000 HPLC system ( Dionex , Amsterdam , The Netherlands ) in-line connected to a LTQ Orbitrap XL mass spectrometer ( Thermo Electron , Bremen , Germany ) and , per LC-MS/MS analysis , 2 µl of sample was consumed . LC-MS/MS analysis and generation of MS/MS peak lists were performed as described [52] . These MS/MS peak lists were then searched with Mascot using the Mascot Daemon interface ( version 2 . 2 . 0 , Matrix Science ) . The Mascot search parameters were set as follows . Searches were performed in the Swiss-Prot database with taxonomy set to human ( UniProtKB/SwissProt database version 2010_05 containing 20 , 286 human protein sequences ) . Trideutero-acetylation at lysines , carbamidomethylation of cysteine and methionine oxidation to methionine-sulfoxide were set as fixed modifications . Variable modifications were trideutero-acetylation , acetylation and 13C2-acetylation of protein N-termini and pyroglutamate formation of N-terminal glutamine . Endoproteinase Arg-C/P ( Arg-C specificity with arginine-proline cleavage allowed ) was set as enzyme allowing no missed cleavages . The mass tolerance on the precursor ion was set to 10 ppm and on fragment ions to 0 . 5 Da . The estimated false discovery rate by searching decoy databases was typically found to lie between 2 and 4% on the spectrum level [48] . Quantification of the degree of N-Ac was done as previously explained [18] . Purified MBP-hNaa60p ( 500 nM ) was mixed with selected oligopeptide substrates ( 200 µM ) and 300 µM of acetyl-CoA in a total volume of 50 µl acetylation buffer ( 50 mM Tris ( pH 8 . 5 ) , 800 µM EDTA , 10% glycerol , 1 mM DTT ) and incubated at 37°C for 35 min . The enzyme activities were quenched by the addition of 5 µl of 10% TFA . Peptide acetylation was quantified using RP-HPLC as described previously [23] . Peptides were custom-made ( Biogenes ) to a purity of 80–95% . All peptides contain 7 unique amino acids at their N-terminus , as these are the major determinants influencing N-terminal acetylation . The next 17 amino acids are essentially identical to the ACTH peptide sequence ( RWGRPVGRRRRPVRVYP ) however; lysines were replaced by arginines to minimize any potential interference by Nε-acetylation . Oligopeptide sequences: SYSM-RRR ( ACTH ( aa138–161 , P01189 ) : [H] SYSMDHFRWGRPVGRRRRPVRVYP [OH]; MDEL-RRR ( NF-kκB p65 , Q04206 ) : [H] MDELFPLRWGRPVGRRRRPVRVYP [OH]; MLGT-RRR ( hnRNP H , P31943 ) : [H] MLGTEGGRWGRPVGRRRRPVRVYP [OH]; MAPL-RRR ( Prot phosphatase 6 , O00743 ) : [H] MAPLDLDRWGRPVGRRRRPVRVYP [OH]; MLGP-RRR ( hnRNP F , P52597 ) : [H] MLGPEGGRWGRPVGRRRRPVRVYP [OH]; SESS-RRR ( High mob . gr . prot A1 , P17096 ) : [H] SESSSKSRWGRPVGRRRRPVRVYP [OH]; MKKS-RRR ( Septin 9 , Q9UHD8 ) : [H] MKKSYSGRWGRPVGRRRRPVRVYP [OH] . The S . cerevisiae MATalpha strain BY4742 ( Euroscarf ) was transformed with pBEVY-U or pBEVY-U-hNAA60 and transformants were selected on plates lacking uracil . The two strains generated , BY4742-pBEVY-U ( yeast normal ) and BY4742-pBEVY-U-hNAA60 ( yeast+NatF ) , were cultivated in 300 ml synthetic medium lacking uracil ( Sigma ) to an OD600nm of ∼3 . After harvesting , cells were washed twice in lysis buffer ( 50 mM Tris , 12 mM EDTA , 250 mM NaCl , 140 mM Na2HPO4 ( pH 7 . 6 ) supplemented with a complete protease inhibitor mixture tablet ( 1 tablet per 100 mL ) ( Roche Diagnostics ) and glass beads were added before several rounds of vortex/ice ( 10× ) . One milliliter of lysis buffer was used for a pellet resulting from 300 mL of yeast culture . The lysates were centrifuged at 5000× g for 10 min and the retained supernatants were analyzed by COFRADIC analyses . Aliquots were analysed by SDS-PAGE and Western blotting using anti-hNaa60p . Solid guanidinium hydrochloride was added to a final concentration of 4 M in order to inactivate proteases and denature all proteins . Subsequently , proteins were reduced and alkylated simultaneously , using TCEP . HCl ( 1 mM final concentration ( f . c . ) ) and IAA ( 2 mM f . c . ) respectively , for 1 h at 30°C . Subsequent steps of the N-terminal COFRADIC protocol were performed as described previously [48] . Aliquots were analysed by SDS-PAGE and Western blotting using anti-hNaa60p . HeLa cells ( epithelial cervix adenocarcinoma , ATCC CCL-2 ) were cultured in Glutamax-containing DMEM medium supplemented with 10% dialyzed foetal bovine serum ( Invitrogen , Carlsbad , CA , USA ) , 100 units/ml penicillin ( Invitrogen ) and 100 µg/ml streptomycin ( Invitrogen ) . Cells were grown in media containing either natural ( 12C6 ) or 13C615N4 L-arginine ( Cambridge Isotope Labs , Andover , MA , USA ) [53] at a concentration of 80 µM ( i . e . 20% of the suggested concentration present in DMEM at which L-arginine to proline conversion was not detectable for HeLa cells ) . Cells were cultured for at least six population doublings to ensure complete incorporation of the labeled arginine . Human K-562 cells ( ATCC CCL-243 ) were grown in Glutamax-containing RPMI-1640 medium supplemented with 10% foetal calf serum , 100 units/ml penicillin and 100 µg/ml streptomycin . Cells were cultured at 37°C and in 5% CO2 . Plasmid transfections were performed using Fugene6 ( Roche ) according to the instruction manual . siRNA transfections were performed using Dharmafect 1 ( Dharmacon ) . In the overexpression experiment , 10×10 cm dishes of cells cultivated in 13C615N4 L-arginine were transfected with control vector and cells cultivated in 12C6 L-arginine were transfected with phNAA60-V5 . Cells were harvested 48 hours post-transfection . Aliquots were analysed by SDS-PAGE and Western blotting using anti-V5 ( Invitrogen ) to confirm efficient overexpression ( See Figure 6A ) . In the knockdown experiment , 10×10 cm dishes of control control cells cultivated in 12C6 L-arginine were transfected with 50 nM si-non-targeting control ( D-001810 , Dharmacon ) and cells cultivated in 13C615N4 L-arginine were transfected with 50 nM sihNAA60 pool ( D-014479 , Dharmacon ) . After 48 hours of transfection , the medium was replaced by new SILAC medium containing 5 µM zVAD-fmk . After 84 hours , cells were harvested , lysed and subjected to COFRADIC analysis as described previously [18] . Aliquots were analysed by SDS-PAGE and Western blotting using anti-hNaa60p ( Custom made affinity purified rabbit antibody targeting a peptide corresponding to aa 69–82 of hNaa60p , Biogenes ) to confirm efficient knockdown ( See Figure 6B ) . Each sample of the knockdown- and overexpression experiments resulted from 10×10 cm dishes of cells and was processed further for N-terminal COFRADIC analyses as described previously [18] . The ratios of Nα-acetylation for all N-termini were quantified using MASCOT Distiller . The extent of Nα-acetylation was calculated after extracting the corresponding peak intensities ( extracted from the resulting rov-files ) . The modified peptide sequences were used to calculate the theoretical isotope peak distribution using the MS-isotope pattern calculator ( http://prospector . ucsf . edu ) . For both variants ( i . e . , in vivo Nα-acetylated ( peak at m/z ) and in vitro 13C2D3-Nα-acetylated ( peak at m/z+5 Da ) ) , the predicted intensity of the 5th contributing isotope was subtracted from the measured intensity of the corresponding monoisotopic peak of the other overlapping isotopic envelopes in order to correct for overlapping isotopic envelopes . Only the corresponding highest scoring MS/MS-spectra were withheld and inspected to evaluate the calculated Nα-acetylation degree ( in case of inconsistencies , whenever possible the second , third or next highest scoring MS/MS-spectra were inspected to evaluate the calculated Nα-acetylation degree , if inconclusive the status was set as “N . D . ” ) . When unclear MS-spectra were observed , the N-Ac status was also documented as “N . D . ” . When no clear isotopic envelope was present for one of the possible variants , the status was set at 0% and 100% or 100% and 0% respectively . Further , if the Nα-acetylation calculated was ≤2% of ≥98% , the overall N-Ac status was accounted for as being free or fully N-Ac respectively . When comparing the degrees of Nα-acetylation from two independent control experiments ( with the degrees of Nα-acetylation of more than 1 , 000 unique N-termini calculated ) and taking into account a [x−10% , x+10%] interval around the calculated x-value ( the x-value being the degree ( % ) of Nα-acetylation for the calculated data point in one dataset ) , the p-value was calculated to be p<0 . 01 , indicating that upon setting these limits , less than 1% of all measured N-Ac values differed more than 10% . Therefore , a significant variation in the degree of Nα-acetylation was set to 10% or more . In the case of free N-termini identified in a control setup however , significance was set to 5% since in this case two isotopic envelopes could clearly be distinguished . Dmel2 cells were cultured at 25°C and RNAi was performed according to standard procedures . To deplete dNaa60 ( CG18177 ) , Dmel2 cells were separately transfected with two different double-stranded RNAs ( dsRNA ) corresponding to fragments of dNaa60 defined by the set of primers ( Forward-1 ) CAACAAACACAGTGCGCC and ( Reverse-1 ) CACATTTCGATAGGGTTTGATTTC or ( Forward-2 ) GACTCGATGGGTCGTTCCGC and ( Reverse-2 ) GTGGATGGCCGCCGTTAAT . GFP-targeting dsRNA was used as control . Each primer incorporates a T7 RNA polymerase binding site . All PCR products were used as template to synthesize dsRNA by use of the T7 RiboMAX Express kit ( Promega ) . Drosophila Dmel2 cells were grown in SFM Medium ( GIBCO ) supplemented with 1× glutamine and 1× PenStrep ( GIBCO ) . Cells were counted and diluted to 2×106 cells/ml in SFM medium supplemented with glutamine . Cells were incubated during 1 h with 40 µg for each dsRNA at a concentration of 1 µg/µl . After 1 h incubation with dsRNA , 3 ml of SFM media supplemented with glutamine and PenStrep ( GIBCO ) was added back . After 93 h dsRNA treatment , 2×106 cells were added to coverslips by 1 h incubation at 25°C . Cells were fixed with 4% formaldehyde , 0 . 03 M PIPES , 0 . 11 M HEPES , 0 . 01 M EGTA and 4 mM MgSO4 for 10 min , followed by two washes in 1× PBS . Permeabilization and blocking was performed for 1 h with PBS-TB ( PBS , 0 . 1% Triton X-100 , 1% fetal bovine serum ) . Primary antibody incubations were done in blocking solution for 2 h at room temperature or overnight at 4°C , followed by three 5 min washes in PBS-TB . Secondary antibody incubations were performed as described for the primary antibodies , including three 5 min washes . Primary antibodies included mouse anti-α-tubulin DM1A ( 1∶500; Sigma ) , rabbit anti-pSer10-Histone H3 ( 1∶500; Upstate Biotechnology ) , chicken anti-Cid ( 1∶500; kindly provided by David Glover's laboratory ) and rabbit anti-Cnn ( 1∶500; kindly provided by Jordan Raff ) . F-actin was stained with rhodamine-conjugated phalloidin ( Sigma ) and DNA was stained with DAPI at 1∶1000 ( stock concentration 1 mg/ml ) , with the addition of 5 µg/ml RNAse A . Visualization of fixed cells was performed using a Delta Vision Core System ( Applied Precision ) using a 100× UplanSApo objective and a cascade2 EMCCD camera ( Photometrics ) . Images were acquired as a series of z-sections separated by 0 . 2-µm intervals . Deconvolution was performed using the conservative ratio method in softWoRx software . Phenotypic quantification was performed using a regular Epifluorescent microscope Leica DMRA2 . | Small chemical groups are commonly attached to proteins in order to control their activity , localization , and stability . An abundant protein modification is N-terminal acetylation , in which an N-terminal acetyltransferase ( NAT ) catalyzes the transfer of an acetyl group to the very N-terminal amino acid of the protein . When going from lower to higher eukaryotes there is a significant increase in the occurrence of N-terminal acetylation . We demonstrate here that this is partly because higher eukaryotes uniquely express NatF , an enzyme capable of acetylating a large group of protein N-termini including those previously found to display an increased N-acetylation potential in higher eukaryotes . Thus , the current study has possibly identified the last major component of the eukaryotic machinery responsible for co-translational N-acetylation of proteins . All eukaryotic proteins start with methionine , which is co-translationally cleaved when the second amino acid is small . Thereafter , NatA may acetylate these newly exposed N-termini . Interestingly , NatF also has the potential to act on these types of N-termini where the methionine was not cleaved . At the cellular level , we further found that NatF is essential for normal chromosome segregation during cell division . | [
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]
| 2011 | NatF Contributes to an Evolutionary Shift in Protein N-Terminal Acetylation and Is Important for Normal Chromosome Segregation |
Through as yet undefined proteins and pathways , the SUMO-targeted ubiquitin ligase ( STUbL ) suppresses genomic instability by ubiquitinating SUMO conjugated proteins and driving their proteasomal destruction . Here , we identify a critical function for fission yeast STUbL in suppressing spontaneous and chemically induced topoisomerase I ( Top1 ) –mediated DNA damage . Strikingly , cells with reduced STUbL activity are dependent on tyrosyl–DNA phosphodiesterase 1 ( Tdp1 ) . This is notable , as cells lacking Tdp1 are largely aphenotypic in the vegetative cell cycle due to the existence of alternative pathways for the removal of covalent Top1–DNA adducts ( Top1cc ) . We further identify Rad60 , a SUMO mimetic and STUbL-interacting protein , and the SUMO E3 ligase Nse2 as critical Top1cc repair factors in cells lacking Tdp1 . Detection of Top1ccs using chromatin immunoprecipitation and quantitative PCR shows that they are elevated in cells lacking Tdp1 and STUbL , Rad60 , or Nse2 SUMO ligase activity . These unrepaired Top1ccs are shown to cause DNA damage , hyper-recombination , and checkpoint-mediated cell cycle arrest . We further determine that Tdp1 and the nucleotide excision repair endonuclease Rad16-Swi10 initiate the major Top1cc repair pathways of fission yeast . Tdp1-based repair is the predominant activity outside S phase , likely acting on transcription-coupled Top1cc . Epistasis analyses suggest that STUbL , Rad60 , and Nse2 facilitate the Rad16-Swi10 pathway , parallel to Tdp1 . Collectively , these results reveal a unified role for STUbL , Rad60 , and Nse2 in protecting genome stability against spontaneous Top1-mediated DNA damage .
Efficient DNA repair suppresses spontaneous genetic alterations that otherwise lead to cell death or transformation . Posttranslational modifications ( PTMs ) can enhance the efficiency of individual repair processes and proteins and/or channel repair through appropriate pathways ( e . g . [1] , [2] ) . Among these PTMs , the small proteins ubiquitin and SUMO have gained increasing recognition as key guardians of chromosomal integrity [1]–[3] . Related enzymatic cascades covalently attach either SUMO or ubiquitin to lysine residues within target proteins to modulate their stability , activity and localization [3] . Each cascade employs dedicated E1 activating enzymes , E2 conjugating enzymes and E3 ligases that contribute to substrate selection and transfer of the modifier from the E2 to the target protein [3] . In contrast to the numerous ubiquitin E3 ligases , there are apparently two major SUMO E3 ligases in fission yeast called Pli1 and Nse2 [4] , [5] . Novel crosstalk between the SUMO and ubiquitin pathways is provided by the recently discovered SUMO-targeted ubiquitin E3 ligases ( STUbLs ) , which ubiquitinate and thereby target SUMO-modified proteins to the proteasome for degradation [6]–[8] . Through this novel activity , STUbLs play key but largely enigmatic roles in maintaining genome stability [9]–[15] . Fission yeast STUbL was recently shown to physically interact with Nse5/6 and Rad60 , providing a potential link between STUbL activity and DNA repair [12] . Nse5/6 are subunits of the Smc5/6 genome stability complex that is architecturally related to the Cohesin and Condensin complexes , but interestingly , contains the SUMO E3 ligase Nse2 [16] , [17] . Mimicry of SUMO was recently discovered as a function of members of the Rad60 DNA repair protein family , which contain two SUMO-like domains ( SLDs ) at their C-termini [18]–[20] . We recently determined that Rad60 SLD2 mimics SUMO by interacting non-covalently with the SUMO E2 conjugating enzyme Ubc9 at the same interface bound by SUMO [21] . Disruption of the Rad60∶Ubc9 interface via a single Rad60 glutamate 380 to arginine mutation ( rad60E380R ) causes genome instability and phenotypes associated with dysfunction of the SUMO pathway [21] . Interestingly , rad60E380R cells , like STUbL mutant cells , are dependent on both the Holliday junction ( HJ ) endonuclease Mus81-Eme1 and the RecA recombinase Rhp51 ( Rad51 ) for viability in the absence of exogenous stress [12] , [21] . Given the specific role of Mus81-Eme1 in replication fork restart [22] , [23] , this suggests that for as yet undefined reasons , replication forks are prone to collapse in Rad60 and STUbL mutant cells . A potential source of fork collapse in these mutant cells are stalled covalent topoisomerase I ( Top1 ) -DNA adducts that are encountered during replication [24] . In budding yeast , covalent Top1-DNA adducts called Top1 cleavage complexes ( Top1cc ) are efficiently removed by several repair factors acting in parallel , including tyrosyl-DNA phosphodiesterase ( Tdp1; [24]–[26] ) . The corresponding fission yeast pathways and their relative contributions to Top1cc repair have not been defined . However , fission yeast Tdp1 was found to process Top1-independent lesions arising from oxidative stress in quiescent fission yeast [27] . In budding yeast , Tdp1 also affects Top1-independent repair processes , such as enhancing the fidelity of non-homologous end-joining by producing a 3′-phosphate at the exposed ends of DNA double strand breaks [28] . Here , we determine that STUbL , together with the physically associated DNA repair protein Rad60 and the Nse2 SUMO E3 ligase , suppresses spontaneous Top1-induced DNA damage . When STUbL , Rad60 or Nse2 functions are compromised , cells require Tdp1 to repair both spontaneous and induced Top1-dependent DNA damage , which otherwise results in genomic instability , cell cycle checkpoint activation and/or cell death . This is a striking result because Tdp1 mutant fission yeast cells are weakly sensitive to the Top1 poison camptothecin ( CPT ) , due to redundancy with as yet unknown factors ( our results and [27] ) . This primary finding provides mechanistic insight on how STUbL , Rad60 and Nse2 dysfunction can negatively impact genome stability . In addition , we show that Tdp1 is redundant with the fission yeast Ercc1-Xpf homologs , Rad16-Swi10 , and that the absence of both pathways is lethal due to an inability to repair spontaneous Top1cc . Epistasis analysis suggests that STUbL acts in the Rad16-Swi10-initiated pathway for Top1cc repair . Furthermore , we find that Tdp1 predominates in the repair of replication-independent Top1cc lesions . Collectively , our data support a function for the evolutionarily conserved STUbL , Rad60 and Nse2 proteins in mitigating DNA damage caused by covalent Top1-DNA complexes , which arise as byproducts of normal cellular metabolism .
To probe the cause of replication fork collapse identified in STUbL mutant fission yeast [12] , we constructed a double mutant between the hypomorphic STUbL allele , slx8-1 , and tdp1Δ , and analyzed their sensitivity to CPT . The slx8-1 allele contains a mutation of a non-conserved cysteine residue ( C218 ) to tyrosine , which is within the RING finger domain but is not expected to affect zinc coordination [29] . Phenotypes of slx8-1 are normally only apparent at the restrictive temperature of 35 . 5°C [12] . Tdp1 is an enzyme largely dedicated to the removal of stalled Top1cc [25] , [26] , [30] . Whereas either single mutant exhibited wild-type sensitivity , slx8-1 tdp1Δ cells were synergistically sensitive to CPT , even at the slx8-1 permissive temperature of 25°C ( Figure 1A ) . This result indicates that STUbL and Tdp1 define parallel or non-overlapping pathways for the repair of Top1cc . Importantly , slx8-1 tdp1Δ cells were as sensitive to the replication fork stalling agent hydroxyurea ( HU ) as the slx8-1 single mutant ( Figure 1A ) , indicating that the genetic interdependency of slx8-1 and tdp1Δ is specific to Top1-dependent lesions . To distinguish between stabilization of Top1ccs or a repair defect downstream of Top1 removal in slx8-1 tdp1Δ cells , we utilized chromatin immunoprecipitation ( ChIP ) of Top1 in the absence of formaldehyde crosslinking , followed by quantitative PCR ( qPCR ) to specifically detect Top1cc . To avoid propagating the sick slx8-1 tdp1Δ cells and possible selection of suppressors , we placed Top1 under the repressible nmt41 promoter at its endogenous locus and included the FLAG epitope for ChIP analyses . With Top1 expression induced , a significant elevation of Top1cc was detected in the slx8-1 tdp1Δ double mutant at 3 out of 4 loci tested ( Figure 1B ) . As ChIP-qPCR in the absence of formaldehyde crosslinking is a novel application for high-sensitivity detection of Top1cc , we performed key controls to verify its utility . First , the low background signal under conditions that repress Top1 expression support the specificity of the assay ( Figure 1B and Figure S1A ) . Furthermore , to control for the contribution of non-catalytic Top1 DNA-binding to the ChIP-qPCR signal , we generated and expressed a Top1 catalytic mutant ( Top1 Tyrosine 773 mutated to Phenylalanine , Top1Y773F ) to compare with the otherwise identical wild-type FLAG-Top1 allele . Despite equivalent levels of protein expression , Top1Y773F generated a weak ChIP-qPCR signal , significantly below that observed for wild-type Top1 in a wild-type background ( Figure S1B and S1C ) . Importantly , the Top1Y773F ChIP-qPCR signal did not change , irrespective of the genetic background e . g . slx8-1 tdp1Δ ( Figure S1B ) . In addition , slx8-1 tdp1Δ top1Y773F triple mutant cells were as insensitive to CPT treatment as slx8-1 tdp1Δ top1Δ triple mutant cells , demonstrating the specificity of the slx8-1 tdp1Δ genetic interaction for Top1cc ( Figure S1D and S1E ) . Finally , because CPT-induced Top1cc would be expected to reverse rapidly under our assay conditions , we measured Top1cc in the presence or absence of CPT . As expected for reversible CPT-induced Top1cc , we did not detect a significant difference in ChIP-qPCR signal between the CPT treated or control samples ( Figure S1F ) . Thus , our controls confirm that in slx8-1 tdp1Δ double mutants there are increased spontaneous covalent Top1-DNA adducts and furthermore , reveal that these Top1cc are not readily reversible , unlike those induced by CPT . Next , we wanted to determine whether STUbL activity was also required to prevent Top1cc-dependent DNA damage during unchallenged growth in Tdp1-deficient cells . Whereas single slx8-1 and tdp1Δ mutants exhibit near wild-type growth , the slx8-1 tdp1Δ double mutant cells are synthetically sick and highly elongated even at the permissive temperature for slx8-1 ( Figure 2A ) . Strikingly , an slx8-1 tdp1Δ top1Δ triple mutant is wild-type in appearance , demonstrating that the sickness of slx8-1 tdp1Δ cells is Top1-mediated ( Figure 2A ) . Consistent with the evolutionary conservation of STUbL function , expression of the human STUbL RNF4 suppresses the slx8-1 tdp1Δ phenotype to a similar extent observed with fission yeast Slx8 ( Figure S2; [12] ) . The elongated phenotype of slx8-1 tdp1Δ cells is reminiscent of fission yeast following exposure to genotoxic agents , activation of the DNA damage checkpoint and consequent delay in cell cycle progression [31] . We therefore assessed checkpoint activation in slx8-1 tdp1Δ cells by monitoring Chk1 phosphorylation and also , abrogated the checkpoint by deleting Chk1 . Western analysis of Chk1 revealed no detectable checkpoint activation in slx8-1 single mutant cells , low level activation in tdp1Δ cells and an apparent increase in slx8-1 tdp1Δ double mutant cells ( Figure 2B ) . Deleting Chk1 in slx8-1 tdp1Δ cells dramatically reduced their length , similar to the effect of deleting Top1 ( Figure 2A ) . Deletion of an upstream activator of Chk1 , Rad3 ( ATR ) , in slx8-1 tdp1Δ cells also suppressed cell elongation ( Figure 2A ) . The highly elongated cell phenotype of slx8-1 tdp1Δ cells is thus due to activation of the DNA damage checkpoint , indicating the presence of spontaneous DNA lesions in these cells . As STUbL degrades certain SUMO conjugated proteins and the slx8-1 tdp1Δ phenotype is Top1-dependent , we tested whether STUbL regulates Top1 itself . We first determined that Top1 is sumoylated in a manner dependent on the major E3 SUMO ligase Pli1 ( Figure S3 ) . We then assayed Top1 sumoylation in slx8-1 , tdp1Δ and slx8-1 tdp1Δ cells versus wild-type at the permissive temperature , or in slx8-1 cells at the restrictive temperature . No detectable change in the sumoylation status of Top1 was observed under any of the conditions tested ( Figure S3 ) . However , we cannot exclude deregulation of a minor fraction of sumoylated Top1 , such as that on chromatin in the form of Top1cc . To assay the presence of DNA damage in slx8-1 tdp1Δ cells , we analyzed Rad52 ( S . pombe Rad22 ) and RPA ( S . pombe Rad11 ) DNA repair foci by live cell fluorescence microscopy in strains that either express YFP-tagged Rad22 or Rad11 from their endogenous loci . Compared to wild-type , both slx8-1 and tdp1Δ exhibited elevated levels of repair foci ( Figure 3A ) . Strikingly , greater than 60% of slx8-1 tdp1Δ double mutant cells contained Rad22-YFP foci ( Figure 3A ) . As activation of the G2 DNA damage checkpoint in slx8-1 tdp1Δ double mutants is Top1-dependent ( Figure 2A ) we analyzed the effect of deleting Top1 on the observed DNA repair foci . Cells deleted for Top1 have a characteristic increase in double Rad22-YFP foci that are associated with nucleoli and likely represent the rDNA loci ( Figure 3A ) . Double mutant slx8-1 tdp1Δ cells that exhibit a profound cell cycle delay have elevated levels of large single Rad22-YFP foci as compared to wild-type and the single mutants , which exhibit no checkpoint-dependent delay in cell cycle progression ( Figure 3A ) . Notably , slx8-1 tdp1Δ top1Δ triple mutant cells have a similar spectrum of Rad22-YFP foci as the top1Δ single mutant and consistently , show no cell cycle delay ( Figure 2A and Figure 3A ) . This observation suggests that the excess of large single DNA repair foci evident in slx8-1 tdp1Δ cells is responsible for checkpoint activation . Furthermore , these results demonstrate that Top1 causes physical DNA damage in slx8-1 tdp1Δ cells . The number and type of Rad22-YFP foci in slx8-1 tdp1Δ was not affected in the chk1Δ background ( Figure 3A ) . The hyper-elongated phenotype of slx8-1 tdp1Δ cells may reflect a role for Slx8 ( STUbL ) in normal resumption of the cell cycle following checkpoint activation . However , when challenged with the DNA damaging agents CPT or methyl methanesulfonate ( MMS ) , slx8-1 cells showed a wild-type profile of cell cycle arrest ( checkpoint activation ) and release ( checkpoint inactivation; Figure 3B ) . When STUbL activity is attenuated and Tdp1-based repair is absent , our data indicate that spontaneously occurring Top1cc's generate DNA damage and activate the DNA structure checkpoints . Such DNA damage would be anticipated to be recombinogenic . Therefore , we measured recombination rates in the slx8-1 tdp1Δ double mutant versus the single mutants using an ade6 heteroallele system [32] . We observed a 12-fold increase in recombination in the slx8-1 tdp1Δ double mutant versus 1 . 5 and 5-fold for slx8-1 and tdp1Δ , respectively ( Figure 3C ) . Notably , the increased recombination rate in the slx8-1 tdp1Δ double mutant is Top1-dependent , consistent with the Top1-dependency of excess DNA repair foci in these cells ( Figure 3A , 3C ) . In addition , we found that slx8-1 tdp1Δ cells depend on the major HR factor Rad51 ( Rhp51 ) for viability ( Table 1 ) . Thus , consistent with the accumulation of HR foci and elevated spontaneous recombination in slx8-1 tdp1Δ cells ( Figure 3A , 3C ) , unrepaired Top1cc generates a recombinogenic substrate that requires Rhp51-dependent HR repair . We have previously shown that Rad60 physically interacts with STUbL and shares several mutant phenotypes with the STUbL slx8-1 allele [12] , [21] . In particular , a Rad60 mutant unable to interact with the SUMO E2 Ubc9 , rad60E380R makes cells prone to replication fork collapse [21] . Thus , we also tested the dependency of rad60E380R cells on Tdp1 , and found that the rad60E380R mutation is synthetically lethal with Tdp1 deletion ( Figure 4A ) . Consistent with Top1cc being the major target of Tdp1 , the lethality of rad60E380R tdp1Δ double mutants is suppressed by concomitant deletion of Top1 ( Figure 4A ) . We extended this tetrad analysis using a random spore approach that allows many meiotic progeny to be analyzed in one experiment . The genotypes of more than 1000 progeny from a cross between rad60E380R top1Δ and tdp1Δ were analyzed . Importantly , the single mutants , the rad60E380R top1Δ and top1Δ tdp1Δ double mutants , and the rad60E380R tdp1Δ top1Δ triple mutant were all readily recovered . However , no rad60E380R tdp1Δ double mutants were identified indicating that rad60E380R cells require Tdp1 for viability as observed in our tetrad analysis . To further analyze this phenomenon , we used the nmt41-Top1 system to regulate Top1 levels , and constructed an nmt41-top1 rad60E380R tdp1Δ strain under conditions that repress Top1 expression . With Top1 expression repressed , rad60E380R tdp1Δ , rad60E380R and tdp1Δ strains all grew similarly in the absence or presence of a low dose of CPT ( Figure 4B ) . Notably however , upon induction of Top1 , rad60E380R tdp1Δ cells grew poorly as compared to either single mutant and exhibited synergistic hypersensitivity to CPT ( Figure 4B ) . These data indicate that in either single mutant Top1cc repair is relatively efficient compared to the rad60E380R tdp1Δ double mutant and that the lethality of rad60E380R tdp1Δ cells is due to Top1 activity . We next applied the Top1cc ChIP-qPCR assay as for slx8-1 tdp1Δ cells , and detected low levels of Top1cc at the tested loci in wild-type , rad60E380R and tdp1Δ single mutants ( Figure 4C ) . Consistent with a defect in the processive repair of Top1cc in the rad60E380R tdp1Δ double mutant , there was a significant increase in Top1cc at 3 out of the 4 loci tested in these cells ( Figure 4C ) . Specificity of the assay for Top1cc was again confirmed by performing ChIP-qPCR on rad60E380R tdp1Δ cells in which either the expression of Top1 was repressed , or the catalytic mutant Top1Y773F was expressed instead ( Figure 4C and Figure S1B-S1D ) . Western analysis shows equal expression of Top1 in these strain backgrounds and the absence of detectable Top1 in the repressed control strain ( Figure S1B-S1D and S1G ) . Thus , like STUbL , the Rad60∶Ubc9 complex constitutes a key activity in the mitigation of Top1-mediated DNA damage in a pathway distinct from that initiated by Tdp1 . Rad60 and STUbL both physically and functionally interact with the Smc5/6 complex , which contains the Nse2 SUMO E3 ligase [12] , [19] , [21] . Given the intimate association of STUbL and Rad60 function with the SUMO pathway , we tested the potential role of Nse2-dependent sumoylation in supporting Top1cc repair in tdp1Δ cells . To do this we combined the SUMO ligase defective Nse2 mutant , nse2-SA , with a Tdp1 deletion . Using ChIP-qPCR with nmt41-Top1 , we detected significantly elevated Top1cc that were specific to the nse2-SA tdp1Δ double mutant background ( Figure 4D and Figure S1B-S1D and S1H ) . Furthermore , nse2-SA tdp1Δ cells were poorly viable and their growth defects were rescued by deletion of Top1 or expression of the catalytic mutant Top1Y773F ( Figure 4E and Figure S1B-S1D ) . We did not observe any growth defect of cells lacking both Tdp1 and the SUMO E3 ligase Pli1 ( not shown ) . Thus , a functionally related “hub” of proteins , including STUbL , Rad60 and the SUMO E3 ligase Nse2 , is required to suppress Top1-dependent DNA damage when Tdp1 activity is compromised . The synthetic sickness and synergistic sensitivity to CPT observed for rad60E380R or slx8-1 with tdp1Δ , indicates that these factors act in non-redundant pathways for the repair of spontaneous and induced Top1cc . In budding yeast , the Xpf-Ercc1 family endonuclease Rad1-Rad10 initiates a major pathway parallel to Tdp1 [33] , [34] . We therefore tested the contribution of the fission yeast Xpf-Ercc1 family endonuclease Rad16-Swi10 to the repair of spontaneous Top1cc in the absence of Tdp1 . Strikingly , tetrad analyses demonstrated that the tdp1Δ swi10Δ double mutant is inviable due to the presence of irreparable Top1-dependent lesions ( Figure 5A ) . This function of Rad16-Swi10 is independent of its nucleotide excision repair ( NER ) roles as deletion of another component of NER , Rad13 ( XPG ) shows no genetic interaction with tdp1Δ ( Table 1 ) . Similarly , deletion of the Uve1 DNA repair endonuclease , which incises 5′ to several DNA lesions , is not synthetic sick with tdp1Δ ( Table 1 ) . Hence , the role of Rad16-Swi10 in repairing/preventing Top1-induced DNA-damage is likely attributable to its 3′-flap endonuclease activity as concluded in S . cerevisiae [33] , [34] . It should be noted , in budding yeast there is apparently additional redundancy in the repair of Top1cc over fission yeast , as cells lacking both Tdp1 and the Rad16-Swi10 homologues Rad1-Rad10 are viable , but exhibit a Top1-dependent growth defect [34] . To examine the parallel functions of Tdp1 and Rad16-Swi10 in fission yeast , we employed our nmt41-Top1-Flag system to generate a viable tdp1Δ swi10Δ double mutant . When Top1 expression was repressed , the tdp1Δ swi10Δ double mutant grew slightly slower than either single mutant , likely due to the inability to completely shut off the nmt41 promoter ( Figure 5B ) . Strikingly , even under Top1-repressed conditions the tdp1Δ swi10Δ double mutant was exquisitely sensitive to CPT , whereas the growth of either single mutant was unaffected ( Figure 5B , upper panels ) . Furthermore , induction of Top1 expression in the absence or presence of CPT rapidly killed the tdp1Δ swi10Δ double mutant , but neither single mutant ( Figure 5B , lower panels ) . Consistently , elevated Top1cc were detected by ChIP-qPCR in the swi10Δ tdp1Δ double mutant versus the single mutants ( Figure S4A and S4B ) . As expected , the toxicity of Top1 in tdp1Δ swi10Δ cells depends on Top1 catalytic activity , as the double mutant is refractory to expression of the Top1Y773F mutant ( Figure S4C ) . Collectively , these data indicate that Tdp1 and Rad16-Swi10 define the predominant pathways for the initiation of the repair of spontaneous and induced Top1cc . The fission yeast Rad32 ( Mre11 ) -Rad50-Nbs1 ( MRN ) complex , which is a central HR factor , has also been implicated in the direct removal of Top1cc [35] . In light of the finding that Tdp1 and Rad16-Swi10 define the essential parallel pathways for Top1cc removal , we believe it is likely that MRN functions mainly downstream of Top1cc removal in its well-defined HR role . Consistent with this hypothesis and distinct from the synthetic lethality/sickness of rad60E380R , slx8-1 , nse2-SA or swi10Δ in combination with tdp1Δ , the rad32Δ tdp1Δ double mutant grows comparably to the rad32Δ single mutant ( Table 1 and Figure 5C ) . Similarly , the rhp51Δ tdp1Δ and mus81Δ tdp1Δ double mutants grow as well as the rhp51Δ and mus81Δ single mutants , respectively ( Table 1 and Figure 5C ) . Thus , HR factors including MRN are not essential for the response to spontaneous Top1cc in tdp1Δ cells . We also tested the CPT sensitivity of rad32Δ tdp1Δ , rhp51Δ tdp1Δ and mus81Δ tdp1Δ , which all exhibited a similar degree of additivity over the respective single HR mutants ( Figure 5C ) . These data are consistent with partially non-overlapping roles of Tdp1 and the HR machinery in CPT-induced Top1cc repair . This could indicate that either Rad51 , Mus81 and Rad32 can directly remove Top1cc , or as we suggest , the delayed removal of Top1cc in tdp1Δ cells causes fork collapse , which then engages the HR-dependent replication restart pathway [22] . The preferred pathways for the removal of Top1cc during either transcription or replication are poorly defined [30] . In wild-type fission yeast , CPT induces cell cycle arrest at the G2/M boundary in a replication-dependent manner [36] . This data indicates that the generation of DNA damage checkpoint-visible lesions requires replication forks to collide with Top1ccs [30] . Interestingly , while testing the acute response of cells lacking either Tdp1 or the Rad16-Swi10 pathways to CPT treatment , we detected a striking difference in growth inhibition of each mutant . The addition of CPT to asynchronous cultures resulted in a rapid growth arrest of tdp1Δ but not wild-type or swi10Δ cells ( Figure 6A ) . This phenomenon was dependent on both Top1 and the G2/M checkpoint kinase Chk1 ( not shown ) . Both wild-type and swi10Δ cells arrested with kinetics consistent with passage through S phase and arrest in the subsequent G2 phase ( Figure 6A ) . Although the growth of unchallenged tdp1Δ cells is indistinguishable from wild-type ( our unpublished observations and [27] ) , we considered the possibility that tdp1Δ cells may exhibit delayed completion of S phase and thus , addition of CPT would cause first cycle replication-coupled DNA damage and G2/M checkpoint activation . Therefore , we treated asynchronous tdp1Δ and wild-type cells with the replication-blocking agent hydroxyurea ( HU ) and monitored growth . As anticipated , tdp1Δ and wild-type cells arrested with the same kinetics ( Figure 6B ) , which is consistent with the known requirement for passage into S phase for the action of HU in fission yeast [37] . To confirm that Tdp1 mediates replication-independent Top1cc DNA damage repair , we co-treated wild-type and tdp1Δ cells with both HU and CPT and scored growth . Again , tdp1Δ but not wild-type cells arrested first-cycle , demonstrating that this is a replication-independent phenomenon ( Figure 6C ) . Top1 is a known SUMO substrate in several species; however , the role of SUMO modification is unknown . As described earlier , Top1 is SUMO-modified in a manner solely dependent on Pli1 ( Figure S3 ) . It has been suggested that sumoylation of Top1 might be a repair response [38] , or required for efficient Top1cc formation [39] . Either of these responses might modulate the rapid arrest kinetics of tdp1Δ cells treated with CPT . However , pli1Δ tdp1Δ double mutant cells that lack Top1 sumoylation arrest with the same rapid kinetics of tdp1Δ cells ( Figure 6D ) , whereas pli1Δ cells arrest with wild-type kinetics ( data not shown ) . Overall , these data reveal a previously undefined dominant role for fission yeast Tdp1 in suppressing replication-independent Top1-induced DNA damage . Given that STUbL is critical in the absence of Tdp1 , we tested whether it acts in the Rad16-Swi10 initiated pathway by generating an slx8-1 swi10Δ double mutant and comparing it to either single mutant . In stark contrast to slx8-1 tdp1Δ , the slx8-1 swi10Δ double mutant did not exhibit synthetic sickness and was no more sensitive to CPT than the swi10Δ single mutant ( Figure 6E ) . In keeping with a key role in nucleotide excision repair , Swi10 mutant cells were hypersensitive to UV irradiation , whereas slx8-1 cells were insensitive to this agent as expected ( Figure 6E ) . The absence of synergistic CPT sensitivity in slx8-1 swi10Δ double mutant cells , coupled with the fact that Tdp1 and Rad16-Swi10 initiate the critical Top1cc repair pathways , is consistent with STUbL facilitating the Rad16-Swi10-dependent pathway . Due to the sickness of swi10Δ tdp1Δ , it was not possible to generate a triple mutant with slx8-1 to perform additional confirmatory epistasis analyses . Further supporting their overlapping functions parallel to those of Tdp1 , both slx8-1 and swi10Δ cells arrest with similar delayed ( wild-type ) kinetics in response to CPT treatment ( Figure 3B and Figure 6A ) .
Suppression and efficient repair of spontaneous DNA damage is crucial to limit genetic changes that can cause cell death , transformation , or accelerate the aging process . Understanding the molecular basis for these defenses is thus vital to improve our current models of disease and aid novel chemotherapeutic strategies . Our collective results show that STUbL , the STUbL-interacting Rad60∶Ubc9 complex , and the Nse2 SUMO E3 ligase are critical for responding to spontaneous Top1cc-mediated DNA lesions . Detection of Top1cc in tdp1Δ cells that are also hypomorphic for STUbL , Rad60 or Nse2 by ChIP-qPCR reveals important information about the nature of these lesions . Top1cc induced by CPT are normally readily reversible upon drug removal , due to completion of the Top1 catalytic cycle [24] . This raises the question: why are the Top1cc we detect in the absence of crosslinking or denaturing conditions stable ? The answer likely lies in the propensity for Top1 to become irreversibly trapped at lesions in DNA , such as nicks or larger gaps , which are potentially common due to failed or stalled base excision repair ( BER ) [40] , [41] . In addition to our ChIP-qPCR data , our genetic analyses provide strong support for the formation of spontaneous and intrinsically stable Top1ccs . For example , deleting Top1 or mutating the Top1 catalytic site suppresses the synthetic lethality of tdp1Δ and swi10Δ mutants ( Figure 5A and Figure S4C ) . Therefore , even in the absence of exogenous agents , Top1 can form stable Top1ccs that require either Tdp1 or Rad16-Swi10-mediated removal to prevent cell death . Thus , ChIP-qPCR is a valuable novel application for the identification of a subset of Top1ccs . In contrast to budding yeast , we determined that fission yeast lacking both Tdp1 and Rad16-Swi10 ( S . cerevisiae Rad1-Rad10 ) are inviable due to their inability to repair spontaneous Top1-dependent DNA damage . In budding yeast , Tdp1 or multiple redundant activities , including Rad1-Rad10 , initiate Top1cc repair [33] , [34] . Thus , the genetic dependency of fission yeast tdp1Δ cells on Rad60 , STUbL and Nse2 indicates that this group of SUMO pathway regulators may facilitate Top1cc processing by Rad16-Swi10 . We showed that the human STUbL , RNF4 , is able to functionally substitute the Slx8-based fission yeast STUbL in Top1cc repair . Further supporting evolutionary conservation of the STUbL-dependent Top1cc repair pathway , the strongest negative genetic interactors of tdp1Δ in budding yeast are the non-essential STUbL components slx5Δ and slx8Δ [42] . Consistent with redundancy in the processing of spontaneous Top1cc in budding yeast , and in keeping with our findings in fission yeast , Tdp1 mutants show no increased dependency on HR factors during unchallenged growth ( [33] , [34] , [42]; Figure 5C ) . The involvement of STUbL , Rad60 and Nse2 in Top1cc repair is intriguing , as each factor is functionally connected with the SUMO pathway [7] , [21] , [43] , and human Top1 is extensively SUMO-conjugated when stalled in the cleavage complex [38] , [39] , [44] , [45] . SUMO conjugation to Top1 may affect many processes including directing Top1 subcellular localization , initiating the repair signal or enhancing Top1cc levels [38] , [39] , [44] , [45] . The human Top1cc is also subject to ubiquitin-dependent proteasomal degradation [46]–[48] , which may allow subsequent access of DNA repair factors to the otherwise occluded Top1 active site tyrosyl-DNA linkage [49] , [50] . A recent mass spectrometric study found that mammalian Top1cc was modified extensively with SUMO-2/3 and ubiquitin following CPT treatment [45] . Such modifications are consistent with SUMO-modified human Top1cc being targeted by STUbL-dependent ubiquitination prior to proteasomal degradation ( see [6] , [7] ) . Unlike human Top1 , we find that fission yeast Top1 is not as extensively SUMO modified , and this sumoylation is not increased upon CPT treatment ( Figure S3 and data not shown ) . In addition , we have not detected hyper-sumoylated Top1 in fission yeast STUbL mutants or STUbL tdp1Δ mutants ( Figure S3 ) . However , as such a small proportion of total Top1 is sumoylated , functionally important changes could be masked . Because a single unrepaired Top1cc per cell could account for the checkpoint response and death of slx8-1 tdp1Δ cells; STUbL-mediated degradation of Top1cc , stimulated by Rad60∶Ubc9 and Nse2 remains a possibility . It should be noted that cells lacking the predominant Top1 SUMO E3 ligase Pli1 are not sensitive to CPT , whereas those lacking the Nse2 SUMO E3 ligase are hypersensitive to CPT [4] , [51] . Furthermore , Rad60 and STUbL physically and functionally associate with the Smc5–6 complex , of which Nse2 is a core component [12] , [18] , [19] . Thus , it is conceivable that an Nse2-dependent sumoylation target other than Top1 is at the hub of this Top1cc repair network . In keeping with this , we find that nse2-SA tdp1Δ double mutants accumulate Top1cc that cause the observed severe sickness of these cells ( Figure 4D and 4E ) . Determining whether Top1 or other potential STUbL , Rad60∶Ubc9 or Nse2 targets are responsible for the observed repair defect in tdp1Δ cells will require the development of novel genetic and proteomic approaches , which will be the focus of future endeavors . Conceptually , either Tdp1 or Rad16-Swi10 could process Top1cc encountered during transcription , initiating single strand break repair to heal the resulting lesion without the involvement of HR ( Figure 7 ) . However , if a replication fork encounters a Top1cc and collapses , HR is required to restart replication . In tdp1Δ cells , the number of lesions encountered during replication may increase due to defective Top1cc removal during transcription , and this could explain the observed additive CPT sensitivity of tdp1Δ rhp51Δ ( and other HR factor ) double mutants . This is supported by our data indicating that in fission yeast , Tdp1 is the predominant replication-independent Top1cc repair activity , most likely acting in a transcription-coupled manner . However , as tdp1Δ cells are dependent on Rad16-Swi10 , but not HR factors for viability , Rad16-Swi10 must be able to initiate HR-independent Top1cc repair . Supporting replication-independent Tdp1 functions , Tdp1 mutation can cause degeneration of post-mitotic cells such as neurons [52] . In the absence of both Tdp1 and Rad16-Swi10 , the burden of unrepaired Top1cc leads to lethality . In this scenario , neither single strand break repair nor HR can proceed . In light of the well-characterized role of Rad16-Swi10 in budding yeast ( Rad1-Rad10 ) as a 3′ flap endonuclease , we propose that in tdp1Δ swi10Δ cells HR cannot engage due to the Top1cc blocking the DNA 3′ terminus ( Figure 7 ) . Within our model , STUbL , Rad60∶Ubc9 and Nse2 facilitate Top1cc removal by Rad16-Swi10 . Our data does not exclude the possibility that STUbL , Rad60∶Ubc9 and Nse2 may also act to suppress genomic lesions that favor stable Top1cc formation . Other factors may act in the removal of Top1cc from 3′ termini such as MRN [35]; however , in light of the synthetic lethality of tdp1Δ and swi10Δ , as opposed to the observed epistasis between tdp1Δ and rad32Δ during normal growth , such contributions appear minor . By combining genetic , physical and mutational analyses , we here identify a unifying and critical role for STUbL , Rad60∶Ubc9 and Nse2 in DNA repair . While these factors have additional non-overlapping roles ( indicated by the Top1-independent lethality of the rad60E380R slx8-1 double mutant [21] ) , they apparently collaborate in processing potentially lethal or genome destabilizing spontaneous Top1cc lesions . High-throughput observations in budding yeast ( see above ) and the RNF4 rescue-experiment indicate that the pathways we have defined in fission yeast are likely to contribute to Top1cc repair in other species . Top1 is an important chemotherapeutic target but acquired resistance to chemotherapy , via up regulation of repair factors or reduction in Top1 levels , is a common cause of therapeutic failure ( [24] , [30] , [53] and refs . therein ) . Thus , our data implicating the evolutionarily conserved STUbL , Nse2 and Rad60∶Ubc9 factors in Top1cc repair may aid therapeutic strategies . Currently , the presented results characterize a critical STUbL-Rad60-Nse2 DNA repair function acting parallel to Tdp1 , potentially in a pathway initiated by the Xpf-Ercc1 family DNA endonuclease Rad16-Swi10 . More broadly , these STUbL-Rad60-Nse2 results provide key knowledge on how cells deal with the genotoxic effects of spontaneous Top1-induced DNA damage .
Standard yeast methods were performed as described [54] . Top1 was N-terminally tagged at its endogenous locus using nmt41-3FLAG , as described previously [55] . The Top1 catalytic mutant was generated by site directed mutagenesis ( Quikchange; Stratagene ) . Drugs were obtained from Sigma–Aldrich . Table S1 lists the strains used in this study . Cells were either grown on solid media at 25°C for 3 days and resuspended in 1 x PBS before imaging , or cultured in supplemented minimal media to mid-log phase at 25°C , and analyzed with a Nikon Eclipse E800 microscope equipped with a Photometrics Quantix charge-coupled device camera . Images were analyzed with ImageJ ( NIH , http://rsbweb . nih . gov/ij/ ) . For cell length comparisons at least 100 cells per strain were measured . Data represents the average cell length in units defined by ImageJ with 95% confidence intervals . For indirect fluorescence at least 300 cells were analyzed for each strain . Data represents the average of three independent experiments with standard deviations . Log phase cultures ( OD600 of ∼0 . 3–0 . 4 ) were treated with 40 µM CPT , 0 . 008% MMS , or 15 mM HU . Septation was monitored using a Zeiss Axioscope 20 . After 4 h , MMS was washed out and cells were transferred to medium without drug to allow recovery . For comparison between the slx8-1 and wild type strain a minimum of 175 cells were scored for each data point . Data represents the average of four experiments with standard deviations . Cultures were grown at the permissive temperature ( 25°C ) for the slx8-1 allele to mid-log phase for all experiments ( except where otherwise indicated ) . Top1-FLAG cells were cultured overnight in minimal media containing thiamine . Cultures were then washed and diluted into minimal media either with or without thiamine , grown for an additional 48 h and harvested . Cells were lysed in 8 M Urea , 50 mM Tris pH 8 , 50 mM NaH2PO4 , 300mM NaCl , and Complete Protease Inhibitors EDTA-free ( Roche , IN ) . Chk1-HA cells were grown over night in rich media , lysed in 50 mM Tris pH 8 , 150 mM NaCl , 2 . 5 mM EDTA , 10% glycerol , 0 . 2% Nonidet P-40 , 90 mM NaF , Complete Protease Inhibitors EDTA-free , and 5 mM phenylmethylsulfonyl fluoride ( PMSF ) . Protein samples were separated by SDS-PAGE on 4-20% Tris-glycine gels ( Invitrogen , CA ) for Top1-FLAG and 8% acrylamide gels ( 99% ) for Chk1-HA . Immunoblotting was performed as previously described [21] , [23] , [56] , [57] . Top1-TAP cells were grown over night at either 30°C , or at the permissive temperature ( 25°C ) , then cultured at the semi-permissive temperature ( 30°C ) on day two , and shifted to the restrictive temperature ( 36°C ) for 6 h on day three , to inactivate the slx8-1 allele . Top1-TAP cells were lysed using the buffer described above for Chk1-HA , supplemented with 60 mM N-ethyl maleimide ( NEM ) that lacked glycerol . Protein extracts were incubated with IgG-Sepharose beads ( GE healthcare ) at 4°C for 2 h , washed and separated by SDS-PAGE on 4-8% Tris-glycine gels ( Expedeon , CA ) followed by western blotting . For denaturing Nickel pull downs , Top1-Myc and 6His-Pmt3 expressing strains were grown overnight in rich media at the permissive temperature ( 25°C ) . Cells were lysed in 8 M Urea buffer described above for Top1-FLAG , supplemented with 60 mM NEM . Equal amounts of total protein per strain were incubated with Ni-NTA Superflow beads ( Qiagen ) for 1 . 5 hrs rotating at room temperature . Beads were washed and analyzed by SDS-PAGE on 4–8% Tris-glycine gels followed by anti-Myc western blotting . ChIP experiments were essentially performed as published [57] with minor modifications . In order to capture covalent Top1-DNA complexes , cells were not treated with formaldehyde . 4 . 5×108 cells were lysed by bead beating in buffer L ( 50 mM HEPES KOH pH 7 . 4 , 140 mM NaCl , 1 mM EDTA , 0 . 1% Triton , 0 . 1% Na-Deoxycholate , Complete Protease Inhibitors EDTA-free , and 5 mM PMSF ) . After bead beating the Triton-X concentration of the buffer was brought to 1% . DNA was sonicated to 500–800 bp using the Sonicator 3000 ( Misonix , NY ) equipped with a cup horn for 3×20 seconds at power level 10 , in 1 minute intervals . Protein extracts were normalized to the lowest protein concentration ( 1–2 mgs final ) between strains . Lysates were then incubated with Protein-G Dynabeads ( Invitrogen , CA ) pre-bound with FLAG antibodies ( M2 , F1804 , Sigma ) . Following a 2 h pull-down at 4°C , immunocomplexes were washed 3 times in buffer L ( 1% Triton-X ) , 2 times in Buffer H ( buffer L with 500 mM NaCl ) , 2 times in buffer D ( 10 mM Tris-HCl pH 8 , 250 mM LiCl , 1 mM EDTA , 0 . 5% NP40 , 0 . 5% Na-deoxycholate ) , and one time in 10 mM Tris-HCl pH 8 , 1 mM EDTA . The DNA was eluted off the Dynabeads by heating in 10 mM Tris-HCl pH 8 , 10 mM EDTA , at 70°C for 15 minutes . The protein was digested with 0 . 5 mg/ml final concentration Proteinase K ( Invitrogen , CA ) for 2 h at 50°C . The DNA was purified using the PureLink PCR purification Kit ( Invitrogen , CA ) . For each experiment , the percentage DNA recovery of ChIP samples relative to the DNA amount in the input was averaged over triplicate qPCR measurements . Data represents the average of at least three independent experiments with standard deviations . Primer sequences for cnt2 , telo2R , mes1 and rDNA2 have been published [58] , [59] . Spontaneous mitotic recombination rates between Adenine heteroalleles were determined by fluctuation tests as described [32] , using the ade6-L469/pUC8/ura4+/ade6-M375 heteroallele system [60] . For each assay , four independent colonies were analyzed . Each assay was repeated independently at least three times . Assays were performed at 25°C . | The failure of cellular DNA repair mechanisms can lead to cancer , neurodegeneration , or premature aging . Although much is known about specific DNA repair mechanisms , an understanding of how these processes are critically orchestrated by post-translational modifiers such as SUMO and ubiquitin is in its infancy . We identified an intriguing family of E3 ubiquitin ligases called STUbLs that act at the interface between the SUMO and ubiquitin pathways , and through undefined proteins and pathways maintain genome stability . Here we show that dysfunction of STUbL , an associated SUMO-like protein called Rad60 , or the Nse2 SUMO E3 ligase converts the normally benign topoisomerase I ( Top1 ) activity into a genome destabilizing genotoxin . Normally , Top1 transiently introduces a break in one strand of the DNA duplex allowing DNA to unwind . However , these transient breaks are converted into recombinogenic DNA lesions when STUbL , Rad60 , Nse2 , and parallel pathways that we identify are compromised . This study reveals important regulatory circuits reliant on STUbL , Rad60 , and Nse2 that insulate the genome from the potentially harmful effects of Top1 , which may otherwise promote cancer or neurodegeneration . Furthermore , Top1 is a major chemotherapeutic target , and so our findings may aid in the development of more efficacious Top1-based therapies . | [
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| 2011 | SUMO-Targeted Ubiquitin Ligase, Rad60, and Nse2 SUMO Ligase Suppress Spontaneous Top1–Mediated DNA Damage and Genome Instability |
Given the continued successes of the world’s lymphatic filariasis ( LF ) elimination programs and the growing successes of many malaria elimination efforts , the necessity of low cost tools and methodologies applicable to long-term disease surveillance is greater than ever before . As many countries reach the end of their LF mass drug administration programs and a growing number of countries realize unprecedented successes in their malaria intervention efforts , the need for practical molecular xenomonitoring ( MX ) , capable of providing surveillance for disease recrudescence in settings of decreased parasite prevalence is increasingly clear . Current protocols , however , require testing of mosquitoes in pools of 25 or fewer , making high-throughput examination a challenge . The new method we present here screens the excreta/feces from hundreds of mosquitoes per pool and provides proof-of-concept for a practical alternative to traditional methodologies resulting in significant cost and labor savings . Excreta/feces of laboratory reared Aedes aegypti or Anopheles stephensi mosquitoes provided with a Brugia malayi microfilaria-positive or Plasmodium vivax-positive blood meal respectively were tested for the presence of parasite DNA using real-time PCR . A titration of samples containing various volumes of B . malayi-negative mosquito feces mixed with positive excreta/feces was also tested to determine sensitivity of detection . Real-time PCR amplification of B . malayi and P . vivax DNA from the excreta/feces of infected mosquitoes was demonstrated , and B . malayi DNA in excreta/feces from one to two mf-positive blood meal-receiving mosquitoes was detected when pooled with volumes of feces from as many as 500 uninfected mosquitoes . While the operationalizing of excreta/feces testing may require the development of new strategies for sample collection , the high-throughput nature of this new methodology has the potential to greatly reduce MX costs . This will prove particularly useful in post-transmission-interruption settings , where this inexpensive approach to long-term surveillance will help to stretch the budgets of LF and malaria elimination programs . Furthermore , as this methodology is adaptable to the detection of both single celled ( P . vivax ) and multicellular eukaryotic pathogens ( B . malayi ) , exploration of its use for the detection of various other mosquito-borne diseases including viruses should be considered . Additionally , integration strategies utilizing excreta/feces testing for the simultaneous surveillance of multiple diseases should be explored .
Spanning 73 countries and territories and placing an estimated 1 . 39 billion individuals at risk of infection , lymphatic filariasis ( LF ) presents a considerable risk to global health [1] . Similarly , with an estimated 198 million malaria infections and 584 , 000 malaria-related deaths in 2013 , the global burden of human malaria is staggering [2] . Yet despite the wide ranging impacts of these diseases , global elimination efforts have made significant strides , spearheaded by mass drug administration ( MDA ) programs supported by large pharmaceutical donors [3–5] and the widespread use of insecticidal bed nets [6–9] . As a result , disease prevalence in many locations has decreased dramatically , enabling a growing number of countries to discontinue their treatment efforts for LF [5 , 10] and spurring the creation of an increasing number of malaria elimination programs [11–13] . However , lessons learned as a result of LF elimination efforts have shown that the cessation of MDA , recommended after the successful passing of a transmission assessment survey [14] , results in an additional set of programmatic challenges . Foremost in such post-intervention settings is the issue of post-MDA surveillance , as vigilant monitoring is required to ensure that recrudescence of disease has not occurred [15] . This monitoring is costly and current efforts for LF are centered upon the periodic sampling of the human population in order to examine circulating levels of filarial antigen [16–17] . While effective , these efforts require blood sampling of the human population . The invasive nature of this practice , coupled with the requirement of informed consent , results in participation challenges [14] that logically increase as populations become further removed from the time of widespread disease transmission . While still largely of future concern , similar challenges likely await the malaria community as control efforts continue to reduce the burden of disease , making this programmatic obstacle one of utmost global importance . Molecular xenomonitoring ( MX ) , the testing of vectors for the presence of parasite genetic material , has been proposed as a non-invasive means of conducting post-MDA surveillance for LF [14 , 17–18] . Although precise correlations between levels of parasite within the vector population and levels within the human population have not been conclusively established , parasite presence within the vector population is indicative of the potential for disease transmission . Furthermore , when monitoring for LF in locations endemic for the Wuchereria bancrofti parasite , a pathogen without a known zoonotic host [19] , presence is directly indicative of active human infection . Yet despite its many advantages , MX is costly and when used for monitoring in a post-MDA setting , typically requires the collection and sampling of many thousands of mosquitoes [18 , 20–21] . Therefore , as a growing number of countries continue to enter the surveillance phases of their LF eradication programs , alternative methodologies for streamlining , simplifying , and reducing the costs associated with post-MDA monitoring will be required . As an alternative to traditional approaches to MX , excreta and feces produced by mosquitoes potentially harboring parasites can be tested for the presence of pathogen DNA . Previous work has demonstrated that vector feces-monitoring for the PCR-based detection of Trypanosoma cruzi can be used as a means of surveying insect host infection status [22] . Similarly , it has been shown that genetic material from the Brugia malayi parasite can be successfully detected in the excreta and feces collected from individual mosquitoes [23] . Building upon these findings , we describe methodological proof-of-principle for the real-time PCR-based monitoring for B . malayi parasite DNA in pools of mosquito excreta/feces as a platform for the surveillance of large numbers of insects . While unconventional , excreta/feces monitoring has the potential to provide significant time , cost , and labor savings over traditional MX methodologies due to its exceptionally high-throughput nature . Furthermore , as excreta/feces collection would likely prove readily adaptable to a variety of both passive and active trapping practices and platforms , its potential feasibility as an exceedingly low cost , long-term surveillance tool is great . Equally promising , initial experiments have demonstrated that this approach to MX can be applied to the detection of Plasmodium vivax DNA , indicating its possible usefulness for the monitoring of both unicellular and multicellular eukaryotic pathogens . Given these encouraging findings , the further exploration of mosquito excreta/feces testing as a new method for disease surveillance purposes is warranted and efforts to adapt this alternative MX approach to other mosquito-borne illnesses should be pursued .
Preliminary experiments were designed to determine the effectiveness/efficiency of extracting DNA from the excreta/feces of mosquitoes potentially infected with the B . malayi parasite . To make this determination , various extraction protocols and techniques were tested in order to evaluate their efficiency ( Table 1 ) . Because the FR3-derived mosquito cartons containing excreta/feces from potentially infected insects were non-waxed , initial samples were either scraped off of the cartons using a metal spatula , or strips of the carton material ( hereafter referred to as carton strips ) were directly used as the starting material for the extraction procedure . The amplification of B . malayi parasite DNA from all extracts was evaluated using the previously described real-time PCR primer-probe pairing [26] . Results demonstrated that DNA extractions performed using the QIAamp DNA Micro Kit ( Qiagen , Valencia , CA ) provided the most consistent and effective detection of parasite DNA . For this reason , this kit was used in all subsequent experiments . To adapt the Qiagen protocol for use with the bulky , brittle mosquito carton material , minor modifications were made to the manufacturer’s suggested instructions for DNA extraction from bloodspots . Briefly , carton strips were soaked in 360 μl of Buffer ATL for 1 hour prior to incubation with Proteinase K at 56°C . Additionally , following incubation at 70°C , samples were centrifuged at maximum speed for 5 min and supernatants were transferred to new 1 . 7 ml microcentrifuge tubes . Tubes were centrifuged for an additional 5 min at maximum speed to pellet residual debris and the supernatants were transferred to QIAamp MinElute columns . Lastly , all samples were incubated in Buffer AE at room temperature for 5 min prior to the elution of samples from the columns . Although preliminary experiments demonstrated that excreta/feces derived from vector mosquitoes fed on B . malayi microfilaria ( mf ) -positive blood resulted in the amplification of parasite DNA , the availability of mf-containing blood does not guarantee that all mosquitoes will feed or ingest parasites while feeding . Additionally , as the FR3’s standard operating procedure ( SOP 8 . 3 ) requires that mosquitoes spend three to five days as adults prior to the time an infective blood meal is introduced , a substantial volume of parasite-negative feces was produced and deposited into mosquito cartons prior to blood feeding . Furthermore , as mosquitoes are known to excrete while taking a blood meal [27] , it is likely that excreta would be deposited before parasite DNA had reached/been incorporated into the voided material . Therefore , a portion of the voided material collected from mosquitoes provided with mf-positive blood would likely not contain parasites and would therefore not result in a positive PCR . For this reason , a large panel of potentially positive excreta/feces samples was tested in order to estimate the rates of sample positivity . In total , 59 independent samples were tested , with each sample consisting of a 0 . 48 cm2 carton strip . Based upon observations of the volume of excreta/feces produced by single mosquitoes housed in 50 ml conical tubes , it was estimated that the volume of excreta/feces on each carton strip was equivalent to the average volume produced by one to two mosquitoes over a 24 hour period . Negative control extractions were performed on similar volumes of mosquito feces collected from uninfected C . quinquefasciatus . All samples underwent DNA extraction using the modified Qiagen procedure described above and were analyzed by 45 cycles of real-time PCR using the published reagent concentrations and cycling protocol [26] . 2 μl aliquots of each DNA extract were tested in triplicate and samples returning two or more positive results were considered positive for B . malayi parasite DNA . In order to determine detection limits for the presence of B . malayi-infected excreta/feces in large pools of uninfected mosquito feces , a titration of samples was created , with each sample containing a 0 . 48 cm2 strip from a carton used to house mosquitoes provided with a B . malayi-positive blood meal mixed with various volumes of uninfected mosquito feces . Feces from uninfected C . quinquefasciatus mosquitoes were removed from cartons using a cotton swab , and the feces-covered cotton was added to each sample . As 50 uninfected mosquitoes were raised in each carton , and adult mosquitoes were observed to survive for a minimum of 10 days ( with most surviving considerably longer ) , it was conservatively estimated that each carton contained a minimum of 500 mosquito feces/days ( i . e . the amount of feces produced by 500 mosquitoes in one 24 hour period , or the amount of feces produced by a single mosquito over a 500 day period ) . While the distribution of feces within cartons was not precisely uniform , by sectioning cartons based upon total internal surface area ( approximately 1 , 050 cm2 ) , it was possible to roughly estimate the number of mosquito feces/days being added to each sample . Samples estimated to contain approximately 62 . 5 , 125 , 250 , and 500 feces/days were prepared . Negative control extractions were also prepared using mosquito feces collected from uninfected C . quinquefasciatus . All samples were extracted and tested in duplicate reactions using the same extraction and detection methods as described above for the evaluation of PCR positivity testing . To test whether the detection of mosquito-borne pathogen DNA from mosquito excreta/feces was possible for species other than the B . malayi parasite , a set of samples was created using mosquito excreta/feces produced by carton-raised A . stephensi that had been fed on P . vivax-positive blood . As was done for B . malayi detection , samples were prepared by excising 0 . 48 cm2 carton strips containing potentially positive excreta/feces . To establish proof-of-principle , 20 samples were prepared and DNA was extracted using the modified Qiagen protocol described above . DNA extracts from each sample were tested using a previously described primer-probe set for the universal detection of Plasmodium species [28] with reaction recipes and cycling conditions remaining consistent with the authors’ published protocol .
Carton strips were excised from containers used to house A . aegypti mosquitoes provided with B . malayi mf-containing blood and testing was conducted to determine the percentage of excreta/feces samples containing B . malayi DNA . Such testing was necessary since the production of feces can occur prior to the provision of an infective blood meal or before the ingestion of a blood meal . Furthermore , the availability of infective blood does not guarantee that each individual mosquito will feed and , dependent upon the mosquito species , localization of parasite material to voided excreta/feces may take time following blood meal ingestion . Accordingly , DNA was extracted from 59 independent samples , each consisting of a carton strip measuring 0 . 48 cm2 and containing excreta/feces from one to two mosquitoes over a 24 hour period ( i . e . one to two mosquito feces/days ) . Real-time PCR testing , using 2 μl of template DNA resulted in positive detection for 21 out of 59 samples tested ( 35 . 6% ) . For positive samples , mean Ct values ranged from 26 . 62 ( +/- 0 . 24 ) to 41 . 98 ( +/- 0 . 03 ) ( Table 2 ) . Because only a fraction of the deposited mosquito excreta/feces would contain parasite DNA , 35 . 6% may be a true indication of the frequency of positive samples . A titration of samples containing potentially positive 0 . 48 cm2 carton strips mixed with varying amounts of uninfected mosquito feces was prepared in order to estimate the limits of detection for B . malayi-based excreta/feces testing . In total , five samples containing an estimated 62 . 5 mosquito feces/days , six samples containing an estimated 125 mosquito feces/days , six samples containing an estimated 250 mosquito feces/days , and two samples containing an estimated 500 mosquito feces/days were assayed . As expected , due to the uncertainty of which samples actually contained B . malayi DNA , a fraction of the samples failed to give positive PCR detection of B . malayi DNA . However , detection of parasite DNA proved possible at all tested levels of sensitivity ( Table 3 ) . To explore whether excreta/feces testing would efficiently detect pathogen DNA from species other than B . malayi , testing for the presence of the human malaria-causing parasite P . vivax was performed . To demonstrate proof-of-concept , 20 samples were prepared and tested by PCR . Each sample was comprised of a 0 . 48 cm2 carton strip excised from a mosquito container having housed A . stephensi female mosquitoes provided with Plasmodium-positive blood . Real-time PCR testing of DNA extracted from each sample clearly demonstrated the adaptability of excreta/feces testing to the detection of P . vivax since all samples were positive with Ct values ranging from 26 . 82 ( +/- 0 . 26 ) to 29 . 21 ( +/- 0 . 80 ) ( Table 4 ) .
While sensitive and less intrusive to the local population than human sampling , the number of studies implementing current MX practices for the surveillance of LF or malaria has been limited . Although such efforts provide valuable data [10 , 17–18 , 21] the routine use of MX for post-MDA LF surveillance or long-term recrudescence monitoring is not yet standard procedure . Despite the existence of effective molecular tools [28–29] , vector monitoring for malaria is even more uncommon and World Health Organization recommendations for infection monitoring and prevalence estimation rely solely on human sampling [2] . Limited implementation has occurred for multiple reasons , including the need to process and test large numbers of mosquitoes from areas suspected of having low parasite density within the vector population [10 , 18 , 21] . Difficulties in establishing a concrete correlation between vector-parasite levels and human prevalence have further restricted MX implementation [21] . Yet despite these shortcomings , MX continues to receive attention as the need for post-intervention disease surveillance continues to grow and mosquito trap designs continue to improve [30–34] . Accordingly , methodologies capable of harnessing the advantageous aspects of MX while making its practice more practical and inexpensive would be of great benefit to global LF and malaria elimination efforts , as well as to monitoring efforts for other vector-borne diseases . The work presented here provides methodological proof-of-concept for a novel approach to MX with the potential to greatly reduce the cost , time , and labor associated with large-scale surveillance efforts . The successful amplification of parasite DNA from pooled mosquito excreta/feces containing B . malayi genetic material has demonstrated that high-throughput MX for LF is feasible . In the past , real-time PCR-based MX for the presence of the filariasis-causing parasites has been restricted to the testing of pools of 25 or fewer mosquitoes . This is because the biological mass of mosquitoes and high yields of mosquito DNA associated with pools of large size results in the inability to detect the presence of small quantities of parasite DNA [35] . However , excreta/feces testing enables the sampling of material obtained from vast numbers of mosquitoes , while simultaneously limiting the biological mass associated with each sample . As we have demonstrated , it is possible to detect trace amounts of parasite DNA in pools containing the voided material from as many as 500 uninfected mosquitoes . Future studies implementing this approach will benefit from the drastic reduction in cost of DNA extractions and PCR ( approximately 20-fold ) . Furthermore , as it has been shown that non-vector mosquitoes rid themselves of parasite material more rapidly than vector species ( as indicated by a shortened period of time during which parasite detection is possible within non-vectors [23] ) , one would expect to find greater quantities of parasite DNA within the excreta/feces of non-vector mosquitoes . Therefore , the testing of mixed pools of vector and non-vector excreta/feces should be possible . While such testing will result in reduced ability to directly correlate the presence of parasite with individual vector species , it will likely increase the sensitivity of detection when surveying for the presence of parasite in post-transmission-interruption settings as both vector and non-vector mosquitoes potentially harboring parasite material will be screened . In addition , it is likely that excreta/feces testing will eliminate the need for the labor intensive and time consuming species-sorting efforts which are commonplace in current MX work [10 , 17–18 , 21 , 36] . By drastically reducing the numbers of pools that must be screened and by eliminating the need for sorting mosquitoes by species , labor-related time and costs are dramatically reduced . While operationalizing this alternative approach to MX presents some implementation hurdles , adaptation of current passive and active trapping methods to the collection of mosquito excreta/feces is possible . Such adaptation could occur by transferring live mosquitoes from a trap to a holding carton , in which they would be sugar fed using a cotton ball , thereby encouraging the voiding of waste material . Expired mosquitoes would then be removed and additional mosquitoes could be added following further collection from the trap . Periodic testing of the accumulated excreta/feces would enable the high-throughput screening of the voided material from a series of such traps . Any trap with the capacity to maintain live mosquitoes could be used for this purpose including the CDC Gravid Trap , the Ifakara tent trap and others [30 , 37] . Alternatively , collection of excreta/feces could occur directly within traps of various designs . One such design proving readily adaptable to excreta/feces collection in preliminary experiments is the “Large Passive Box Trap” developed by Ritchie , et al [38] . While work aimed at evaluating the adaptability of this trap to the collection of various species of mosquitoes is currently ongoing , and further efforts to optimize this trap for the purpose of excreta/feces collection will be required , simply lining the internal surfaces of this passive trap with waxed paper provides an uncomplicated method for collecting the accumulated material voided by trapped mosquitoes ( S1 Fig ) . Swabbing the excreta/feces from the waxed paper then enables the PCR analysis of pooled material . Additional testing will be required to determine the stability of parasite DNA in mosquito excreta/feces over time and under field conditions . However , in the proof-of-concept experiments described in this paper , mosquito excreta/feces containing parasite DNA was allowed to accumulate for 14–16 days prior to transfer to cold storage . In this setting , parasite DNA remained stable and detectable ( Table 3 ) . While further validation under conditions mimicking tropical temperatures and humidity will be required , these results are encouraging , as DNA stability within tropical and sub-tropical climates could present another hurdle when operationalizing this method in the field . Since production of feces can occur prior to the provision of a parasite-positive blood meal and since this provision does not ensure that all mosquitoes will ingest and/or metabolize a parasite , a percentage of the excreta/feces samples collected will likely test negative for parasite DNA . It is therefore difficult using blood-fed mosquitoes to definitively assess the consistency of detection of parasite DNA in excreta/feces . During initial testing , we demonstrated that 21 out of 59 samples comprised of 0 . 48 cm2 carton strips derived from containers used to rear mosquitoes with a B . malayi-positive blood source were positive ( Table 2 ) . However , although sufficient to fulfill our primary aim of providing methodological proof-of-concept , it cannot be conclusively determined whether the remaining 38 samples were all truly negative for parasite DNA . While spiking uninfected excreta/feces samples with extracted B . malayi genomic DNA would provide clear positive and negative samples , this approach is extremely artificial and has decreased biological relevance since it eliminates any possible effects of mosquito metabolism on the integrity of parasite DNA . Since the major uses of excreta/feces testing will likely center on mapping and long-term , low-cost , post-transmission-interruption recrudescence monitoring , marginally reduced consistency of detection has diminished significance as continuous , sustainable , high-throughput surveillance would enable detection of even low-levels of parasite prevalence . The high-throughput nature of this testing was clearly demonstrated by the positive detection of parasite DNA derived from pools containing various volumes of negative feces up to 500 mosquito feces/days ( Table 3 ) . Detection proved possible at all tested sensitivity levels and with overall sample positivity rates similar to those obtained when testing potentially positive excreta/feces samples without the addition of negative feces ( 36 . 8% vs . 35 . 6% respectively ) . Thus , the inclusion of large amounts of negative feces does not appear to alter detection efficiency . Given these findings , sustainable , high-throughput surveillance efforts using excreta/feces screening could serve as a “first-alert” platform , with positive detection serving as a “red flag” for recrudescence in settings of known transmission interruption . In such a scenario , detection would spur the implementation of more traditional surveillance and monitoring studies . By successfully detecting P . vivax DNA in pools of excreta/feces produced by Plasmodium-positive-blood fed A . stephensi , we have provided proof-of-principle for the application of this platform to the detection of malaria parasites . Furthermore , the increased rates of sample positivity and decreased Ct values seen when assaying for P . vivax are not entirely surprising and indicate this system may work even better for malaria than LF . Estimates have suggested that the ratio of Plasmodium merozoites to gametocytes within the peripheral blood is as great as 156:1 [39–40] . Given this ratio , the vast number Plasmodium merozoites ingested during a blood meal ( up to 32 per infected erythrocyte [41] ) , and knowledge that merozoites obtained during blood feeding are unable to undergo further development within the mosquito host ( only gametocytes undergo further development [42] ) , the great majority of ingested parasites are simply metabolized and/or eliminated by the mosquito . In contrast , while mosquito hosts possess measures that provide partial protection against filarial infection [43–44] , and environmental conditions are thought to impact rates of parasite survival [45] , all filarial parasites taken up as part of a blood meal are of the correct lifecycle stage ( mf ) to potentially undergo further development within the vector host . Therefore , due to the varying natures of their lifecycles , it follows that a greater percentage of filarids ingested during a blood meal are able to successfully develop within the mosquito host as compared to Plasmodium . Since successful parasite development would likely mean the absence of parasite DNA in mosquito excreta/feces , the lower levels of sample positivity and the more modest Ct values observed during B . malayi testing compared to P . vivax testing seem logical . With its adaptability to both B . malayi and P . vivax , MX of mosquito excreta/feces for various other mosquito-borne pathogens should be explored . Given the successes realized with the detection of these parasites , it is extremely likely that similar detection will prove possible for W . bancrofti and other malaria species . However , the applicability of this new platform to other types of pathogens should also be examined , since improved high-throughput screening for RNA viruses such as Dengue , Chikungunya , and Zika would be welcomed programmatic tools . Furthermore , since all species of biting insects draw from the same reservoir of blood within a target host , the possibility of cross-vector monitoring should also be considered . For example , excreta/feces samples collected from mosquitoes could be monitored for the presence of disease-causing agents having unrelated insect hosts ( such as Leishmania ssp . or Loa loa ) . Adaptability to various pathogens and the possibility of cross-vector monitoring could also make excreta/feces sampling an attractive strategy for tropical disease integration efforts . In light of these factors , and the potential time , cost , and labor savings associated with such applications , we believe that this proof-of-concept study suggests that further evaluation of this new method is warranted . | As a non-invasive method of indirectly monitoring insect-borne disease , molecular xenomonitoring ( MX ) , the molecular testing of insects for the presence of a pathogen , can provide important information about disease prevalence without the need for human sampling . However , given the successes of tropical disease elimination programs , including many lymphatic filariasis and malaria elimination efforts , parasite levels in many locations are declining . This decrease in prevalence requires the sampling of increased numbers of vectors for disease surveillance and recrudescence monitoring . Such increased sampling poses a challenge since it results in additional costs and labor . In light of these difficulties , high-throughput methodologies for MX are necessary to provide elimination programs with cost-reducing alternatives to long-term disease surveillance . Here we demonstrate proof-of-concept for a new method that samples large numbers of mosquitoes using PCR to screen excreta/feces for filarial or malarial parasites . If operationalized , this approach to MX will provide a practical “first-alert” system that will enable cost-minimizing surveillance in post-transmission-interruption settings . Given this potential , the applicability of this approach to the monitoring of various mosquito-borne diseases should be explored further , as this platform will prove useful for surveillance efforts for a wide variety of pathogens . | [
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| 2016 | A Novel Xenomonitoring Technique Using Mosquito Excreta/Feces for the Detection of Filarial Parasites and Malaria |
Schistosomiasis is a neglected tropical disease that is responsible for almost 300 , 000 deaths annually . Mass drug administration ( MDA ) is used worldwide for the control of schistosomiasis , but chemotherapy fails to prevent reinfection with schistosomes , so MDA alone is not sufficient to eliminate the disease , and a prophylactic vaccine is required . Herein , we take advantage of recent advances in systems biology and longitudinal studies in schistosomiasis endemic areas in Brazil to pilot an immunomics approach to the discovery of schistosomiasis vaccine antigens . We selected mostly surface-derived proteins , produced them using an in vitro rapid translation system and then printed them to generate the first protein microarray for a multi-cellular pathogen . Using well-established Brazilian cohorts of putatively resistant ( PR ) and chronically infected ( CI ) individuals stratified by the intensity of their S . mansoni infection , we probed arrays for IgG subclass and IgE responses to these antigens to detect antibody signatures that were reflective of protective vs . non-protective immune responses . Moreover , probing for IgE responses allowed us to identify antigens that might induce potentially deleterious hypersensitivity responses if used as subunit vaccines in endemic populations . Using multi-dimensional cluster analysis we showed that PR individuals mounted a distinct and robust IgG1 response to a small set of newly discovered and well-characterized surface ( tegument ) antigens in contrast to CI individuals who mounted strong IgE and IgG4 responses to many antigens . Herein , we show the utility of a vaccinomics approach that profiles antibody responses of resistant individuals in a high-throughput multiplex approach for the identification of several potentially protective and safe schistosomiasis vaccine antigens .
Schistosomiasis is a chronic , often debilitating , parasitic disease affecting over 200 million people worldwide and killing at least 300 , 000 people annually [1] . The disability adjusted life years ( DALYs ) lost to schistosomiasis are potentially as high as 70 million [2] , [3] . Adult flukes live in the portal and mesenteric veins ( Schistosoma mansoni and S . japonicum ) or in the veins of the bladder ( S . haematobium ) , as male/female pairs , and survive for many years producing hundreds of fertilized eggs per day . Severe morbidity results from the host immune responses to eggs that become trapped in the tissues , including periportal fibrosis , portal hypertension , urinary obstruction and bladder carcinoma [4] . Currently , chemotherapy with praziquantel ( PZQ ) is the standard treatment for schistosomiasis . Control programs based on mass drug administration ( MDA ) with PZQ have been complicated by rapid and frequent re-infection of treated individuals , and the difficulties and expense of maintaining continuous MDA over the long term [5] . Additionally , resistance to PZQ can be induced in the laboratory [6] , and field isolates displaying reduced susceptibility to the drug have been reported ( reviewed in [7] ) . Despite recent large-scale MDA efforts [8] , integrated control programs aimed at limiting schistosomiasis by improving education and sanitation , molluscicide treatment programs to reduce the population of the intermediate snail host , and chemotherapy have had limited success [5] , [9] . A vaccine that induces long-term immunity to schistosomiasis is therefore necessary to reach our goals of elimination . The high prevalence of chronic schistosomiasis in endemic populations suggests that sterile immunity is rarely generated . However , the decline in infection intensity at an earlier age in populations with high infection intensity [10] , and more rapid development of resistance to re-infection after several rounds of PZQ treatment ( drug-induced resistance ) [11] , indicates that non-sterilizing immunity , though slow to develop , can occur . Despite the slow acquisition of non-sterile immunity over time , there is still an urgent need for a prophylactic vaccine , particularly one that targets children , who represent the most at-risk population . There are two major obstacles to the development of an efficacious schistosomiasis vaccine . The first is the ability of schistosomes to employ a range of strategies for evasion of the host immune response . Central to the parasite's ability to evade immune clearance is its unique host-interactive outer surface , or tegument , consisting of a single , contiguous , double-bilayered membrane that covers the entire worm [12] . At this interface essential functional interactions with the human host occur , such as nutrient uptake and environmental sensing . The tegument is also the primary site where the parasite defends itself against immune recognition . The host-interactive surface is indeed the target of the few successful examples of metazoan parasite vaccines , such as those targeting the cattle tick Boophilus microplus [13] , the gastrointestinal nematode Haemonchus contortus [14] and the cestodes , Taenia ovis [15] and Echinococcus granulosus [16] . The second major obstacle to the development of a schistosomiasis vaccine resides in the historic approach to antigen discovery for this pathogen . To date , only one schistosomiasis vaccine , rSh28GST from S . haematobium , is currently in phase I clinical trials , where it was shown to be safe and immunogenic [17] . Other vaccine antigens for S . mansoni are in pre-clinical and clinical development [18] , [19] , with safety and immunogenicity results yet to be reported . We [19]–[21] and others [22] , [23] have advocated for the utility of tegument proteins as a basis for subunit vaccines against schistosomiasis . Three of the current lead candidate antigens are located in the tegument and are exposed on the surface of the parasite [24]–[26] . The genomes for the three major human schistosomes have been sequenced [27]–[29] , and coupled with proteomic studies that characterised the surface proteomes of S . mansoni [30] and S . japonicum [31] , have provided researchers with a catalogue of proteins for discovery and development of a new panel of vaccine antigens . To best mine this extensive proteomic data and identify antigens that are preferentially recognised by antibodies from naturally resistant individuals resident in areas of high transmission for schistosomiasis , we have utilized a clinical cohort of individuals referred to as Putative Resistants ( PRs ) . As part of a ten year longitudinal study of individuals from high S . mansoni transmission areas of Brazil , we identified a cohort of individuals who were constantly exposed to S . mansoni infection as determined by extensive water contact and epidemiological studies , but remained egg-negative over the course of the study [32]–[34] . In addition to this unique epidemiological profile , these individuals mounted an immune response that displayed a markedly different phenotype from that of chronically infected ( CI ) individuals [35]–[37] . Indeed , two of the current antigens in pre-clinical development - Sm-TSP-2 [26] and Sm29 [24] – were discovered as a result of their selective recognition by PR subjects , highlighting their utility as a tool for discovery of protective vaccine antigens . Herein , we describe the screening of the first protein microarray for a human helminth parasite , and only the second such array for a eukaryotic parasite other than Plasmodium sp . We developed a targeted array consisting primarily of tegument derived proteins from both S . mansoni and S . japonicum [38]and screened the array with sera from PR and CI individuals with low , medium and high intensity infections , and then compared and contrasted antibody/antigen recognition profiles to determine antibody signatures that characterised natural resistance or susceptibility to infection . We assessed IgG subclass and IgE responses such that potential vaccine antigens could be assessed for their protective properties as well as their safety profiles in terms of exacerbating allergic IgE responses [39] . We showed that individuals with medium and heavy intensity infections generally recognized more antigens and with higher magnitude than did PR individuals and those with low infection intensities . Moreover , we found that PR individuals did not mount an intense IgE response to these antigens compared to CI individuals , but instead produced IgG1/3 ( cytophilic ) antibody responses to only a few membrane bound antigens . We successfully utilized this approach to identify new , and confirm existing , vaccine antigens via their selective IgG1/IgG3 recognition profiles by PR individuals in the absence of a potentially deleterious IgE response .
Details of the microarray production and associated QC have been described elsewhere [38] and are shown in Figure S1 and Table S1 , however this is the first report that describes probing of the microarray with human sera . We included on the array two dilutions of the ubiquitously immunoreactive Epstein Barr virus protein EBNA-1 at two concentrations , 0 . 1 and 0 . 3 mg/ml , as a non-schistosome control for the rapid translation system ( RTS ) used to express schistosome recombinant proteins . Both antigens were consistently recognized by IgG1 antibodies from all individuals tested ( Figure S2 ) , indicating that sera from all cohorts were of sufficient integrity for further analyses . Given the distinct roles of different immunoglobulin isotypes and IgG subclasses in chronic helminth infections , and to gain a comprehensive picture of antibody reactivity from PR versus CI individuals , we analyzed IgG1 , IgG3 , IgG4 and IgE responses to soluble worm antigen preparation ( SWAP ) and a panel of schistosome antigens . PR subjects mounted the strongest anti-SWAP IgG1 and IgG3 responses whereas the moderate and heavily infected groups mounted the strongest IgG3 responses to SWAP ( Figure S3 ) . One hundred and sixteen ( 116 ) from a total of 215 ( 54% ) RTS proteins spotted were recognized by at least one antibody isotype/subclass from at least one cohort of exposed individuals ( reactive proteins ) ( Table S2 and Table S3 ) . Individuals with medium and heavy intensity infections generally recognized more antigens and with stronger SI than did PR individuals and those with low infection intensities ( Figures 1A-D ) . CI-Mod and CI-Heavy cohorts had significantly higher IgG4 , IgG3 and IgE responses than PR and CI-Light cohorts ( Figures 1A-C ) ( *p<0 . 05; **p<0 . 01 , ***p<0 . 001 , ****p<0 . 0001 ) . In contrast , the PR cohort had significantly higher IgG1 ( Figure 1D ) responses than CI-Mod and CI-Heavy cohorts , although the total number of antigens above the cut-off was lower for this antibody subclass . In general , there was a strong correlation between infection intensity and the number of antigens recognized by combinations of IgG3 , IgG4 and IgE from infected individuals ( Table S4 and Table S5 ) . Of the 116 reactive proteins ( RTS and recombinants ) 41 were recognized by just a single antibody isotype/subclass: 8 proteins were recognized by only IgG1 , 24 proteins were recognized by only IgG3 , one protein was recognized by only IgG4 and 10 proteins were recognized by only IgE . Eleven proteins were recognized by all antibody isotypes/subclasses ( Figure 1E ) ( Table S2 ) . IgE responses were detected to 79 different antigens ( Figure 2 , Table S2 ) and most of these were restricted to the CI-Mod and CI-Heavy groups . Significant differences ( P≤0 . 05 ) between mean antibody responses from 2 or more of the endemic groups were detected to all 79 proteins ( Tables S2–4 ) . The only purified recombinant protein ( non-RTS ) that was the target of an IgE response was Sm29 . Antigens for which the strongest IgE responses were detected included proteins that were predicted and/or proven to be located on the tegument membrane ( including tetraspanins , Ly6/CD59-like proteins such as Sm29 , and glucose transporters ) and predicted intracellular proteins including mitochondrial enzymes , chaperones and glycolytic enzymes such as triose phosphate isomerase ( Table S1 and Table S2 ) . IgG4 responses were detected to 21 proteins ( Figure 3 ) – 20 RTS proteins and purified recombinant Sm29 expressed in E . coli . Significantly different IgG4 responses were detected for all reactive antigens between at least two of the endemic cohorts ( Table S3 , Table S4 ) . Sm29 was recognized weakly by IgG4 but was considered a cross-reactive protein because the US non-endemic control group had a low level IgG4 response against this protein ( Figure 3 , Table S2 ) . IgG3 responses were detected to 96 proteins , 95 of which were RTS and 1 E . coli-derived purified recombinant proteins ( Figure 4 , Table S2 ) . Of the 96 reactive proteins , only 3 displayed no significant differences between the cohorts ( Figure 4 , Table S4 ) . IgG1 responses were detected to 43 proteins ( Figure 5 , Table S2 ) , including purified recombinant Sm29 and Sm-TSP-2 . Significantly different IgG1 responses between endemic cohorts were detected for 31 of these reactive antigens ( Table S4 ) . Twenty-two proteins were the targets of an IgG1 response in the PR cohort that was significantly different to at least one of the CI groups ( Table S4 ) . The most robust of these PR-specific IgG1 responses were aimed at the two positive control recombinant proteins , Sm-TSP-2 and Sm29 , and the RTS protein Smp_139970 , a calmodulin-3 like protein that we have termed Sm-CAM-3 . Sm-CAM-3 shared 46% and 23% amino acid identities with its closest S . mansoni and primate ( macaque ) homologues respectively ( Figure S4 ) . Correlations between different isotype responses to the same proteins were calculated ( Table S5 ) . The strongest correlations detected ( r2>0 . 9 ) were between IgG4/IgE responses in all the schistosome-exposed cohorts ( P<0 . 0001 , Figure S5 ) and IgG3/IgG4 and IgG3/IgE responses in the CI-Mod and CI-Heavy cohorts . All the 215 proteins printed on the array were subjected to cluster analysis to identify proteins with similar reactivity profiles . Two different methods of unsupervised clustering were applied: partitional and hierarchical clustering . Considering all of the possible combinations of antibody reactivity patterns , we used classical multidimensional scaling ( MDS ) cluster analysis to generate clusters of proteins . In this analysis , the reactivity of each protein was described with the average SI for each cohort . Proteins with an average SI below the cut-off in the evaluated group were considered to be zero and only proteins with an average signal intensity above the cut-off for at least one isotype/subclass were considered for clustering . For partitional clustering , working with 4 antibody isotypes/subclasses and 5 cohorts , proteins fell into one of 7 clusters determined by K-means methodology [40] . To facilitate visualization of the process ( and avoid superimposing data points in a 2-dimensional format ) , we compressed the 20 dimensions into just 2 dimensions ( Figure 6A ) . For hierarchical clustering , a dendrogram was designed using complete linkage [41] to combine two proteins and color coded to match the k-means clusters; identities of the proteins within each cluster can be found in Table S2 and Figure S5 . There was good correlation between partitional and hierarchical clustering , indicating that the division of proteins in these groups was robust . To further enhance the visualization process , clustered proteins were distributed in 2 dimensions based on isotype/subclass specific responses of each cohort to each individual protein ( Figure 6B ) . A number of clusters of interest for vaccine development were observed . Clusters 4 and 5 are characterized by proteins that are moderate to strong targets of IgE and IgG3 or IgG1 responses , respectively , particularly in the CI-Mod and CI-Heavy groups . Cluster 7 predominantly consists of non-reactive proteins and a small handful of proteins that were exclusively targeted by IgG1 responses of the individual sera in the PR group but not the CI or non-endemic control groups . Of the strongly reactive PR IgG1 proteins , Sm29 was also recognized by all IgG subclasses as well as IgE and belonged to cluster 2; Sm-TSP-2 and Sm-CAM-3 on the other hand were uniquely targeted by PR IgG1 and not other isotypes or subclasses and belonged to cluster 7 ( Figure 6 , Figure S6 and Table S2 ) . Other cluster 7 proteins that were uniquely recognized by PR IgG1 responses , albeit relatively weak responses , included Smp_124240 ( Na/K transporting ATPase beta subunit ) and Sj_AY915291 ( fatty acid CoA synthetase ) . Both of these proteins have multiple predicted membrane spanning domains ( not shown ) . We examined the antibody recognition profiles of individuals within the cohorts to some of the current antigens that are under various stages of pre-clinical development as human schistosomiasis vaccines , including Sm-TSP-2 , Smp80 ( calpain ) and Sm14 , and bovine vaccines to interrupt zoonotic transmission ( Sj23 ) . We compared the responses of these known vaccine antigens with selected RTS proteins including the PR IgG1-specific target Smp_139970 ( Sm-CAM-3 ) and 2 proteins that were significant targets of IgE and/or IgG4 in CI-Mod and CI-Heavy cohorts , Smp_050270 and Smp_008310 ( Figure 7 ) . Different antigens displayed distinct IgE and IgG subclass profiles . Sm-TSP-2 was the target of a strong IgG1 response that was unique to the PR group , in agreement with the published literature [26] . Smp_139970 ( Sm-CAM-3 ) showed the same recognition profile as that targeting Sm-TSP-2 . Mean SI values for IgE were below the cut-offs for all of the vaccine antigens , however varying numbers of individuals in the CI-Moderate and CI-Heavy groups were positive for some of the antigens , although SI values were weak compared with other RTS proteins such as Smp_008310 . Similarly , IgG4 responses were mostly below the cut-off for the established vaccine antigens . The mean IgG3 responses were mostly negative but weakly positive for Smp80 and Sm-CAM-3 in the CI-Mod cohort . IgG1 responses to the known ( and potentially new ) vaccine antigens were the most noteworthy in terms of unique recognition by the PR cohort: strong IgG1 responses to both Sm-TSP-2 and Sm-CAM-3 were detected in just the PR group and none of the CI groups ( Figure 7 ) .
Herein we describe the first immunomics-based approach to study the humoral immune response to a multi-cellular pathogen . The “immunome” can be defined as the entire set of antigens or epitopes that interface with the host immune system [42] . Recent advances in high order multiplexing , or megaplexing , such as the protein microarray discussed below , provide a practical , high-throughput and affordable approach to estimating the immunomic profiles of humans or animals to a pathogen [43] , [44] . This approach permits investigators to assess the repertoire of antibodies created in response to infections or vaccinations from large collections of individual sera . Further , it can be used to perform large-scale sero-epidemiological , longitudinal and sero-surveillance analyses not possible with other technologies . Numerous passive transfer studies [45] , [46] support the critical role of antibodies in immunity to S . mansoni infection in rodent models . Perhaps the most compelling evidence that the humoral immune response targets the tegument and can kill parasites comes from studies with rats , which are semi-permissive to S . mansoni [47] , [48] . Resistance to schistosomiasis can be passively transferred via serum from resistant rats , and protective antibodies can be removed by adsorption on the surface of schistosomes [49] . Indeed , two of the recombinant antigens used on our array - Sm-TSP-2 and Sm29 - have proven efficacious in a mouse challenge model and were the major targets of single chain antibodies from resistant rats adsorbed from the surface of live schistosomes [50] . The role of antibodies in protective immunity against schistosomiasis in humans is , however , somewhat contentious . Unlike experimentally infected rats , protective immunity to schistosomes in humans develops slowly ( over many years ) and is rarely sterilizing in nature . Distinct molecular mechanisms are thought be critical in the acquisition of immunity in different transmission scenarios . For example , some individuals can successfully mount a protective antibody-mediated response that targets adult S . mansoni antigens after repeated rounds of PZQ therapy [51] , [52] – this drug-induced resistance is mediated by IgE and T helper type 2 ( Th2 ) cytokines , and can be accelerated and augmented by repeated drug treatment [11] , [53] , [54] . We show here that CI individuals make robust IgE responses to many antigens , and the number of antigens recognized increases with increasing intensity of infection as measured by eggs per gram of feces . This would appear to contrast with the protective role that is often associated with IgE in helminth infections , including schistosomiasis [55] . In contrast to drug-induced resistance to schistosomiasis , naturally acquired resistance has been reported in a subset of people who have constant exposure to schistosomes but have never been treated with PZQ ( exemplified by the PR cohort in our study ) – these individuals generate robust T cell responses against the surface of the larval schistosomulum and are characterized by elevated levels of IFN-γ [35] , [56]–[58] . We show here that PR individuals , despite constant exposure to S . mansoni , do not appear to mount a strong IgE response to the proteins on the array . Unlike CI individuals , PR subjects are repeatedly negative for schistosome eggs in the feces , and are therefore unlikely to receive the IgE-inducing stimulus of eggs trapped in the bowel wall and the subsequent hepato-portal inflammation that typifies chronic schistosomiasis . PR individuals are likely to kill juvenile schistosomes before they reach sexual maturity , either in the skin or the lungs . The strong recognition of just a handful of tegument antigens by IgG1 from PR individuals therefore implies a protective role for IgG antibodies ( and/or T cells ) targeting these proteins . A major outcome of this study is the development of a tool by which the immunogenicity and probable safety profile ( i . e . IgE recognition ) of an antigen can be rapidly assessed , and a putative association of that antigen-antibody interaction with resistance or susceptibility to infection inferred . In terms of vaccine antigen discovery , we employed the following principles to a given antigen: ( 1 ) up-selection for further evaluation based on preferential recognition by IgG1 and/or IgG3 from the PR but not the CI cohorts; ( 2 ) down-selection based on recognition by IgE from either PR or CI individuals . Our rationale for down-selection of IgE-inducing antigens is primarily due to safety concerns [19] . In a recent phase I clinical trial of the of the Na-ASP-2 hookworm vaccine in a Necator americanus-endemic area of Brazil , hookworm exposed individuals were vaccinated with a recombinant protein which , despite proving safe and immunogenic in helminth-naïve individuals in the USA [59] , induced an immediate hypersensitivity ( urticarial ) response in vaccinees , resulting in suspension of the clinical trial and further development of this antigen as a vaccine [39] . The allergenicity of the hookworm vaccine was linked to pre-existing IgE to parasite-derived Na-ASP-2 in the circulation of exposed individuals . Rather than completely excluding antigens that are the targets of pre-existing IgE responses from further development as vaccine antigens , we suggest that IgE antigens that associate with resistance might be carefully progressed towards studies that assess their anaphylactogenic potential , particularly given the large body of data that shows a protective role for IgE in resistance to human helminths . For a vaccine targeting infants prior to natural exposure to schistosome-infected water , a vaccine that incorporates antigens which are the targets of IgE in older individuals is feasible , and might even harness the putative protective capacity of this immunoglobulin isotype as children first become exposed to the parasite and undergo boosting and isotype class switching . Using these criteria described above to set the parameters for multi-dimensional clustering , a small number of antigens that made up cluster 7 are noteworthy as targets of IgG1 from the PR cohort but not IgE from any cohort . One of these antigens , Sm-TSP-2 , was already known to be a selective target of PR IgG responses [26] , and its recognition profile on the microarrays served to confirm its potential as a vaccine antigen , as well as the utility of our approach for identifying protective antigens . At least three RTS proteins from cluster 7 were noteworthy as targets of PR IgG1 but not IgE from any exposed cohort . Of these antigens , the strongest IgG1 response was aimed at Smp_139970 ( Sm-CAM-3 ) . Sm-cam-3 mRNA ( and its S . japonicum ortholog , contig 8758 ) is upregulated in cercariae [60] , [61] ( Figure S4A ) and encodes a member of the calmodulin family of calcium-sensing proteins . Calmodulins respond to changes in calcium ion concentrations by undergoing a conformational change upon binding , which in turn , facilitates interactions with other signaling proteins . Using gene silencing and quantitative parasite motility assays , a schistosome calmodulin dependant kinase ( CamKII ) was shown to minimise the impact of PZQ treatment against adult S . japonicum [62] . Sm-CAM-3 is a small ( ∼8 kDa ) protein that contains a single Ca2+ binding EF-hand motif and shares moderate homology with mammalian and other parasite calmodulins , the majority of which are larger than Sm-CAM-3 and contain multiple EF-hands . Of the four calmodulin family members present in S . mansoni , two have been characterized ( SmCaM1 and SmCaM2 ) and were detected in the tegument of adult worms [30] , [63] and the epidermal and tegumental layers of larval stages in the snail host [64] . Indeed RNAi-mediated silencing of these genes resulted in stunted larval development [64] . Generic calmodulin antagonists have been shown to inhibit the in vitro growth and egg-hatching ability of schistosomes [64] , [65] and the growth of Plasmodium [66] but have limited use as anti-parasitic interventions due to the highly conserved nature of calmodulins across species . It is therefore noteworthy that Sm-CAM-3 shares only ∼20% identity with primate calmodulins ( Figure S4B ) , supporting its development as a safe schistosomiasis vaccine that is unlikely to induce antibodies which cross-react with homologous human proteins . Although Sm-CAM-3 is immunogenic in PR individuals , it is a small protein and might not be overly immunogenic as a subunit vaccine . An ideal schistosomiasis vaccine might therefore incorporate Sm-CAM-3 as part of a larger chimeric construct with other vaccine antigens such as Smp80-calpain or Sm-TSP-2 , a strategy that was recently shown to boost efficacy in a mouse challenge model [67] . Two other cluster 7 RTS proteins were unique targets of just IgG1 in the PR cohort , albeit with relatively weak responses – Smp_124240 ( Na/K transporting ATPase beta subunit ) and Sj_AY915291 ( fatty acid CoA synthetase ) . Both proteins have multiple predicted membrane spanning domains , and warrant expression of defined extracellular domains in a cell-based system for further investigation as vaccine antigens . In addition to Sm-TSP-2 , two other high profile vaccine antigens , Smp80-calpain ( Smp_137410 ) and Sm14 ( Smp_095360 ) , were contained within cluster 7 . Although some individuals mounted IgG and to a lesser extent IgE responses to these RTS products , the mean SI values for any isotype/subclass to either antigen were below the cut-offs . The apparent absence of a positive mean SI ( indicative of a strong antibody response ) to an RTS protein on the array should be treated with due caution – the benefit of RTS protein production is its inherent high-throughput nature and its suitability for printing onto arrays in nanoliter quantities . One of the major limitations of RTS protein production however is the absence of complex secretory machinery and the dependence on secretory pathways for correct folding and processing of some proteins . While there are RTS systems that contain eukaryotic microsomal membranes , these systems are not widely used for protein microarray production , and would add substantial cost to the production of large arrays . Sm-TSP-2 was available to us in recombinant , cell-derived form , and was not successfully translated in RTS form . Smp80-calpain and Sm14 were successfully produced in RTS form ( Figure S1 ) , but we cannot guarantee faithful replication of all the native epitopes . We therefore urge caution in the interpretation of a “negative” result using this microarray approach , but we have confidence in assigning a “positive” antibody response . Proteome microarrays have been used to identify candidate vaccine antigens for a number of infectious diseases of viral and bacterial origin [43] . To date , Plasmodium is the only eukaryotic parasite for which proteome microarrays have been described [68] . Screening of P . falciparum proteome arrays with sera from well-defined clinical cohorts resident in malaria-endemic areas [68]–[70] and recipients of radiation attenuated vaccines [71] has resulted in the identification of a suite of new vaccine antigens , some of which have proven efficacious in mouse models of malaria ( DLD , unpublished observations ) . Our proteome microarray included a small number of carefully selected S . mansoni and S . japonicum proteins , working on the assumption that tegument surface proteins are most likely to be the targets of PR protective immune responses . Based on our findings here , notably the large number of immunogenic proteins , a second generation array that consists of a much larger number of S . mansoni proteins , both extracellular and intracellular , would likely yield many more immunogenic proteins , including potential vaccine and diagnostic antigens . This is particularly relevant in the context of antigen discovery using sera from individuals who have developed resistance to schistosomiasis after repeated rounds of PZQ therapy ( drug induced resistance - DIR ) , where the immune response is primarily aimed at intracellular molecules released by dying worms [72] or other means of protein export such as exosomes [73] . We are now screening our array ( and subsequent generation arrays ) with sera from DIR individuals . Indeed , if a schistosomiasis vaccine is developed , it is likely to be incorporated into an integrated control program that couples chemotherapy with vaccination [19] , so a comprehensive assessment of the targets of DIR immunity will prove to be an essential component of future schistosomiasis vaccine antigen discovery . We have shown here that proteome microarrays provide an ideal means by which to explore humoral immunity and vaccine antigen discovery for parasitic helminth infections . The approach is less labor intensive and more sensitive than traditional immunoproteomics based approaches that employ 2D Western blots followed by protein extraction from SDS gels [72] . Moreover , antigens can be readily up-selected for their protective properties and down-selected for potentially deleterious allergic properties , in a high-throughput fashion with large numbers of sera . The power of this technology lies with the nature of the assembled cohorts – whether they are well-characterized groups of naturally resistant and susceptible individuals , or animals that have been experimentally rendered resistant by vaccination ( e . g . irradiated schistosome cercariae [74] , [75] ) . With the recent sequencing of the S . haematobium genome [27] , and the enormous burden of disease that is attributed to urogenital schistosomiasis in Sub-Saharan Africa [76] , it is now essential to apply a systems vaccinology approach to the integrated control of all the major schistosomes infecting humans . Future efforts will explore the replication of conformational epitopes in prokaryotic ( as done herein ) versus eukaryotic ( eg . insect cell lysates ) RTS systems , and larger protein microarrays containing the entire parasite secretome will be produced to allow a more comprehensive screen and ensure that a pipeline of schistosomiasis vaccine antigens is generated for progression towards clinical trials .
All subjects provided written informed consent using forms approved by the Ethics Committee of Centro de Pesquisa René Rachou ( reference number Fev/04 ) , the Federal Institutional Review Board of Brazil or CONEP ( 25000 . 029 . 297/2004-58 ) and the George Washington University School of Medicine Institutional Review Board ( GWUMC IRB# 100310 ) . Individuals aged 18–60 ( inclusive ) from a S . mansoni endemic areas in Minas Gerais State , Brazil were followed longitudinally . Individuals were determined to be Putative Resistant ( PR ) if they had regular contact with infected water as determined by water contact studies and surveys for infected snails [26] , and no S . mansoni eggs in their feces after three days of examination of fecal samples by ether sedimentation and Kato Katz fecal thick smears ( 2 slides per fecal sample ) over a period of 10 years of investigation ( n = 20 ) . The PR group were matched with sera taken from individuals deemed to be chronically infected ( CI ) with S . mansoni as determined by the fecal exam methods described above and stratified in the following groups by the intensity of S . mansoni infection as expressed in eggs per gram of feces ( epg ) by Kato Katz fecal thick smear: CI-Light ( n = 30; epg <100 ) , CI-Moderate ( n = 20; epg = 101–400 ) and CI-Heavy ( n = 20; epg>401 ) . Individuals in each CI intensity strata were age , sex , and water contact matched with a PR individual . The PR and some of the CI sera were the same as those used in Tran et al . [26] . We included negative control groups from non-endemic areas of both Brazil ( Belo Horizonte , Minas Gerais; n = 12 ) and the U . S . ( n = 12 ) . Donor sera from the U . S . were taken at the George Washington University under an IRB approved protocol . Table S6 contains the demographic information on the different cohorts utilized in this study . A subset of potentially immunogenic open reading frames ( ORFs ) were selected for expression and printed from publically available coding sequences for S . mansoni ( n = 63 ) and S . japonicum ( n = 214 ) [38] . Most of these sequences were selected based on bioinformatic , proteomic and transcriptomic data using the following criteria: high sequence homology among the two schistosome species; expression in the immunologically vulnerable schistosomulum stage; predicted or known to be localized on or in the parasite tegument; and limited sequence similarity with mammalian homologs . Primer design and PCR amplification from S . mansoni and S . japonicum cDNA libraries were performed as described [38] . Amplicons were cloned into the custom made pXi T7 vector containing N-terminal 10-His and C-terminal HA tags by homologous recombination , as described previously [77] . Of the sequences selected , 88% ( n = 244 ) were successfully amplified and the resultant plasmids purified , and the inserts were verified by PCR and sequencing [38] ( Table S1 ) . A total of 217 high-yielding plasmids ( 45 from S . mansoni and 172 from S . japonicum ) with correct inserts were expressed in an in vitro cell-free system based on Escherichia coli ribosomes ( Roche RTS 100 ) , and the protein extracts were contact-printed without purification onto nitrocellulose glass ONCYTE slides . As controls , the following purified recombinant antigens expressed in yeast or E . coli were printed onto the array in two dilutions ( 0 . 1 and 0 . 3 mg/ml ) : Sm-TSP-2 ( Smp_181530 ) , Sm29 ( Smp_072190 ) , Sj-TSP-2/238 ( NCBI ABQ44513 ) and Sj silencer ( NCBI AAP06461 ) . Non-schistosome control proteins/RTS reactions were also spotted onto the microarray as described [38] , and included Epstein Barr virus protein EBNA-1 as well as purified human immunoglobulins . The printed in vitro expressed proteins were quality checked using antibodies against incorporated N-terminal poly-histidine ( His ) and C-terminal hemagluttinin ( HA ) tags . The efficiency for in vitro expression was higher than 95% , where positive features were considered to have detectable His or HA tags ( Figure S1 ) . Sera were pre-adsorbed for anti-E . coli antibodies by rocking for 30 min at RT with E . coli lysate before probing of arrays . Protein arrays were blocked in blocking solution ( Maine Manufacturing ) for 2 hours at RT prior to probing with human sera ( diluted 1∶50 ) at 4°C overnight with gentle constant rocking [71] . Arrays were washed 3 times for 5 min with TBS/0 . 05% Tween 20 ( TTBS ) then isotype and subclass specific responses were detected using biotinylated monoclonal antibodies against human IgG1 , IgG3 , IgG4 ( Sigma ) and IgE ( Hybridoma Reagent Laboratory , Baltimore , MD ) diluted 1∶100 for 2 h at RT . Arrays were washed again then incubated for 2 h in streptavidin Cy-5 diluted 1∶400 and washed with TTBS followed by TBS then MQ water , 3×5 min in each solution . Air-dried slides were scanned on a Genepix 4200AL scanner ( Molecular Devices ) and signal intensities ( SI ) quantified using the ScanArray Express Microarray Analysis System Version 3 . 0 ( Molecular Devices ) . Raw SI were corrected for spot-specific background using the Axon GenePix Pro 7 software . Data were analyzed using the “group average” method [78] whereby the mean SI was considered for analysis . Briefly , the SI for negative control spots ( empty vector ) was calculated for each serum and each antibody type . This value was considered as the background and was subtracted from the SI of each protein spot . To determine if an antigen was recognized a cut-off for each antibody type was defined . The cut offs were defined as one standard deviation above the average of the negative control spots for all groups of sera ( Negative BZL , Negative USA , PR , CI-Light , CI-Mod , CI-Heavy ) after correcting for spot-specific background . The cut offs were- IgE: 2828 . 8; IgG4: 2767 . 3; IgG3: 1557; and IgG1: 316 . 4 . All 217 RTS proteins as well as purified E . coli-derived Sm29 and yeast-derived Sm-TSP-2 were used to conduct a spatial analysis . When mean SI was below the cut-off for a given antibody isotype/subclass , the SI value was adjusted to zero for this analysis only . To identify clusters containing proteins with the same antibody reactivity profiles we generated a distance matrix estimated from the pairwise Euclidian distance of log transformed SI for each antigen based on the cut-off values for each antibody isotype/subclass in the different cohorts . Complete linkage clustering methodology was used to create a dendrogram analysis of pairwise Euclidian distances for each protein according to the equation below: Where D ( i , j ) represents the distance between the proteins i and j , E is the antibody isotypes ( IgG1 , IgG3 , IgG4 and IgE ) and S is the set of individual subjects; f iap is the fluorescence signal relative to antibody isotype present in the serum of subject p reactive against protein i; f jpa is the fluorescence signal for the same sample against protein j . To provide a visual representation of each distance matrix , we used a multidimensional scaling ( MDS ) plot with two dimensions ( 2D ) . The unsupervised methodology k-means algorithm with 1 , 000 interactions [40] was used to define seven clusters . Clusters were validated using clValid , a R software package for cluster validation [79] . The distance matrix , MDS , clustering and graphing were performed using the R software platform ( www . r-project . org ) [80] . Graphics representing specific relativities that characterized each cluster were designed using GraphPad Prism 5 . 0 . The raw SI values grouped into clusters are provided in Table S2 . Kruskal-Wallis with Dunn's multiple comparison test was used to compare more than two independent samples ( Figure 1 ) and to calculate the statistical differences between the groups if the protein was classified as reactive ( Figures 2–5 ) . Correlations were calculated using the Spearman Test . Statistical analyses were performed with GraphPad Prism 5 . 0 . | Schistosomiasis is a neglected tropical disease that kills as many as 300 , 000 people each year . Mass drug administration is widely used to control schistosomiasis , but fails to prevent rapid reinfection in endemic areas . There is a desperate need for a prophylactic vaccine; however , very few candidates have been developed . Herein , we take advantage of recent advances in systems biology and longitudinal studies in schistosomiasis endemic areas to pilot an immunomics approach to the discovery of vaccine antigens . The emerging field of immunomics enables the determination of an “antibody signature” to a pathogen proteome for both resistant and susceptible individuals . We constructed the first protein microarray for a multi-cellular pathogen and probed it with sera from naturally resistant vs . susceptible individuals from a high transmission area in Northeastern Brazil . Using multi-dimensional cluster analysis , we showed that resistant individuals mounted a distinct and robust IgG1 antibody signature to a small set of newly discovered and well-characterized surface antigens in contrast to infected individuals . This antigen discovery strategy can lead to identification of several potentially protective and safe schistosomiasis vaccine antigens . | [
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| 2014 | An Immunomics Approach to Schistosome Antigen Discovery: Antibody Signatures of Naturally Resistant and Chronically Infected Individuals from Endemic Areas |
Cytomegalovirus ( CMV ) causes a persistent , lifelong infection . CMV persists in a latent state and undergoes intermittent subclinical viral reactivation that is quelled by ongoing T cell responses . While T cells are critical to maintain control of infection , the immunological factors that promote CMV persistence remain unclear . Here , we investigated the role of regulatory T cells ( Treg ) in a mouse model of latent CMV infection using Foxp3-diphtheria toxin receptor ( Foxp3-DTR ) mice . Eight months after infection , MCMV had established latency in the spleen , salivary gland , lung , and pancreas , which was accompanied by an increased frequency of Treg . Administration of diphtheria toxin ( DT ) after establishment of latency efficiently depleted Treg and drove a significant increase in the numbers of functional MCMV-specific CD4+ and CD8+ T cells . Strikingly , Treg depletion decreased the number of animals with reactivatable latent MCMV in the spleen . Unexpectedly , in the same animals , ablation of Treg drove a significant increase in viral reactivation in the salivary gland that was accompanied with augmented local IL-10 production by Foxp3-CD4+T cells . Further , neutralization of IL-10 after Treg depletion significantly decreased viral load in the salivary gland . Combined , these data show that Treg have divergent control of MCMV infection depending upon the tissue . In the spleen , Treg antagonize CD8+ effector function and promote viral persistence while in the salivary gland Treg prevent IL-10 production and limit viral reactivation and replication . These data provide new insights into the organ-specific roles of Treg in controlling the reactivation of latent MCMV infection .
The immune system has evolved multiple innate and adaptive strategies to control pathogens[1] . Likewise , in order to ensure their persistence , pathogens have developed sophisticated and elaborate mechanisms to avoid the host immune system establishing latent infections that are never cleared from the host [2–7] . Human cytomegalovirus ( HCMV ) and its murine homolog ( MCMV ) are well-studied examples of pathogens that have developed multiple means to establish latency [8–10] . MCMV is a reasonable model for HCMV as it shares multiple biological characteristics and significant homology to the genome of HCMV[11] . A large number of HCMV and MCMV genes are involved in modulating innate and adaptive host immune responses [12–15] . During primary infection , these viruses vigorously replicate and disseminate by infecting many cell types , including epithelial , endothelial , smooth muscle , and connective tissue cells , as well as specialized parenchymal cells in multiple tissues [16] . Primary CMV infection is well controlled by a robust early NK cell response followed by CD4+ and CD8+ T cell responses that ultimately results in control of virus replication , although the virus is not eliminated and persists for the lifetime of the host [17–20] . Interestingly , prior work shows that the control of lytic virus in different tissues requires distinct immune cell populations [21–25] . In the spleen , epitope-specific CD8+ T cells are sufficient to control acute MCMV infection [26] . Whereas in the salivary gland ( SG ) , CD4+ T cells and , in particular their production of IFN-γ , are crucial for terminating lytic viral replication . Further , IL-10R blockade or the absence of CD4+ cell-derived IL-10 enhanced the accumulation of IFN-γ–producing CD4+ T cells and inhibited MCMV persistence in the SG [21 , 22 , 25 , 27–29] . This role of CD4+ T cells in the SG is critical , as there appears to be a reservoir of MCMV in non-hematopoietic cells within the SG that downregulate MHC class I and become resistant to CD8+ T cell killing [22] . However , recent work showed that CD8+ T cells can play a role in controlling MCMV in the SG after local re-infection [30] . The likely explanation for this differential role of CD8+ T cells during re-infection is because of the higher expression of class I MHC in acutely infected cells in contrast to cells harboring latent virus . Regulatory T cells ( Treg ) have also been shown to contribute to immune-mediated control of acute MCMV infection . In vitro , Treg were shown to suppress the function of MCMV-specific CD8+T cells via secretion of TGF-ß [31] . In vivo , during acute MCMV , Treg depletion resulted in enhanced MCMV-specific T cell responses and decreased viral load [32] . Nonetheless , following the termination of lytic infection , the virus persists in a latent state in which viral genomes are present in the absence of replicating virus . MCMV establishes latency in a number of tissues similar to HCMV , including the spleen , lungs , and bone marrow [33–36] . This latent form of HCMV drives a substantial amount of morbidity when it reactivates in immune suppressed individuals ( e . g . aged individuals , persons with HIV infection , transplant patients ) [37–39] . During latent infection , HCMV persists in CD34+ hematopoietic stem cells as well as more committed myeloid lineage progenitor cells , monocytes , and macrophages [40–44] . Both myeloid lineage cells [33 , 45] , as well as non-hematopoietic cells such as endothelial cells in many organs are considered cellular sites of latent infection for MCMV [33 , 46 , 47] . While a fair amount is known regarding immune mechanisms controlling acute MCMV infection , significantly less is known about immune mechanisms contributing to the control of latent MCMV infection . It has been reported that once latency is established , the cooperative function of lymphocytes including NK , CD4+ , CD8+ T cells and CMV-specific antibodies prevent the production of lytic virus from latent pools in the spleen and lungs and SG [48] . In most visceral organs , CD8+ T cells play a crucial role in preventing the emergence of lytic viral replication . For example , CD8+ T cells maintain latency by epitope-specific sensing of transcriptional reactivation in the lungs and killing these cells [49] . Similar to their role in controlling acute infection in the SG , CD4+ T cell production of IFN-γ is critical to prevent lytic virus production from latently infected cells [22 , 50 , 51] . During latency , there is substantial epigenetic suppression of viral immediate-early genes that must be overcome by cellular signals to exit from latency [42 , 44 , 52] . Two models have been proposed for MCMV reactivation during latency . First , a two-step model proposed by Hummel et al . [53] in which an inflammatory immune response can drive the activation of the major immediate-early ( MIE ) gene , initiating reactivation from latency . The second step requires an immune-suppressive environment , such as that which occurs during γ-irradiation or immune-deficiency . This immune-suppressive environment allows the virus to actively replicate , facilitating the production of lytic virus . In one example of this two-step scenario , the production of inflammatory mediators like tumor necrosis factor alpha ( TNF-α ) , interleukin-2 ( IL-2 ) , and gamma interferon ( IFN-γ ) following allogeneic transplantation of kidneys in mice initiated IE gene expression [54] . However , full lytic reactivation of MCMV was only found in immune-deficient recipients [55] . Similarly , in human transplant patients , HCMV reactivation correlated with increased inflammatory responses [56 , 57] . A second model focusing on latency and reactivation in the lung suggests that virus is reactivating frequently in immunocompetent hosts as shown by detectable levels of the immediate-early ie1/ie3 transcripts but that checkpoints exist which prevent full production of infectious virus following transcriptional activation [58–60] . Increased TNF-α levels were not enough to fully reactivate virus , although five-fold higher levels of IE transcripts were detected . Presumably memory T cells , specific for the IE proteins , lyse the reactivating cells before virus is produced [9 , 58–61] . Multiple immune cells such as CD4+ and CD8+ T cells and other factors such as antibody and IFN-γ have also been shown to be important for maintaining latency [48 , 62] . However , the immune cells and molecular factors that control viral latency and whether these cells and factors are the same between different tissues remains unclear . In recent work , recurrent virus reactivation was accompanied by a concomitant increase in Treg frequency and suppressive functionality [63] . Thus , while Treg have been associated with viral reactivation , a causative role in control of latent CMV/MCMV has not been established . Here , we investigated the role of Treg in controlling latent MCMV infection . Strikingly , we found that Treg had opposing effects in distinct tissues harboring latent virus . In the spleen , Treg promoted viral persistence and suppressed local MCMV-specific effector T cell responses , while in the SG Treg were required to prevent viral reactivation/replication and IL-10 production by Foxp3- CD4+ T cells . These data show that Treg play divergent and tissue-specific roles in controlling viral reactivation from latency depending upon the type of T cells ( effector or regulatory ) they inhibit .
Foxp3+ regulatory T cells ( Treg ) can suppress effector T cells and promote MCMV replication in the spleen and salivary gland ( SG ) during acute MCMV infection [32] . However , there are no studies examining the role of Treg during latent MCMV infection . To investigate the role of regulatory T cells ( Treg ) during latent MCMV infection , we infected cohorts of C57BL/6 and Foxp3-DTR mice with MCMV and waited for 8 months . Notably , in the C57BL/6 background , low level virus replication continues in the SG and spleen for months longer than is observed in mice on a BALB/c background . Thus , it took 7 to 8 months following infection for the establishment of latency , as measured by the inability to detect replicating virus in multiple tissues , including spleen , lung , liver , pancreas , and SG ( S1 Table ) . Next , we examined the levels and activation status of Treg in the spleen of latently infected mice . Interestingly , compared to uninfected mice of the same age , the frequency of Treg ( Fig 1A and 1B ) , and in particular “effector” Treg ( as assessed by CD69 expression ) were significantly increased in MCMV infected mice ( Fig 1C and 1D ) , although the total number of these cells was not different ( S1 Fig ) . Thus , latent MCMV infection favors the preferential accrual of activated Treg in the spleen . Next , we determined the role of Treg in latent MCMV infection using Foxp3-DTR mice , which allows the specific depletion of Treg using diphtheria toxin ( DT ) [64] . Before DT administration the levels of MCMV-specific T cells were not significantly different between WT and Foxp3-DTR mice ( S2A Fig ) . After DT administration , the frequency and total numbers of Treg were substantially reduced relative to DT-treated controls ( S2B Fig ) . As MCMV-specific T cells control viral replication , we determined the role of Treg in suppressing MCMV-specific T cell responses during latent infection . Treg depletion resulted in a significant increase in MCMV-specific CD8+ T and CD4+ T cells , relative to control animals ( Fig 2A ) . MCMV-specific CD8+ T cells in Treg depleted mice expressed markers of differentiation ( KLRG-1 ) and proliferation ( Ki67 ) ( S2C and S2D Fig ) . Functionally , the frequency of TNF-α or IFN-γ single producers and the numbers of single and double producers were significantly increased in Treg depleted mice ( Fig 2B ) . Similarly , CD107α expressing MCMV-specific CD8+ T cells were significantly increased after Treg depletion ( Fig 2C ) . Thus , Treg suppress effector T cell responses during latent MCMV infection . Given the differences in effector T cell responses following Treg depletion , it was important to evaluate if loss of Treg would modulate the latent MCMV viral load . Seven days after initial DT treatment there was no actively replicating virus in the spleen in either controls or Foxp3-DTR mice as assessed by plaque assay ( S2 Table ) . Using a spleen explant assay [65] , we examined the impact of Treg on the latent viral pool . The spleen explant assay provides a functional assay to assess reactivation from latency , and thus , indirectly provide information on viral load levels since a lower latent viral load is expected to be less efficient at reactivation [65] . As detected in the explant assay , for the WT control mice , 6 out of 9 mice had evidence of viral reactivation by 28 days post explant culture , whereas MCMV reactivated only in 3 out of 9 Foxp3-DTR mice ( Fig 3A ) . Further , titers of reactivating virus in WT mice were significantly higher in the first two weeks than those detected in the few Treg-depleted mice that had reactivated MCMV ( Fig 3B ) . To further evaluate whether reduced virus reactivation from the spleen also correlated with reduced levels of viral DNA following Treg depletion , we performed qPCR on total splenic DNA to quantify viral genomes . Although there was variation from one experiment to the next , we saw a consistent decrease in viral genome levels in spleens from Foxp3-DTR mice relative to C57BL/6 controls across four independent experiments ( S3 Fig , average percent decrease of 62 . 72% +/- 9 . 4; p<0 . 007 ) . Thus , Treg are critical to maintain the reactivatable latent pool of MCMV in the spleen . However , the increase in overall spleen cellularity ( and hence DNA content ) following Treg depletion could also contribute to a decreased detection of viral load . Given the role of Treg in promoting viral persistence in the spleen , we next determined the role of Treg in the control of latent MCMV infection in the SG . Similar to the spleen , latency in the SG was established by 8 months as evidenced by the lack of replicating virus prior to DT administration ( Fig 4A ) . However , in stark contrast to the reduction in viral load in the spleen upon Treg depletion at day 7 , Treg depleted mice had detectable replicating virus in the SG . While 3 out of 11 WT mice had a barely detectable viral reactivation , 9 out of 10 DT-treated Foxp3-DTR mice had a significant increase in viral reactivation and viral titers in the SG ( Fig 4A and 4B ) . Indeed , in several experiments , virus was not detected in the WT SG whereas actively replicating virus was consistently detected in the SG of the Foxp3-DTR mice ( Fig 4A and 4B ) . Additionally , reactivation occurred as early as day 4 after Treg depletion with evidence of increased , albeit low , viral titers in reactivating mice observed at that time ( S4A and S4B Fig ) . Thus , in stark contrast to the spleen , Treg are critical to prevent lytic viral reactivation in the SG . The SG provides a site for MCMV viral persistence and is a major site for accrual of tissue resident MCMV-specific memory CD8+ and CD4+ T cells [30 , 66] , although MCMV-specific CD8+ T cells are unable to drive viral clearance in this organ [22] . Given that Treg inhibited viral reactivation in the SG , we next investigated the SG T cell responses following Treg depletion in latently MCMV-infected mice . First , we established that DT administration efficiently depleted Treg in the SG ( S5A Fig ) . In contrast to the spleen , we did not observe any change in the MCMV-specific CD8+ T cells ( Fig 4C ) or tissue resident MCMV-specific CD8+ T cells ( CD103+CD69+ ) ( S5B Fig ) in Treg depleted mice . However , depletion of Treg resulted in an increase in the number of MCMV-specific CD4+ T cells in SG ( Fig 4D ) . Thus , Treg suppress CD4+ but not CD8+ T cells in the SG during latent infection . Prior work showed that IL-10 is critical for promoting viral replication in the SG [27 , 28] . Indeed , we found that depletion of Treg drove a significant increase in SG IL-10 mRNA levels ( Fig 5A ) . Given the increase in CD4+ T cell responses in the SG , we next examined the potential cellular sources of IL-10 . Importantly , CD4+Foxp3-cells were the predominant source for IL-10 production compared to CD8+ T cells and non–T cells ( Fig 5B ) . Combined , these data show that Treg suppress a population of CD4+ Foxp3- IL-10+ cells , consistent with a potential role of these cells in MCMV replication in the SG . As the effects of Treg depletion on viral reactivation/latency were quite different in the SG compared to the spleen , we examined IL-10 levels in the spleen as reduced IL-10 could potentially explain the different viral loads . Similar to the SG , IL-10-producing Foxp3- CD4+ T cells were increased in the spleen ( S6A Fig ) . However , unlike the SG , total IL-10 mRNA was not increased in the spleen , suggesting that the total amount of IL-10 in the spleen is not substantially increased ( S6B Fig ) . To further investigate the role of IL-10 in the SG after Treg depletion , we blocked IL-10R signaling by administration of a blocking IL-10R antibody with and without Treg depletion using Foxp3-DTR mice ( Fig 5C ) . As expected , Treg depletion was again accompanied by an increase in the number of mice harboring reactivating virus as measured by plaque assay ( Fig 5D ) . Strikingly , neutralization of IL-10 during Treg depletion significantly reduced the total number of mice with MCMV reactivation ( 9/13 to 3/13 ) ( Fig 5D ) . Thus , our data show that Treg limit IL-10 production in the SG and that IL-10 is essential for viral reactivation/replication in the SG after Treg depletion .
Investigating the role of regulatory T cells in latent MCMV has been hampered by the lack of appropriate tools . Our ability to assess the role of Treg in latent MCMV infection was greatly facilitated by two essential tools . First , Foxp3-DTR mice , which express diphtheria toxin receptor under the Foxp3 promoter allowed for depletion of Treg upon DT administration[64] . Second , the use of a sensitive tissue explant assay and qPCR allowed us to assess the reactivatable latent viral pool in mice with Treg depletion [65 , 67] . Herein , we found that depletion of Treg during latent MCMV infection had profound consequences . In the spleen , Treg restrained MCMV-specific CD4+ and CD8+ T cells and promoted the latent viral pool . In stark contrast , in the SG , Treg were essential to limit viral reactivation because they prevented the emergence of IL-10- secreting Foxp3- CD4+ T cells . Thus , we demonstrate unique tissue-specific functions of Treg in control of latent MCMV infection . During latent MCMV infection , we found a significant increase in activated Treg in the spleen . We speculate that the increase in Treg maybe a direct consequence of the chronic stimulation due to the periodic low-level viral reactivation during latency . In agreement , in other chronic infections like Leishmaniasis , hepatitis C virus ( HCV ) , hepatitis B virus ( HBV ) and human immunodeficiency virus ( HIV ) , Treg frequency was substantially increased in the spleen and other tissues [68–70] . Interestingly , human herpes virus 6 ( HHV-6 ) infection , another herpes virus closely related to HCMV , induces virus-specific CD4+ and CD8+ regulatory T cells [71] . Thus , chronic infections appear to promote their own persistence by driving Treg accrual . Strikingly , Treg depletion resulted in markedly reduced latent virus in the spleen as measured by two assays . Some mice had undetectable viral DNA levels in the spleen , suggesting that viral load had been reduced to levels that were below the limit of detection in our assay . We acknowledge that the increase in spleen size and cellularity could contribute to the observed reduction in latent viral load . However , in Treg-depleted mice where viral DNA load was undetectable , the spleen size and cellularity was similar to mice with detectable levels of viral DNA . Nonetheless , future experiments will investigate this in more detail , examining the number of cells harboring latent virus and the levels of virus within such cells . The spleen explant assay allowed us to assess one aspect of latent MCMV infection , the ability to reactivate from latency as a measure of whether reactivatable latent viral loads in the spleen were affected by Treg depletion . In our hands , if virus is replicating in the spleen at the time of isolation , this is detected by seven days post explants [65 , 72] . However , if virus is latent in the tissue at the time of isolation , it takes longer for the virus to be detected in the explant assay , usually by day 14 , and then rapidly spreads and replicates within the culture . Although this assay is not quantitative per se , we recently demonstrated that a mutant virus was unable to reactivate from the spleen by explant culture and had reduced levels of viral DNA [65] , ( Cardin , manuscript in preparation ) . Here , our results show that the Foxp3-DTR mice were less efficient at reactivation in this assay , suggesting lower levels of latent virus in the spleen . Importantly , we found that both the number of Foxp3-DTR mice with reactivating virus and also the levels of virus replication per mouse in the spleen explant cultures were reduced . Decreased reactivation from the spleen was highly reproducible between studies , and indeed , qPCR analysis of viral DNA in the spleen could indicate lower levels of latent virus in mice depleted of Treg , with the caveat of spleen size as mentioned earlier . Alternatively , we cannot rule out that the increased numbers of virus-specific T cells in the Treg-depleted spleens are functional after placement into the explant culture and thus elimination of latently-infected cells could also occur in vitro; however , these T cells likely have a very limited survival in vitro . It is important to note that we were unable to carry out long-term Treg depletion ( greater than 14 days ) , because of the rampant and lethal autoimmunity that ensues with long-term Treg depletion in these mice [64] . Further , the impact of Treg depletion on latent viral genomes likely only impacted those genomes that underwent a reactivation event and became detectable by the immune system due to viral antigen expression . Given that Treg depletion was accompanied by a substantial increase in cytotoxic MCMV-specific CD8+ T cells in the spleen , it is likely that Treg depletion led to the reactivation of some viral genomes , which was likely quickly quelled in the spleen by an MCMV-specific CD8+ T cell response , possibly even before lytic virus was produced [49] . Additional studies are needed to address this further . One important question raised by our study is the mechanism ( s ) by which Treg suppress CD8+ T cell responses to MCMV . Prior in vitro work suggested that Treg utilize TGF-β to suppress MCMV- specific T cell responses [31] , although CTLA-4 and IL-10 may also have a regulatory role [73 , 74] . In a chronic Friend virus ( FV ) infection model , FV-specific CD8+ T cells were also partially restrained by Treg , although the mechanism ( s ) appear to be independent of PD-1 and Tim-3 [75] . Interestingly , our previously published data show that Treg from aged mice have significantly increased levels of IL-35p19 , suggesting that IL-35 may contribute to suppression of MCMV-specific CD8+ T cell responses [76] . However , a recent paper showed that IL-35 produced by NK cells during acute MCMV infection promotes , rather than inhibits IL-10 production [77] . Nonetheless , further studies are required to determine the mechanism by which Treg restrain MCMV-specific CD8+ T cell effector functions and thus promote latent infections . It was very surprising that , while the depletion of Treg resulted in reduction in viral load in the spleen , it was accompanied by an augmented reactivation and/or replication of the latent viral pool in the SG . We envision three major potential mechanisms that may explain these results . First , it is possible that under chronic infection such as that observed in MCMV infected SG , Treg could lose immunosuppressive abilities and acquire the phenotype of a more pathogenic or anti-viral Treg . There is precedence for Treg metamorphosis in tumor models and under chronic inflammatory conditions [78–81] . This makes teleological sense as chronic inflammation may favor Treg with more effector function to replace exhausted effector T cells . However , we looked for Treg expression of pathogenic markers ( i . e . T-bet , RORγt ) on SG Treg and failed to find expression of these markers . Second , it is possible that the kinetics of latency establishment between the two tissues explains the differential effects on latent virus ( clearance & reactivation ) in the two tissues . For example , because the virus established latency in the spleen after three months but was latent in the SG after 7 months , perhaps clearance was easier to achieve in the spleen due to a lower level of latent viral load . However , we performed several experiments examining Treg depletion 2–5 months after primary infection and found that , similar to 8 months after infection , that loss of Treg drove significant decrease in MCMV replication in the spleen ( S7 Fig ) . Thus , it is likely that the tissue microenvironment rather than the timing controls the tissue-specific role of Treg . Third , it is possible that Treg maintain their suppressive capacity and instead of their “classical” role of inhibiting pro-inflammatory T cells , it is possible that they similarly control anti-inflammatory T cells . If so , the result of suppressing anti-inflammatory cells would be the promotion of an immune-suppressive environment which is permissive for viral reactivation/replication . Our data are most consistent with this latter possibility . In this regard , CD4+ T cell production of IL-10 was increased in both the spleen and SG after Treg depletion . One obvious question is why didn’t this increase in IL-10 drive viral reactivation in the spleen ? One likely explanation is that , in the spleen , where CD8+ T cells are critical to prevent lytic virus production , their levels of IL-10R are significantly decreased , making them insensitive to IL-10 [82] . Our data also show that Treg depletion drives viral reactivation in the SG and implicates IL-10 in the process . However , current data in the literature suggest that IL-10 contributes more to viral replication than outright reactivation [83] . Indeed , prior work has shown that the specific production of IL-10 from Foxp3- CD4+ cells attenuates acute antiviral immune-responses and leads to persistent viral replication in the SG [27] . Thus , while our data clearly show that inhibition of IL-10R signaling restored viral control , more work is required to conclusively determine whether IL-10 promotes outright reactivation or promotes viral replication . However , in our study , we initiated IL-10R antibody treatment following the initiation of Treg depletion , thus , if virus had started to reactivate , it could have been effectively controlled . The effect of IL-10 on viral control could be direct or indirect . IL-10 can directly affect CD4+ T cell production of effector cytokines like IFN-γ [84] . Indeed , IFN-γ is indispensable for the control of viral load in the SG [25 , 85] . However , there was no diminution in IFN-γ production upon Treg depletion , instead IFN-γ production was actually increased ( S8 Fig ) . Alternatively , IL-10 can indirectly compromise CD4+ T cell responses in the SG by interfering with the responsiveness of APC to IFN-γ . For example , it is well known that IL-10 inhibits the effect of IFN-γ by interfering with IFN-γinduced genes , preventing the phosphorylation of STAT-1 molecules and activation of monocytes [86] . Thus , while our data clearly show a role for IL-10 , more work is required to determine the cellular targets of IL-10 that regulate reactivation/replication . Notably , the cellular site ( s ) of MCMV latency in host tissues is a long debated issue . Like HCMV , MCMV can establish a latent infection in cells of the myeloid lineage and these cells are able to respond to IL-10 . However , other , non-hematopoietic targets , like endothelial cells cannot be excluded . This divergent and tissue-specific role in controlling MCMV viral latency by Treg raises the question as to whether Treg manipulation is a useful therapy in latent HCMV infection . In HCMV , manipulating Treg is viewed as a promising therapeutic approach to control latent viral reactivation in immune-compromised hosts like organ transplant patients . In a prior study , the use of immune-suppressive drugs like daclizumab ( anti-CD25 ) , steroids and calcineurin inhibitors in CMV sero-positive renal transplant patients , led to reduction of Treg levels that correlated with enhanced levels of CMV-specific effector T cells , suggesting that modulation of Treg favors maintenance of CMV-specific immunity [87] . However , Treg depletion may also promote viral reactivation in sites such as SG that are not assessed in treated patients . Thus , manipulating Treg could be a double-edged sword . Treg in CMV infection might exert different functional activity depending on their localization within the infected host and the T-cell responses that they regulate . Our data , suggesting that Treg could regulate another suppressor CD4+Foxp3-IL-10+ cells in the SG and limit viral reactivation opposed to their conventional role in regulating effector T cells as observed in the spleen , is a novel concept . It is unclear whether the mechanisms employed by Treg to inhibit effector T cell responses vs . other regulatory cell populations are distinct . Understanding such mechanisms might be crucial however to enhance the suppressive effects of Treg on other immune suppressive cell populations in some instances ( chronic infections ) or block the suppressive effect of Treg on others ( i . e . autoimmunity ) .
NIH 3T3 cells ( ATCC CRL1658 ) were grown in Dulbecco’s modified Eagle’s medium ( DMEM , Media tech , Herndon , VA ) supplemented with 10% fetal bovine serum ( FBS , Hyclone , Logan , UT ) , 7 . 5% Sodium Bicarbonate , 4 mM HEPES , 2 mM L-glutamine , and gentamicin in a humidified 5% CO2 incubator at 37°C . Parent stocks of the wild type MCMV K181 ( originally a kind gift from Dr . Ed Mocarski , Stanford University ) were prepared in NIH 3T3 cells from a SG-derived virus stock as previously described [65] . Virus titers were determined by plaque assay on NIH 3T3 cells . All virus stocks were stored at -70°C and re-titered before use in experiments . Young C57BL/6 mice were purchased from Taconic Farms ( Germantown , NY ) . Foxp3-IRES-DTR-GFP knock-in C57BL/6 mice were a generous gift from Dr . A . Rudensky . ( Rockefeller University , NY ) . For depletion of Foxp3 Treg cells , 1μg DT was injected at day 0 . Followed by 0 . 25μg DT on day 3 , 6 . Mice were sacrificed on day 7 . For Treg depletion and IL-10R neutralization , mice were injected with 1μg DT at day 0 , followed by 0 . 25μg DT on day 3 , 6 and 8 . In addition , mice received antiIL-10R blocking antibody ( Clone: 1B1 . 3A BioXcell ) or ratIgG1 isotype control ( Clone: HRPN BioXcell ) 500μg on day 2 , 5 , 8 and 250μg at day 8 . Mice were sacrificed on day 10 . All mice were injected by intraperitoneal route ( i . p . ) . For MCMV infection , five to six week-old mice were infected with MCMV K181 strain , via i . p . inoculation with 1 × 106 PFU . Mice were sacrificed upon establishment of latent MCMV at various times ( > 6–7 months post infection in C57Bl/6 mice ) . Mice were maintained under specific-pathogen-free conditions at Cincinnati Children's Hospital Medical Center . All animal protocols were reviewed and approved by the Institutional Animal Care and Use Committee at the Cincinnati Children’s Hospital Research Foundation ( CCHRF ) under IACUC2016-0087 ( 1D03023 ) . The care and use of laboratory animals at CCHMC is in accordance with the principles and standards set forth in their Principles for Use of Animals ( NIH Guide for grants and Contracts ) , the Guide for the care and Use of Laboratory Animals ( Department of Health , Education , and Welfare DHEW , Public Health Service PHS , National Institutes of Health NIH Publ . 8th edition , Rev . 2011 ) . The provisions of the Animal Welfare Acts ( P . L . 89–544 and its amendments ) , and other applicable laws and regulations . For plaque assays , dilutions of virus stocks , 10% ( w/v ) mouse tissue sonicates , and sonicated leukocyte cell suspensions were adsorbed onto 70% confluent NIH 3T3 monolayers for one hour at 37°C , and then overlaid with 1:1 carboxymethyl cellulose ( CMC ) : 2X modified Eagle’s medium as previously described[85] . At 6 days , the overlay was removed and the cells were fixed with methanol and stained with Giemsa to determine the number of plaques . For measurement of tissue virus replication following mouse infection , tissues were placed in pre-weighed tubes or were homogenized in media to prepare cell suspensions , followed by sonication on ice to disrupt cells and release free virus . Tissue homogenates were titered by plaque assay similar to virus stocks as described above . Explant reactivation assays of tissues from latently infected mice were established as previously described [45 , 65] . After infection when virus was no longer replicating and establishment of latency ( > 6–7 months post infection in C57Bl/6 mice ) and following DT treatment , the SGs and spleens were collected . In some experiments , other tissues such as lungs and liver were also collected for plaque assays . The SGs were sonicated and titered to detect persistent replicating virus as the SG is a major site of viral persistence . In some experiments , to analyze populations of infiltrating leukocytes in the SGs , the SGs were first homogenized to prepare a cell suspension , an aliquot was removed for sonication and plaque assay analysis , and the remaining sample was treated as described below under ‘cell isolation’ . For the explant reactivation assays , the spleens were minced and primary spleen cultures were established as previously described [45] . In some studies , SGs and lungs were also analyzed by explant reactivation assay . Briefly , the spleen explant assay was established by dividing the spleens into three parts , with each part placed into a well of a 6-well tissue culture plate containing 5 ml of media . The cultures were followed for up to 6 weeks and culture media from each well were collected weekly , sonicated and titered in plaque assays to detect the presence of reactivating or replicating virus . Plaques detected in any wells were counted as a reactivation event . Tissues were isolated from infected and uninfected mice using dissection tools pre-treated with DNA Away ( Molecular BioProducts ) . DNA was isolated from cell suspensions following tissue homogenization using QIAamp DNA Mini Kit ( Qiagen #51306 ) according to manufacturer’s protocol . Quantitative PCR was performed as previously described[88] using the following primers: E1 forward primer 5’-TCGCCCATCGTTTCGAGA-3’; E1 reverse primer 5’-TCTCGTAGGTCCACTGACGGA-3’ to yield an amplified 106bp product . The TAMRA Taqman E1 probe was purchased from Applied Biosystems . Spleens were harvested and crushed through 100-μm filters ( BD Falcon ) to generate single-cell suspensions . For the SG , tissue was digested with collagenase ( 4 ) or with liberase in media ( RPMI 1640 containing 0 . 5 mg/ml digestion enzyme ( Sigma ) , 5mM CaCl2 , 0 . 2 mg/ml of DNase I ( Sigma ) and 5% FBS ) incubated for 45minutes at 37°C , followed by a two-step discontinuous Percoll gradient ( Little Chalfont , UK ) . Gradient samples were centrifuged at 25000rpm , room temperature , for 25 min with the brake off . The lymphocytes were harvested at the interface between 30% and 70% Percoll layers . A total of 1–2 million single-cell suspensions from either spleen or SG surface stained with a combination of the following tetramers and antibodies: M45 –HGIRNASFI , m139 –TVYGFCLL , M38 –SSPPMFRV , IE3 –RALEYKNL andM25 –NHLYETPISATAMVI . Tetramers were all synthesized by the NIH tetramer core facility . Cells were then incubated with Fc block and surface stained with Surface Abs: αCD4 , αCD8α , αTCRβ ( BD Biosciences , San Diego , CA , USA ) , αCD44 , αKLRG1 , αCD127 , αCD69 , αCD103 , αCX3CR1 , αCD16/32 and αLy6C ( eBioscience , San Diego , CA , USA ) . For CD8 T cell intracellular staining , cells were stained in media and fixed with 2%Methanol free formaldehyde ( MF FA ) for 1 hour and then intracellularly stained with αKi67 ( eBioscience ) using eBio permeabilization buffer . For CD8 T cell peptide stimulation using M38 , M139 at 2μg/ml ( a gift from Dr . Edith Janssen , Cincinnati Children's Hospital Medical Center , Cincinnati , OH ) cells were stimulated for 5 h in the presence of anti-CD107a antibody and in the presence of brefeldinA for the final 4 h . Cells were then fixed with 2%MF FA for 1 hour and then in 0 . 05% saponin . Cells were stained for IFN-γ and TNF-α production ( all from eBioscience ) . For CD4 T cells , cells were stimulated with 50ng/ml PMA and 1μg/ml ionomycin for 5 hours , in the presence of brefeldinA for the final 4 h and fixed with 2%MF FA for 1 hour followed by intracellular staining for IL-10 , IFN-γ , TNF-α ( all from eBioscience ) and Foxp3 ( eBioscience ) staining was done according to eBioscience Foxp3 staining kit and protocol . Data were acquired on an LSRII flow cytometer ( BD Biosciences ) and analyzed using FACSDiva software ( BD Biosciences ) . Samples were homogenized and total cellular RNA was extracted and quantified . DNase-treated RNA was then used to synthesize cDNA . The primer sequences used for detection of IL-10 were: 5′- GCTCTTACTGACTGGCATGAG -3′ and 5′- CGCAGCTCTAGGAGCATGTG -3′ . The primer sequences used for detection of β-actin as an internal control were 5′-GGCCCAGAGCAAGAGAGGTA-3′ and 5′-GGTTGGCCTTAGGTTTCAGG-3′ . Quantitative real-time PCR was performed with Roche LightCycler 480 SYBRGreen 1 Master Mix using the Roche LightCycler 480 II instrument ( Roche Diagnostics ) . Data were analyzed using GraphPad Prism software and Excel software . Statistical analysis was performed using either a Student’s t-test or a Fisher’s exact test in different experiments . A p-value of <0 . 05 was considered significant . | Cytomegalovirus ( CMV ) infection in both mice and humans is normally initially contained by a vigorous adaptive immune response that drives the virus into latency in multiple tissues . However , the immunologic mechanisms that control latency are not well understood . In this report , we have examined the role of regulatory T cells ( Treg ) in a mouse model of CMV infection . Interestingly , depletion of regulatory T cells had profound consequences on MCMV latent infection , depending upon the tissue . In the spleen , Treg depletion enhanced CD8+ T cell responses and reduced reactivatable latent infection from the spleen . In striking contrast , in the salivary gland , Treg depletion enhanced the production of IL-10 from CD4+ T cells as well as viral reactivation . Thus , Treg play divergent and tissue specific roles in controlling MCMV reactivation from latency . | [
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| 2017 | Tissue-specific control of latent CMV reactivation by regulatory T cells |
Pseudomonas aeruginosa infection can be disastrous in chronic lung diseases such as cystic fibrosis and chronic obstructive pulmonary disease . Its toxic effects are largely mediated by secreted virulence factors including pyocyanin , elastase and alkaline protease ( AprA ) . Efficient functioning of the endoplasmic reticulum ( ER ) is crucial for cell survival and appropriate immune responses , while an excess of unfolded proteins within the ER leads to “ER stress” and activation of the “unfolded protein response” ( UPR ) . Bacterial infection and Toll-like receptor activation trigger the UPR most likely due to the increased demand for protein folding of inflammatory mediators . In this study , we show that cell-free conditioned medium of the PAO1 strain of P . aeruginosa , containing secreted virulence factors , induces ER stress in primary bronchial epithelial cells as evidenced by splicing of XBP1 mRNA and induction of CHOP , GRP78 and GADD34 expression . Most aspects of the ER stress response were dependent on TAK1 and p38 MAPK , except for the induction of GADD34 mRNA . Using various mutant strains and purified virulence factors , we identified pyocyanin and AprA as inducers of ER stress . However , the induction of GADD34 was mediated by an ER stress-independent integrated stress response ( ISR ) which was at least partly dependent on the iron-sensing eIF2α kinase HRI . Our data strongly suggest that this increased GADD34 expression served to protect against Pseudomonas-induced , iron-sensitive cell cytotoxicity . In summary , virulence factors from P . aeruginosa induce ER stress in airway epithelial cells and also trigger the ISR to improve cell survival of the host .
The Gram-negative bacterium Pseudomonas aeruginosa is an opportunistic pathogen that increases morbidity and mortality in chronic lung diseases , such as cystic fibrosis ( CF ) and chronic obstructive pulmonary disease ( COPD; GOLD stages III-IV ) ) [1–3] . P . aeruginosa often causes chronic infection due to its ease of developing antibiotic resistance and its ability to form biofilms in these patients . Furthermore , its survival in the host in the early stages of infection is supported by the secretion of toxins and virulence factors , including pyocyanin and its proteases elastase and alkaline protease ( AprA ) ( reviewed in [4 , 5] ) . Interestingly , their production appears to be lower in the later stages of infection [6 , 7] . Therefore , the specific role of these virulence factors in chronic infections is incompletely understood . Pyocyanin is a redox-active toxin that causes cellular senescence [8] , ciliary dyskinesia [9] , increased expression of IL-8 [10] and disruption of calcium homeostasis [11] in human lung epithelial cells . Pyocyanin inactivates α1-antitrypsin , thereby contributing to the protease-antiprotease imbalance found in CF lungs [12] , while P . aeruginosa elastase additionally cleaves many proteins of the extra-cellular matrix , including collagen , fibrinogen and elastin , and opsonin receptors , thus contributing to the invasion of bacteria into the lung parenchyma [13] . AprA is thought to modulate the host response and prevent bacterial clearance by degrading proteins of the host immune system , including TNFα and complement factors [14–16] . P . aeruginosa requires iron both for its respiration and for biofilm formation [17 , 18] . Competition with the host is fierce and so P . aeruginosa has evolved specific strategies to obtain iron [19] . It produces redox-active phenazine compounds to turn insoluble Fe3+ to the more soluble Fe2+ , siderophores to scavenge iron and receptors for the uptake of iron-siderophore complexes , proteases to degrade host iron-binding proteins , and bacteriocins to eliminate competitors ( reviewed in [19] ) . Moreover , iron availability regulates the production of virulence factors such as pyocyanin , AprA and exotoxin A [20] . The endoplasmic reticulum ( ER ) functions to fold secretory and membrane proteins and its quality control systems ensure that only properly folded proteins exit the organelle . Accumulation of incompletely folded proteins can impair ER homeostasis and induces “ER stress” , which activates intracellular signal transduction pathways collectively called the “unfolded protein response” ( UPR; Fig 1 ) . This response restores ER homeostasis by reducing the influx of new proteins into the lumen of the ER and by enhancing the organelle’s capacity to fold proteins; however , if the stress cannot be resolved then apoptotic cell death pathways are invoked ( reviewed in [21] ) . Three distinct sensors detect ER stress: protein kinase RNA ( PKR ) -like ER kinase ( PERK ) , inositol-requiring enzyme 1 ( IRE1 ) and activating transcription factor 6 ( ATF6 ) [21] . Early during ER stress , the kinase PERK phosphorylates eukaryotic translation initiation factor 2 on its alpha subunit ( eIF2α ) causing the inhibition of protein synthesis and thus preventing the load on the ER from increasing further [22–24] . In addition , this promotes the translation of specific mRNAs , for example that encoding the transcription factor ATF4 [25] . One important target of ATF4 is the transcription factor called C/EBP homologous protein ( CHOP ) , and both individually can trans-activate the GADD34 gene [26] . GADD34 is a phosphatase that selectively dephosphorylates eIF2α , completing a negative feedback loop and enabling the translation of other targets of the UPR [27] . In parallel , IRE1 initiates the unconventional splicing of the mRNA encoding X-box binding protein-1 ( XBP-1 ) [28] . Spliced XBP-1 mRNA encodes an active transcription factor that , in concert with ATF6 , induces expression of UPR genes , such as the chaperones GRP78 ( also known as BiP ) and GRP94 [28–30] . The phosphorylation of eIF2α is a point at which the responses to several forms of stress are integrated [31] . During ER stress , PERK phosphorylates eIF2α , but eIF2a can also be phosphorylated by PKR responding to double-stranded RNA during viral infection [32 , 33] , by GCN2 during amino acid starvation [25 , 34 , 35] , and by HRI during iron deficiency ( reviewed in [31] ) . For this reason , the events initiated by eIF2α phosphorylation have been termed the “integrated stress response” ( ISR; Fig 1 and [36] ) . Abnormal function of the ER has been implicated in the pathogenesis of many diseases , including diabetes mellitus , atherosclerosis , Alzheimer’s disease and cancer [21 , 37] . Remarkably , the ER also plays an important role during immune responses to infection and malignancy . For example , during bacterial infection , Toll-like receptor ( TLR ) activation triggers splicing of XBP1 mRNA , possibly in response to the increased biosynthesis of secreted inflammatory mediators , increasing the capacity for protein secretion and thus contributing to an augmented inflammatory response [38–40] . In addition , induction of GADD34 is required for cytokine expression during viral infection; however , in contrast to ER stress , pathogen-induced induction of GADD34 appears to be independent of CHOP [41 , 42] . Nevertheless , sustained activation of the UPR can impair the immune response by triggering cell death [26 , 43] . Previously , it has been shown that infection of airway epithelia or Caenorhabditis elegans with P . aeruginosa can elicit an UPR [39 , 44 , 45] . In worms , activation of the IRE1-XBP-1 branch of the UPR was dependent on p38 MAPK-signalling [39] , but it is unknown if this signalling response is conserved in humans . Moreover , it is unclear whether living bacteria are required for the induction of ER stress or if unidentified secreted factors are sufficient . In the present study , we set out to test the hypothesis that virulence factors secreted by P . aeruginosa trigger the UPR in human cells via the p38 MAPK pathway . We found that p38 MAPK signalling was required for the response of human epithelial cultures to bacterial conditioned medium and that the secreted factors pyocyanin and AprA contribute to the induction of ER stress . Furthermore , we showed that induction of the ISR target GADD34 is mediated by the iron-regulated kinase HRI and this induction protects the host against the toxic effects of P . aeruginosa .
Infection with live P . aeruginosa has previously been shown to induce the UPR in mouse macrophages and human immortalized bronchial epithelial cells [40 , 45] . To identify whether P . aeruginosa could induce the UPR in primary bronchial epithelial cells ( PBEC ) and whether living bacteria were necessary for this , we stimulated PBEC with filter-sterilised conditioned medium ( CM ) from P . aeruginosa strain PAO1 ( CM-PAO1 ) , containing secreted virulence factors without living bacteria . Treatment with CM-PAO1 induced ER stress in a time- and dose-dependent manner , as evidenced by a 9 . 9-fold increase of splicing of XBP1 mRNA ( p<0 . 01 ) , a 12 . 8-fold increase of CHOP mRNA ( p = 0 . 02 ) and a 16 . 2-fold increase of GADD34 mRNA ( p<0 . 05 ) after 8–12 hours ( Fig 2A and 2B ) . This was accompanied by an increase in phosphorylation of eIF2α and protein expression of GADD34 and GRP78 ( Fig 2C ) . This increase in phosphorylated eIF2α was accompanied by a decrease in global protein translation as assessed by puromycin incorporation in nascent proteins ( Fig 2D ) [46] . In line with previous reports [47–49] , CM-PAO1 gradually impaired epithelial integrity until the monolayer was completely disrupted after 24 hours . Although the epithelial layer was disrupted by CM-PAO1 ( as reported by trans-epithelial resistance; S1A Fig and visualised by light microscopy; S1B Fig ) , the cell membranes themselves remained intact as reported by exclusion of trypan blue stain ( S1B Fig ) . Infection of C . elegans with P . aeruginosa has been reported to cause splicing of XBP1 mRNA in a p38 MAPK-dependent manner [39] . To exclude the effects of donor variation and complex nutrient/growth factor requirement of primary cells , we tested whether exposure of 16HBE cells , a SV-40 transformed bronchial epithelial cell line , to P . aeruginosa conditioned medium would trigger phosphorylation of p38 MAPK and activate the UPR . We observed that CM-PAO1 caused prolonged phosphorylation of p38 MAPK in 16HBE cells up to 6 hours ( Fig 3A ) . We reasoned that the activation of p38 MAPK after 15 minutes might represent the activation of TLR signalling , since stimulation of HEK-TLR2 or HEK-TLR4 cells [50] with CM-PAO1 demonstrated robust TLR2 and TLR4 activation . The sustained activation was similar to that observed in C . elegans infected with Pseudomonas [39] , which suggests the importance of p38 MAPK in the induction of the UPR . To examine if p38 MAPK signalling was required for the ER stress response , we pre-treated 16HBE cells with an inhibitor of p38 MAPK ( SB203580 ) or an inhibitor of TAK1 ( 5Z-7-oxozeanol , better known as LL-Z1640-2 ) , a kinase upstream of p38 MAPK . We then exposed cells to CM-PAO1 and observed that both compounds markedly reduced activation of p38 by CM-PAO1 ( Fig 3B ) . In addition , both compounds reduced secretion of IL-8 in response to CM-PAO1 treatment ( Fig 3C ) . Of note , these compounds strongly inhibited splicing of XBP1 mRNA and abrogated the induction of CHOP and GRP78 mRNA ( Fig 3D ) . However , the induction of GADD34 was insensitive to the inhibitors ( Fig 3D ) suggesting the involvement of an additional pathway independent of CHOP . To prepare P . aeruginosa conditioned medium , cultures were grown for 5 days ( see Experimental procedures and [47] ) to a high optical density , at which quorum-sensing is activated in this strain , thus triggering the production of a variety of virulence factors among which the cytotoxic exoproduct pyocyanin . When pyocyanin levels in CM-PAO1 were measured , values up to 5 . 5 μg/ml ( 26 μM ) were detected ( Fig 4A ) , which were similar to values observed in sputum of CF patients colonised with P . aeruginosa [51] . We first wished to determine if pyocyanin was an important mediator of the observed ER stress response by CM-PAO1 . To this end , P . aeruginosa bacterial cultures were supplemented with iron to suppress pyocyanin production together with other iron-regulated factors ( Fig 4A ) . The conditioned medium prepared in this manner was significantly less efficient at triggering the splicing of XBP1 mRNA and at increasing expression of GRP78 mRNA ( Fig 4B ) . Surprisingly , CHOP mRNA was not significantly affected ( Fig 4B ) , whereas GADD34 mRNA induction was completely abrogated . These experiments provided only indirect support for the involvement of pyocyanin , since iron supplementation also affects production of other P . aeruginosa virulence factors and may also affect host cells . We therefore tested whether purified pyocyanin could induce ER stress in 16HBE cells . Treatment with purified pyocyanin caused dose-dependent splicing of XBP1 mRNA , induction of CHOP and GRP78 mRNAs and expression of GRP78 and GRP94 protein ( Fig 4C and 4D ) , maximal at 10 μM ( 2 . 1 μg/ml ) . In contrast , GADD34 mRNA continued to rise up to a maximum at ≥ 30 μM ( 6 . 3 μg/ml ) of pyocyanin ( Fig 4D ) . Once again , this suggested that induction of GADD34 in this system might not simply reflect activation by ER stress . As expected , pyocyanin potently induced secretion of IL-8 by 16HBE cells ( Fig 4E ) [10] . Since pyocyanin is a redox active toxin , we tested the effect of co-administration of the anti-oxidants N-acetylcysteine ( 10 mM ) and glutathione reduced ethyl-ester ( 10 mM ) for 24 hours . Both failed to ameliorate the ER stress response suggesting that pyocyanin caused ER dysfunction independent of causing oxidative stress [52 , 53] ( see online repository ) . Taken together , these observations suggested that conditioned medium of P . aeruginosa caused ER stress via multiple virulence factors , including pyocyanin . Furthermore , the induction of GADD34 appeared to involve an additional pathway independent of CHOP . Having found evidence for the involvement of multiple virulence mechanisms in the induction of ER stress , we next attempted to determine their identities . The P . aeruginosa AB toxin exotoxin A is known to cause translational attenuation by catalysing the ADP-ribosylation of elongation factor 2 ( EF2 ) [54] . We investigated whether purified exotoxin A could also induce ER stress , but detected no increase in spliced XBP1 , CHOP , GADD34 or GRP78 mRNA ( S2A Fig ) nor the phosphorylation of eIF2α ( S2B Fig ) . Next , to more broadly explore the involvement of other potential virulence factors , we made use of strains of P . aeruginosa that lacked specific toxic products: PAN8 , a lasB aprE double mutant , which is deficient in the production of elastase [55] and the secretion of AprA; PAN11 , an xcpR lasB mutant , which is deficient in the production of elastase and the secretion of all other substrates of the type II protein secretion system but still produces AprA; and PAO25 , a leu arg double mutant derivative of PAO1 and the direct parental strain of both mutants ( Table 1 ) . CM-PAO25 did not differ from CM-PAO1 in the content of all toxins measured ( S3A and S3B Fig ) and in inducing spliced XBP1 , CHOP , GADD34 and GRP78 mRNA ( S3C Fig ) . In spite of the aprE mutation , still traces of AprA were detected in the culture supernatant of the PAN8 strain ( Fig 5A ) , presumably due to cell lysis during the 5-days growth period . When 16HBE cells were incubated with CM-PAN8 ( lacking elastase and AprA ) , XBP1 mRNA splicing and induction of GRP78 mRNA were completely abolished , and only low induction of CHOP mRNA remained ( Fig 5B ) . In contrast , the response of 16HBE cells to CM-PAN11 ( containing AprA , but no elastase or other substrates of the type 2 secretion system ) was much less affected relative to CM-PAO1 treatment ( Fig 5B ) , indicating that the reduced response to CM-PAN8 is primarily due to the absence of AprA in this CM rather than to the absence of elastase . Indeed , stimulating 16HBE cells with purified elastase did not elicit an ER stress response within 24 hours ( see online repository ) . On the other hand , incubation with 10 nM purified AprA induced the splicing of XBP1 mRNA , and up-regulated CHOP and GRP78 mRNA ( Fig 5C ) . These experiments suggested that , in addition to pyocyanin , AprA also contributed to the induction of ER stress in 16HBE cells . We therefore next generated conditioned medium of a series of specific AprA and pyocyanin mutant strains to demonstrate the relative contribution of AprA and pyocyanin to the induction of ER stress . However , these experiments were inconclusive because the corresponding wild type strains did not induce sufficient ER stress ( see online repository ) . Remarkably , once again the induction of GADD34 mRNA followed a distinct trend from the other markers of ER stress . Particularly a lack of AprA ( in CM-PAN8 ) was correlated with an increased expression of GADD34 ( Fig 5B ) , whilst purified AprA did not induce GADD34 mRNA ( Fig 5C ) . This suggested that an unrelated mechanism regulated GADD34 induction by CM-PAO1 and that this might be independent of ER stress . To examine the involvement of ER stress-dependent and-independent responses to CM-PAO1 , we next made use of the specific inhibitor of IRE1 , 4μ8C , which blocks splicing of XBP1 mRNA during ER stress ( [56] and Fig 6 ) . Of note , this compound not only attenuated the splicing of XBP1 mRNA elicited by CM-PAO1 , but interestingly , it also attenuated the secretion of IL-8 by 16HBE in response to CM-PAO1 ( S4A Fig ) . During ER stress , the kinase PERK phosphorylates eIF2α , thereby activating the ISR . When Perk-/- mouse embryonic fibroblasts ( MEFs ) were exposed to CM-PAO1 , the induction of Gadd34 mRNA was unaffected , while the response to the ER stress-inducing agent tunicamycin ( Tm ) was abrogated ( Fig 7A ) . However , phosphorylation of eIF2α was required for the induction of Gadd34 mRNA in response to CM-PAO1 as demonstrated by the failure of the conditioned medium to induce Gadd34 mRNA in fibroblasts homozygous for the eIF2αAA mutation , which renders them insensitive to all eIF2α kinases ( Fig 7B ) . Moreover , ATF4 , a transcription factor translationally up-regulated upon phosphorylation of eIF2α , was essential for the induction Gadd34 mRNA by CM-PAO1 ( Fig 7C ) . As we have shown previously [26] , CHOP was only partially required for tunicamycin ( ER stress ) -induced expression of Gadd34 mRNA ( S4B Fig ) . The same was observed for CM-PAO1 , although it did not reach statistical significance ( S4B Fig ) . Interestingly , murine fibroblasts stimulated with CM-PAO1 failed to splice Xbp1 mRNA ( S4C Fig ) , suggesting that activation of IRE1 by CM-PAO1 may be less important in this cell type than in human epithelial cells . However , reassuringly , ISR-dependent signalling in response to pseudomonal toxins was preserved in these cells and , once again , expression of Chop mRNA was regulated via eIF2α and ATF4 . As had been observed for Gadd34 , Chop induction was independent of PERK , suggesting that in MEFs treated with CM-PAO1 , Chop was induced by a stimulus other than ER stress ( S4D–S4F Fig ) . We next examined which eIF2α kinase was responsible for activation of the ISR by CM-PAO1 . To this end , we made use of Pkr-/- , Gcn2-/- and Hri-/- MEFs [25 , 57 , 58] and observed a significant deficit of CM-PAO1 induction of Chop and Gadd34 mRNA in Hri-/- cells , suggesting the involvement of the iron-sensing kinase HRI ( Fig 7D–7F and S4G–S4I Fig ) . In contrast , although it has been suggested previously that GCN2 is involved in the stress response induced by P . aeruginosa in gut epithelial cells [59] , we observed no significant effect on the induction of Gadd34 mRNA in Gcn2-/- cells ( Fig 7E ) . We therefore went on to deplete either GCN2 or HRI in HeLa cells using two separate siRNA oligonucleotides for each gene and obtained similar results: whereas both siRNAs directed against HRI decreased induction of Gadd34 mRNA , one siRNA directed against GCN2 had no effect whereas the other even increased Gadd34 mRNA expression ( Fig 7H and S4J Fig ) . Whereas we cannot exclude the possibility that this increasing effect of one siRNA directed against GCN2 may result from putative off-target effects , we conclude that these data support a role for HRI rather than GCN2 . Since RPMI is an iron-poor medium , we reasoned that the CM-PAO1 would limit iron availability to epithelial cells , e . g . by the presence of siderophores [60] , which might activate HRI through depletion of iron from the culture medium . We therefore first evaluated the effect of iron depletion of the epithelial cell culture medium using deferoxamine ( DFO ) . DFO treatment resulted in a marked increase in the expression of the ISR and UPR related genes CHOP and GADD34 , whereas GRP78 and spliced XBP1 were not affected ( Fig 7H ) . This is line with selective activation of the ISR by iron depletion . We next confirmed the presence of the iron-chelating siderophore pyoverdine in the CM-PAO1 by the bright fluorescence of the medium upon exposure to UV light ( see online repository ) . To test the possible involvement of iron depletion in CM-PAO1-mediated Gadd34 induction , we supplemented the epithelial cell culture medium with iron , which indeed completely suppressed the induction of Gadd34 mRNA ( Fig 7I and S4K Fig ) . Taken together , these data demonstrate that CM-PAO1 induces splicing of XBP1 mRNA ( ER stress ) in human bronchial epithelial cells , while induction of GADD34 predominantly reflects an iron-dependent ISR mediated by the eIF2α kinase HRI . During chronic ER stress in cell and animal models of disease , the induction of GADD34 appears to mediate cellular toxicity [26 , 43] . In contrast , during the acute stress of SERCA pump inhibition by thapsigargin , GADD34 has been shown to be protective [61] . To test the role of ER stress-independent induction of GADD34 by exposure to CM-PAO1 , we made use of Gadd34ΔC/ΔC MEFs [61] , which lack GADD34 phosphatase activity . Cells expressing wild-type GADD34 were more resistant to the cytotoxic effects of CM-PAO1 compared with Gadd34ΔC/ΔC fibroblasts , as reported by the release of lactate dehydrogenase ( LDH ) ( Fig 8A ) . To confirm these findings , we repeated these experiments in HeLa cells expressing GADD34 from a tetracycline-responsive promoter . The induction of GADD34 with doxycycline significantly increased cell viability upon exposure to CM-PAO1 ( Fig 8B ) . When the cell culture medium of wild-type cells was supplemented with iron , the release of LDH was prevented ( Fig 8C , left panel ) . Iron supplementation was also observed to rescue cell viability reported by MTT assay ( Fig 8C , right panel ) . Taken together , these data suggest that the toxicity of CM-PAO1 is sensitive to iron and that HRI-mediated induction of GADD34 is protective in this context . Supplementation with iron relieves both the cytotoxicity and the requirement for induction of GADD34 .
It is known that a normal response to ER stress is required for an efficient innate immune response to bacterial infection [39] , but whether live bacteria are required for this has been unclear . In this study , we have shown that secreted virulence factors of P . aeruginosa cause ER stress in primary bronchial epithelial cells and in a cell line , and that this is mediated by TAK1 and phosphorylated p38 MAPK . In addition , we have identified GADD34 induction via an ER-stress independent ISR . We have demonstrated pyocyanin to be one of the factors eliciting these responses , while AprA contributes to the activation of the UPR . We were however unable to establish the relative contribution of pyocyanin and AprA to the activation of the UPR . In contrast , activation of the ISR with induction of GADD34 mRNA is most likely a response to reduced iron availability and may serve a cytoprotective role during exposure to conditioned medium of P . aeruginosa . In line with these observations , phosphorylation of p38 MAPK has previously been shown to be involved in the splicing of XBP1 upon infection with P . aeruginosa [39 , 45] , although the involvement of TAK1 upstream of p38 MAPK and its essential involvement in the activation of CHOP and GRP78 are novel findings . Interestingly , GADD34 , classically a downstream target of CHOP , was regulated independently of the TAK1-p38 MAPK pathway . The induction of GADD34 is only partially dependent on CHOP ( S4B Fig and [26] ) , but it is absolutely reliant on phosphorylation of eIF2α and ATF4 [26] . This is concordant with the recent description of a virus-induced “microbial stress response” mediated via the PKR/eIF2α/ATF4 pathway , which fails to induce CHOP , but potently induces GADD34 [41 , 42] . In contrast to the response of human airway epithelial cells , P . aeruginosa conditioned medium failed to cause splicing of Xbp1 mRNA in murine fibroblasts , suggesting that ER stress may not be a conserved feature of the cellular response to this insult . This is unsurprising , as induction of ER stress is known to be highly cell-type dependent [40] . In the absence of ER stress in the murine fibroblasts , the induction of Chop and Gadd34 suggests that activation of the ISR by the secreted virulence factors may be a more conserved response . Of note , in human bronchial epithelial cells , the induction of CHOP seems primarily subordinate to an ER stress-induced ISR , rather than the microbial stress response ( S7 Fig ) . Consequently , induction of CHOP was dependent on the TAK1-p38 MAPK pathway in those cells ( Fig 3D ) and its induction was only partially inhibited when bacterial cultures were supplemental with iron ( Fig 4B ) , in contrast to MEFs where Chop induction was dependent on HRI ( S4I Fig ) . Recent evidence suggests that bacterial components may function as triggers for the UPR . Flagellin has been shown to induce an atypical ER stress response in CF bronchial epithelial cells during live infection [45] , while N- ( 3-oxo-dodecanoyl ) homoserine lactone ( C12 ) has been observed to phosphorylate eIF2α and activate p38 MAPK [62] . We have now shown that at least two secreted virulence factors , pyocyanin and AprA , also contribute to this ER stress response to Pseudomonas . More research has to be done to assess the involvement of ( other ) individual virulence factors . High concentrations of pyocyanin also mediated an ER stress-independent , ISR-dependent induction of GADD34 ( Fig 4E ) . We were able to identify a crucial role for iron availability and for the iron-sensing kinase HRI in this response , although we cannot fully exclude a role for the kinase GCN2 that has been previously implicated in responses to Pseudomonas spp [59] . Of note , it is possible that the protective effect of GADD34 is unrelated to its ability to dephosphorylate p-eIF2alpha . Interestingly , AprA was not involved in the induction of the ISR response but rather appeared to dampen it , since considerably higher GADD34 expression was observed when conditioned medium of the aprE mutant PAN8 was used to stimulate the cells ( Fig 5B ) . Among other possibilities , an explanation for this observation could be that AprA present in the conditioned medium of the wild-type strain partially degrades HRI , a possibility that warrants further investigations . The discovery of this ER-independent ISR may plausibly offer novel potential therapeutic targets . It has been shown recently that spliced XBP1 is required for C12-mediated apoptosis [62] . Remarkably , exposure of cells to C12 does not itself trigger the splicing of XBP1 mRNA suggesting that basal levels of XBP1 splicing are both necessary and sufficient for this response . Moreover , the transcriptional activity of spliced XBP1 does not appear to be required for this cell death , indicating that the spliced XBP1 protein may have additional , as yet unidentified , activities . C12 appears able to trigger the ISR in an ER stress-independent matter , although the mechanism for this remains to be determined . It would be interesting to determine if C12 can activate HRI . Chronic elevation of GADD34 in ER stress can mediate cellular toxicity [26] , but GADD34 has been shown to be protective during the acute stress of SERCA pump inhibition with thapsigargin , which depletes the ER of calcium [61] . As with thapsigargin , P . aeruginosa has been associated with altered ER calcium signalling [38 , 44] . It is therefore of interest that expression of GADD34 reduced cell toxicity and increased cell survival upon iron deficiency caused by treatment with conditioned medium from P . aeruginosa . It has been shown that lungs of cystic fibrosis patients lack the ability to induce GADD34 [45] , which might plausibly lead to increased cytotoxicity or altered innate immunity due to Pseudomonas infection of the lungs of CF patients . However , future in vivo studies are required to confirm the observed cytoprotective effect of GADD34 induction during Pseudomonas infections . In summary , secreted virulence factors of the PAO1 strain of P . aeruginosa , including pyocyanin and AprA , are sufficient to elicit an ER stress response but the relative contribution of these virulence factors remains to be investigated . In contrast to these virulence factors , our findings strongly suggest that iron depletion mediated by the secretion of siderophores causes an ER stress-independent ISR . The induction of GADD34 by this may serve to ameliorate the toxic effects of P . aeruginosa conditioned medium .
All strains used in this study are listed in Table 1 . CM was prepared as described previously with slight modifications [47] . Briefly , overnight bacterial cultures in Luria Broth were inoculated 1:50 into RPMI 1640 ( Gibco , Life Technologies , Breda , the Netherlands ) and incubated at 37°C shaking at 200 rpm . After 5 days , the cultures were centrifuged and supernatants were filter-sterilized through 0 . 22 μm pore-size filter ( Whatman , Dassel , Germany ) to obtain CM . Pyocyanin and AprA levels in CM were measured as described previously [63 , 64] . PBEC were obtained from tumour-free resected lung tissue by enzymatic digestion as described previously [65] . 16HBE cells ( passage 4–15; kindly provided by Dr . D . C . Gruenert , University of California , San Francisco , CA , USA ) were cultured in MEM ( Invitrogen ) supplemented with 1 mM HEPES ( Invitrogen ) , 10% ( v/v ) heat-inactivated FCS ( Bodinco , Alkmaar , the Netherlands ) , 2 mM L-glutamine , 100 U/ml penicillin and 100 μg/ml streptomycin ( all from BioWhittaker ) . All MEFs were maintained as described previously [23 , 26 , 36 , 66 , 67] . HEK-TLR2 and HEK-TLR4 [50] were a kind gift from Prof . Dr . M . Yazdanbakhsh ( Leiden University Medical Center , the Netherlands ) . HeLa cells were transfected for 6 hours with two different ON-TARGETplus Human EIF2AK1 siRNA ( GCACAAACUUCACGUUACU and GAUUAAGGGUGCAACUAAA ) and knockdown was assessed 48 hours after transfection ( S5 Fig ) . GADD34-N1-eGFP ( kind gift form S . Shenolikar , Duke-NUS Graduate Medical School Singapore , Singapore ) was excised with BglII and NotI and ligated into pTRE2-hyg plasmid ( Clontech Laboratories , Mountain View , CA , USA ) digested with BamHI and NotI . HeLa Tet-On advanced cells ( Clontech Laboratories ) were transfected with the pTRE2-hyg_GADD34-eGFP plasmid and selected with 600 μM hygromycin to generate a stable cell line conditionally expressing GADD34-GFP ( S6 Fig ) . Positive cell clones were visualised by GFP expression in response to 1 μg/ml of doxycycline . Once identified , expanded and characterized , these clones were maintained in DMEM ( Sigma ) supplemented with 10% FBS and antibiotics ( 100 U/ml penicillin G , 100 μg/ml streptomycin , 200 μg/ml G418 and 200 μM hygromycin ) . Expression of GADD34 was typically induced using 1 μg/ml doxycycline ( Sigma ) for 24 hours . Cells were exposed at 80–90% confluence for 24 hours ( unless stated otherwise ) to CM-PAO1 ( 1 in 5 dilution , unless stated otherwise ) , pyocyanin ( 1–30 μM ) , ammonium iron ( III ) citrate ( 100 μM; Fe3+ ) , exotoxin A ( 1–10 ng/ml ) , AprA ( 10 nM ) , elastase ( 16–64 μg/ml ) and/or DFO ( 1–100 nM ) as indicated ( all from Sigma ) . Puromycin ( 10 μg/ml; Sigma ) was added 30 minutes before harvesting . Thapsigargin ( 100 nM; Sigma ) , TNFα and IL-1β ( both 20 ng/ml; Peprotech , Rocky Hill , NJ ) were used as positive controls . The compounds SB203580 ( 10 nM; Sigma ) and 5Z-7-oxozeanol ( also called LL-Z1640-2; 100 nM; TebuBio , Heerhugowaard , the Netherlands ) were added 30 minutes before stimulation for the inhibition of p38 MAPK and TAK1 , respectively . The specific IRE1-inhibitor 4μ8C ( 30 μM ) [56] was a kind gift from Prof . Dr . D . Ron , University of Cambridge . Cells were lysed in buffer H ( 10 mM HEPES , pH 7 . 9 , 50 mM NaCl , 500 mM sucrose , 0 . 1 mM EDTA , 0 . 5% ( v/v ) Triton X-100 , 1 mM PMSF , 1X Complete protease inhibitor cocktail ( Roche Applied Science , Mannheim , Germany ) ) supplemented with phosphatase inhibitors ( 10 mM tetrasodium pyrophosphate , 17 . 5 mM β-glycerophosphate , and 100 mM NaF [25 , 27] ) for detection by antibodies directed against phospho-eIF2α ( Cell Signaling Technology , Danvers , MA , USA ) , eIF2α ( gift from Prof . Dr . D . Ron ) , KDEL ( Enzo Life Sciences ) , GADD34 ( ProteinTech , Chicago , IL , USA ) , puromycin ( Millipore , Billerica , MA , USA ) , β-actin and GAPDH ( CellSignalling ) , or in sample buffer ( 0 . 2 M Tris-HCl pH 6 . 8 , 16% [v/v] glycerol , 4% [w/v] SDS , 4% [v/v] 2-mercaptoethanol , 0 . 003% [w/v] bromophenol blue ) for detection by antibodies directed against ( phospho- ) p38 MAPK ( both CellSignalling ) . The proteins in the samples were separated using a 10% SDS-PAGE gel and transferred onto a nitrocellulose membrane . After blocking with PBS containing 0 . 05% Tween-20 ( v/v ) and 5% skimmed-milk ( w/v ) , the membrane was incubated overnight with the primary antibody ( 1:1000 ) in TBS with 0 . 05% Tween-20 ( v/v ) and 5% BSA ( w/v ) at 4°C . Next , the membrane was incubated with HRP-labelled anti-mouse or anti-rabbit antibody ( Sigma ) in blocking buffer for 1 hour and developed using ECL ( ThermoScientific ) . Total RNA was isolated using Qiagen RNeasy mini kit ( Qiagen/Westburg , Leusden , the Netherlands ) . Quantitative reverse-transcriptase polymerase chain reaction ( qPCR ) was performed as described previously [68] using the primer pairs as defined in Table 2 . Relative mRNA concentrations of RPL13A and ATP5B ( GeNorm , PrimerDesign Ltd . , Southampton , UK ) were used as housekeeping genes for human genes and Actb ( β-actin ) and Sdha for mouse genes to calculate normalized expression . IL-8 was measured using commercially available ELISA kit according to manufacturer’s instructions ( Sanquin , Amsterdam , the Netherlands ) . LDH release was measured with a LDH-cytotoxicity colorimetric assay kit following manufacturer’s instructions ( Biovision , Milpitas , CA , USA ) . Thiazolyl blue tetrazolium bromide ( MTT; Sigma ) was dissolved in a 5 mg/ml stock concentration in sterile water and cells were incubated with a 1:10 dilution for 2 hours at 37°C . Next , the water-insoluble formazan formed from MTT in viable cells was dissolved in isopropanol for 10 min before the absorbance was read at 570 nm wavelength . Epithelial barrier function was measured using ECIS ( Applied Biophysics , Troy , NY , USA ) as described previously [69] . Resistance was measured at 1000 Hz and cells were stimulated with CM-PAO1 when the resistance was stable . Results are expressed as mean ± SEM . Data were analysed using one- or two-way analysis of variance ( ANOVA ) and corrected with the Bonferroni post-hoc test . Differences with P-values <0 . 05 were considered to be statistically significant . | Pseudomonas aeruginosa causes a devastating infection when it affects patients with cystic fibrosis or other chronic lung diseases . It often causes chronic infection due to its resistance to antibiotic treatment and its ability to form biofilms in these patients . The toxic effects of P . aeruginosa are largely mediated by secreted virulence factors . Efficient functioning of the endoplasmic reticulum is crucial for cell survival and appropriate immune responses , while its dysfunction causes stress and activation of the unfolded protein response . In this study , we found that virulence factors secreted by P . aeruginosa trigger the unfolded protein response in human cells by causing endoplasmic reticulum stress . In addition , secreted virulence factors activate the integrated stress response via a parallel independent pathway . Both stress pathways lead to the induction of the protein GADD34 , which appears to provide protection against the toxic effects of the secreted virulence factors . | [
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| 2015 | Virulence Factors of Pseudomonas aeruginosa Induce Both the Unfolded Protein and Integrated Stress Responses in Airway Epithelial Cells |
The membrane proximal external region ( MPER ) of the HIV-1 glycoprotein gp41 is targeted by the broadly neutralizing antibodies 2F5 and 4E10 . To date , no immunization regimen in animals or humans has produced HIV-1 neutralizing MPER-specific antibodies . We immunized llamas with gp41-MPER proteoliposomes and selected a MPER-specific single chain antibody ( VHH ) , 2H10 , whose epitope overlaps with that of mAb 2F5 . Bi-2H10 , a bivalent form of 2H10 , which displayed an approximately 20-fold increased affinity compared to the monovalent 2H10 , neutralized various sensitive and resistant HIV-1 strains , as well as SHIV strains in TZM-bl cells . X-ray and NMR analyses combined with mutagenesis and modeling revealed that 2H10 recognizes its gp41 epitope in a helical conformation . Notably , tryptophan 100 at the tip of the long CDR3 is not required for gp41 interaction but essential for neutralization . Thus bi-2H10 is an anti-MPER antibody generated by immunization that requires hydrophobic CDR3 determinants in addition to epitope recognition for neutralization similar to the mode of neutralization employed by mAbs 2F5 and 4E10 .
The trimeric HIV-1 envelope glycoprotein ( Env ) , composed of its receptor binding subunit gp120 and the fusion protein gp41 , is the main target for neutralizing antibodies . Although recent studies have demonstrated the potential of the human immune system to produce broadly neutralizing antibodies ( bnAbs ) directed against gp120 [1]–[10] , generation of antibodies with broad cross-clade neutralization activity via recombinant Env immunization has been rare [11]–[14] . This may be due in part to the long time frame required to generate such antibodies as well as to multiple evasive strategies developed by the virus [15]–[17] . Because Env gp41 contains highly conserved sequences that are exposed during the conformational changes leading to membrane fusion [18] , [19] a considerable effort is underway to target gp41 with a focus on the membrane proximal external region ( MPER ) . The MPER is recognized by the broadly neutralizing antibodies ( bnAbs ) 2F5 , Z13 , 4E10 and 10E8 [9] , [20]–[23] . They interact with linear epitopes of the MPER [9] , [24]–[26] and gp41-mAb interaction most likely prevents refolding of gp41 into the six-helical bundle conformation [27]–[29] . Notably , 2F5 and 4E10 are among the broadest cross-reactive human neutralizing antibodies directed against HIV-1 gp41 while recently-described 10E8 combines this extensive breadth with substantially increased potency [9] , [22] . The potencies of 2F5 and 4E10 are confirmed by their ability to prevent HIV-1 transmission in rhesus macaques by passive immunization [30]–[35] . Numerous studies have been performed with purified gp41 proteins and gp41-derived peptides in an attempt to induce such antibodies by immunization; however , with very limited success so far [36]–[45] . This was partly attributed to the fact that both 4E10 and under some experimental conditions also 2F5 display lipid binding and potential polyreactivity [46] , which may be a special feature of anti-HIV antibodies [47] . However mAb 10E8 does not bind lipids and is not polyreactive [9] . 2F5 and 4E10 contain hydrophobic residues within the third complementarity-determining region of the heavy chain ( CDR H3 ) , which do not contact the antigen directly , but are required for virus neutralization [48]–[51] . CDR H3 was suggested to insert into the viral membrane and extract membrane-embedded MPER leading to tight binding [52] , [53] . In addition CDR H3 of 2F5 may function in destabilizing the helical region downstream of the core 2F5 epitope leading to the extended-loop conformation of the 2F5 epitope [54] . Both models are consistent with the finding that neutralization activity of MPER antibodies depends on the transmembrane region allowing functional MPER exposure [55] . Furthermore , MPER antibody epitopes of most primary isolates become only accessible after receptor/co-receptor-binding induced conformational changes in Env [56] , [57] . In addition , MPER mAb Env recognition was reported to induce gp120 shedding leading to irreversible neutralization effects [58] . Moreover , MPER antibody epitope access may be size-restricted [59] , which is consistent with the proposal that such antibodies target the transient fusion-intermediate conformational state of gp41 [60] that is also difficult to access by HR1-specific neutralizing antibodies [61]–[63] . Accordingly , anti-MPER-like neutralization activity in patient sera was reported to be rare [64]–[67] or absent [68] , [69] . This was attributed to the fact that MPER-specific antibodies in acutely infected individuals are polyreactive [70] and their B cells may undergo clonal deletion [71] . However , a recent study suggested that 10E8-like potent anti-MPER antibodies occur frequently in sera with high neutralizing breadth and potency without exhibiting autoreactivity such as lipid interaction [9] . Thus a major challenge remains to induce MPER-specific neutralizing antibodies upon immunization . Therefore we immunized two llamas with proteoliposomes containing trimeric gp41 consisting of the HIV-1 Env transmembrane region and MPER . Llamas ( Lama glama ) and other Camelidae produce classical antibodies as well as heavy chain-only antibodies [72] . Notably , VHH ( variable domain of the heavy chain of a heavy chain only antibody ) specific for gp120 with broad neutralizing activity have been successfully produced by immunization before [73]–[78] . Here we report the functional and structural characterization of the llama anti-MPER VHH 2H10 . Our data suggest that this is the first MPER-specific antibody induced by immunization that employs similar structural features as mAbs 2F5 and 4E10 for neutralization . 2H10 requires a hydrophobic CDR3 for neutralization but not for interaction with its epitope , like mAbs 2F5 and 4E10 . Futhermore , 2H10 binds to a 2F5 overlapping epitope that is in an alpha-helical conformation , which provides important insights for the development of MPER based vaccines .
This study was carried out in strict accordance with the Dutch Experiments on Animals Act 1997 . In accordance with article 18 of the Act the protocol was assessed and approved by the Animal Ethics Committee of the Utrecht University ( Permit Number: DEC#2007 . III . 01 . 013 ) . All efforts were made to minimize discomfort related to immunizations and blood sampling . The animal welfare officers of the Utrecht University checked the mandatory administration and supervised the procedures and the well-being of the llamas that were used . The HIV-1 gp41 constructs ‘gp41CHRTM’ , clade B C-terminal Heptad Repeat ( CHR ) , MPER and transmembrane ( TM ) regions ( residues 629–706 , HXB2c numbering ) , ‘gp41-GCN’ , gp41 residues 629–683 ( HXB2c numbering ) fused to a trimeric GCN4 leucine zipper [79] , and ‘gp41INT’ , which has a similar design as reported [60] with the exception that the trimeric GCN4 zipper was fused in frame with part of gp41 NHR , were cloned into pETM-11 ( EMBL , Heidelberg ) or pETM-13 using standard PCR techniques . Proteins were expressed in Escherichia coli strains C41 or BL21 . Cells were grown to an OD600 of 0 . 8 and induced with 1 mM isopropyl-ß-D-thiogalactopyranoside ( IPTG ) . Cells were harvested by centrifugation and lysed in either phosphate buffered saline ( PBS ) supplemented with 1% 3-[ ( 3-cholamidopropyl ) dimethylammonio]-1-propanesulfonate ( CHAPS ) or lysed in 20 mM tris ( hydroxymethyl ) aminomethane hydrogen chloride ( Tris-HCl ) pH 8 . 0 , 100 mM NaCl , or in 20 mM Bicine pH 9 . 3 , 100 mM NaCl , for ‘gp41CHRTM’ , ‘gp41-GCN’ and ‘gp41INT’ respectively . Purification was performed using a Ni2+ affinity or Q-sepharose column ( ‘gp41INT’ ) and gel filtration on a Superdex 200 column ( GE Healthcare ) . Gp41528–683 was produced as described [29] . The concentration of gp41CHRTM was adjusted to 1 mg/ml . Lipids POPC , POPE , POPS , Cardiolipin/Sphingomyelin and cholesterol ( Avanti Polar Lipids ) were dissolved in chloroform and mixed in a 1∶2∶1∶2∶5 ratio ( w/w ) . After solvent evaporation , the lipid film was dried under vacuum . Multilamellar vesicles were obtained by resuspending the lipid film in PBS to a final concentration of 1 mg/ml . After three quick freeze/thaw rounds the liposome suspension was extruded through a 100 nm membrane . Liposomes and gp41CHRTM were mixed in a 1∶1 ratio ( w/w ) . CHAPS was removed by extensive dialysis with PBS containing 0 . 1 mg/ml BioBeads ( BioRad ) , changing the solution every eight hours for two days . The quality of gp41CHRTM proteoliposomes was confirmed by dynamic light scattering ( Malvern Nano ZS ) . Two llamas ( L6 and L7 ) were immunized intramuscularly with the gp41CHRTM reconstituted in the trimeric form into synthetic liposomes ( gp41 proteoliposomes ) without adjuvant . The immunization scheme consisted of a priming immunization at day 0 followed by 5 boosts , which were given at days 7 , 14 , 21 , 28 and 35 . During the prime and the first boost 0 . 25 mg of gp41 and 0 . 5 mg of lipids were used per injection . These amounts were halved for the remaining boosts . Importantly no adjuvants were used . The immune response was measured in the serum taken at day 21 and compared to day 0 . At day 43 lymphocytes were collected from the two llamas for construction of two separate phage libraries , as described previously [80] . The genes encoding for the VHH were amplified and cloned in the phage-display plasmid , pAX50 . The plasmids were transferred to E . coli strain TG1 [supE hsdΔ5 thi Δ ( lac-proAB ) F′ ( traD36 proAB+ , lacIq lacZΔM15 ) ] . Two rounds of selection with phage libraries were carried out by panning phages onto gp41-proteoliposomes immobilized in microtiter plate wells . In the first round selection , gp41 proteoliposomes at 5 , 1 and 0 . 2 µg were coated in the wells of PolySorp ( Nunc ) microplate overnight at 4°C followed by PBS containing 4% Marvel dried skimmed milk ( Premier International Foods UK Ltd ) for one hour at room temperature . approximately 1010 of the phages , pre-incubated for 30 minutes in 2% skimmed milk in PBS and empty liposomes to reduce nonspecific binding , were then added to the wells for 2 hours followed by extensive washing ( 15 times ) with PBS containing 0 . 05% Tween 20 ( PBS-T ) ( the 5th , 10th and 15th wash steps were done for 10 min ) and three times with PBS . The bound phages were eluted with 100 mM TEA , neutralized with 1 M Tris-HCl pH 7 . 5 , and rescued by infection of the E . coli strain TG1 , which were then plated on LB agar plates containing 100 µg/ml ampicillin and 2% glucose . The phages eluted from 1 µg coated gp41 proteoliposomes in the first round yielded the highest enrichment of ∼100 times . These were therefore chosen for further selection in a second round . The selected polyclonal phages were amplified in E . coli strain TG1 after infection with helper phages VCSM13 ( Stratagene ) . After purification from the culture supernatant , phages were used as input for the second round selection . Lower concentrations of proteoliposomes ( 0 . 5 and 0 . 1 µg per well ) were coated . ∼109 phages of the amplified phages were incubated in each well . After extensively washing away unbound phages , the bound phages were eluted by 100 µl of 150 µg/ml human monoclonal antibodies of either 2F5 or 4E10 . TG1 cells were infected with the eluted phages . The infected TG1 cells were grown on LB agar plates containing 100 µg/ml ampicillin and 2% glucose to obtain individual colonies . The infected TG1 were also spotted on the LB agar plates supplemented with 100 µg/ml ampicillin and 2% glucose for the assessment of the efficiency of the selection . The enrichment of specific phages was estimated in comparison with the phages that were eluted by an irrelevant human monoclonal antibody ( Remicade , Schering-Plough ) or with the phages eluted from the empty wells . DNA inserts of a number of selected phages binding to gp41 were recloned to plasmid pAX51 using the restriction sites SfiI and BstEII . MaxiSorp ( Nunc ) plate wells were coated overnight at 4°C , with gp41-GCN in PBS . After blocking of the wells with 4% skimmed milk in PBS , they were incubated with VHH fused to M13 phages in the presence of 2% skimmed milk . The wells were washed with PBS-T , and bound phages were detected by incubation with a mouse monoclonal antibody against M13 phage coupled to horse radish peroxidase ( HRP ) . The amount of HRP was visualized by the addition of O-phenylenediamine ( OPD ) supplemented with 0 . 03% H2O2 . The reactions were stopped by the addition of 1 M H2SO4 and the optical density was measured at 490 nm . TG1 strains expressing monoclonal VHH were cultivated in 2×YT medium supplemented with 100 µg/ml of ampicillin until the optical density of culture medium reached 0 . 5 at 600 nm . Then 1 mM IPTG was added to induce VHH production for 5 hours at 37°C . VHH were extracted from the periplasmic fraction and bound to Talon metal-affinity resin ( Clontech 635504 ) . After washing away unbound material , the bound VHH were eluted by 300 mM imidazole in PBS . The eluted VHH were dialyzed against PBS with two buffer changes . The VHH were dialyzed twice for 1 . 5 hours at room temperature , followed by dialysis overnight at 4°C . The VHH were analyzed on 15% acrylamide sodium dodecyl sulfate polyacrylamide gel electrophoresis ( SDS-PAGE ) stained by Coomassie Brilliant Blue and a Western blot using the antibody against the myc-tag ( 9E10 ) . The binding of the VHH to HIV-1 envelope was performed on the immobilized gp41 and gp140-92UG037 in the microtiter plate ( MaxiSorp from Nunc ) in ELISA . 200 ng Gp41-GCN or 280 ng gp140-92UG037 protein were coated in the wells of microtiter plates overnight at 4°C followed by incubation with 4% skimmed milk to block non-specific binding . Dilution series of purified VHH were incubated with the coated wells for one hour at room temperature followed by the removal of unbound VHH by washing three times with PBS-T and one time with PBS . Bound VHH were detected with anti-myc tag antibody ( 9E10 ) and donkey anti mouse peroxidase ( DAMPO ) . The antibodies were incubated for one hour and washed with PBS-T and PBS between each incubation . The plates were developed using OPD containing 0 . 03% H2O2 . The reaction was stopped with 1 M H2SO4 . The optical density was measured at 490 nm . Titrating amounts of antibodies were added to JR-FL HIV-1 Env-transfected 293T cells expressing either cleaved or uncleaved Env , incubated for 1 h at 37°C and washed with FACS buffer ( PBS , 5% heat-inactivated fetal calf serum [HIFBS] , 0 . 02% azide ) . MAbs b6 , b12 , and 4E10 were detected with a secondary anti-IgG Ab conjugated to phycoerythin ( Jackson ImmunoResearch ) . MAb b12 and the VHH 2H10 , bi-2H10 and bi-2H10 W100A were biotinylated and detected with streptavidin PE . MAb b6 binding is weak on cleaved Env , confirming that most Env is cleaved [81] . Binding was analyzed using flow cytometry , and binding curves were generated by plotting the mean fluorescence intensity of antigen binding as a function of antibody concentration . PCR was used to amplify the VHH sequences . Different primer sets designed to amplify the VHH that will be located at the N terminus and the VHH that will be located at the C terminus of the bivalent VHH . The primers at the 3′ of the N-terminal VHH and at the 5′ of the C-terminal VHH , may encode a flexible sequence glycine-serine ( GS ) linker represented by a repeat of the pentapeptide “G-G-G-G-S” , also called 5GS . These same primers contain a unique restriction site ( BamHI ) . We constructed bi-2H10 with 5GS , 15GS , and 25GS linkers and with the linker GGGGSGGGGSGGGGGGS , called 17GS . For the studies we used the bi-2H10 with the 17GS linker , unless specified differently . After PCR amplification , the generated fragments were cleaved at a unique N-terminal restriction site ( SfiI restriction site ) and BamHI for the VHH that will be located at the N terminus , and with BamHI and at a unique C-terminal restriction site ( BstEII ) for VHH that will be located at the C terminus . The fragments are ligated into pAX51 , which was digested with SfiI and BstEII . Antibodies were mixed with liposomes having the lipid composition as described above in PBS and their buffer was adjusted to 40% sucrose . The 40% sucrose mixture was overlaid stepwise with 30 , 20 , 10 and 5% sucrose and centrifuged in a Ti55 swinging bucket rotor for 18 h at 50 K rpm . Six fractions were recovered from the gradient and samples thereof were analyzed by SDS-PAGE . For ELISA analysis , cardiolipin from bovine heart ( SIGMA , C0563 ) and l-α-phosphatidyl-l-serine from bovine brain ( SIGMA , P7769 ) were dissolved in ethanol to a concentration of 5 mg/ml and further diluted with PBS to 50 µg/ml of which 100 µl was coated per well in PolySorp plates ( Nunc ) overnight at room temperature without a cover foil to allow complete evaporation of the sample . As a control gp140-92UG037 was coated on a MaxiSorp plate ( Nunc ) . Dilution series were prepared of 2H10 , bi-2H10 , 2F5 and 4E10 . 2F5 and 4E10 were detected with the mouse anti-human IgG monoclonal 3E8 ( SANTA CRUZ ) . The rest of the ELISA was performed as described above . HIV-1 IIIB was obtained from the Centralised Facility for AIDS Reagents ( CFAR ) , NIBSC and propagated in H9 cells . Replication-competent NL43 stocks were prepared from a replication-competent HIV-1 molecular clone by transfection of 293T cells . All other used HIV-1 viruses were HIV-1 envelope pseudotyped viruses and were produced in 293T cells by co-transfection with the pSG3Δenv plasmid and the relevant envelope plasmid . The subtype B HIV-1 reference panel of envelope clones and the subtype C clones were obtained through the NIH AIDS Research and Reference Reagent Program , Division of AIDS , NIAID , NIH , USA . AC10 . 0 clone 29 and SC422661 clone 8 were contributed by David Montefiori , Feng Gao and Ming Li; ZM109F . PB4 was contributed by David Montefiori , Feng Gao , S Abdool Karim and G Ramjee; RHPA4259 clone 7 was contributed by B . H . Hahn and J . F . Salazar-Gonzalez . The SHIV clones were constructed at the BPRC by Zahra Fagrouch and Ernst Verschoor . The panel of transmitted subtype B ( T/F ) clones ( WEAU-d15 . 410 . 787 , 1006-11 . C3 . 1601 , 1054-07 . TC4 . 1499 , 1056-10 . TA11 . 1826 , 1012-11 . TC21 . 3257 ) were provided by George Shaw and Beatrice Hahn . TZM-bl cells were obtained through the NIH AIDS Research and Reference Reagent Program from J . C . Kappes , X . Wu , and Tranzyme , Inc . , and cultured in Dulbecco's modified Eagle medium ( Invitrogen ) containing 10% ( v/v ) fetal calf serum ( FCS ) . Neutralization was measured using pseudovirus or 200 50% tissue culture infective doses ( TCID50 ) of virus in the TZM-bl cells as targets following the methods of Derdeyn et al . [82] Wei et al . [83] and Li et al . [84] , with Bright-Glo luciferase reagent ( Promega , Southampton , UK ) . The neutralization activity of each VHH was assayed in duplicate . No virus inactivation was observed with a negative control VHH . VHH IC50 titers were calculated using the XLFit4 software ( ID Business Solutions , Guildford , UK ) . The HIV-1 neutralization potency of llama 6 & 7 serum/plasma samples were also evaluated in TZM-bl . Neutralization was measured using 200 TCID50 of IIIB virus in the TZM-bl cell-based assay , as described above , using a Glomax plate reader ( Promega ) . Serum samples were heat-inactivated to inactivate complement by incubation at 56°C for 1 h before use in neutralization assays . Threefold serial dilutions of llama sera were then tested , starting at a 1∶5 dilution . The binding of VHH and mAb to peptides was assessed in a Pepscan-based ELISA [85] . Structural aspects of antibody antigen interaction were revealed through small random peptide libraries [86] . Each VHH and mAb was titrated to ensure that optimal binding was achieved and that nonspecific binding was avoided . Each well in the card contained covalently linked peptides that were incubated overnight at 4°C with VHH and mAb , between 1 and 10 ng/ml in PBS containing 5% horse serum ( v/v ) , 5% OVA ( w/v ) , and 1% ( v/v ) Tween 80 , or in an alternative blocking buffer of PBS containing 4% horse serum ( v/v ) , and 1% ( v/v ) Tween 80 . After washing , the plates were incubated with a HRP-linked rabbit anti-Ab ( DakoCytomation ) for 1 h at 25°C . After further washing , peroxidase activity was assessed using substrate ( 0 . 5 g/l 2 , 2′-azino-di[3-ethyl-benzthiazolinesulfonate ( 6 ) ]diammonium salt ( ABTS ) ) with 0 . 006% H2O2 in 0 . 18 M Na2HPO4 , 0 . 22 M citric acid was added until the solution had a pH of 4 . The color development was quantified after 60 minutes using a charge-coupled device camera and an image-processing system . The cDNA corresponding to 2H10 was synthesized ( GeneArt ) and cloned into vector pMEK220 ( without 6his-tag or myc tag ) and 2H10 was expressed in E . coli strain BL21gold ( DE3 ) ( Invitrogen ) . Cells were grown to an OD600 of 0 . 7 and induced with 1 mM IPTG at 37°C . After 5 hours cells were harvested by centrifugation and lysed by sonication in 0 . 02 M 4- ( 2-hydroxyethyl ) -1-piperazineethanesulfonic acid ( HEPES ) , 0 . 1 M NaCl , pH 7 . 4 ( HEPES buffer ) . 2H10 was purified by affinity chromatography using a Protein A column ( Roche ) . 2H10 was eluted with 0 . 01 M glycine , pH 2 . 5 , and fractions containing 2H10 were neutralized with 1 M Tris-HCl , pH 8 . These fractions were further purified by gel filtration on a Superdex 200 column ( GE Healthcare ) in HEPES buffer . Purity of the sample was confirmed on a 12% SDS-Tris-Tricine gel . 2H10 in complex with peptide 903 was subjected to crystallization at a concentration of 12 mg/ml . Crystals were obtained by the vapor diffusion method in hanging drops , with equal volumes of antibody and reservoir solution ( 0 . 1 M Bicine , 3 . 2 M NH4SO4 , pH 9 ) . The crystal was cryo-cooled at 100 K in reservoir buffer containing 25% ( v/v ) glycerol . A complete dataset was collected at the ESRF ( Grenoble , France ) beamline ID14-4 . Data were processed and scaled with MOSFLM [87] , and SCALA [88] . The crystals belong to space group I23 with unit cell dimensions of a = 89 . 95 Å , b = 89 . 95 Å , c = 89 . 95 Å and α = 90 . 00° . The structure was solved by molecular replacement using PHASER [89] and the VHH structure from Protein Data Bank ( PDB ) ID 3EZJ as a search model . An initial model was built with ARP/wARP [90] and completed by several cycles of manual model building with Coot [91] and refinement with refmac [92] using data to 1 . 3 Å resolution . The final model contains 2H10 residues 1–122 . Molecular graphics figures were generated with PyMOL ( W . Delano; http://www . pymol . org ) . Coordinates and structure factures have been deposited in the Protein Data Bank with accession ID 4B50 . A 13C , 15N-labeled protein sample was prepared at a concentration of 0 . 95 mM in 20 mM HEPES buffer and 75 mM NaCl at pH 6 . 7 with 10% D2O . For the study of the peptide∶2H10 complex , a solution of gp41 peptide ( 655KNEQELLELDKWASL669 . ) was prepared at 7 . 9 mM in the same HEPES buffer and 5 µl were added step by step to the 13C , 15N-labeled protein sample . A final protein∶peptide ratio of 1∶1 . 5 was obtained and 15N HSQC experiments were recorded for each peptide addition in order to control the complete disappearance of the free form . Sequential backbone resonance assignments of free and peptide bound 2H10 were performed by recording series of 3D experiments using the BEST scheme ( HNCO , HNCA , HNcoCACB , HNCACB ) and the BEST-TROSY scheme ( hNcocaNH , hNcocaNH ) [93] . All NMR experiments were performed at 37°C on Agilent VNMRS 600 or 800 MHz spectrometers equipped with triple-resonance ( 1H , 13C , and 15N ) coldprobes and shielded z-gradients . NMR data were processed with NMRPipe [94] and analyzed using the CcpNmr Analysis routines of CCPN [95] . Chemical shifts were referenced with respect to the H2O signal at 4 . 66 ppm relative to DSS , using the 1H∶X frequency ratios of the zero point according to Markley et al . [96] . Surface plasmon resonance ( SPR ) analysis was performed with a Biacore T100 ( GE Healthcare ) . As a flow buffer 10 mM HEPES , 150 mM NaCl , pH 7 . 4 with 0 . 005% Tween-20 was used . Gp140-92UG037 was immobilized to 1500 response units using 50 µg/ml protein in flow buffer on an activated CM-5 sensor chip ( GE Healthcare: BR-1000-50 ) according to the manufacturer's instructions . Specific binding to the target protein was corrected for nonspecific binding to the deactivated control channel . The flow rate was 30 µL/min . Regeneration of the sensor chip was achieved with 4 M MgCl2 for 60 seconds at 50 µL/min . Single concentration injections were all performed with 100 nM of VHH for 60 seconds each . All data were measured in duplo and the curves superpose quite well . Data were analyzed with the Biacore T100 software version 2 . Curves obtained for 2H10 could not be fitted well with the 1∶1 Langmuir or heterogeneous ligand algorithms , but only with the two-state reaction algorithm . To establish whether 2H10 indeed binds with a two-state reaction , we injected 2H10 with different injection durations . This clearly showed that the dissociation rate is dependent on the injection time . The longer the duration of the injection , the stronger the interaction becomes and the dissociation rate decreases . We , therefore , fitted the curves belonging to the monovalent 2H10 VHH with the two-state reaction model . Curves of the bivalent 2H10 should be fitted with a combination of two-state reaction and bivalent analyte model , but due to a lack of availability of this model , the curves were fitted with the heterogeneous ligand fit and the two-state reaction fit . Additional SPR analysis was performed using a Biacore 3000 ( GE Healtcare ) . As a flow buffer 10 mM HEPES , 150 mM NaCl , pH 7 . 4 , 50 µM EDTA with 0 . 005% P-20 ( GE Healthcare ) was used . Gp41INT or gp41528–683 was immobilized to 1000 response units in a sodium acetate buffer , pH 4 . 5 , on an activated CM-5 sensor chip according to the manufacturer's instructions . Specific binding to the target protein was corrected for nonspecific binding to the deactivated control channel . The flow rate was 20 µl/min . Regeneration of the sensor chip was achieved with 25 mM NaOH for 30 seconds at 50 µl/min . Data were analyzed with the BIAevalution software version 4 . 1 . The curves for 2H10 and 2H10-W100A on gp41INT were fitted with separate association and dissociation , because global fittings , either 1∶1 langmuir or two-state reaction , did result in reasonable Chi2 scores and residuals . Isothermal Titration Calorimetry ( ITC ) measurements were carried out at 25°C on a VP-ITC microcalorimeter ( MicroCal ) . Both 2H10 and peptides were purified or dissolved , respectively , in a solution containing 20 mM HEPES , pH 7 . 4 and 100 mM NaCl . The samples were degassed for 20 min and centrifuged to remove any residuals prior to the measurements . The concentration of 2H10 in the sample cell was 10 µM and that of peptide 903 or 904 in the syringe was approximately 100 µM . The peptides injected into buffer alone was used as a negative control . Data were fit to a single binding site model and analyzed using Origin version 5 ( MicroCal ) software . Site directed mutagenesis was performed by PCR with complementary oligos which carried the desired mutation , to amplify the whole plasmid . The oligo's were designed to be between 42–50 nucleotides long and have a melting temperature ( salt adjusted , calculated on http://www . basic . northwestern . edu/biotools/oligocalc . html ) of approximately 80°C . The PCR reaction was performed with 20 ng of template DNA ( 2H10-wild type plasmid ) , 125 ng each primers , 2 mM DNTPs , Pfu buffer with 2 mM , MgSO4 and 0 . 5 units of Pfu polymerase ( Fermentas ) . The protocol consisted of an initial denaturation step at 95°C for 5 min followed by 16 cycles of 95°C for 30 seconds , 60°C for 1 minute and 68°C for 7 minutes , and a final step extension at 68°C for 10 min . DNA was cleaned with PCR Clean-up kit ( Macherey-Nagel ) . Parental methylated DNA was digested with DpnI ( Fermentas ) , followed by transformation into TG1 E . coli cells . Models of gp41 peptide docked onto 2H10 were built with the webserver version “The HADDOCK web server for data-driven biomolecular docking” of HADDOCK2 . 1 [97] using CNS1 . 2 [98] for the structure calculations . The coordinates of 2H10 were taken from the crystal structure , and the gp41 peptide coordinates were based on those of the helical peptide ( 653-QEKNEQELLELDKWASL-669 ) from the crystal structure of a late fusion intermediate of HIV-1 gp41 ( PDB code: 2X7R [29] ) . Ambiguous interaction restraints were defined , based on the NMR chemical shift perturbation ( CSP ) mapping using the program SAMPLEX [99] and the epitope mapping . The SAMPLEX program takes as input a set of data and the corresponding three-dimensional structure and returns the confidence for each residue to be in a perturbed or unperturbed state . Residues 47–51 , 57–62 and 95–99 identified as perturbed in 2H10 upon binding to gp41 and residues 657 , 658 , 661 , 662 and 665 identified from the epitope mapping , were used as interaction restraints for docking . Phi-psi angles were deduced from the 13C chemical shifts using TALOS+ [100] and the resulting dihedral and H-bond restraints were used in the modeling as well . The docking was performed with default HADDOCK parameters , except that random removal of restraints was turned off and the clustering cutoff was decreased to 2 . 5 Å because of the small size of the peptide . Non-bonded interactions were calculated with the OPLS force field using a cutoff at 8 . 5 Å . The electrostatic potential ( Eelec ) was calculated by means of a shift function , while a switching function – between 6 . 5 and 8 . 5 Å – was used for the Van der Waals potential ( Evdw ) . The HADDOCK score is used to rank the generated models . It consists of a weighted sum of intermolecular electrostatics , Van der Waals , desolvation ( ΔGsolv ) [101] and ambiguous interaction restraint ( AIR ) energies , defined as: Haddock Score = 0 . 2Eelec+1 . 0EVdW+1 . 0EΔGsolv+0 . 1EAIR
Two llamas ( L6 and L7 ) were immunized with the gp41 proteoliposomes composed of an HIV-1-like lipid envelope [102] and membrane-anchored gp41CHRTM ( Fig . 1 ) . The presence of anti-gp41CHRTM proteoliposome antibodies in the plasma was confirmed by ELISA at day 21 post immunization . However , no significant neutralization was detected in the sera/plasma at day 21 and 43 compared to those of day 0 . Two phage display libraries were constructed and the selection of VHH targeting gp41 was performed in two rounds by direct panning of the phage display library on immobilized gp41 proteoliposomes . In the first round triethyl-amine ( TEA ) was used to elute the phages . In the second round a specific competitive elution with bnAbs 2F5 or 4E10 was performed . Monoclonal VHH expressing TG1 clones from first and second round selections were screened for binding to detergent-solubilized gp41CHRTM and gp41-GCN ( with pIIGCN in place of the transmembrane region ) by phage ELISA . Approximately 80% of all monoclonal VHH displaying phages were positive for binding . In order to find VHH targeting specific binding sites , a competition ELISA between phages and bnAb 2F5 or 4E10 was performed . The phages with either strong binding signals , and/or binding signals that were inhibited by bnAb 2F5 or 4E10 were chosen for further investigation with purified VHH . Using ELISA , three of the VHH were found to bind to gp41-GCN , gp41 proteoliposomes and gp140-92UG037 . 2H10 , which originated from llama 7 ( L7 ) , exhibited the highest maximum signal and was therefore chosen for the subsequent studies ( see Fig . 2 for the sequence of 2H10 ) . 2H10 competed for binding to gp41 with 2F5 , but not with 4E10 . To determine the epitope of 2H10 , its binding to a set of overlapping linear and cyclic peptides covering the gp41 MPER was measured . The best binding cyclic 15-mer peptide covered the gp41 region from amino acid 655 to 669 ( 655KNEQELLELDKWASL669 ) . This peptide was used as a seed for a library in which the amino acids on each position of the peptide was substituted by all other 19 natural amino acids . Probing this library with 2H10 and 2F5 Fab allowed the mapping of the 2H10 epitope and identified five residues ( E657 , Q658 , L661 , E662 and K665 ) important for 2H10 binding . Notably , three out of the five are part of the 2F5 epitope ( Fig . 3A and 3B ) . Next , we tested whether 2H10 recognizes its epitope in a helical conformation . To achieve this , the peptide sequence ( residues 655–669 ) was fused to a short coiled-coil that was shown to favor a helical conformation of the fused sequences [103] . The fusion construct was designed in such a way that the 2H10 residues required for interaction are exposed . Binding assays demonstrated that 2H10 binding was unaffected when the MPER peptide was fused to the coiled-coil template ( Fig . 3C ) . In contrast , 2F5 , which binds its epitope in a β-hairpin conformation [24] shows a dramatically reduced binding to the coiled-coil MPER construct ( Fig . 3D ) . Replacement of heptad positions by Gly , which are helix breakers and thus do not support a helical MPER conformation , further leads to the loss of 2H10 interaction ( Fig . 3C ) . Because MPER antibodies primarily target the fusion intermediate state of gp41 [60] , [104] , [105] , binding of 2H10 was tested to a soluble form of the fusion intermediate conformation of gp41 , gp41INT [60] . SPR measurements revealed a KD of 29 nM for the 2H10 monohead ( Fig . S1A ) . The avidity increased substantially to 2 . 5 nM , when two 2H10 VHH were linked with a 15-GS linker ( bi-2H10 ) ( Fig . S1B ) . Surprisingly , 2H10 and bi-2H10 interacted with similar affinities of 18 nM and 1 . 1 nM , respectively with gp41523–883 , a potential late fusion intermediate conformation [29] ( Figs . S1C and S1D ) . In addition binding to gp140 from clade 92UG037 was recorded . 2H10 interaction with gp140 was fit with a two state reaction model , because an experiment with different injection durations with 2H10 indicated that it binds in two states , a low affinity followed by a high affinity state ( Fig . S1E ) , which may be due to heterogeneous conformational states of gp41 present in recombinant gp140 consistent with the presence of non-native Env on virions [106] . Again the affinity of 2H10 ( KD = 4 nM ) is inferior to bi-2H10 binding ( KD = 0 . 15 nM ) , based on both the heterogeneous ligand fit and the two state reaction fit ( Figs . S1F and S1G ) . In order to confirm the pepscan 2H10 binding to the linear gp41 peptide epitope , isothermal titration calorimetry was used to determine the affinity of 2H10 to linear and cyclic forms of peptide 655KNEQELLELDKWASL669 . This revealed affinities of 35 nM and 84 nM , respectively , indicating that cyclisation of the peptide epitope is unfavorable and reduces the binding affinity . However , the KD of 35 nM is in good agreement with the SPR data and indicates that no other determinants of gp41 ( present in gp41int ) contribute to binding . We next tested whether 2H10 interacts with wild type native Env expressed on the plasma membrane , which revealed no interaction with either cleaved or uncleaved forms of JR-FL Env comparable to the lack of significant 4E10 interaction ( Fig . 4 ) . We conclude that 2H10 interacts with a linear sequence with a helical conformation present in two gp41 conformations tested . The 2H10 VHH monohead did not neutralize any of the HIV-1 strains tested ( Table 1 ) . However , bivalent 2H10 , which showed an increased affinity for gp41 as compared to the monohead , was able to neutralize HIV-1 isolates and pseudoviruses in a TZM-bl assay . No substantial difference was observed for bi-2H10 with a 15 or 17 amino acid linker . The activity of bi-2H10 with the 17 amino acid linker ( bi-17GS ) was mainly directed towards clade B , neutralizing 14 of 26 B isolates , one clade A ( i . e . 92UG037 ) and none of the clade C isolates due to the sequence conservation of the epitope . Eight strains neutralized are Tier 1 and seven are Tier 2 viruses . Bi-2H10 also neutralized both the neutralization sensitive and resistant variants of SHIV SF162 consistent with SF162 . LS neutralization ( Table 1 ) . The IC50 values were between equal to and 100-fold lower than those of mAb 2F5 , which neutralized 18 out of 23 clade B viruses tested ( Table 1 ) . Bi-2H10 , like 2H10 , did not interact with cell surface expressed JR-FL Env ( Fig . 4 ) , although the virus was neutralized . We conclude that that bi-2H10 acts downstream of receptor-induced conformational changes in Env and the increased avidity of bi-2H10 renders the VHH neutralization active . To study the interaction of 2H10 with its epitope , several attempts were made to obtain crystals of 2H10 in complex with various MPER peptides based on the Pepscan analyses . However , in each attempt , diffraction quality crystals were obtained that contained the VHH , but not the peptide . The best 2H10-containing crystals diffracted to 1 . 3 Å resolution and the structure was solved by molecular replacement ( Table 2 ) . The structure resembles the framework of known VHH structures [76] , with the major exception that most other VHH structures have CDR3 loops that fold back on the framework of the VHH , whereas the CDR3 of 2H10 protrudes from the framework of 2H10 . However , it should be noted that its orientation may have been influenced by crystal contacts . Its most notable feature is the exposure of W100 at the tip of CDR3 ( Fig . 5A ) . CDR3 W100 resembles the presence of hydrophobic residues within the CDR3 heavy chains of 2F5 and 4E10 [24] , [25] , which are not essential for epitope interaction although changes in affinity have been reported depending on the nature of the antigen for some mutants . In contrast , mutations of hydrophobic CDR3 residues affect the neutralization potency of these antibodies dramatically [48]–[50] . Similarly , several 2H10 W100 mutants ( i . e . W100A , W100F and W100L ) interacted with gp41INT and gp140 with affinities comparable to wild type 2H10 ( Fig . 6A and Fig . S1H ) , confirming that W100 is non-essential for epitope interaction . To study whether W100 is required for neutralization , the W100A mutation was introduced into bi-2H10 ( bi-2H10-W100A ) . SPR confirmed that bi-2H10-W100A binds to gp140 comparable to wild type bi-2H10 revealing the same dissociation rate together with a slightly slower on-rate ( Fig . 6B ) . Although not essential for gp41 interaction , the exchange of W100 to alanine completely abrogated neutralization of most strains . Four other strains tested showed a reduced potency as evident from the 4-and 6-fold increased IC50 values for these viruses ( Table 1 ) . In addition , bi-2H10 W100A did not interact with surface-expressed Env as expected ( Fig . 4 ) . We thus conclude that CDR3 W100 is dispensable for epitope interaction but is essential for neutralization similar to the role of hydrophobic CDR3 heavy chain residues present in mAbs 2F5 and 4E10 [48]–[50] . In order to map the gp41 binding site on 2H10 the following mutants ( S29F , D53T/R54S , R54S , R56A , R71S , R93A , E95A , R97A ) were designed based on the structure ( Fig . 5A ) and the VHH germline sequence of CDR1 and CDR2 ( Fig . 2 ) . Assessment of binding by SPR revealed that the single mutants R56A , R93A , E95A , and R97A lost binding to gp140-92UG037 ( Fig . 6A ) . All other mutants ( S29F , D53T/R54S , R54S , R71S ) still bound gp140 , although the affinities for some seemed to be marginally lower than for wild-type 2H10 . This thus implicates the charged residues R56 in CDR2 and R93 , E95 and R97 of CDR3 in gp41 recognition . Because it was reported that mAbs 2F5 and 4E10 have lipid binding activity , we tested whether 2H10 also binds liposomes or lipids . Sucrose gradient flotation analyses showed that 2H10 does not float with liposomes having the lipid composition of native HIV-1 virions . 2H10 is only found in the bottom fractions of the gradient while a substantial amount of 4E10 floats to the upper fraction of the gradient in this assay . However , 2F5 did not reveal any liposome binding either ( Fig . S2A ) . This is consistent with ELISA data which demonstrated binding of mAb 4E10 to cardiolipin and phosphatidylserine , while 2H10 , bi-2H10 and 2F5 revealed no binding activity ( Fig . S2B ) . We conclude that 2H10 exerts no measurable lipid binding activity . 15N HSQC spectra of 2H10 were recorded in the absence or presence of an excess of the gp41 peptide and revealed a sufficient spectral resolution to allow the 1H , 15N , and 13C backbone assignment of the two forms using heteronuclear experiments ( Fig . S3 ) . Chemical shift index ( CSI ) calculated from the CB , CA , N and CO chemical shifts reveal similar secondary structures for the free and the bound forms ( Fig . S4 ) . The specific shift of individual residues was observed using 15N HSQC spectra recorded at different ratios of gp41 peptide to 2H10 and depend on the peptide concentration ( Fig . 5B ) . These data reveal a slow exchange process between the bound and the free form in agreement with the affinity constant measured for this complex . The complete list of chemical shift differences ( CSD ) induced in 2H10 upon peptide binding is listed in Figure S5 ( Fig . S5 ) , which confirmed further that peptide binding , did not induce conformational changes in the tertiary structure of 2H10 . Notably most significant CSDs map to one surface of the 2H10 structure providing a first clue on the gp41 ligand position ( Fig . 5C ) . Modeling of the 2H10 interaction with a helical gp41 peptide conformation with HADDOCK [97] readily produced a top cluster with an average cluster score significantly lower than any other solution ( −96 . 3±0 . 5 a . u . ) . This cluster corresponded also to the most populated one with 173 members out of 200 models generated . Its electrostatic energy is −437 . 0±31 . 1 kcal/mol and its Van der Waals energy −36 . 9±6 . 9 kcal/mol . The model reveals that the short gp41 helix binds in a parallel fashion to the 2H10 beta-sheet involving a network of salt bridges ( E654-K60; K665-E95; D664-R56 ) and hydrogen bonds ( E654-Q44; E657-S62 amide; Q658-amide-L47; E662-Y37; R96-L669 carbonyl ) . In addition , R93 stabilizes the orientation of Y36 and E95 , which contact E662 and K665 , respectively ( Fig . 7A ) . The network of interactions is consistent with the chemical shift perturbation ( Fig . 5B ) and mutagenesis data , which implicate R56 , R93 , E95 and R97 in the interaction . CDR3 S99 and S100D show additional chemical shift differences upon peptide binding . This may indicate that the CDR3 may be flexible enough to contact the peptide or alternatively , that peptide binding induces major changes within the CDR3 conformation or orientation . Among the interacting residues are five of the six framework residues that deviate from the germline ( S35 , F50 , K60 , V61 and R93 ) by at least two nucleotide changes in their codons ( Fig . 2B ) . The sixth residue ( L91 ) is very close to the interaction site . Their occurrence in VHH is rather rare ( F50 , 1 . 5% of VHH sequences , K60 , 0 . 8% , V61 , 0 . 7% and R93 , 0 . 52% of VHH sequences ( https://fungen . wur . nl/ ) . The model also corroborated that CDR3 W100 does not interact with gp41 . Subsequent superpositioning of the Cα atoms of the VHH-peptide model ( Fig . 7A ) and the trimeric structure of the late fusion intermediate [29] yielded a trimer model without any clashes ( Fig . 7B ) . Importantly , it revealed that the side chain of W100 is positioned in such a way that it could dip into the membrane bilayer in the same plane as gp41 MPER residues W678 , W680 and Y681 ( Fig . 7B ) . The latter have been proposed to insert into the membrane during the conformational changes of gp41 leading to membrane fusion [29] . We conclude that 2H10 binds a helical epitope of gp41 , which allows positioning of the CDR3 W100 towards the membrane . Due to the small size of the VHH , 2H10 may stay associated with gp41 until late stages of membrane fusion .
The HIV-1 envelope fusion subunit gp41 contains highly conserved epitopes within its MPER that are recognized by the potent and broadly neutralizing antibodies 2F5 and 4E10 . Although these bnAbs recognize linear epitopes , immunization with gp41 peptides or proteins has not yet produced such antibodies [107] . In this study , we isolated and characterized a VHH from a llama immunized with gp41 proteoliposomes . Similar gp41 proteoliposomes , with a different liposome formulation and gp41 construct , used in a previous immunization study of mice using a different immunization strategy , yielded sera with no significant neutralization [37] . The VHH 2H10 recognizes an epitope ( EQELLELDK ) that partially overlaps with the 2F5 epitope [20] , [24] and binds to its epitope present in various Env conformations with low nanomolar affinity . One hypothesis is that neutralizing antibodies directed against MPER target epitopes that are only transiently exposed during the entry process [60] , [104] . Consequently their binding to the native Env structure may be weak , as demonstrated for mAb 2F5 , which interacts only with few Env gp140 oligomers [60] and the absence of significant binding of MPER antibodies to membrane-anchored Env with the exception of 10E8 [9] . Because neither monomeric 2H10 nor bi-2H10 interacted with cleaved or uncleaved JR-FL Env , we hypothesize that their target is an Env conformation induced by receptor binding . In contrast 2H10 binds to recombinant Env gp140 derived from strain 92UG037 . Binding of 2H10 is best described by a two-state reaction of a low affinity contact followed by a high affinity interaction . Thus 2H10 may induce its epitope in the soluble version of gp140 , which may be facilitated by the fact that the C-terminal end of this recombinant gp140 protein is not constrained by the transmembrane region . Due to the potentially transient nature of the 2H10-epitope exposure , we tested binding to a soluble form of the fusion intermediate conformation of gp41 , gp41INT [108] . SPR measured a 30 nM affinity for 2H10 binding to gp41INT , which is approximately 30 times lower than the 2F5 Fab affinity for the fusion intermediate conformation of gp41 from strain 92UG037 , 92UG-gp41-inter-Fd [60] . Bi-2H10 showed a more than tenfold increase in binding to gp41INT , but still a lower affinity when compared to the KDs of Fab or scFv interactions of 2F5 and 4E10 with 92UG-gp41-inter-Fd [60] . It is thus possible that the lower affinity of monovalent 2H10 for gp41INT may affect its neutralization capacity of any of the tested HIV-1/SHIV strains . However , increasing the affinity by an increased avidity as demonstrated for bi-2H10 led to the neutralization of selected strains including some Tier 2 viruses with IC50 values ranging from 0 . 2 to 49 µg/ml , when using the stringent TZM-bl cell assay . Bi-2H10 may achieve HIV-1 entry inhibition by binding two gp41 molecules from the same trimer or cross link two gp41 trimers . The crystal structure of the 2H10 VHH highlighted the longer than usual CDR3 that displays a solvent exposed tryptophan ( W100 ) at its tip . Mutagenesis showed that W100 is not required for interaction with gp41INT or gp140 . However , bi-2H10 with a W100A mutation either no longer neutralized or showed a largely reduced neutralization potency ( increases of IC50 values ∼6- and 4-fold ) against Tier 1 and Tier 2 strains despite its wild-type-like interaction with gp41INT . The presence of W100 in the CDR3 of 2H10 is reminiscent of hydrophobic determinants within the CDR3 H3 regions of mAbs 2F5 and 4E10 , which are not required for gp41 interaction , but are important for neutralization activity [48]–[50] . In case of mAb 2F5 , single Ser substitutions resulted in a 15- to 500-fold reduction in neutralization potency , while double mutations to Ser completely abrogated 2F5-mediated neutralization [50] . Even though most HIV-1 strains were no longer neutralized by the bi-2H10 W100A mutant , it is conceivable that a W100 to Ser mutation may have been even more efficient in affecting neutralization due to the polar propensity of Ser . Similarly , single mutations in the CDR3 of mAb 4E10 reduced its neutralization potency [48] and it was noted that Asp substitutions exerted a greater effect on neutralization potency than Ala substitutions [49] . The effect of the bi-2H10 W100A mutant also suggests that neutralization by bi-2H10 is not solely due to heteroligation of two antigen binding domains via an increased avidity [59] , [109] . We therefore propose that CDR3 W100 plays a crucial role in neutralization by bi-2H10 , which resembles the role of the hydrophobic CDR3 of mAbs 2F5 and 4E10 in neutralization . Numerous studies suggested that the hydrophobic CDR3 residues are implicated in lipid/membrane interaction thereby facilitating antigen binding [46] , [110] , [111] . One possibility is that MPER itself becomes membrane-embedded during the fusion reaction and MPER-specific antibodies need to recognize their epitope within the membrane context [52] , [112] . We therefore presented MPER in a lipid bilayer context to allow selection of antibodies that can recognize lipid and protein determinants . However , despite the presence of W100 in CDR3 , 2H10 as well as the control mAb 2F5 did not show any detectable lipid binding specificity in vitro . All HIV-1 strains that were neutralized by bi-2H10 have the epitope sequence determined by pepscan analyses except for 92UG037 . A9 which has Q658 replaced by a lysine . However , SPR measurements of 2H10 to gp140 env derived from 92UG037 . A9 corroborate 2H10 binding . The discrepancy may be explained by the use of cyclic peptides in the pepscan analysis , which showed a lower affinity in the ITC experiment . Therefore a Q658K exchange may have had a larger effect on binding within the cyclic version than within natural contexts of the epitope . Sequence analysis of 2300 Env sequences revealed that ∼28% have the epitope 657-E ( Q/K/H ) -XX-LE-XX-K-665 . Considering only B clades , the number of isolates covered would rise to ∼61% . In comparison , 2F5 and 4E10 have been shown to neutralize 68% and 97% of all clade B strains tested and 60% and 98% of all clades , respectively [1] . The combined structural approach using peptide modeling , NMR , docking and mutagenesis showed that 2H10 interacts with a helical linear gp41 epitope . VHH maturation played an important role in optimizing antigen binding , because somatic mutations involve both CDR and framework residues at the interaction site . High framework mutation rate has been observed in broadly neutralizing human antibodies as well [5]–[7] , [113] . The helical epitope conformation is present in the late fusion intermediate conformation of gp41 , gp41528–683 [29] that interacts with 2H10 . This is unexpected , because 4E10 whose epitope is exposed and accessible in gp41528–683 [29] does not interact with this conformation due to molecular clashes of the light chain with gp41 heptad repeat region 1 [29] . The fact that the small size of 2H10 allows binding to gp41528–683 may constitute a disadvantage for using small llama VHH over complete antibodies; the VHH may thus less efficiently prevent refolding of gp41 into the six helical bundle post fusion conformation . Nevertheless , an interesting feature surfaced from the 2H10-gp41528–683 complex model . The model poses 2H10 CDR3 W100 in the same plane as gp41 MPER residues W678 , W680 and Y681 , which may insert into the membrane during the fusion process [29] . This indirectly corroborates the hypothesis that the necessity of W100 for HIV-1 neutralization is in agreement with the suggestion that part of MPER may be membrane embedded . Because MPER , as observed in the late fusion-intermediate gp41528–683 structure , may be already membrane-embedded at an earlier stage , anti-MPER antibodies such as 2H10 may act much earlier to block the fusion process . Alternatively , bi-2H10 binding may prevent membrane extraction of MPER , which is likely required to finalize membrane fusion [29] . The epitope of 2H10 overlaps with the helical epitope of mAb 13H11 , which is , however , non-neutralizing due to the absence of a long hydrophobic heavy chain CDR3 [114] . The helical epitope of 2H10 is part of the helical epitope recognized by the broadly neutralizing anti-MPER mAb 10E8 , isolated from a patient [9] . In this MPER conformation , which may represent the structure of MPER within native Env trimers , residues 656 to 683 form two short L-shaped helices of which the C-terminal helix provides most antibody binding contacts . Most notably , mAb 10E8 employs hydrophobic CDR3 residues Phe 100A and W100B for interaction with its epitope . Because mAb 10E8 displays no autoreactivity it presents a new class of potent anti-MPER antibodies [9] . The VHH 2H10 , which was elicited by gp41 proteoliposomes as antigen stresses the importance of a membrane component in generating anti-MPER neutralizing antibodies . Although there have been many attempts to generate neutralizing MPER-specific anti gp41 antibodies in animals , no protein-based immunization scheme has produced sera or antibodies that neutralized HIV-1 in the stringent TZM-bl assay [16] , [17] , [107] . In this study , we have demonstrated that it is possible to elicit anti-MPER antibodies that neutralize HIV-1 by employing proteoliposomes containing the native Env transmembrane region that may orient MPER optimally with respect to the lipid bilayer , which in turn is important for antibody neutralization [55] . Although bi-2H10 lacks the potency and breadth of 2F5 , 4E10 or 10E8 , optimization of the immunization protocol such as longer immunization schemes which may produce more extensive somatic mutations could yield antibodies with higher breadth and potency . | Due to the absence of an effective vaccine or cure for acquired immunodeficiency syndrome ( AIDS ) , HIV-1 infections still result in high mortality . Two antibodies , 2F5 and 4E10 , previously isolated from HIV-1 infected patients , prevent infections by binding to the MPER of gp41 , a part of the virus that is difficult to access and only transiently exposed . Here , we immunized llamas with a gp41-based immunogen and subsequently isolated a small antibody fragment ( VHH ) that can easily access and recognize the MPER . We showed that a unit of two VHH , named bi-2H10 , was indeed capable of preventing HIV-1 from infecting cells . We determined the three dimensional structure of the VHH and mapped its interaction site to an MPER region that overlaps with the 2F5 epitope . The 2H10 VHH displays a membrane binding component important for neutralization that resembles that of 2F5 . In conclusion , we have developed an immunogen and a small antibody that may have great potential for development of novel anti-HIV/AIDS vaccines and treatments . | [
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| 2013 | A gp41 MPER-specific Llama VHH Requires a Hydrophobic CDR3 for Neutralization but not for Antigen Recognition |
A homologue of the ecdysone receptor has previously been identified in human filarial parasites . As the ecdysone receptor is not found in vertebrates , it and the regulatory pathways it controls represent attractive potential chemotherapeutic targets . Administration of 20-hydroxyecdysone to gerbils infected with B . malayi infective larvae disrupted their development to adult stage parasites . A stable mammalian cell line was created incorporating the B . malayi ecdysone receptor ligand-binding domain , its heterodimer partner and a secreted luciferase reporter in HEK293 cells . This was employed to screen a series of ecdysone agonist , identifying seven agonists active at sub-micromolar concentrations . A B . malayi ecdysone receptor ligand-binding domain was developed and used to study the ligand-receptor interactions of these agonists . An excellent correlation between the virtual screening results and the screening assay was observed . Based on both of these approaches , steroidal ecdysone agonists and the diacylhydrazine family of compounds were identified as a fruitful source of potential receptor agonists . In further confirmation of the modeling and screening results , Ponasterone A and Muristerone A , two compounds predicted to be strong ecdysone agonists stimulated expulsion of microfilaria and immature stages from adult parasites . The studies validate the potential of the B . malayi ecdysone receptor as a drug target and provide a means to rapidly evaluate compounds for development of a new class of drugs against the human filarial parasites .
Diseases caused by the human filarial parasitic nematodes are a significant public health problem faced by developing countries . Recent reports estimate that there are over 140 million individuals suffering from human filarial parasites in over 80 countries worldwide . Approximately 1 billion people are at risk for contracting the filarial infections [1] . Lymphatic filariasis ( caused by infection with Brugia malayi , Brugia timori or Wuchereria bancrofti ) and onchocerciasis ( caused by infection with Onchocerca volvulus ) are severely debilitating diseases , together resulting in a loss of 5 . 7 million disability adjusted life years [2] . Both lymphatic filariasis ( LF ) and onchocerciasis have been designated as neglected tropical diseases by the international community , and both have been targeted for elimination in the London Declaration on Neglected Tropical Diseases ( NTDs ) [3] . The efforts to eliminate LF and other NTDs have generally relied on mass drug administration ( MDA ) programs . Currently , a combination of ivermectin plus albendazole ( ALB ) or diethylcarbamazine ( DEC ) plus ALB are employed for MDA for filariasis ( depending upon the region ) , while ivermectin ( IVM ) monotherapy is the standard for MDA targeting onchocerciasis . However , recent clinical trials with a single-dose triple-drug therapy of DEC , IVM and ALB [4] , and twice-yearly treatment with ALB + IVM [5] have shown very promising results when compared to the two-drug regimens currently used by traditional filariasis MDA programs . Though MDA programs have on the whole been well formulated and executed , they face potential challenges that may threaten their ultimate success . These include the development of resistance to the limited number of drugs that are deployed by the MDA programs [6] and limits on the use of MDA in certain areas . For example , DEC + ALB is used to treat LF by MDA programs throughout India , South America and Southeast Asia [7 , 8] . However it cannot be used in Africa , as it causes severe ocular side effects and systemic complications in individuals co-infected with O . volvulus , the causative agent of onchocerciasis , a disease that is endemic throughout most of sub-Saharan Africa [9 , 10] . Similarly , ivermectin , which is the only drug used by the MDA programs worldwide to treat onchocerciasis , cannot be easily used in much of Central Africa . This area is endemic for the eyeworm Loa loa , and treatment of L . loa infected individuals with ivermectin can result in severe neurological reactions , including coma and death [11] . Thus , new treatments are desperately needed for these infections . Insect growth regulators ( IGRs ) have been used in veterinary medicine to treat ectoparasites like ticks , fleas , lice and mites . The IGRs interfere with the larval molt and embryogenesis by targeting one of two pathways: ( i ) chitin inhibitors acting on cuticle synthesis and degradation , and ( ii ) hormonal ( ecdysone and juvenile hormone ) analogs [12] . Ecdysteroids are master regulators of development in arthropods , and are thought to also play a central role in controlling development in other organisms in which molting is a central feature of the life cycle ( the ecdysoans ) [13] . In insects , molting and other developmental processes ( including embryogenesis ) are controlled through variation in the levels of the molting hormones , or ecdysteroids , which induce molting , and the juvenile hormones , which inhibit molting [14 , 15] . This process is mediated through a heterodimer of the ecdysone receptor ( EcR ) and ultraspiracle , the homologue of retinoid X receptor ( RXR ) which controls the transcriptional activity of the developmental genes regulating molting and metamorphosis [16] . The fact that ecdysis is a central developmental pathway in insects and is absent in vertebrates has made it an attractive target for the development of compounds that might act selectively against invertebrates [17 , 18] . Thus , the agricultural industry has targeted the EcR in pesticide development , as insects represent one of the largest classes of ecdyzoans on Earth . This high degree of species-specific activity makes the EcR an excellent target for pest management . For example , tebufenozide has insecticidal activity against lepidopteran pests but shows low activity against the hymenopteran insects [18] . Several lines of evidence suggest that like insects , many developmental processes in parasitic nematodes may be controlled in part by ecdysteroids . First , ecdysone and related compounds have been found in many parasitic nematodes , including O . volvulus ( for a review , see [19] ) . Second , ecdysteroid levels vary during nematode development . In Ascaris suum , the level of 20-hydroxyecdysone ( 20E ) has been shown to peak just before the third and fourth larval molts , similar to the temporal pattern seen during the larval molts in insects [20] . Third , parasitic nematodes are capable of catabolizing ecdysone [21] . Fourth , ecdysteroids have been shown to have developmental effects on parasitic nematodes . For example , ecdysone was shown to stimulate microfilarial release in Brugia pahangi and to promote embryogenesis in ovaries of D . immitis adult females [22] . Fifth , a homolog of the EcR has been identified and proven to be functional in Brugia malayi [13 , 23] . Homologues of the EcR have also been identified in the genomes of other human filarial parasites . Previous studies have demonstrated that treatment of the filarial worm with ecdysteroids can affect embryogenesis [23] , molting [24] and viability of adult parasites [25] , suggesting that the EcR might represent an attractive chemotherapeutic target against filarial infections . In this study , we validate the Brugia malayi ecdysone receptor ( BmaEcR ) as a chemotherapeutic target and report progress towards the development of a high-throughput screening assay to identify agonists and antagonists against the BmaEcR . We also report the development of a homology model of the BmaEcR that may be useful in optimizing leads identified by high throughput and in-silico approaches .
All animal work was conducted according to guidelines outlined by the National Institutes of Health Office of Laboratory Animal Welfare , and was approved by The Institutional Animal Care and Use Committee ( IACUC ) of the University of South Florida , under protocol IS00000261 . Brugia malayi infective larvae ( L3 ) were obtained from the Filariasis Research Reagent Resource Center ( FR3 ) at the University of Georgia , USA . A total of four male gerbils were infected intraperitoneally with 150 L3s each , under aseptic conditions . The experimental animals ( n = 2 ) received 20E ( Adipogen , CA ) at a dose of 5mg/kg/day/animal for 150 days . The control animals ( n = 2 ) received ethanol , the vehicle used for dissolving 20E . To avoid the potential for trauma induced by having to carry out multiple gavages on each animal , Alzet mini-osmotic pumps ( Model number 2006 ) were used to provide a steady supply of 20E in ethanol or ethanol alone to the infected animals . The pumps were surgically implanted on the dorsal aspect of the gerbils two days post-infection . The pumps were replaced every 42 days following the manufacturer’s instructions . Animals were euthanized after 150 days post-infection and peritoneal gavage was performed to recover microfilariae . Necropsy was then conducted to recover adult worms from the peritoneal cavity . Previous studies utilized a system in which plasmids were transiently transfected into NIH3T3 cells ( American Type Culture Collection- www . atcc . org; CRL-1658 ) [13] . However , in our studies the plasmids were transfected into HEK293 cells ( American Type Culture Collection- www . atcc . org; CRL-1573 ) as we observed improved growth and enhanced assay characteristics when these cells were used . To adapt the transient transfection assay into a high throughput screen , a stable mammalian cell line was developed , in which all three of the necessary constructs were integrated into the HEK293 genome , employing the Gateway recombination system ( Invitrogen , CA ) . In brief , three cassettes were constructed . The first cassette consisted of the ligand-binding domain ( LBD ) of the BmaEcR fused to a heterologous GAL4 DNA binding domain . This fusion ORF was placed under the control of the CMV intermediate early promoter and terminated with an Sv40 poly-A addition signal . The second cassette contained the open reading frame ( ORF ) for the human RXR chimera fused to the VP16 activation domain . This chimeric ORF was placed under the control of the SV40 promoter and terminated with the bovine growth hormone poly-A addition signal . Finally , a reporter Gaussia princeps secreted luciferase reporter ORF was created that was preceded by five copies of the GAL4 response element and a minimal promoter . The reporter ORF was terminated with the human B globin poly-A addition signal . All cassettes were prepared using standard molecular cloning procedures and amplified by PCR with appropriate primers to produce products ready for recombination cloning . The three cassettes were then cloned by recombination into three pDONR vectors of the multisite Gateway Pro system ( S1 Fig ) . The sequence of the resulting entry clones were confirmed and the functionality of the entry clones confirmed by transiently transfecting all three pDONR plasmids into HEK293 cells and testing for reporter activity in cells cultured in the presence and absence of 20E , as described below . The entry vectors were then inserted by three-way recombination into the destination vector pJTI Fast Dest ( Invitrogen , CA ) to create a destination vector containing all three cassettes ( S1 Fig ) . This construct was validated by DNA sequencing and by functional assays , as described above . The destination vector was then used to create a stable mammalian cell line in HEK293 cells , using the Jump In Fast Gateway Targeted Integration System ( Invitrogen , CA ) . Clones were selected in the presence of 30ug/mL hygromycin following the manufacturer’s protocol , and individual clones selected and screened for reporter expression in the presence and absence of 20E , in order to identify the clone producing the greatest signal to noise ratio and minimal well-to-well variation . The clone that exhibited highest signal to noise ratio was cultured and maintained in T75 flasks using 1x MEM media fortified with 10% FBS . An ecdysone analog library consisting of compounds found to be active against various insect species was tested against the BmaEcR [26–28] . Thirty-five thousand cells of the cell line developed as described above were seeded into each well of a 96 well tissue culture plate . The cells were grown under the same conditions as described above . Cells were incubated at 37°C for 18–24 hours until they reached a density of 70–90% . Compounds were added to wells containing the cells at a concentration of 10μM and the cells incubated in the presence of the compounds for 48 hours . Control wells received ethanol , the vehicle of 20E . A total of 10μl of the culture media was removed from each well , and assayed for the presence of secreted luciferase using the renilla assay reagent in the Dual Luciferase assay kit ( Promega , WI ) following the manufacturer’s protocol . Each compound was assayed in triplicate . Student’s t-test was used to calculate the significance of any agonist activity observed , and the Bonferroni correction was applied to the results to adjust for multiple comparisons . Tenfold dilutions of compounds that resulted in a statistically significant increase in reporter activity ( p < 0 . 05 , Bonferroni corrected ) were then assayed in triplicate to calculate EC50 values . To assess cytotoxic effects of the compounds on the mammalian cell line , cell viability was assayed with Alamar Blue ( Thermo-Fisher Scientific , MA ) according to manufacturer’s protocol . Z’ values for the assay were calculated as previously described [29] . The tertiary structure of the BmaEcR was predicted using the Schrödinger’s Prime 3 . 1 comparative homology modeling module ( Prime . Schrodingers , LLC New York , NY version 3 . ed . 2012 ) [30] . Due to the lack of an experimental crystal structure of the BmaEcR , a search for suitable template structures upon which to model the BmaEcR tertiary structure was undertaken using BLAST ( Basic Local Alignment Search Tool ) [31] . The hemipteran Bemisia tabaci EcR ( PDB ID: 1Z5X ) was predicted to be the template with highest homology to BmaEcR ( BLAST-bit score: 145 . 2 ) with excellent residue conservation . Based on the template secondary structure , Clustalω [32] was used to optimize the placement of the BmaEcR ( LBD ) α-helices and β-sheets using a hidden Markov model ( HMM ) . After the Clustalω alignment , backbone atoms for aligned regions and conserved residue side chain atoms were directly transferred from the template to the query sequence to create an initial structure , followed by adding insertions and closing gaps using a knowledge based approach . The B . tabaci EcR native substrate ponasterone A was retained in the binding site during the homology model construction . The prime serial loop sampling protocol was used to refine one particular loop of importance ( residues 162–175 ) that constitutes an intrinsic part of the binding site . To alleviate possible steric clashes between residues after model construction , Truncated Newton Conjugate Gradient ( TNCG ) minimization was performed using implicit solvent ( VSGB 2 . 0 ) with the OPLS-2005 force-fields , until convergence was reached ( < 0 . 01 kcal . mol-1 Å-1 ) [33] . The quality of homology model was verified by generating Ramachandran plots and further compared to the homology model generated with the I-Tasser web-server ( details in S1 Appendix ) for additional structural validation purposes [34] . To further refine the homology model , Molecular Dynamics ( MD ) simulations were performed for 65 ns while monitoring structural properties such as radius of gyration ( Rg ) , root mean square deviation ( RMSD ) , root mean square fluctuation ( RMSF ) , and total energy of the system ( details in S1 Appendix ) . A snapshot of the BmaEcR homology model that was most representative of the complete 65 ns simulation ( i . e . , a snapshot that likely details biologically relevant conformations ) was selected according to a simulation-clustering scheme ( S1 Appendix ) ; this structure was used for subsequent molecular docking studies . All ligand structures were prepared using Schrödinger’s LigPrep 2 . 3 [35] module to produce appropriate 3D structures with correct stereochemistry , protonation states , and ring conformations . All ligands were then virtually docked in the BmaEcR homology model using the Glide 5 . 8 docking program . Glide uses a user-defined grid to determine the shape and properties of the binding site . The BmaEcR grid was defined by selecting the centroid of ponasterone A with otherwise default settings . The ligands were first docked and scored according to Glide SP ( standard precision ) and then re-docked with the Glide XP ( extra-precision ) protocol . Glide Scores ( GScores ) were calculated to estimate the relative predicted binding affinity amongst the ligands for the EcR binding site [36] . As part of the default Glide docking procedure , the van der Waals radii of non-polar hydrogen atoms were scaled by a factor of 0 . 8 and all calculations were performed with the OPLS-2005 force field . To illustrate the utility of combining virtual screening results with experimental assays , we generated a small virtual compound library to identify potential novel BmaEcR LBD hits . This small library was assembled from searches done on extensive and diverse online databases ( i . e . , the PDB and PubChem ) . The initial search used the ProBiS-CHARMMing / ProBiS-Ligands structural bioinformatics tool ( available at http://probis . nih . gov ) , which compares the binding site of a provided structure to all known protein binding sites in the non-redundant PDB ( currently 42 , 270 in total ) [37 , 38] . The BmaEcR homology model was submitted to ProBiS , with the EcR LBD identified as the binding site of interest . Four proteins were identified with similar binding sites to the EcR LBD and each of these binding sites had several associated ligands; all of these were added to our virtual library . Secondly , a ponasterone A structure similarity search was performed on the PubChem compound library ( consisting of ~83 million compounds ) [39] . Ultimately , 104 compounds were identified via the two search protocols and added to the virtual library . This library was then virtually screened according to the following protocol: ( 1 ) compounds were docked in the EcR LBD with Glide SP , ( 2 ) structures that resulted in binding scores of -7 . 25 kcal/mol ( the binding score of 20-hydroxyecdysone ) were re-docked into the EcR LBD using Glide XP docking , and again ( 3 ) those structures with Glide XP docking scores of less than -7 . 25 kcal/mol were docked into the EcR LBD with a flexible ligand-flexible receptor docking method known as Induced Fit Docking ( IFD ) [40 , 41] , which allows for better binding mode prediction , albeit at a higher computational cost [42–44] . Four of the compounds identified by the virtual screen ( AM580 , BMS493 , 22-R-Hydroxycholesterol , TTNPB ) were found to be readily commercially available . These were obtained from Sigma-Aldrich ( St . Louis MO , USA ) , and tested for activity and cytotoxicity using the assays described above . Adult females were cultured in a 6-well plate ( five parasites per well ) in CF-RPMI media ( RPMI 1640 supplemented with 25 mM HEPES buffer , 2 mM glutamine , 100 U/ml streptomycin , 100 μg/ml penicillin , 0 . 25 μg/ml of amphotericin B , and 10% heat-inactivated fetal calf serum ) . Two biological replicates were conducted per treatment group . Parasites were allowed to acclimatize for 24 hours before treatments commenced . After the acclimatization period , Muristerone A , Ponasterone A and 20E ( separate wells ) were added to a final concentration of 10 μM to the experimental wells . The control wells received ethanol ( the vehicle for ponasterone A , muristerone A and 20E ) . The parasites were then cultured for an additional four days . The culture media was changed every 24 hours . Three 20ul aliquots from each biological replicate were removed every 24 hours from each well and the number of progeny present ( aborted eggs and embryos , immature microfilariae and mature microfilariae ) counted . Thus , three technical replicates were performed on each biological replicate at each time point .
Previous studies have demonstrated that 20E stimulated expulsion of immature progeny ( eggs , embryos and immature microfilariae ) from adult female parasites cultured in the presence of 20E [45] . However , by analogy to its role in insects , the BmaEcR could be hypothesized to play an important role in molting in the filarial parasites as well . To test this hypothesis , the effect of 20E on the development of infective larvae to the adult stage was assessed in gerbils intraperitoneally infected with B . malayi infective larvae . Infected animals were given 20E at a dosage of 5mg/kg/day for a total of 150 days , the time necessary for infective larvae to develop into fertile adult parasites . After 150 days , the animals were euthanized and the peritoneal cavity was examined for parasites . A total of 25 and 28 adult female parasites respectively were recovered from the peritoneal cavity of the two control animals given only ethanol , the vehicle for 20E ( Fig 1A ) . In contrast , no parasites were recovered from one of the animals given 20E , while a single incompletely developed female parasite was recovered from the second 20E treated animal ( Fig 1B and 1C ) . In keeping with this observation , a large number of mature microfilariae were recovered from both control animals while no microfilariae were observed in the peritoneal cavity of the two treated animals . Previous studies have demonstrated that mammalian cells transiently transfected with three plasmids containing ORFs for the BmaEcR LBD domain , a heterodimeric RXR partner and a secreted luciferase reporter exhibited an up-regulation of reporter activity when cultured in the presence of ponasterone A , a potent ecdysone analog [13] . Mean luciferase activity in the media of HEK293 cells transiently transfected with these three plasmids increased roughly seven-fold when compared to the reporter activity in the media of cells cultured in the absence of 20E ( Fig 2A ) . The Z’ calculated for this assay was 0 . 72 , suggesting that an assay based upon such a platform might be suitable for use in a high throughput format to identify agonists or antagonists for the BmaEcR . However , the transient transfection process varied in efficiency from day to day and the need to repeatedly transiently transfect cells limited the throughput of the assay , both of which were impediments to adapting the assay to a high throughput format . To overcome these obstacles , a stable cell line was created that incorporated the three cassettes necessary for the assay ( the BmaEcR LBD , the RXR partner and , the reporter ) into the genome of HEK293 cells . Assays using the stably transfected cells revealed a performance comparable or better than that of the assay using the transiently transfected cells , with a signal to noise ratio ranging from 4 . 5 to 5 . 5 and a Z’ that ranged from 0 . 78 to 0 . 88 when conducted on different days ( Fig 2B ) . The stably transfected cells were used to screen a focused library comprised of 40 compounds belonging to the diacylhydrazine , tetrahydroxyquinoline and the steroidal ecdysone analog families . These compounds were tested for activity against BmaEcR using the screening strategy described in Materials and Methods . The initial screen identified seven compounds that resulted in a significant increase in reporter activity in the stable cells cultured as compared to the activity in the cells cultured in the presence of solvent alone ( Fig 3 , p < 0 . 05 , with Bonferroni correction for multiple comparisons ) . In particular , muristerone A and ponasterone A demonstrated a higher level of activity than 20E in this assay . The compounds identified as agonists were then tested at a series of concentrations to determine the EC50 values . The EC50 values for the initial hits were low , with some exhibiting values that were in the sub-μM range ( Fig 4 ) . None of the compounds exhibited cytotoxicity at a concentration of 10 μM . A homology model of the BmaEcR LBD was then constructed as described in Materials and Methods . The Ramachandaran plots before and after MD simulations were created to discern potential changes in ϕ /ψ distributions . The number of residues within the favored and allowed regions increased from 89 . 6% to 98 . 7% as a result of MD simulations ( S2 Fig ) . Fig 5 presents the structure of the B . malayi EcR LBD homology model with Ponasterone A occupying the active site . The homology model was used for virtual screening studies of agonists identified with the molecular assay above to verify the model performance . The structure with the most favorable protein—ligand interaction energy based on the MD simulation ( according to an RMSD clustering scheme of the final 20 ns ) was chosen as the representative structure for molecular docking studies ( details in S1 Appendix ) . The 3D structures of the ligands were docked and scored using both Glide SP ( standard precision ) and Glide XP ( extra precision ) ( Fig 6 ) . The predicted lowest energy for each agonist ( ligand ) is described in detail in the S1 Appendix and S2 Table . A comparison of computational and experimental results is shown in Table 1 . A Pearson correlation coefficient of 0 . 78 was found between experimental log EC50 values and Glide XP docking scores , suggesting that the model predictions trended well with the data collected from the mammalian cell assay . As a first test of the utility of the model to identify potential compounds capable of interacting with the BmaEcR , a number of databases were initially screened as described in Material and Methods , resulting in a library of 104 potential hits . A total of 25 compounds survived the selection protocol described in Materials and Methods ( i . e . had Glide XP predicted binding scores more negative than -7 . 25 kcal/mol ) . The binding modes of these 25 compounds were further refined via flexible ligand—flexible protein docking ( i . e . , IFD ) . The docking scores for these 25 compounds are shown in S3 Table . Of particular note , two compounds found to be active were isolated without using the ligand’s structural information ( i . e . , were obtained via ProBis CHARMMing / ProBis Ligands ) . Out of these 25 compounds , four ( AM580 , BMS493 , 22-R-Hydroxycholesterol , TTNPB ) were commercially available . When tested on the cell-based assay , AM580 and BMS493 were active agonists with micromolar EC50 values ( Fig 7 ) . The remaining two compounds ( 22-R-Hydroxycholesterol and TTNPB ) were found to be cytotoxic at all concentrations tested , and could not be evaluated in the cell based assay . As mentioned above , previous studies have demonstrated that culturing gravid adult females in the presence of 20E induced expulsion of immature eggs , embryos and microfilaria . Ponasterone A and muristerone A were the compounds that gave a better performance than 20E in the cell assay as well as having a high predicted binding score in the active site of the BmaEcR homology model . Thus , it was of interest to determine if these compounds could elicit a similar response . As predicted , gravid adult female worms cultured in presence of 10 μM muristerone A and ponasterone A exhibited a significant increase ( p<0 . 001 , t-test ) in expulsion of aborted eggs , embryos and microfilariae as compared to the parasites cultured in the presence of the 20E ( Fig 8 ) .
Brugia malayi is an ecdysozoan that exhibits molting and developmental changes during the lifecycle similar to those seen in arthropods . Studies have shown a high expression of the BmaEcR transcripts in B . malayi in adult females , L3 , eggs and embryos and mature microfilariae [23] . In keeping with this expression pattern , recent studies have demonstrated that 20E affected egg development and embryognesis in adult female B . malayi [45] . The data presented above , though limited , suggests that 20E also interferes with the development of infective larvae to adult parasites , a process involving two molts . In these studies , animals infected with L3 , when treated with 20E for 150 days ( the time necessary for the parasites to develop from L3 to fecund adult parasites ) exhibited a dramatic decrease in the number of adult worms recovered after treatment , along with a complete inhibition of microfilarial production . While the experiment was designed to look for an effect at any point during this prolonged devellopmental process , it is likely that 20E was actually interfering with some temporally limited critical developmental processes ocurring during this period , such as the L3-L4 molt or the molt from L4 to immature adult parasites . Thus , treatment throughout the entire 150 day period may have not been necessary . In support of this hypothesis , L3-stage larvae cultured in-vitro in the presence of 20E molt earlier ( on day 7–8 ) when compared with larvae cultured in the absence of 20E , which between day 9–11 ( S4 Table ) . It is therefore possible that administration of 20E results in inappropriate activation of the BmaEcR , resulting in premature molting and death of the developing parasites in vivo . This result , when combined with the effect of 20E on cultured adult females , suggests that 20E has effects on both embryogenesis and development of B . malayi from the infective larvae stage to the adult stage . This finding would be similar to the role that 20E plays in insects , where is serves as a master regulator of both vitellogenesis and molting [46] . Further , these observations suggest that agonists of 20E may represent an important potential chemotherapeutic space for the development of novel drugs against the human filaria . Previous studies demonstrated that mammalian cells transfected with plasmids encoding three individual ORFs could be used to detect activation of the BmaEcR [13] . The initial assay utilized NIH3T3 cells , which grow slowly and are therefore not optimal for use in a high throughput assay format , where large quantities of cells are required . For this reason , we decided to attempt to utilize HEK293 cells , which grow more quickly and therefore would be more useful for high throughput assays . The assay , when optimized with HEK293 cells , demonstrated an improved signal to noise ratio and Z’ value when compared to the transient transfection assay utilizing NIH3T3 cells ( S5 Table ) . However , because the assay relied on transiently transfected cells , it was cumbersome and subject to day to day variations . For this reason , we developed a cell line into which all three of the necessary cassettes were inserted into the host cell’s genome . This assay exhibited performance characteristics that equaled or exceeded those of the original assay , making it much more amenable to a high throughput format . As a first test of the assay , a small library of compounds that have previously been shown to act as ligands for EcRs of other species , as well as ligands for other members of the NHR family were screened for activity aginst the BmaEcR . The results of this study suggested that members of the diacylhydrazine ( DAH ) family were active ligands for the BmaEcR . This class of compounds has been previously exploited by the agricultural industry to develop insecticides targeting insect EcRs . The DAHs cause precocious molting , leading to the death of the the target organism . These compounds also show high species specificity . Furthermore , there is no evidence of detrimental effects of DAH on the vertebrates . Taken together , these data suggest that members of the DAH family may have good potential as chemotherapeutic agents against the filaria . As described above , ecdysone analogs ( both steroidal and non-steroidal ) often demonstrate species-specific activities . In the studies reported here , we see similar evidence of species-specific activities for compounds active against the BmaEcR . For example , muristerone A , a known ecdysone analog , was one of the compounds in the library tested with the BmaEcR cell based assay . Muristerone A has demonstrated activity in the insect system [47] , but it did act as an agonist when tested against the BmaEcR . Apart from the DAH family , several other families of compounds , including the tetrahydroquinolones , butyl-benzamide and acylaminoketone , have been shown to act as ecdysone agonists . Other potential sources of chemotherapeutic agents may include compounds showing activity against other members of the NHR family; e . g . , natural extracts from plants and fungi . The development of the assay based upon the stable cell line described above should be useful in implementing high-throughput screens to identify potential additional leads from these sources . To complement the high throughput assay , a homology model was also created to permit in-silico screening of potential ligands against the BmaEcR . When using homology models to predict ligand binding , it is important to verify whether the resulting models are: a ) likely to be found in nature , b ) structurally relatable to other proteins in their family ( i . e . , have the same overall fold ) , and c ) are reproducible . Analysis presented via Ramachandaran plots and the direct comparison to known protein relatives together suggests that the homology model reported above meets these critera and that it closely represents the true structure of the BmaEcR LBD . Furthermore , the model reflects the known mechanism of binding of the EcR to its cognate ligand , in that upon binding of 20-hydroxyecdysone , a conformational change allows H12 to interact with the ecdysone response elements present in the promoters of ecdysone responsive genes , activating transcription . The fact that the model captures this mechanistic feature provides added support for its potential utility in virtual screening studies . In the analysis of predicted binding poses for each ligand , a pattern was observed: orientation of the potential steroidal ligands were optimized such that hydrophobic tails were buried in a hydrophobic region , while the central steroid nucleus structure was surrounded by more hydrogen bond donating/accepting side-chains . When the agonists identified in our preliminary screen were docked in the BmaEcR LDB , they interacted almost exclusively with the hydrophobic region , resulting in very favorable hydrophobic enclosure and lipophilic terms ( as listed in XP energy decomposition analyses , Table 1 ) . The DAH family occupied a very different orientation in the active site when compared to the steroidal ligands , yet displayed comparable activity to stimulate the receptor . Ponasterone A and muristerone A displayed a higher fold change activity than 20E in the stable cell line assay as well as being predicted to bind firmly in the active site of the BmaEcR homology model . In confirmation of this , ponasterone A and muristerone A increased expulsion of microfilariae and aborted eggs and embryos from adult female parasites cultured in their presence . This short-term culture assay can therefore be of use in providing a preliminary confirmation of the biological activity of potential leads identified in the high throughput and in-silico screens prior to undertaking time consuming and expensive in-vivo studies in infected animals . In conclusion , the data presented here validate the filarial ecdysone receptor as a potential chemotherapeutic target , and describe a series of assays that can be used to identify and validate potential lead compounds against this target . Given the success of the agricultural industry in targeting this receptor , these studies lay the groundwork for the exploitation of this receptor to develop anti-filarial agents that could supplement those currently used in the elimination programs worldwide . | The human filarial parasites are the causative agents of two neglected tropical diseases targeted for elimination by the international community . The current elimination programs rely upon the mass distribution of a limited number of drugs , leaving the programs open to failure in the event that resistance develops . Thus , there is a critical need for novel chemotherapeutic agents to supplement the current arsenal . The filarial parasites are ecdysozoans , whose developmental processes are controlled by a master regulator , the ecdysone receptor . Here we validate the potential of the filarial ecdysone receptor as a chemotherapeutic target and report the development of high throughput and virtual screening assays that may be used to compounds that target it . | [
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| 2016 | Identification of Ecdysone Hormone Receptor Agonists as a Therapeutic Approach for Treating Filarial Infections |
Activation of Notch1 signaling in neural progenitor cells ( NPCs ) induces self-renewal and inhibits neurogenesis . Upon neuronal differentiation , NPCs overcome this inhibition , express proneural genes to induce Notch ligands , and activate Notch1 in neighboring NPCs . The molecular mechanism that coordinates Notch1 inactivation with initiation of neurogenesis remains elusive . Here , we provide evidence that Prox1 , a transcription repressor and downstream target of proneural genes , counteracts Notch1 signaling via direct suppression of Notch1 gene expression . By expression studies in the developing spinal cord of chick and mouse embryo , we showed that Prox1 is limited to neuronal precursors residing between the Notch1+ NPCs and post-mitotic neurons . Physiological levels of Prox1 in this tissue are sufficient to allow binding at Notch1 promoter and they are critical for proper Notch1 transcriptional regulation in vivo . Gain-of-function studies in the chick neural tube and mouse NPCs suggest that Prox1-mediated suppression of Notch1 relieves its inhibition on neurogenesis and allows NPCs to exit the cell cycle and differentiate . Moreover , loss-of-function in the chick neural tube shows that Prox1 is necessary for suppression of Notch1 outside the ventricular zone , inhibition of active Notch signaling , down-regulation of NPC markers , and completion of neuronal differentiation program . Together these data suggest that Prox1 inhibits Notch1 gene expression to control the balance between NPC self-renewal and neuronal differentiation .
Notch1 signaling plays an important role in the maintenance of NPCs in the undifferentiated state by inhibiting neuronal differentiation [1]–[3] . In the developing nervous system , NPCs initially undergo symmetric divisions to expand the available pool of progenitor cells , while at later stages , during the neurogenic phase , they switch to asymmetric divisions generating one progenitor cell and one neuronal precursor destined to become a neuron . In these nascent neurons , proneural genes induce the expression of Notch ligands such as Delta1 and Jagged , which in turn activate Notch1 in neighboring NPCs . The interaction of Notch1 with its ligands results in cleavage of the Notch intracellular domain ( NICD ) by the presenilin/γ-secretase complex [4] and its translocation to the nucleus where it forms a complex with RBP-J ( CBF1 ) . This complex activates transcription of the basic helix-loop-helix ( bHLH ) genes Hes1 and Hes5 , which act as downstream effectors of Notch1 to down-regulate proneural gene expression and inhibit neurogenesis [5]–[9] . By this process , also known as lateral inhibition , newly produced neurons are thought to feedback and inhibit neighboring NPCs from differentiating , thus regulating the number of neurons born at a given time and maintaining a pool of NPCs for generation of subsequent neurons and glia . However , in these asymmetrically produced precursors that have been instructed to become post-mitotic neurons , active Notch1 signaling has to be terminated [2] , [10] . Although the reduced expression of Notch1 ligands by neighboring cells contributes to a decrease in Notch1 signaling , additional mechanisms should operate to protect nascent neuronal precursors against Notch1 signals from neighboring cells and maintain neuronal fate and specification . In vertebrate CNS the molecular mechanism that inactivates Notch1 signaling in cells destined to become neurons during the initial phases of differentiation remains largely unknown [10] . To this end , we hypothesized that proneural genes , which are expressed into the Notch1 positive area in the neural tube [5] , may activate downstream effector genes that will coordinate cell-autonomously the induction of neuronal differentiation with the inhibition of Notch1 signaling . In this regard two possible mechanisms , non-mutually exclusive , may be envisaged: either inactivation of key component ( s ) in the Notch1 signaling axis via protein-protein interactions and/or degradation , or down-regulation by transcriptional repression . Detailed expression studies have previously shown that newly-born neurons readily down-regulate Notch1 expression at the mRNA level so that cells that exit the ventricular zone ( VZ ) rapidly suppress Notch1 gene expression [11]–[13] . This observation indicates that a potential mechanism , operating in neuronal precursors to desensitize them from responding to Delta/Jagged signal-sending cells , is to down-regulate the expression of the Notch1 itself by transcriptional repression . Prox1 , a homeobox transcription repressor , acting downstream of proneural genes during neurogenesis , is a good candidate for such a function . Interestingly , Prospero , the Drosophila homologue of Prox1 in vertebrates , is a critical regulator of the balance between self-renewal and differentiation in neural stem cells ( NSCs ) [14] , [15] . Prospero suppresses the genetic program for self-renewal of NSCs and cell cycle progression , while it activates genes for terminal neuronal differentiation [15] , [16] . Neuroblasts that lack Prospero form tumors in the embryonic nervous system of Drosophila [15] . Notch signaling appears to have the opposite function by promoting self-renewal and neuroblast identity [17]–[19] . Most important , Prox1 in vertebrates is expressed in the boundaries between Notch1+ and Notch1− cells during development of the spinal cord and is transiently expressed in neuronal precursors but not in terminally differentiated neurons in this region [20]–[22] . Multiple lines of evidence suggest important roles for Prox1 in different aspects of embryonic development and morphogenesis , while mouse embryos deficient in Prox1 die at E14 . 5 , a critical time point in the development of many different organs [23] , [24] . Thus , Prox1 has been previously shown to have essential roles during lymphatic , hepatocyte , pancreatic , heart , lens , retinal , and spinal cord development [20] , [23] , [25]–[29] . Collectively , these observations indicate that Prox1 is involved in many key developmental decisions during organ morphogenesis . Here we provide functional evidence that Prox1 is directly implicated in Notch1 gene suppression during neurogenesis in the developing spinal cord and thus co-ordinately regulates Notch1 signaling inactivation with neuronal differentiation . In particular , we showed that physiological levels of endogenous Prox1 are sufficient to allow binding at the Notch1 promoter locus in vivo , and gain-and-loss-of-function studies suggest that Prox1 levels are essential for proper regulation of the endogenous Notch1 gene in vivo . Moreover , these studies indicate that Prox1-mediated Notch1 suppression controls cell cycle exit and differentiation of NPCs . Together our data imply that Prox1 is involved in the transition of NPCs from self-renewal to neuronal differentiation via direct suppression of Notch1 .
There is an inverse spatiotemporal correlation between expression of Notch1 and neuronal markers such as SCG10 , in a way that differentiated neurons expressing SCG10 appear later in development and localize in the mantle zone ( MZ ) , whereas Notch1 and Hes5 are highly expressed early in development while later reduced and detected in NPCs of the VZ ( Figure 1A , 1B , 1D , 1E , 1G , 1H and unpublished data ) [11]–[13] . Moreover , an intermediate zone of cells , negative for both markers , is also evident . These observations suggest that prior to acquisition of a terminally differentiated neuronal character the expression of Notch1 mRNA has to be strongly down-regulated . However , the molecular mechanism that actively directs suppression of Notch1 expression to relieve its inhibitory action on neurogenesis remains totally unknown . As Prox1 acts downstream of proneural genes and given the inverse correlation between proneural genes and active Notch1 signaling , we considered Prox1 as a potential factor that could be involved in this suppression . Comparative analysis of Prox1 , Notch1 , and SCG10 expression by single and double in situ hybridization revealed that Prox1 is expressed in the intermediate zone of cells between Notch1+ NPCs and SCG10+ post-mitotic neurons ( Figure 1 and Figure S1 ) . Most important , Prox1 positive signal is observed in a cell population that laminates and demarcates the area of Notch1+ cells , both in the chick and mouse embryonic spinal cord ( Figure 1J , 1K , 1N , 1O and Figure S1Q–S1T ) , probably marking a transitory cell population in the medio-lateral axis , that migrates towards the MZ to differentiate into post-mitotic neurons ( Figure 1R ) . This expression pattern is consistent with a role in suppressing Notch1 expression at the mRNA level . To initially test this hypothesis , we performed transcriptional assays in mouse neuroblastoma Neuro2A cells ( N2A ) . We first used previously published promoter-luciferase constructs for mammalian Notch1 [30] or Hes1 and Hes5 genes [31] , [32] to evaluate the effect of Prox1 on transcription regulation . After transient transfections of N2A with a mouse ( Figure 2A ) or human ( Figure S2A ) Prox1 expression vector , the activity of Notch1 promoter was significantly decreased , whereas a control construct carrying the human Thymidine-kinase ( TK ) promoter remained unaffected . Prox1 was also able to suppress the activities of Hes1 and Hes5 promoters , presumably via its action on Notch1 ( Figure 2A ) . To determine whether Prox1 affects in a similar manner the expression of endogenous genes , we stably overexpressed Prox1 in N2A . These cells showed reduced levels of Notch1 mRNA and protein levels , as well as Hes1 and Hes5 mRNA levels ( Figure 2B and 2C ) , indicative of a significant repression in Notch signaling . We next asked whether Prox1 directly interacts with the chromatin of the proximal Notch1 gene promoter by performing chromatin immunoprecipitation ( ChIP ) experiments ( Figure 2D ) . An antibody to Prox1 co-precipitated the proximal Notch1 promoter sequence in chromatin prepared from Prox1-transfected cells , but not from mock-transfected N2A cells . In addition , sequences from the distal 3′-end of the Notch1 gene were not precipitated with this antibody . Similarly , control IgGs were not able to precipitate the proximal Notch1 promoter sequence , further suggesting a direct interaction between Prox1 and chromatin from Notch1 promoter . To better understand the suppressive function of Prox1 on Notch1 promoter , different mutants of both Notch1 promoter construct and Prox1 protein were constructed and utilized in luciferase assays . We first identified on Notch1 promoter two overlapping consensus binding sites for Prox1 [33] , conserved between human , mouse , and rat , located 597 bp upstream of the translation start site . However , by mutating both of these sites ( mut-N1-Luc ) or deleting a 426 bp sequence containing these sites ( 535-N1-Luc ) , we observed that Prox1 is still sufficient to suppress Notch1 promoter activity ( Figure 2E ) . Furthermore , deletion of the DNA binding domain ( DBD ) from the carboxy terminal of Prox1 protein was not sufficient to relieve the Prox1-mediated suppression in Notch1 promoter activity ( Figure 2E and Figure S2B ) , indicating that Prox1 acts on Notch1 as transcriptional co-repressor via another factor . There are two previously reported transcription factors that utilize Prox1 as co-repressor via direct interactions to suppress gene expression in other systems , NR5A2 ( Nuclear Receptor 5A2 ) and SF1 ( Steroidogenic Factor 1 ) , both belonging to the family of orphan nuclear receptors [34]–[36] . NR5A2 is able to activate Notch1-luc construct in N2A cells ( Figure S2D ) and is expressed in the mammalian CNS ( Figure 3B and Figure S3 ) [37] . Most important , both wt Prox1 and ΔDBD-Prox1 are sufficient to suppress the NR5A2-mediated induction of Notch1-luc construct ( Figure 2F ) . By performing ChIP experiments with the NR5A2 antibody on NR5A2-transfected and mock-transfected N2A cells , we showed that NR5A2 specifically binds to the proximal Notch1 promoter sequence ( Figure 2G ) , indicating that Prox1 is likely to be recruited on the Notch1 promoter to suppress its activity via direct interactions with NR5A2 . Moreover , by using a series of deletion mutants for the Prox1 protein in luciferase assays , we were able to map the repressive function in the N-terminal domain ( Figure 2H and Figure S2C ) . It has been shown that Prox1 achieves its repressive action on gene promoters via a previously described domain in the N-terminal end of the protein , known as Repression Domain ( RD ) [35] , [38] , which is not involved in the direct interaction with the NR5A2 [35] . Deletion of this domain abolishes the ability of Prox1 to suppress Notch1 promoter or the NR5A2-mediated induction of Notch1-luc ( Figure 2H and Figure S2E ) , and instead Prox1ΔRD is now able to enhance Notch1 promoter activity ( Figure 2H ) . It has also been previously reported that the RD domain has a repressive function on transcription by interacting and utilizing the activity of Histone Deacetylase 3 ( HDAC3 ) [35] , [38] . Consistently , treatment of N2A cells with an HDAC inhibitor ( trichostatin-A , TSA ) is sufficient to derepress endogenous Notch1 gene transcription and Notch1-luc vector activity , as well as to abolish the Prox1-mediated Notch1 suppression ( Figure 2I and Figure S2F and S2G ) . Moreover , HDAC3 over-expression suppresses Notch1 promoter activity , which is further aggravated by Prox1 co-expression ( Figure 2J ) , indicating that HDAC3 synergizes with Prox1 to suppress Notch1 promoter . We then directly addressed whether Prox1 interacts with HDAC3 in the mouse CNS . First , by performing RT-PCR , Western blot , and immunostainings , we showed that HDAC3 is expressed in mouse embryonic spinal cord and mouse NPCs isolated from the same tissue and cultured in vitro ( Figure S4 and unpublished data ) . In addition , a co-immunoprecipitation assay showed that Prox1 could be co-precipitated with HDAC3 from protein extracts of mouse E12 . 5 CNS tissue ( Figure 2K ) . Moreover , by performing ChIP experiments with the HDAC3 antibody on HDAC3-transfected and mock-transfected N2A cells , we showed that HDAC3 specifically binds to the proximal Notch1 promoter sequence ( Figure 2L ) . These data indicate that endogenous Prox1 and HDAC3 proteins indeed form a complex in the developing mouse spinal cord and that Prox1 utilizes HDAC3 inhibitory activity on transcription to suppress Notch1 gene expression . We next investigated whether physiological levels of endogenous Prox1 and NR5A2 are sufficient to allow binding at the Notch1 promoter locus in vivo . Thus , by performing in vivo ChIP experiments , we showed that endogenous Prox1 protein directly interact with the Notch1 promoter locus in the mouse embryonic CNS . In particular , an antibody to Prox1 co-precipitated the proximal Notch1 promoter sequence , but not the 3′ Notch1 coding sequence , in chromatin prepared from E12 . 5 mouse CNS ( Figure 3A ) . To address the same question for NR5A2 , we first showed that this gene is expressed in E12 . 5 mouse embryonic spinal cord and NPCs isolated from the same tissue and cultured in vitro ( Figure 3B and Figure S3 ) . Similar to Prox1 results , an antibody to NR5A2 was sufficient to specifically precipitate the proximal Notch1 promoter sequence in chromatin prepared from E12 . 5 mouse CNS ( Figure 3C ) . Therefore we conclude that Prox1 and NR5A2 specifically bind in vivo the Notch1 gene promoter . We next tested whether Prox1 and NR5A2 could form a complex on chromatin over the Notch1 promoter locus by performing sequential ChIP assays ( re-ChIP ) in the mouse embryonic CNS . In these experiments , Prox1 , NR5A2 , or control IgG containing chromatin , purified by the first immunoprecipitation , was eluted from the protein A/G beads and subjected to a second immunoprecipitation with antibodies recognizing NR5A2 or Prox1 in a crosswise manner . As shown in Figure 3D , re-ChIP for NR5A2 following an initial ChIP for Prox1 generated specific enrichment of the Notch1 promoter sequence , but not of the 3′ Notch1 coding sequence or Notch1 promoter after initial ChIP with control IgG . Reciprocal re-ChIP assays , in which the order of the antibodies was inverted , generated identical results ( Figure 3E ) . These data together with the observation that Prox1 acts on Notch1 promoter as a co-repressor imply that the effect of Prox1 on Notch1 expression could be mediated through NR5A2 . To investigate whether the effect of Prox1 on Notch1 gene expression is consistent with the function of Notch1 signaling in self-renewal and differentiation of NPCs , we employed an in vitro culture system using NPCs from embryonic mouse spinal cord , which have the ability to self-renew and proliferate when cultured in the presence of growth factors ( GFs ) , as well as to differentiate into neurons , astrocytes , and oligodendrocytes upon withdrawal of GFs [39] . In these cultures , endogenous Prox1 is up-regulated upon induction of differentiation at the mRNA level and a concomitant induction in the number of Prox1+ cells is observed upon withdrawal of GFs ( Figure 4A–4E ) . In contrast , Notch1 mRNA levels are reduced and inversely correlated with the induction of Prox1 upon withdrawal of GFs ( Figure 4E and 4F ) , supporting the repressive action of Prox1 on Notch1 transcription . Interestingly , 2 d after in vitro differentiation , Prox1 is mainly expressed in the majority of βIII-tubulin+ early differentiating neurons but also in a fraction of nestin+ NPCs that remained undifferentiated ( Figure 4G–4L ) . These Nestin+/Prox1+ cells may represent a transitory cell population of intermediate precursors similar to the transitory cell population observed in the intermediate zone of the chick and mouse spinal cord ( Figure 1 and Figure S1 ) [20] , [22] . Moreover , Prox1 is also expressed in a subset of oligodendrocytes , although the numbers of these cells are generally low in this experimental system ( Figure 4K and 4L ) . Most important , Prox1 is excluded from GFAP+ astrocytes ( Figure 4J and 4L ) , which is consistent with the previously reported data that active Notch1 signaling is required for the differentiation of astrocytes [2] . This suggests that the absence of Prox1 expression in astrocytes may be required for Notch1 receptor expression and astrocyte differentiation . This observation could also explain the difference in slope between the curves for Prox1 and Notch1 mRNA levels upon withdrawal of GFs ( solid lines in Figure 4E and 4F , respectively ) , since under these conditions a significant number of astrocytes are generated that express Notch1 but not Prox1 and contribute to mRNA levels . Finally , in agreement with these in vitro differentiation assays , a similar expression pattern for Prox1 was observed in vivo in embryonic mouse spinal cord ( Figure S5 ) . To further test Prox1 function , we transfected NPCs with various constructs using the AMAXA electroporation system ( Figure S6 ) [39] . We first verified that Prox1 suppresses Notch1 transcription in these cells ( Figure 5A and 5B ) . Similar to N2A data , the NR5A2 was sufficient to induce Notch1 mRNA levels and Notch1-luc activity , while Prox1 was able to inhibit this induction ( Figure 5C and 5D ) . To assess whether the inverse correlation between Prox1 and Notch1 expression has functional importance , we used NPCs as a model system to analyze the effect of Prox1 misexpression on Notch1-mediated self-renewal/proliferation and/or differentiation of NPCs ( Figure 4M ) [2] , [40] . We first examined the effect of Prox1 overexpression on NPC identity and proliferation under conditions that favor self-renewal ( +GFs ) ( Figure S6 ) . BrdU incorporation analysis 48 h after plating , followed by 2 h of BrdU-pulse , revealed a strong reduction in BrdU incorporation by 92 . 5% in a cell-autonomous manner ( Figure 5E–5G ) . Similarly , a dramatic decrease by 85% in the proportion of Nestin+ cells was specifically observed in Prox1 transfected cells ( Figure 5H–5J ) . Therefore , both indices show that Prox1 negatively affects self-renewal and proliferation of NPCs , with no indication of increased cell death as estimated by staining for activated caspase 3 ( unpublished data ) . We next asked whether Prox1 overexpression also influences astrogliogenesis and neurogenesis . Under differentiation conditions , generation of astrocytes was severely impaired ( by ∼90% ) in the Prox1-electroporated NPCs , as evidenced by measuring the index of GFAP+ cells ( Figure 5K–5M ) . Conversely , a significant increase in the proportion of βIII-tubulin+ cells by ∼3-fold was observed in the Prox1 electroporated cells ( Figure 5N–5P ) . Collectively our results demonstrate that Prox1 through its action to negatively regulate the expression of Notch1 is sufficient in a cell-autonomous manner to arrest self-renewal of Nestin+ NPCs and enhance their differentiation towards the acquisition of early βIII-tubulin+ neuronal identity , as opposed to depletion of GFAP+ astrocytes . To further evaluate this conclusion in vivo , we misexpressed Prox1 unilaterally in the neural tube by in ovo electroporation of HH stage 12–14 ( E2 ) chick embryos , following a previously reported protocol [39] . A striking reduction in the expression of Notch1 at the mRNA level was observed in the neural tube of Prox1-electroporated embryos at both 24 h and 48 h after electroporation ( a . e . ) , as compared to control GFP-transfected embryos ( Figure 6A , 6B , 6E , 6F , 6I , and 6J ) . A concomitant reduction in expression of the Notch1 target gene Hes5 was also evident ( Figure 6C , 6G , 6K , and 6Q ) , suggesting that Notch signaling is counteracted by Prox1 in a cell-autonomous manner . In addition , no evidence of apoptosis was observed either at 24 h or later at 48 h a . e . ( Figure S7A and unpublished data ) , excluding the possibility that cells were depleted due to an apoptotic effect of Prox1 . To further test whether the Prox1-mediated effect on Notch1 expression is responsible for Hes5 down-regulation , we co-expressed together with Prox1 the constitutively active intracellular domain of mammalian Notch1 ( NICD ) ( Figure S7B–S7D ) . Co-expression of NICD was sufficient to rescue the negative effect of Prox1 on Hes5 expression , excluding a direct action of Prox1 on Hes5 transcription or another pathway ( Figure 6M–6O and 6Q ) . We have previously shown that forced depletion of Notch1 gene expression in the VZ results in ectopic induction of neurogenesis [39] , [41] . Similarly , misexpression of Prox1 caused ectopic neuronal differentiation , as evidenced by induction of βIII-tubulin , an early marker of post-mitotic neurons ( Figure 6D , 6H , 6L , and 6R ) . This observation has been previously reported by another group [20] . However , here , we wanted to examine whether Prox1 achieves its effect on neuronal differentiation via Notch1 gene suppression . To this end , we again co-expressed NICD together with Prox1 . Interestingly , NICD strongly inhibited the Prox1-mediated generation of ectopic βIII-tubulin+ neurons in the VZ ( Figure 6P and 6R ) . We then asked whether the capacity of Prox1 to suppress Notch1 might affect self-renewal and proliferation of NPCs in the VZ . As previously reported [20] , a striking reduction in the number of BrdU-incorporating cycling progenitors was observed in the neural tube of Prox1-transfected embryos 24 h a . e . compared with control GFP-transfected embryos ( Figure 6S , 6T , and 6V ) . Similar to neuron differentiation , we showed here that co-expression of NICD abolishes the ability of Prox1 to arrest self-renewal of NPCs ( Figure 6U–6V ) , indicating that Prox1-mediated suppression of Notch1 is essential for this function . In agreement , it was previously shown that Prox1 misexpression in chick spinal cord reduced the numbers of cycling Pax6+ and Pax7+ NPCs in the VZ [20] . Taken together our results support the hypothesis that Prox1 is involved in the transition of NPCs from self-renewal to neuronal differentiation via direct regulation of Notch1 . Given the opposite functions of Prox1 and Notch1 in NPCs , we then directly addressed the possibility that Notch1 antagonizes Prox1 expression in a cross-inhibitory manner . To examine this , we first used the γ-secretase inhibitor DAPT in mouse NPCs . We verified that this treatment inactivated Notch signaling , as evidenced with reduced expression of Hes1 and Hes5 genes ( Figure 7A ) . Under these conditions inhibition of Notch signaling caused a significant induction in endogenous Prox1 mRNA expression by ∼3-fold ( Figure 7B ) , suggesting that active Notch signaling in NPCs suppresses Prox1 expression . Furthermore , when we misexpressed NICD in chick neural tube , we observed that ectopic activation of Notch1 signaling was sufficient to down-regulate endogenous expression of Prox1 in vivo ( Figure 7C–7H' ) . We conclude from these experiments that a cross-inhibitory interaction between Prox1 and active Notch1 signaling regulates the expression of both genes . The balance of this cross-inhibition might also regulate self-renewal and differentiation of NPCs . We next examined the requirement of Prox1 for suppressing Notch1 and thus regulating neurogenesis in vivo . To inhibit Prox1 expression in the chick neural tube , we utilized a pCAGGs-based shRNA system fused with GFP reporter gene to follow the expression of shRNAs ( Figure S8A ) [42] , [43] . The effective knock-down of endogenous chick Prox1 in the neural tube was assessed in mRNA and protein levels , by in situ hybridization and antibody staining , respectively ( Figure S8B , S8C , S8F , and S8G ) . In addition , inhibition of endogenous Prox1 did not affect programmed cell death , as evidenced with TUNEL assay ( Figure S8J ) . Most important , knock-down of Prox1 clearly induced the expression domain of Notch1 towards the MZ by 41 . 8% , as compared to shControl ( Figure 8A , 8B , and 8E ) . This induction was accompanied by a concomitant induction in Hes5 gene expression in a manner similar to the induction caused by constitutively active NICD misexpression , indicative of ectopic Notch signaling activation ( Figure 8A–8C and 8F ) . Consistently , expression of the proneural gene Cash1 was reduced in response to Prox1 depletion in a similar manner to NICD overexpression , indicative of enhanced Notch signaling that suppresses the initial phases of neurogenesis ( Figure 8G–8I ) . To exclude off-target effects and assess the specificity of Prox1-shRNA phenotypes , we performed two types of control experiments . First , we electroporated a control shRNA construct , containing a nucleotide sequence that does not target chick Prox1 and has no effect on chick Prox1 expression ( Figure S8D , S8E , S8H , and S8I ) . This control construct did not alter the expression of Notch1 , Hes5 ( Figure 8B , 8E , and 8F ) , or Cash1 ( Figure 8H ) . Second , the Prox1-shRNA construct was co-electroporated with a rescue construct containing the murine Prox1 coding sequence , which is quite divergent from the chick sequence and therefore is not targeted by the Prox1-shRNA sequence . A 6xHIS epitope tag was also included to be able to follow its expression ( Figure S9A–S9C ) . Co-expression of murine Prox1 was sufficient to block the Prox1-shRNA-mediated induction of Notch1 and Hes5 , as well as the suppression of Cash1 ( Figure 8D , 8E , 8F , and 8J ) . Instead , it was able to revert the Notch1 and Hes5 phenotypes by suppressing the expression of both genes . Collectively , these lines of evidence strongly support that depletion of endogenous Prox1 is responsible for the effects observed on the expression of Notch1 , Hes5 , and Cash1 . Furthermore , we addressed whether shRNA-mediated knock-down of Prox1 affects the expression of other markers for NPCs . Thus , we examined the expression of the homeodomain factors , Pax7 , Pax6 , and Nkx2 . 2 , which subdivide the VZ in the dorso-ventral axis , into defined progenitor domains with restricted developmental potentials . In each domain , whether dorsal or ventral , an induction in the number of cells that express these markers was observed in areas where Prox1-shRNA construct was misexpressed ( Figure S10 ) . Conversely , it was previously shown that forced Prox1 expression in chick spinal cord reduced the number of Pax7+ and Pax6+ NPCs [20] . Interestingly , Prox1 knock-down expanded the expression of Pax7 , Pax6 , and Nkx2 . 2 in the medio-lateral axis , towards the MZ , without affecting their dorso-lateral boundaries of expression . This suggests that Prox1 affects fundamental properties of NPCs relating to proliferation versus differentiation decisions , without interfering with their sub-type identity . In all cases overexpression of NICD phenocopied the effect of Prox1 knock-down ( Figure S10E–S10G , S10L–S10N , and S10S–S10U ) , further suggesting that Prox1 is required for suppression of NPC markers in spinal cord via inhibition of Notch signaling . These data indicate that Prox1-shRNA suppresses the initial phases of neurogenesis . To evaluate this conclusion , we examined expression of early and late markers of post-mitotic neurons , namely βIII-tubulin and SCG10 , respectively . Our analysis was focused on dorsal interneurons , since Prox1 is mainly expressed in interneuron precursors ( Figure 1 and Figure S1 ) [20] . As expected , Prox1 depletion strongly impaired neuronal differentiation by ∼50% ( Figure S11A , S11E , S11F , and S11J ) , while no effect was seen in control shRNA transfected embryos ( Figure S11B , S11E , S11G , and S11J ) . As previously reported [44] , we also showed that NICD overexpression impaired neuronal differentiation , and thus it is sufficient to recapitulate the effect of Prox1 ablation on interneuron differentiation in spinal cord ( Figure S11C , S11E , S11H , and S11J ) . Moreover , co-expression of murine Prox1 with shProx1 was able to rescue the negative effect on neurogenesis , since the numbers of βIII-tubulin+ and SCG10+ cells were restored ( Figure S11D , S11E , S11I , and S11J ) . Taken together these results suggest that Prox1 is necessary for proper regulation of Notch1 gene expression , through which it controls induction of interneuron differentiation .
In contrast with the wealth of information on the role of Notch signaling in neural development [1]–[3] , [45] , little is known of the upstream molecular mechanisms that control Notch1 gene expression and regulate its inactivation in NPCs during neuronal differentiation [10] , [46] . We show here that Prox1 directly interacts with the chromatin over the Notch1 promoter in vivo , and physiological levels of endogenous Prox1 are critical for the proper regulation of Notch1 gene expression in vivo . In particular , this study shows that Prox1 exerts a repressive action on Notch1 mRNA expression in the boundaries between VZ and MZ during the neurogenic phase of vertebrate spinal cord development . This repressive action is sufficient for proper regulation of cell cycle exit and initiation of neuronal differentiation in NPCs , as evidenced by induction of βIII-tubulin , an early marker for post-mitotic neurons . In addition , Prox1 activity is necessary for suppression of active Notch signaling , down-regulation of NPC markers , and completion of neuronal differentiation program . We propose that Prox1 is directly involved in the molecular mechanism that couples and coordinates termination of Notch1 signaling with induction of neuronal differentiation ( Figure 8K ) . It was previously shown that Notch1 signaling is modulated by a number of other mechanisms such as glycosylation , differential trafficking , and ubiquitin-dependent degradation of receptors and ligands , all of which play important roles in CNS development [2] , [3] , [47] , [48] . However , as early neuronal precursors begin to differentiate and migrate outside the VZ , they cease to express Notch1 at the mRNA level [11]–[13] . Thus , mechanisms that inhibit Notch1 receptor activity via protein modifications and/or stability in neuronal precursors may function as an early and immediate response in the VZ , whereas gene inactivation could act later in a more permanent way when cells migrate out of the VZ . Consistently , Numb , which acts as a Notch1 inhibitor via protein-protein interactions , begins to be expressed in neuronal precursors while in the VZ [49]–[51] , whereas Prox1 is expressed later in the neuronal lineage when cells withdraw from the cell cycle and exit the VZ [20] , [22] , [25] , [40] . Moreover , recent observations indicate that NPCs in the undifferentiated/self-renewing state express Notch ligands , such as Dll1 , in an oscillatory manner [1] , indicating that Notch ligands are constantly available in the neuroepithelium . Thus , we suggest that Prox1 , by suppressing Notch1 gene expression , prevents newly produced neuronal precursors from receiving signals from Notch ligands present in the membrane of neighboring cells and allows them to complete and sustain a neuronal differentiation program ( Figure 8K ) . Consistently , Prox1 is induced by proneural genes and is required for implementation of their neurogenic program [20] , [22] . In particular , Prox1 is co-expressed with Mash1 and Ngn2 in the subventricular zone of murine brain and chick spinal cord during the initial stages of neurogenesis [20] , [22] . Overexpression of these factors in the chick spinal cord [20] or murine NPCs is sufficient to induce Prox1 expression [22] . Conversely , Prox1 levels are reduced in the embryonic brain of Mash1 knockout mice [22] , further suggesting that Prox1 expression during neurogenesis is dependent on proneural genes . This epistatic relationship between proneural genes and Prox1 may also explain the inhibitory action of active Notch signaling on Prox1 expression ( Figure 7 ) , since active Notch signaling directly suppresses the expression of proneural genes [6] , [44] , which then cannot induce Prox1 ( Figure 8K ) . In support , we were able to identify a number of conserved E-box sequences on the Prox1 gene locus representing putative proneural protein binding sites ( Figure S12 ) . However , based on our data we cannot exclude the possibility of a direct action of Notch signaling on Prox1 expression . Moreover , this epistatic relationship could also explain how proneural genes , after a certain number of oscillatory cycles of expression , manage to overcome in a cell-autonomous manner the Notch1-mediated inhibition on neurogenesis without depleting NPCs [1] . In this regard , an intriguing question is why oscillations of proneural genes , such as Ngn2 , in NPCs are unable to induce neuronal differentiation whereas their sustained expression does elicit differentiation . It has been proposed that only a subset of downstream genes , perhaps those already expressed in the VZ , respond quickly to changes in proneural gene expression , whereas genes expressed outside the VZ respond more slowly and cannot be induced when Ngn2 expression oscillates [1] . Thus , Prox1 may belong to these slowly responsive genes , induced upon sustained Ngn2 expression to terminate Notch1 signaling and facilitate neuronal differentiation . In this scenario , NPCs are not depleted despite short pulses of Ngn2 expression but are maintained by sustaining activated Notch signaling , which in turn prevents neuronal differentiation . Although Prox1 acts downstream of the bHLH proneural genes [20] and is able to promote early neurogenic events , such as ectopic inactivation of Notch signaling , cell cycle exit of NPCs , and ectopic induction of βIII-tubulin+ cells in the VZ , it is not sufficient to induce a full neurogenic expression program [20] . This observation suggests that Prox1 acts in concert with other factors downstream of proneural proteins to induce neurogenesis ( Figure 8K ) . In agreement , proneural proteins are capable to promote the expression of multiple downstream factors , such as NeuroD , NeuroM , Nscl1 , Delta1 , Cend1 , and Sox4/11 , which are involved in the implementation of a full neurogenic cascade [5] , [39] , [52] , [53] . These factors are sufficient to induce a partial array of neuronal specific phenotypes that can be fully achieved by bHLH proneural factors . For example , Sox4/11 promote the expression of specific neuronal markers , but they are not sufficient to induce the exit of NPCs from the cell cycle or suppress progenitor-specific gene expression [52] . Conversely , Prox1 is able to achieve the latter but cannot fully phenocopy the effect of Sox4/11 on inducing neuronal markers . Thus , proneural proteins appear to activate multiple complementary downstream programs to potentiate full neuronal differentiation . Moreover , genetic or pharmacological inhibition of Notch signaling in chick neural tube is sufficient to induce proneural gene expression , cause NPCs to exit the cell cycle , downregulate progenitor identities , and induce a full neurogenic expression program [44] . Although Prox1 misexpression in the same system is able to inhibit Notch signaling , it cannot precisely mimic the effect of Notch inhibition on terminal neuronal differentiation [20] . These observations suggest that Prox1 exerts an extra action on NPCs , which is not compatible with terminal neuronal differentiation in the spinal cord . Consistently , endogenous Prox1 expression is downregulated prior to acquisition of terminal neuronal identity in this region of the CNS [20] . In addition to this function , Prox1 might be involved in the specification of neuronal sub-types , since its expression is excluded from the pMN domain ( MN progenitors ) of the ventral spinal cord . In agreement , genetic inactivation of Notch1 from NPCs of the ventral spinal cord suppresses MN identity and induces V2 interneurons , suggesting that Notch1 activity is required for generation of MNs [54] . Thus , Prox1 exclusion from pMN could be associated with this requirement . Furthermore , Prox1 is also expressed in several other regions of the CNS , apart from the spinal cord , during embryonic and postnatal stages of development , including the cortex , dentate gyrus , thalamus , and cerebellum [21] , [55] , [56] . Interestingly , in adult brain , Prox1 expression remains high in mature neurons of the hippocampus and cerebellum [55] . This expression pattern is quite distinct from the transient pattern of Prox1 expression in the embryonic spinal cord , suggesting that Prox1 function may be different in the developing or mature nervous system [55]–[57] . Given that Prox1 is a pleiotropic factor affecting many diverse signaling pathways and transcriptional networks in other tissues and organs [16] , [24] , [26] , [27] , [29] , [34] , [35] , [36] , this differential expression pattern may also imply a different mechanism of Prox1 action in these regions of CNS . Therefore , based on our observations in the embryonic spinal cord , we cannot rule out the possibility that other signaling pathways may be involved in mediating the function of Prox1 in other CNS regions independently of its ability to counteract Notch1 in a cell-autonomous manner . In agreement , it was recently published that ablation of Prox1 in postnatal dentate gyrus leads to an increase in apoptosis of intermediate progenitors and the absence of adult neurogenesis through a non-cell autonomous mechanism [57] . A key question arising from our observations is whether other Notch receptors ( Notch2 , 3 , and 4 ) are implicated in the Prox1-mediated inactivation of Notch signaling as documented by Hes5 gene down-regulation . Although , based on our data , we cannot exclude a possible function of Prox1 in inhibiting other Notch receptors , the fact that overexpression of the constitutively active intracellular domain of Notch1 is sufficient to overcome the effect of Prox1 on Hes5 gene down-regulation , cell-cycle exit , and ectopic neuronal differentiation of NPCs suggests that these effects may be achieved primarily through the Notch1 receptor . Moreover , previous studies that report the phenotypes of Notch1 , 2 , 3 , and 4 gene deletions in mouse embryos imply Notch1 as the most important Notch receptor in CNS development and differentiation [3] . Notch3 and Notch4 deletions do not affect neuronal differentiation or CNS development [58]–[60] . On the other hand , Notch2−/− embryos die around E11 and undergo massive cell death in the CNS similar to Notch1−/− mice . However , they do not show alterations in Hes5 expression in striking contrast to Notch1−/− mutants [61] , [62] . Consistently , Notch1 and Notch2 are differentially expressed in mouse embryonic CNS [12] , [63] . Moreover , transcriptional assays in cell lines and NPCs indicate that Prox1 acts as a transcriptional co-repressor in suppressing Notch1 and its action may be mediated by NR5A2 and HDAC3 proteins that are co-expressed and interact with Prox1 . We suggest that these proteins facilitate Prox1 recruitment on Notch1 promoter and chromatin-mediated transcriptional repression , respectively . Despite the fact that both proteins are expressed in primary NPCs and embryonic mouse and chick CNS , their role in CNS development is not known . Interestingly , the Prox1/NR5A2 complex , where Prox1 acts as a co-repressor , plays a crucial role in the proper regulation of a set of genes , which are very important for liver development , regeneration , and function [27] , [35] , [36] , [64] , [65] , suggesting that this role might have been conserved in CNS development and function . In support of this hypothesis , we showed here that physiological levels of endogenous Prox1 and NR5A2 are sufficient to allow binding at the Notch1 promoter locus in the embryonic mouse CNS , and most important , re-ChIP assays in the same tissue suggest that Prox1 and NR5A2 could form a complex on chromatin over the Notch1 promoter . To conclude , in this study we have unveiled for the first time to our knowledge a novel means of regulation of Notch signaling in the developing spinal cord , which involves transcriptional repression of Notch1 by Prox1 . In addition , we have demonstrated that this transcriptional repression has profound implications in cell cycle exit and differentiation of neuronal precursors as they exit the VZ and migrate towards the MZ to acquire terminally differentiated phenotypes . This mechanism is of paramount importance for generating the correct number of neurons from a duly sustained pool of NPCs and we would like to propose that part of the important roles of Prox1 in many different aspects of embryonic development , organ morphogenesis , and cancer pathogenesis [23] , [25]–[29] , [66] may be mediated through its ability to counteract Notch signaling .
All animals were handled in strict accordance with good animal practice as defined by the relevant European and Greek animal welfare bodies . Total RNA was isolated by using the RNAeasy Kit ( Qiagen ) followed by treatment with RQ1 DNase . Quantitative real time RT-PCR analysis was performed as described [39] . Primer sets used in RT-PCR assays: mProx1-For: CAGCGGACTCTCTAGCACAG mProx1-Rev: GCCTGCCAAAAGGGGAAAGA mNotch1-For: GCCGCAAGAGGCTTGAGAT mNotch1-Rev: GGAGTCCTGGCATCGTTGG mHes1-For: TCAACACGACACCGGACAAACC mHes1-Rev: GGTACTTCCCCAACACGCTCG mHes5-For: CTCCGCTCCGCTCGCTAATCGC mHes5-Rev: GCTTCATCTGCGTGTCGCTGGC mNR5A2-For: TGAGTGGGCCAGGAGTAGTA mNR5A2-Rev: ATCAAGAGCTCACTCCAGCA mHDAC3-For: TATGCAGGGTTTCACCAAGA mHDAC3-Rev: CAGAGATGCGCCTGTGTAAC mGAPDH-For: AACTCCCTCAAGATTGTCAGCAA mGAPDH-Rev: ATGTCAGATCCACAACGGATACA Transient transfections and luciferace reporter assays were performed with Lipofectamine ( Invitrogen ) and luciferase/β-galactosidase kits ( Promega ) , respectively , as previously described [67] , [68] . For Notch1-luc , Hes1-luc , Hes5-luc , and TK-luc constructs we have used 0 . 4 µg per transfection and 1 . 6 µg of expression vectors ( Prox1 , NR5A2 , or HDAC3 ) . All experiments were done in quadruplicate at least three times , and statistical analysis was performed by the paired two-sample Student's t test . To generate the mut-N1-Luc a PCR-based approach was used , with the following primer sets: N1promoter-EXT-For: AAGTAAGCTTCTTGGGGGAGCGGGGCACA N1promoter-EXT -Rev: TCTTCCATGGGCCTCCCCACCGGCT N1promoter-INT-For: CCGCCCCGGGATAATACGATTATTCACATGCAAATTTCA N1promoter-INT-Rev: ATGTGAATAATCGTATTATCCCGGGGCGGAATGGGGA By this approach the two overlapping Prox1 consensus binding sites [33] on Notch1 promoter were mutated from CTCCTCCGCT to ATAATACGAT . To generate the 535-N1-Luc construct , a BamHI/NcoI fragment from the WT Notch1-Luc construct was digested and inserted into the parental pGL-2basic vector . All plasmid constructs were verified with sequencing . ChIP assays were performed essentially as previously described with minor modifications [69] . 10 to 25 µg of chromatin were used per IP reaction with up to 3 µg of antibody . Chomatin-antibody immunocomplexes were formed using affinity purified antibodies to Prox1 ( ReliaTech , 102-PA32 ) , HDAC3 ( Santa Cruz Biotechnology , SC-11417 ) [70] , and NR5A2 ( kindly provided by Dr Talianidis ) [71] . Antibody bound chromatin was retained on protein A/G-magnetic beads ( Invitrogen ) . DNA was extracted from the immobilized bound immunocomplexes reversed , ethanol precipitated , and analyzed by semi-quantitative PCR . The following primer pairs were used to amplify the promoter and ORF genomic loci from mouse Notch1 gene as indicated in Figure 2D: N1-Promoter-5: AGTGCCTGGCCTCAATCCTCC ( 21 bp ) N1-Promoter-3: TGTAGCCGCCCTCTGCGACAT ( 21 bp ) N1-ORF-5: GCCAGTACAACCCACTACGG ( 20 bp ) N1-ORF-3: CACTGAGGTGTGGCTGTGAT ( 20 bp ) The re-ChIP experiments were performed as previously reported [71] . Briefly , the immunocomplexes were pulled down with the first antibody , treated with 20 mM DTT , followed by a 20-fold dilution before performing the immunoprecipitation with the second antibody . Cross-linking was reversed and DNA was purified and subjected to PCR using the above mentioned primer sets . Co-immunoprecipitation assays were performed from embryonic mouse CNS tissue ( E12 . 5 ) , including both brain and spinal cord . Tissues were lysed in 20 mM Tris HCl ( pH 7 , 5 ) , 140 mM NaCl , 1% Νοnidet-P40 , 2 mM EDTA , 1 mM Phenylmethylsulfonyl fluoride ( PMSF ) , and cocktail inhibitors ( SIGMA ) . The following antibodies were used: anti-HDAC3 ( Santa Cruz Biotechnology , SC-11417 ) [70] , anti-Brd2 ( Santa Cruz Biotechnology , SC-46805 ) , anti-Prox1 ( Chemicon , Mab5654 ) , and control IgGs from DAKO . To retain antibodies protein-A agarose beads were utilized ( Pierce ) . Non-radioactive in situ hybridization on cryosections and preparations for digoxigenin- or fluorescein-labeled probes were carried out as described [39] , [72] . Prox1 was detected using a rabbit polyclonal anti-Prox1 antibody ( ReliaTech ) or a mouse monoclonal antibody ( Chemicon ) . Anti-BrdU monoclonal antibody was purchased from Dako and detected as previously described [39] . Anti-Nestin and anti-O4 monoclonal antibodies were from Chemicon; monoclonal anti-GFAP was from Sigma; anti-activated caspase 3 , anti-HDAC3 , anti-Brd2 , and anti-Notch1 from Santa Cruz; and monoclonal anti-βIII-tubulin was from Covance ( USA ) . Anti-GFP and anti-FLAG were purchased from Molecular Probes and Sigma , respectively . Pax6 , Pax7 , and Nkx2 . 2 antibodies were obtained from Developmental Studies Hybridoma Bank ( University of Iowa , Iowa City ) . Detection of cells undergoing apoptosis on sections was carried out with the TUNEL kit from Roche ( Mannheim , Germany ) using Streptavidin Texas Red ( Amersham ) . Secondary antibodies conjugated with AlexaFluor 488 ( green ) or 546 ( red ) were from Molecular Probes . Cell nuclei were labeled with Hoechst 33258 or DAPI ( 1∶1000 , Molecular Probes ) . Specimens were viewed and analyzed with a Leica confocal microscope . Statistical analysis was performed with the two-tailed paired Student's t test . NPCs cultures from embryonic mouse spinal cords were performed as previously described [39] , with some modifications . NPCs were prepared from E14 . 5 mouse embryo spinal cords and maintained as neurosphere cultures in a serum-free medium [1∶1 mixture of DMEM and F-12 with penicillin ( 100 units/ml; Invitrogen ) and streptomycin ( 100 µg/ml; Invitrogen ) ] , containing the B-27 supplement ( 1 ml/50 ml medium; Invitrogen ) , insulin ( 20 µg/ml; Sigma ) , recombinant human basic fibroblast growth factor ( bFGF 20 ng/ml; R&D Systems ) , and epidermal growth factor ( EGF 20 ng/ml; R&D Systems ) . After 5–7 d in culture , floating neurospheres were trypsin-dissociated and allowed to re-form spheres at least three times before further use . Proliferation studies were performed after dissociation to single cells , plating onto poly-l-lysine coated coverslips in 24-well plates at a density of 6×104 , and further culture for 2 d in the presence of EGF/bFGF . For differentiation , dissociated neurospheres were plated on poly-l-lysine-coated coverslips at a density of 7×104and maintained for 2 or 3 d in the absence of growth factors . For Prox1 or GFP overexpression , cells were transfected using an AMAXA electroporator ( Lonza ) . The pCDNA3-Prox1 and control GFP mammalian expression vectors were used , driving transgene expression under the control of CMV promoter ( 5 µg of plasmid DNA per electroporation ) . Transfected neurospheres were cultured for 24 h , after which , either they were enzymatically dissociated and plated as single cells in poly-l-lysine-coated 10-mm-diameter coverslips or they were cultured for a further 24 h and collected for RNA extraction , luciferase assays , or Real-Time RT-PCR analysis . For quantification of proliferation , the index of BrdU+ cells was determined by scoring the transgene ( Prox1 or GFP ) and BrdU double-positive cells versus the total number of transgene positive cells for each set of electroporations ( Prox1 and GFP ) from five independent experiments . For quantification of differentiation , the indices of Nestin+ , βIII-tubulin+ , GFAP+ , and O4+ cells were determined by scoring the transgene ( Prox1 or GFP ) and Nestin or βIII-tubulin or GFAP or O4 double-positive cells versus the total number of transgene positive cells for each set of electroporations ( Prox1 and GFP ) from five independent experiments . Statistical analysis was performed by the paired two-sample Student's t test . Unilateral overexpression of transgenes and shRNA constructs in the chick neural tube by in ovo electroporation method was performed as previously described [39] . Briefly , white Leghorn chicken eggs were incubated at 38 °C until stages 12–14 of development ( E2 ) . Supercoiled plasmid for electroporation was used at a concentration of 1–2 mg/ml in TE ( 10 mM Tris-HCl , 1 mM EDTA , pH 7 . 5 ) with 0 . 025% Fast Green ( Sigma ) . Embryos were injected with DNA solution into the lumen of the neural tube and then subjected to electroporation . The electrodes were spaced 4 mm apart and positioned such that the DNA was driven into the cells on only one side of the neural tube . Embryos were pulsed 2×3 times for 30 ms each at 28 V . Eggs were then re-incubated for 1 or 2 d before the embryos were fixed for 4 h at 4 °C in 4% paraformaldehyde in PBS . Embryos were then washed in PBS , cryoprotected with 20% sucrose , mounted in OCT ( Tissue-Tek ) , and sectioned at 12–14 µm . For BrdU-labeling , embryos received 10 µg BrdU in PBS 2 h before fixation . To distinguish the expression of exogenous transgenes from that of the endogenous genes , pCaggs based expression constructs were co-electroporated alongside a GFP expression plasmid . To create pCAGGs-Prox1 and pCaggs-NICD expression vectors , the respective mouse and human cDNAs were cloned into pCAGGS empty vector . For the construction of the shRNA vectors targeting chick Prox1 , we followed previously published methods [42] , [43] . In particular , to generate the appropriate vectors we used the following primer sets exactly as previously described [42]: W: GGCGGGGCTAGCTGGAGAAGATGCCTTCCGGAGAGGTGCTGCTGAGCG Y: GGGTGGCTTAAGAAGAGGGGAAGAAAGCTTCTAACCCCGCTATTCACCACCACTAGGCA Target: CTTCCTGGAAGAAGGCCATATA RNAi-cProx1-B-For: GAGAGGTGCTGCTGAGCGATTCCTGGAAGAAGGCCATATATAGTGAAGCCACAGATGTA RNAi-cProx1-B-Rev: ATTCACCACCACTAGGCACTTCCTGGAAGAAGGCCATATATACATCTGTGGCTTCACT Target sequences on cProx1 were identified with a genescript free online tool as described [42] . | Early during development , neural progenitor cells ( NPCs ) can either proliferate or differentiate into neurons . Thus , generation of the correct number of neurons is governed by a tightly regulated balance between proliferation and differentiation , and disruption of this balance can result in severe developmental deficits , malformations , or cancers . Notch1 is a member of the Notch family of receptors , which make up a highly conserved cell signaling system . Notch1 signaling has been shown to inhibit NPC differentiation and to promote self-renewal , thereby allowing NPCs to divide and progressively generate the enormous number of neurons present in the central nervous system . The molecular mechanism by which NPCs overcome Notch1-mediated inhibition in order to differentiate into neurons , however , is not completely understood . In this study , we show that Prox1 , a homeobox transcriptional repressor , plays a fundamental role in the switch to differentiation by suppressing the expression of Notch1 receptor , thereby preventing newly produced neuronal precursors from receiving inhibitory signals from Notch ligands present in neighboring cells . This transcriptional repression may regulate cell cycle exit and differentiation of NPCs as they migrate towards different regions and adopt their final cell fates . We suggest that Prox1 may exert its known influence on embryonic development , organ morphogenesis , and cancer through its ability to counteract Notch1 signaling . | [
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| 2010 | Prox1 Regulates the Notch1-Mediated Inhibition of Neurogenesis |
Despite the well-documented role of remote enhancers in controlling developmental gene expression , the mechanisms that allocate enhancers to genes are poorly characterized . Here , we investigate the cis-regulatory organization of the locus containing the Tfap2c and Bmp7 genes in vivo , using a series of engineered chromosomal rearrangements . While these genes lie adjacent to one another , we demonstrate that they are independently regulated by distinct sets of enhancers , which in turn define non-overlapping regulatory domains . Chromosome conformation capture experiments reveal a corresponding partition of the locus in two distinct structural entities , demarcated by a discrete transition zone . The impact of engineered chromosomal rearrangements on the topology of the locus and the resultant gene expression changes indicate that this transition zone functionally organizes the structural partition of the locus , thereby defining enhancer-target gene allocation . This partition is , however , not absolute: we show that it allows competing interactions across it that may be non-productive for the competing gene , but modulate expression of the competed one . Altogether , these data highlight the prime role of the topological organization of the genome in long-distance regulation of gene expression .
Differential regulation of gene expression transforms shared genomic information into the cell type-specific programs underlying organismal development and homeostasis . In vertebrates , it is not uncommon to find gene regulatory elements , in particular enhancers , hundreds of kilobases away from their target gene ( reviewed in [1] , [2] ) . The mere scale of this genomic distance raises the question of how enhancers and promoters can find each other , and how enhancers distinguish between their specific target and other neighboring genes , which may even lie much closer . Understanding the molecular basis of such specific interactions is essential as their impairment can lead to mis-expression of the normal target gene [3] , [4] or to inappropriate activation of neighboring genes [5]–[8] , with often severe phenotypic consequences [7] , [9]–[12] . Enhancers can typically activate transcription from different promoters , a property that is part of their initial definition [13] and which has been amply used to assess enhancer activity . Many enhancers act pervasively across their endogenous genomic surroundings [14] , [15] , and enhancer sharing is not unusual between neighboring genes , particularly within multigenic clusters [16]–[22] . Noteworthy , this can also occur between genes with no functional relationship except genomic proximity [9] , [23]–[25] . Nonetheless , in many loci , adjacent genes exhibit distinct expression patterns , implying the existence of mechanisms that limit the promiscuous potential of enhancers . Different mechanisms and genomic elements have been invoked to explain enhancer-target gene specificity . They can be divided in two main categories , depending on whether they may promote interactions ( eg . nature of the promoter , tethering elements [26] , [27] ) , or block them . Amongst the latter , insulators prevent contact of an enhancer with an adjacent promoter , when placed in between [28]–[30] . This capacity of insulators to organize the genome in separate regulatory compartments designate them as critical components in ensuring specificity of cis-regulatory interactions [31] . However , only a handful of insulator elements have been functionally assessed in their native genomic context , and therefore their mode ( s ) of action is still poorly understood . Contrary to earlier models , a growing body of evidence suggests that insulators do not function autonomously , but rather through higher-order 3D conformations [32] . The necessity to consider the genome's three-dimensional organization is further highlighted by genome-wide high-resolution interaction maps obtained by chromosomal conformation capture techniques [33] . These studies revealed that the genome is compartmentalized in topologically-associating domains ( TADs ) [34] , [35] . TADs have been proposed to contribute to gene expression by limiting enhancer action [36] , [37] . In support of this view , genes located within the same TAD tend to be expressed coordinately [35] , [38] , and TADs have been found to encompass the regulatory domains defined by long-range enhancer activities [15] , [39] . Recent works have addressed the finer-scale structural organization of TADs , revealing a complex hierarchy of interactions , which may contribute to mediate long-distance interactions between enhancers and promoters [40] , [41] and to subdivide them into distinct regulatory domains [15] . In most instances , the functionality of structural contacts is difficult to evaluate precisely and the causal relationship between structural conformation and gene regulation remains unclear . To better understand the relationships between 3D structural properties of the genome and enhance-promoter allocations , we focused on a large interval of approximately 0 . 5 Mb containing two different developmental genes , Bmp7 and Tfap2c . These two genes , which encode a secreted signaling molecule and a nuclear transcription factor , respectively , are active in multiple tissues and organs during embryogenesis [42]–[48] . Both genes have promoter architectures compatible with tissue-specific and long-distance regulatory inputs [49] . Their expression overlaps in the limbs , forebrain and branchial arches of mid-gestation mouse embryos , while in other contexts , their expression is specific of one or the other and exclusive . Therefore this locus constitutes an ideal system to study the control of long-distance enhancer specificities . To investigate the regulatory organization of this locus , we used a transposon/recombination-based chromosomal engineering approach [14] . We show here that the genomic interval consists of two largely independent regulatory domains , corresponding to each of the two genes . Analysis of the chromatin conformation of re-engineered genomic configurations identified a central transition zone ( TZ ) that defines different topological sub-domains . Importantly , the allocation of enhancers to one or the other gene is determined by this partition . Altogether , our data support the view that the topological organization of the genome restricts enhancers to specific domains , determining therefore their “specific” target gene choice . Interestingly , we found that the presence of Bmp7 in cis has a mild influence on the expression level of Tfap2c in the developing forebrain , indicating that the position of the two genes to different topological domains does not lead to an absolute insulation .
To determine the regulatory organization of the Tfap2c-Bmp7 locus , we adapted the GROMIT ( Genome Regulatory Organization Mapping with Integrated Transposons ) strategy [14] . Firstly , at the 3′ end of the endogenous Bmp7 gene , we inserted a transgene consisting of a Sleeping Beauty transposon comprising 1 ) a regulatory sensor gene ( a LacZ reporter under the control of a short naïve synthetic promoter region derived from the human β-globin gene [14] , [50] ) and 2 ) a loxP site . After establishment of a mouse line carrying the correct insertion , we removed the selection marker used to identify candidate targeted ES clones , a step which left behind an additional loxP site , next to the Sleeping Beauty transposon . We designated this allele as SB-B ( 3end ) ( Fig . 1 ) . By serial remobilisation of the transposon in vivo [14] , we obtained several insertions located in this region of mouse chromosome 2 ( S1 Table ) . Of these , seven insertions were distributed along the Tfap2c-Bmp7 locus ( Fig . 1A ) : three very close to SB-B ( 3end ) ( within 23 kb ) , one ( SB-B ( up ) ) 20 kb upstream of Bmp7 , and another one in the first intron of Bmp7 ( SB-B ( in ) ) . The remaining two ( SB-A1 and SB-A2 ) lie within the large intergenic region separating Tfap2c and Bmp7 . In parallel , we established a mouse line ( BA0758 ) from an ES clone carrying a βgeo gene-trap insertion in Tfap2c [51] . We analyzed the expression pattern of the regulatory sensor at different insertion sites in E10 . 5 to E12 . 5 mouse embryos , at stages when Tfap2c and Bmp7 show both shared and specific expression patterns ( Fig . 1 , S1 Fig . ) . The two insertions located between Tfap2c and Bmp7 ( SB-A1 and -A2 ) showed very similar LacZ staining in the oro-facial region , the branchial arches , and in the forebrain ( Fig . 1B , left ) . These three expression domains are strikingly consistent with reported expression patterns of Tfap2c [42] and particularly with the Tfap2c LacZ gene-trap allele ( Fig . 1B- S1 Fig . ) . This overlap and agreement in expression suggested that SB-A1 and -A2 were included in the Tfap2c “regulatory domain” [15] . The expression of the reporters showed however different relative intensity between the lateral and medial part of the forebrain: while BA0758 and SB-A1 were preferably expressed in the lateral forebrain , with weaker expression in the medial region , SB-A2 showed the inverse pattern , with a stronger medial than lateral LacZ staining . Such position-effects ( the promoter is the same for SB-A2 and SB-A2 ) are not uncommon within regulatory domains [15] , [52] . They may reflect the presence in the locus of several forebrain enhancers with distinct medial/lateral activity and different range of action . These forebrain expression domains were not observed with any of the four insertions located within the 23-kb region at the 3′end of Bmp7 ( Fig . 1B , S1 Fig . ) , suggesting that the telomeric limit of Tfap2c regulatory domain is upstream of this region . More distant insertions in Bmp7 ( SB-B ( in ) ; SB-B ( up ) ) showed weak medial-only forebrain expression at E11 . 5 , with no lateral expression detected , as also observed for Bmp7 [44] . None of the six telomeric insertions showed the characteristic oro-facial expression observed with the Tfap2c-associated insertions . In contrast , they shared several common expression domains not reported by the SB-A1 and –A2 insertions ( Fig . 1B ) . The four insertions at the 3′end of Bmp7 and SB-B ( in ) showed all prominent staining for LacZ expression in the developing heart ( from E10 . 5 to E12 . 5 ) , and in the interdigital mesenchyme ( at E12 . 5 ) . SB-B ( up ) displayed only faint LacZ staining in the interdigital mesenchyme , and no staining in the heart . However , LacZ expression from this position overlapped characteristically with other SB-B insertions in the whiskers , nasal pits , and forebrain ( S1 Fig . ) , defining collectively a regulatory domain distinct from the one associated with Tfap2c . This domain includes Bmp7 , and accordingly , several of the reported activities overlap with known Bmp7 expression domains [47] , [53] . Some regions of the Bmp7 expression domain were not reflected accurately in the activity of the SB reporters , being either missing or spatially expanded . These differences may arise from the limited range of action of some promoter-proximal enhancers [53] , and/or from the different post-transcriptional stability and dynamics of LacZ compared to the endogenous Bmp7 transcripts . Overall , the regulatory activities detected by the sensor differed significantly between the centromeric and telomeric part of the locus , and highlighted two distinct and non-overlapping regulatory domains , each defined by multiple distinct tissue-specific activities , one domain corresponding to Tfap2c and the other to Bmp7 . We focused for subsequent analyses on the forebrain ( medial and lateral ) and heart , as representative markers of these two domains . In these two tissues , the expression pattern of the different genes is stable from E10 . 5 and E12 , contrasting with the dynamic expression of these genes in the developing limbs and face . Also , for these two expression domains , it is technically possible to dissect from embryos the part where the gene or the enhancer is active , without the contribution of too many non-expressing cells . To further characterize the functional relevance of these two domains and associated enhancers , we used in vivo Cre-mediated recombination to engineer chromosomal deletions removing either the telomeric half or the whole of the intergenic region ( Fig . 2 ) . Each deletion was produced using a combination of loxP sites in cis and trans [54] in order to keep the LacZ sensor at the deletion breakpoint ( see Materials and Methods ) . With the TAMERE strategy , we also obtained a large duplication , reciprocal to del3 ( S2 Fig . ) . All three deletions led to a complete loss of LacZ expression in the embryonic heart and forebrain ( Fig . 2B ) suggesting that the enhancers detected by SB-A1 and SB-B ( 3end ) lie in the region encompassed by del1 . Dup3-lacZ embryos showed LacZ expression in the heart similar to SB-B ( 3end ) , corroborating the presence of the heart enhancer ( s ) at the 3′ side of Bmp7 ( S2 Fig . ) . These deletions also provided information on the locations of additional enhancers associated with other expression domains ( S2 Fig . ) . We next determined if the enhancers present in the del1 interval contributed to Tfap2c and Bmp7 expression by whole-mount in situ hybridization and RT-qPCR ( Fig . 2C–D ) . In del1 homozygous embryos , Bmp7 expression was drastically reduced in the heart compared to wild-type littermates , while the very weak expression of Tfap2c in the heart was unaffected ( Fig . 2C ) . In the forebrain , where both genes are expressed , we found an almost complete loss of Tfap2c expression in both the medial and lateral parts of del1 embryos . In contrast , Bmp7 expression was barely affected and showed only a slight reduction in the lateral forebrain ( Fig . 2C ) . These analyses demonstrated a critical role of elements located within the del1 segment for the specific expression of Tfap2c in the forebrain and of Bmp7 in the heart , respectively . Several peaks enriched for chromatin marks associated with active enhancers ( H3K27ac , EP300 ) have been detected within this region in the forebrain and the heart of E11 . 5 embryos [55]–[57] ( S3 Fig . ) . Interestingly , the distribution of these regions is coincident with the location of the two regulatory domains . Many forebrain H3K27ac peaks are located between Tfap2c and SB-A1/A2 , while the only ones present around Bmp7 lie in the first intron of the gene . Conversely , heart H3K27ac-enriched elements cluster around the 3′ end of Bmp7 . H3K27ac peaks were also identified outside of the del1 region around the locus . The forebrain H3K27ac peak adjacent to Bmp7 could account for its unaffected expression in del1; however , the role of the predicted forebrain and heart enhancers located respectively centromeric and telomeric to del1 , respectively , remained unclear , as they were seemingly unable to confer significant activity to the reporter gene or to the endogenous genes in these tissues , in the absence of del1 sequences . To confirm that del1 contained enhancers with the expected activities , we cloned FB1 , an evolutionarily conserved element enriched for both H3K27ac and EP300 in the forebrain , upstream of the regulatory sensor construct . In this transgenic assay , FB1 drove specific and reproducible LacZ expression in the forebrain in E11–12 embryos ( Fig . 2E ) , including the Tfap2c expression domain . However , FB1 appeared broadly and equally active in both medial and lateral forebrain , contrasting with the restricted expression detected by the same reporter gene than the one used in the transgenic assay when inserted in the endogenous locus on either side of FB1 . In this context , it showed alternatively preferential expression in the lateral ( SB-A1 , like Tfap2c ) or medial ( SB-A2 ) . These differences suggested that additional factors – possibly the other H3K27ac-region present in the vicinity ( see below ) – may modulate FB1 intrinsic activity in a position-dependent manner . Amongst the predicted heart enhancers , one of them ( mm75 ) had been tested previously [58] and reported to have broad enhancer activity in the heart of E11 . 5 mouse embryos ( Fig . 2F ) . Taken together , these data demonstrated that the del1 region contained heart-specific and forebrain-specific regulatory element ( s ) critical for the expression of Bmp7 in the heart , and of Tfap2c in the forebrain , respectively . Importantly , these elements appeared to be dispensable for the regulation of one another's genes . These selective influences and the separate location of the different enhancers further confirmed the partition of this genomic interval into two distinct regulatory domains containing enhancers which act exclusively on one or the other gene ( Fig . 2G ) . We next investigated how the regulatory subdivision of the locus corresponded to its topological organization . Hi-C data available for mouse ES cells and cortex [34] suggests that the locus has a relatively loose topological structure , confined between two prominent topologically associating domains ( Fig . 3A , S4 Fig . ) . To determine the pattern of physical contacts involving Tfap2c and Bmp7 , we carried out circular chromatin conformation capture experiments followed by high-throughput sequencing ( 4C-Seq ) using the promoters of these two genes as viewpoints ( Fig . 3 ) . We performed these 4C-Seq analyses on dissected samples where one and/or the other gene were expressed ( E11 . 5 heart , medial and lateral forebrain ) and whole body of E11 . 5 embryos ( where most cells are non-expressing either of the two genes ) . We also included samples from E12 . 5 limbs , which comprised a majority of non-expressing cells . For both viewpoints , the 4C profiles highlighted a large primary interaction domain characterized by high 4C read counts ( Fig . 3B , C ) . We applied a segmentation algorithm [59] to delineate this primary domain in the different conditions ( S2 Table ) . The calculated primary interaction domains for a given viewpoint were nearly identical across the different tissue samples . The 4C profiles were predominantly similar between samples , with the exception of a moderate increase of the 4C signals over the enhancers associated with each gene in the tissues in which they are active ( for Tfap2c: FB1 and flanking H3K27ac-enriched regions in the brain samples; for Bmp7 mm75 and surrounding H3K27ac-enriched regions in the heart sample ) . We confirmed the increased interactions of Tfap2c with FB1 and of Bmp7 with mm75 in an independent 3C experiment ( S5 Fig . ) . Importantly , the reciprocal 3C experiment with FB1 as a viewpoint showed that it contacted strongly Tfap2c in the forebrain , but not in the heart , and had much weaker/rarer contacts with Bmp7 . Noteworthy , the Tfap2c domain and the Bmp7 domain end shortly before the edges of the flanking TADs detected in mouse ES cells [34] , consistent with the notion that these 4C primary domains corresponded to the structural conformation adopted by the locus . In all samples , the primary contact domains of one gene included the enhancer regions we found associated with it , but excluded the ones associated with the other gene . Nevertheless , we observed a consistent overlap between the two domains , demarcating a region of about 10- to 30-kb region , which we termed the transition zone ( TZ ) . To further characterize this region , we used two additional viewpoints for 4C analysis ( Fig . 3D–E ) . Contacts observed from a viewpoint located just before the centromeric end of the Bmp7 primary domain showed extensive overlap with the latter , extending broadly over Bmp7 but not stopping almost abruptly at the TZ ( Fig . 3D ) . Similarly , FB1 showed only weak contact with positions located on the other side of the TZ ( S5C Fig . ) . This asymmetry in the distribution of contacts suggested the TZ indeed corresponds to a conformational transition between two different conformations . Importantly , a viewpoint located in the TZ itself showed prominent contacts extending towards both genes ( Fig . 3E ) , consistent with the strong 4C signals observed over the TZ in the reciprocal 4C experiments . Next , we performed 4C analyses on del1 homozygous embryos , where the TZ region was deleted together with a larger part of the locus , including the different enhancers ( S6 Fig . ) . In this context , we observed a wide extension of the contacts made by Tfap2c ( resp . Bmp7 ) in the telomeric ( resp . centromeric ) region , over distances larger than the size of the deleted region . At the same time , the centromeric ( resp . telomeric ) profiles remained highly similar between WT and del1 . Interestingly , the intervals with frequent contacts by Tfap2c and Bmp7 now largely overlapped , as if they “merged” into one domain only limited by the adjacent TADs ( S6 Fig . , S3 Table ) . These new extended contacts supported the notion that the TZ may contribute to delineate two distinct structural domains . However , as del1 also significantly reduced the linear distance between Tfap2c and Bmp7 , we decided to use other types of alleles to challenge the structural and regulatory organization of the locus and to test the influence of the TZ on these . We used insertions carrying loxP sites in the opposite orientation to the one left at the SB-B ( 3end ) position in cis to engineer three balanced inversions by CRE-mediated recombination ( Fig . 4A , S1 Table ) . In INV-L1 and -L2 , the distance between Bmp7 and the heart enhancer increased to 5 . 7 and 1 . 1 Mb , respectively , whereas the relative order and distances between Tfap2c , the enhancers and the TZ region were unchanged ( S7 Fig . ) . In INV-M , the heart enhancer was now equidistant from Bmp7 and Tfap2c ( 187 and 207 kb , compared to distances of 80 kb and 312 kb in the wild-type allele , with mm75 taken as reference ) . However , in this allele , the TZ was now located between Bmp7 and the heart enhancer ( s ) . With each inversion , the LacZ reporter remained adjacent to the heart enhancer region and displayed its normal heart expression ( Fig . 4B , S7 Fig . ) , demonstrating that these rearrangements did not disrupt heart enhancer activity . In the three inversions , Bmp7 expression was strongly reduced in the heart , comparable to levels observed with del1 ( Fig . 4C ) . In contrast , Tfap2c expression was enhanced by a thousand-fold in the heart of INV-M animals ( Fig . 4D ) , implying that in this genomic configuration , the heart enhancers now activated Tfap2c instead of Bmp7 . This complete switch of the heart enhancer ( s ) from Bmp7 to Tfap2c coincided with the new relative position of the TZ . The importance of the position of the TZ was further supported by a lack of up-regulation of Tfap2c in INV-L1 and INV-L2 ( Fig . 4D ) , where its location with regards to the TZ/heart enhancers remained unchanged . In INV-L1 , we instead found an up-regulation of Ptgis ( Fig . 4E ) , which was now located on the other side of the TZ , next to mm75 . As Ptgis was closer to the heart enhancer ( S7A Fig . ) we were unable in this case to fully rule out a possible influence of distance on promoter choice . However , in INV-L2 , Dok5 , the new gene juxtaposed “next to” the heart enhancer ( s ) opposite to TZ was much further away than Tfap2c ( 1 . 1 Mb versus 0 . 3 Mb ) . In this context , neither Dok5 ( Fig . 4F ) nor Tfap2c were up-regulated in the heart , ruling out the possibility that the heart enhancer ( s ) act simply by default the nearest gene . To examine at the consequences of these rearrangements on the structural conformation of the region , we performed 4C experiments on INV-M and INV-L2 embryos ( Fig . 5 , S8–S10 Figs . ) . In INV-M , as in WT controls , Tfap2c showed robust interactions over a domain extending up to the TZ . Due to the inversion , this domain now included the heart enhancer , which displayed much stronger interaction with Tfap2c than those observed in WT ( S8A Fig . , pink versus grey arrow ) , a result consistent with mm75 now activating Tfap2c . Conversely , the new primary interaction domain of Bmp7 stopped at the TZ , with a very reduced 4C signal over the heart enhancer in INV-M when compared to WT ( S8D Fig . , grey versus pink arrow ) . The viewpoint located between mm75 and TZ , which was part of the Bmp7 interaction domain in WT , showed in INV-M broad and extended contacts overlapping with the Tfap2c interaction domain , ending at the TZ region ( Fig . 5B ) . Interestingly , the inversion had no effect on the 4C profile of the TZ-associated viewpoint , which extended on both sides in all configurations . Thus , in INV-M as in WT , the locus appeared structurally partitioned at the TZ: instead of maintaining their normal contacts and regulatory preferences , genes and regulatory elements established new interactions , depending on their respective position in relation to the TZ . In INV-L2 embryos , the 4C profile of Tfap2c appeared generally unchanged and did not expand across the TZ into its new flanking region . The TZ-flanking viewpoint remained still limited by the TZ , but highlighted on the other side a broad domain of nearly 1 Mb in the Dok5-Cbln4 gene desert , which is now adjacent to it . The 4C signal was strongly diminished before reaching the promoter of Dok5 , which may explain the lack of up-regulation of this gene in the heart of INV-L2 embryos ( Fig . 4F ) . Again , the TZ itself contacted both flanking regions , the relocated Tfap2c domain , and the new Dok5-Cbln4 . Importantly , in INV-L2 , Bmp7 showed broad contacts over the region now present at its 3′end , extending for up to 0 . 5 Mb further in the Cbln4 locus , supporting the notion that the presence of the TZ limited the extent of the Bmp7 contact range ( Fig . 5C , S3 Table ) . Remarkably , the new distribution of 4C contacts in the different rearrangements appeared to follow quite strictly the relative position of the TZ . It did not appear to depend on the nature of the flanking sequences themselves . The directional bias of contacts made by the viewpoint flanking the TZ is the same in the different configurations ( WT , INV-M and INV-L2 ) ( S10 Fig . , on the right ) , irrespectively of the flanking sequences . The expression and structural changes observed in the heart suggested that the TZ behaved as a simple insulator region . In INV-L1 and INV-L2 , the Tfap2c domain was fully maintained and unaffected by the genomic rearrangements . Therefore one would expect little impact on Tfap2c . However , we observed an up-regulation of Tfap2c in the medial telencephalon in both alleles ( Fig . 6A–B ) . This up-regulation is unlikely to be caused by the juxtaposition of new forebrain enhancers , as the regulatory sensor did not detect any forebrain activity in L1 and L2 position , in either the inverted or non-inverted configurations ( Fig . 6C ) . We noted that in INV-L1 and –L2 , Bmp7 , which is strongly expressed in the medial forebrain , was relocated away from Tfap2c and its forebrain enhancer . This rearrangement had no effect on Bmp7 expression in the forebrain , suggesting that it was the presence of Bmp7 in cis that negatively influenced Tfap2c . Supporting this hypothesis , we did not observe any up-regulation of Tfap2c in the medial forebrain of INV-M embryos ( Fig . 6D ) , where Bmp7 remained adjacent to the Tfap2c . These observations prompted us to re-examine the 4C profiles . As stated before , the intensity of the 4C signals diminished strongly beyond the TZ region . However , we observed that the 4C contacts made by the Bmp7 promoter , albeit weak , were stronger over the Tfap2c domain than over the region located symmetrically from the viewpoint ( S9 Fig . , green boxes ) . Reciprocally , Tfap2c showed weak but consistent interactions with the Bmp7 region in WT and INV-M ( S9 Fig . , blue boxes ) , interactions which are not observed with a symmetrically located region , or with the region at the equivalent place in INV-L2 . To further test if the INV-L1 and –L2 up-regulation of Tfap2c depended on the removal of Bmp7 , we produced INV-Bmp7 which consists in a simple inversion of the gene itself . Consequently , Bmp7 remained adjacent to the Tfap2c domain , and separated from it by the TZ ( S11A Fig . ) . In this configuration , we did not observe significant changes of Bmp7 or Tfap2c expression , with the exception of a small reduction of Bmp7 expression in the lateral forebrain . Altogether , these results supported that the simple presence of an active Bmp7 in cis , despite the presence of the TZ region , can affect Tfap2c expression in the medial forebrain . We also noted that INV-M led to a significant reduction of Tfap2c expression in the lateral forebrain ( Fig . 6D ) , even if the genomic region between Tfap2c and FB1 was unaffected . This reduction could result from the relocation to the other side of the TZ of two forebrain-specific H3K27ac-enriched regions included in INV-M . As we observed neither a concomitant up-regulation of Bmp7 ( Fig . 6D ) nor changes in the activity reported by the sensor ( Fig . 6E ) , it is possible that these elements may not act autonomously but rather modulate the long-range action of FB1 .
We show here that the neighboring Tfap2c and Bmp7 genes are controlled by distinct set of enhancers acting specifically on one or the other gene . Since we observed in a balanced genomic rearrangement a switch of enhancer-gene preferences , the specificity of these enhancers for one or the other gene cannot result exclusively from differences in their promoter structures , as proposed for other situations [49] , [60] . In contrast , our results indicate that , for this locus , the regulatory interactions are in a large part determined by the relative position of the different elements , as reported for other complex regions [7] , [61] , [62] . Our 4C experiments showed that Bmp7 and Tfap2c lie in genomic domains that share limited physical contacts . These domains were only weakly demarcated in the available Hi-C data in ES cells [34] . Therefore , it is unclear if the Tfap2c and Bmp7 domains correspond to adjacent sub-TADs [41] , or weak TADs in a rather unstructured region . However , the distinction between these different levels of spatial segregation of the genome may in part be semantic , based on arbitrary thresholds , which may not be pertinent for gene regulation . We showed here that the distinct enhancers that regulate each gene ( this work , [53] , [63] ) reside and act within the corresponding conformational domain , further supporting the functional relevance of the structural partition we described in establishing distinct domains of regulation [15] . Furthermore , we showed that a balanced rearrangement exchanging the relative position of genes , enhancers and the TZ region led to a concomitant redistribution of physical and regulatory interactions . The switch of the heart enhancer from Bmp7 to Tfap2c and the patterns of contacts observed in this configuration demonstrate together that the topological separation in two distinct domains is key to allocate distant enhancers to one or the other gene . We observed extensive similarities in the 4C profiles between the different cell tissues assayed , irrespective of the expression state of the corresponding genes . This indicates that the Tfap2c-Bmp7 locus adopts a rather generic conformation which undergoes limited changes in response to transcriptional activity . Such a constitutive folding has also been described for other loci [34] , [40] , [64]–[66] . It suggests that the structural partitioning of the locus into two domains pre-exists and guides regulatory interactions , instead of deriving from directed interactions between active genes and enhancers . Our functional dissection of the locus highlights that the transition zone separating the two domains has an important role in organizing this topological subdivision . The fusion of the interaction profiles of the two promoters and the centromeric extension of the Bmp7 interaction domain upon removal of the TZ strongly argue in favor of the TZ preventing interactions between Bmp7 and Tfap2c . The different balanced inversions further demonstrate that the TZ organizes this topological separation irrespectively of the nature of its flanking sequences . Interestingly , the TZ region interacts robustly with both flanking regions , suggesting that the topological segregation between Tfap2c and Bmp7 may arise from its action as an interaction sink or decoy , not as a blocker or repulsive element . TAD “boundaries” often displayed strong interactions with regions flanking them on both side [34] , suggesting that this behavior could be a rather general feature of topological transitions . The TZ does not appear to coincide with a region of constitutive transcription , contrarily to a large subset of typical TAD boundaries [34] . It is flanked by and includes several constitutive CTCF sites [38] . CTCF sites have been proposed to anchor long-range interactions and to act , together with cohesin and Mediator complexes , as master regulators of the chromosomal 3D conformation [67] , [68] . However , as only a subset of CTCF sites act as insulators [15] , [69] , and as depletion of CTCF only mildly impacts chromosomal topologies [70] and long-range gene regulation [71] , the precise role of these sequences – and of other regions of the TZ – would need to be directly assessed . With regard to the allocation of the heart enhancer , the TZ behave similarly to a classical insulator ( Fig . 7 ) . However , the analysis of INV-L1 and –L2 indicates that the TZ does not provide complete shielding from external influences , as the presence , beyond the TZ , of an active Bmp7 promoter can interfere with the expression of Tfap2c in the medial forebrain . Although contacts between Bmp7 and Tfap2c and its associated forebrain enhancer ( s ) are limited and even insufficient to lead to productive interactions ( i . e . activation of Bmp7 ) , they are nonetheless present at higher than background level . Our data suggests that they may be frequent and/or strong enough to perturb the regulation of Tfap2c by its forebrain enhancer ( s ) , most probably through promoter competition . Several studies have reported that promoters have a tendency to come into close proximity [40] , [72] , [73] , particularly when they are co-active and linked . Our analysis indicates that the TZ appears to counteract this generic promoter clustering by limiting admixing of the two domains , but it does not however totally prevent the diffusion of regulatory influences between them . The functional impact of these influences underscores the difficulties of defining functional thresholds for the interaction data obtained with 4C or Hi-C . It also emphasizes that topological domains should not be considered as strict autarchic units: topological separation does not exclude neighborly relationships and semipermeable borders . Transformation of the intrinsically broad forebrain activity of FB1 into the graded expression pattern shown by Tfap2c may involve additional neighboring enhancer elements , as hinted to by the INV-M data . However , our observations suggest that the permeability of the TZ to active Bmp7 may also contribute to this fine-tuning ( Fig . 7C ) . In operational terms , the TZ should be considered as a rheostatic controller rather than as a strict insulator . Interestingly , a sequence orthologous to FB1 is present between Tfap2c and Bmp7 in the coelacanth , but not in teleosts or sharks ( S12 Fig . ) . This indicates that the origin of FB1 can be traced back to the ancestor of the lobe-finned fishes . In contrast , the sequence of the TZ region is far less conserved , suggesting a more recent origin . Expression of Bmp7 in the forebrain is likely an ancestral feature , as it is shared amongst Bmp7 orthologues and paralogues [44] . Conversely , Tfap2c is the only member of its family expressed in the forebrain [42] , [45] , and the only one directly adjacent to a Bmp gene . The evolution of FB1 as a forebrain enhancer may have been favoured by the pre-existing expression of Bmp7 in this tissue , as suggested for other loci [74]–[76] . In this scenario , we suggest that Bmp7 may have initially been the primary target of this emerging enhancer . The evolution of a region with insulating-like activity would have make FB1 available to Tfap2c . Interestingly , the forebrain expression of Tfap2c regulates the formation of basal progenitors in the developing cortex in mammals [77] and variations of this expression levels , in space and time , have been proposed to account for the increased number of cortical neurons present in higher primates [77] . Changes in gene expression changes are usually attributed to evolution of enhancers or promoters [78] . Our results indicate that a simple change of the filtering capacity of the TZ may also provide evolution with means of modulating gene expression .
The initial allele used to produce SB-B ( 3end ) was obtained by homologous recombination in ES cells ( E14 ) . The targeting construct comprised: the SB8 transposon [79]; an additional loxP site outside of the transposon; a neomycin resistance gene under the control of the PGK promoter that are flanked by two FRT sequences . The homology arms ( chr2:172686051–172689701 and chr2:172689702–172694528 ( NCBI37/mm9 ) ) were amplified by PCR and then attached to the targeting construct above . After transformation and selection in ES cells , correctly targeted clones were injected into donor C57BL/6J blastocyst . Germline transmission was obtained from one chimera . The FRT-flanked selection cassette was then removed by breeding with hACTB-FLPe mice , leaving only the transposon and the loxP sequence outside of it at the site ( allele SB-B ( 3end ) ) . The ES clone BA0758 was obtained from BayGenomics , verified by PCR genotyping , and injected to establish a Tfap2c-gene trap line . The SB transposon was remobilised and new insertions were mapped as described before [14] . Alleles carrying the different deletions , duplications and inversions were produced by in vivo genomic engineering [18] , [54] , using the 129S1/Sv-Hprttm1 ( cre ) Mnn/J CRE line [80] . Deletions del1 and del3 were obtained by recombination in cis between the static loxP site at the end of Bmp7 and the one moved along with the transposed insertion SB-A1 and SB-Sall4 , respectively . To keep the regulatory sensor at the deletion breakpoint , we also produced another version of these deletions , del1-LacZ and del3-LacZ , by CRE-mediated recombination in trans [54] , between the loxP site from SB-B ( 3end ) and the one at SB-A1 and SB-Sall4 , respectively . For the del2-lacZ allele , we used a recombination in trans , between SB-B ( 3end ) and BA0758 . Mice were genotyped by PCR ( see Supplemental Experimental Procedures ) . Mouse experiments were conducted in accordance with the principles and guidelines in place at European Molecular Biology Laboratory , as defined and overseen by its Institutional Animal Care and Use Committee , in accordance with the European Convention 18/3/1986 and Directives 86/609/EEC and 2010/63/EU . LacZ staining and whole-mount in situ hybridization was carried out following standard protocols . For RT-qPCR , total RNA was extracted from the frozen tissues using RNeasy kit ( QIAGEN ) , and then cDNA was synthesized using the ProtoScript II First Strand cDNA Synthesis Kit ( New England Biolabs ) . The quantitative PCR was performed using StepOne Real-Time PCR System with SYBR green reagent ( Applied Biosystems ) . Gapdh was used to normalize expression level for each sample . The extra-embryonic membranes were used for PCR-genotyping of the embryos . We cloned the FB1 enhancer ( chr2:172551998–172555000 , NCBI37/mm9 ) upstream of the reporter gene used in SB8 , in a lentiviral vector [81] . The transgenic provirus was produced in HEK293 cells as described elsewhere [81] . Briefly , the virus was micro-injected under the zona pellucida of one-cell embryos which were maintained in culture up to the blastocyst stage . Embryos were then reimplanted into foster mothers and , at stage E11 . 5 or E12 . 5 , stained for LacZ activity and genotyped . To prepare the 3C library we dissected out the heart and the lateral and medial forebrains from E11 . 5 C57BL/6 embryos . The cells were dissociated , fixed and then processed following the protocol in Splinter et al . [82] . The fixed genomic DNA was digested with NlaIII enzyme and subsequently self-ligated . To quantify the ligation products of interest , we conducted qPCR with TaqMan probes . qPCR was performed with four technical replicates , and for each value , mean and standard deviation were plotted . For the 4C analyses , the 3C libraries were first prepared as described above from the respective tissues with NlaIII enzyme . They were then subjected to digestion by DpnII and ligation . After purification of the circularized DNA , inverse PCR was performed to obtain 4C libraries . Reading primers had 3–6 nucleotides of tag sequence , to allow for demultiplexing of the pooled libraries after sequencing . PCR products were purified , mixed altogether and sequenced on a HiSeq 2000 ( Illumina ) . For data analysis , we first demultiplexed the FASTQ files of the 4C sequencing libraries and then aligned them to the mm9 reference genome using Bowtie version 1 . 0 . 0 [83] . To normalize with regard to library size , we divided the counts by the total number of counts on the viewpoint chromosome ( chr2 ) for each library and multiplied these values by 1 , 000 , 000 ( “RPM normalization” ) . We then smoothed the counts over adjacent fragments , using a window size of 11 fragments . Details are available in Supplementary Information . Sequencing data of the 4C libraries is deposited at ENA ( Study Accession ERP005557 ) | The specificity of enhancer-gene interactions is fundamental to the execution of gene regulatory programs underpinning embryonic development and cell differentiation . However , our understanding of the mechanisms conferring specificity to enhancers and target gene interactions is limited . In this study , we characterize the cis-regulatory organization of a large genomic locus consisting of two developmental genes , Tfap2c and Bmp7 . We show that this locus is structurally partitioned into two distinct domains by the constitutive action of a discrete transition zone located between the two genes . This separation restricts selectively the functional action of enhancers to the genes present within the same domain . Interestingly , the effects of this region as a boundary are relative , as it allows some competing interactions to take place across domains . We show that these interactions modulate the functional output of a brain enhancer on its primary target gene resulting in the spatial restriction of its expression domain . These results support a functional link between topological chromatin domains and allocation of enhancers to genes . They further show that a precise adjustment of chromatin interaction levels fine-tunes gene regulation by long-range enhancers . | [
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| 2015 | A Discrete Transition Zone Organizes the Topological and Regulatory Autonomy of the Adjacent Tfap2c and Bmp7 Genes |
The let-7 microRNA ( miRNA ) regulates cellular differentiation across many animal species . Loss of let-7 activity causes abnormal development in Caenorhabditis elegans and unchecked cellular proliferation in human cells , which contributes to tumorigenesis . These defects are due to improper expression of protein-coding genes normally under let-7 regulation . While some direct targets of let-7 have been identified , the genome-wide effect of let-7 insufficiency in a developing animal has not been fully investigated . Here we report the results of molecular and genetic assays aimed at determining the global network of genes regulated by let-7 in C . elegans . By screening for mis-regulated genes that also contribute to let-7 mutant phenotypes , we derived a list of physiologically relevant potential targets of let-7 regulation . Twenty new suppressors of the rupturing vulva or extra seam cell division phenotypes characteristic of let-7 mutants emerged . Three of these genes , opt-2 , prmt-1 , and T27D12 . 1 , were found to associate with Argonaute in a let-7–dependent manner and are likely novel direct targets of this miRNA . Overall , a complex network of genes with various activities is subject to let-7 regulation to coordinate developmental timing across tissues during worm development .
MicroRNAs ( miRNAs ) are an abundant class of regulatory genes that control many cellular and developmental processes [1] . The biogenesis of miRNAs requires multiple steps , beginning with transcription by RNA polymerase II to produce capped and polyadenylated primary transcripts [2] , [3] . These transcripts are processed sequentially by the RNase III enzymes Drosha and Dicer , resulting in the ∼22 nucleotide ( nt ) single stranded mature miRNA . The mature miRNA is incorporated into the RNA induced silencing complex ( RISC ) , which uses the miRNA as a sequence specific guide to find and mediate regulation of target mRNAs . The miRISC usually induces translational repression and destabilization of the target mRNA through mechanisms that are still being determined [4] , [5] . let-7 was originally discovered as a miRNA controlling developmental timing in Caenorhabditis elegans [6] , [7] . The lethality associated with mutations in this gene is at least partly due to vulval rupturing , where internal organs burst out of the egg-laying pore . Additionally , lateral hypodermal seam cells fail to terminally differentiate at the larval to adult transition in let-7 mutants . These phenotypes place let-7 in the heterochronic pathway , which includes genes that regulate the temporal identity of cell divisions and fates [6] , [8] . let-7 regulates developmental timing , in part , through the direct target genes lin-41 and hbl-1 [6] , [7] , [9] , [10] . These genes , in turn , regulate the transcription factor lin-29 , which directly controls terminal differentiation in the hypodermis [6] , [7] , [9] , [10] . Several transcription factors , such as the nuclear hormone receptor daf-12 , the forkhead transcription factor pha-4 and the zinc finger protein die-1 , genetically interact with let-7 and are also likely direct targets [11] . Genetic mutation or RNAi depletion of any one of these let-7 targets is sufficient to at least partially rescue the lethality of let-7 mutants . The let-7 miRNA is a widely conserved animal miRNA and its role in regulating differentiation also appears to be conserved [12] , [13] , [14] . Typically , expression of let-7 family miRNAs is negligible in stem cells and in early embryonic tissues and is then up-regulated as cells take on more differentiated fates . In worms and mammalian cells , the LIN-28 RNA binding protein is largely responsible for keeping let-7 miRNA levels low during early development [15] . LIN-28 prevents the maturation of let-7 family miRNAs by blocking Drosha or Dicer processing or promoting destabilization of let-7 precursors [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] . The abnormally low expression of let-7 detected in various types of tumors has been linked , in some cases , to aberrant up-regulation of LIN-28 [24] . Additionally , let-7 and LIN-28 have opposing effects on insulin sensitivity in mice [25] , [26] . This is due at least in part to direct targeting of several metabolic genes by let-7 miRNA . Consistent with its role in promoting differentiated states , decreased expression of let-7 miRNA has been associated with numerous types of cancer [14] . In fact , one of the first discovered targets of let-7 in humans is RAS , a notorious oncogene [27] . Since then , many genes that promote cell division or antagonize the differentiated state have been implicated as direct or indirect targets of let-7 regulation [28] , [29] , [30] , [31] , [32] , [33] . Remarkably , the introduction of let-7 miRNA into lung or breast tumors in mouse models has been shown to halt tumor growth in vivo [31] , [34] , [35] . Thus , let-7 functions as a tumor suppressor in at least in some settings , where it represses the expression of genes needed for oncogenesis . To understand how let-7 or any miRNA controls a cellular process , the genes it regulates must be identified . Many computational prediction approaches have been taken to match miRNAs to targets [11] , [36] , [37] , [38] , [39] , [40] , [41] . However , the limited overlap of predicted targets between programs suggests that a consensus regarding the rules for target recognition is yet to be reached . The best defined motif for target recognition is perfect pairing of miRNA nucleotides 2–7 , called the “seed” region , with a target sequence [42] . Deviations from seed pairing can be compensated for by strong pairing of the 3′ end of the miRNA or “centered sites” , where the middle portion of the miRNA forms consecutive base pairs with the target [42] , [43] . Several validated target sites fail to conform to any of these motifs [42] , [44] . Furthermore , other features , such as location within an mRNA and RNA secondary structure surrounding the miRNA complementary sequence influence whether a target site will be recognized in vivo . Ultimately , the endogenous context of the target site and the cellular environment will determine which sequences will be recognized and regulated by miRISC . Numerous experimental methods have complemented the in silico endeavors to match miRNAs with direct targets . Traditional genetic as well as RNAi-based suppressor screens have uncovered major targets of the first described miRNAs in C . elegans [6] , [7] , [11] , [27] , [45] , [46] . More high-throughput methods have been based on the general role of miRNAs in down-regulating mRNA and protein levels of their targets [5] , [47] . Microarray or RNA-seq analysis of transcripts up-regulated when a miRNA is absent can provide lists of potential direct targets [48] , [49] , [50] , [51] . Likewise , large-scale proteomics analyses have been used to detect proteins sensitive to changes in expression of specific miRNAs [52] , [53] , [54] . More recently , ribosome profiling has been developed as an indirect method for assessing changes in the translation status of mRNAs , leading to the conclusion that regulation by miRISC generally results in target mRNA destabilization [55] , [56] . A more direct approach for detecting targets of miRISC is to capture mRNAs associated with Argonaute complexes . RNA immunoprecipitation ( RIP ) or cross-linking followed by IP ( CLIP ) protocols have been used to identify entire transcripts or the actual mRNA sequences in contact with Argonaute , respectively [57] , [58] , [59] , [60] , [61] , [62] , [63] , [64] , [65] , [66] . These types of experiments demonstrate that an mRNA is recognized by miRISC but do not necessarily reveal the identity of the miRNA involved or if the interaction is regulatory . We combined several molecular and genetic methods to identify physiologically relevant targets of let-7 in C . elegans . Our approach for discovering new let-7 regulatory targets takes advantage of let-7 dependent expression differences of the known targets , including lin-41 [67] , [68] . We postulated that other direct targets would also be mis-regulated in let-7 mutants . Therefore , in vivo expression changes were analyzed in wild-type ( WT ) and let-7 mutant animals using microarray analysis to identify a list of relevant candidate target genes . This list of genes was further refined by computational target predictions and expression analysis in the downstream heterochronic mutant , lin-29 . The relevance of the up-regulated genes for let-7 phenotypes was tested through RNAi-based suppressor screens . These genetic analyses revealed twenty new downstream effectors of let-7 phenotypes , including multiple transcription factors and metabolic proteins . Several of these genes also affect let-7 dependent phenotypes seen in lin-28 mutants revealing a complex genetic interaction with let-7 . By showing let-7 dependent association with Argonaute , we were able to confirm three new direct targets of let-7 with binding sites in the 3′ UTRs as well as in coding regions .
We have previously shown let-7-dependent mRNA destabilization of known direct targets [67] , suggesting that in addition to giving a general picture of let-7 function , microarray analysis of gene-misregulation in let-7 mutants will provide a basis for the discovery of new direct targets . The let-7 ( n2853 ) mutation changes the fifth G to an A in the mature let-7 miRNA [6] , which destabilizes target interactions and results in up-regulation of lin-41 mRNA , an established let-7 target [7] , [67] , [70] . To identify globally the genes regulated by let-7 , six independent and paired wild-type and let-7 ( n2853 ) fourth larval stage ( L4 ) RNA samples were labeled and hybridized to Affymetrix arrays . 2216 genes were up-regulated , and 1905 genes were down-regulated in the let-7 ( n2853 ) mutants compared to WT worms ( FDR<0 . 05 ) ( Table S1 ) . By microarray analysis , most of the differentially expressed genes were only modestly mis-regulated , as only 42 genes were up-regulated >2-fold ( Table 1 ) and 49 were down-regulated by >2-fold ( Table 2 ) . Illustrating the role of let-7 as a master regulator of development , the up-regulated genes were enriched for Biological Process Gene Ontology ( GO ) terms representing larval growth and development ( Table S1 ) . The up-regulated genes represent direct , including the known targets lin-41 , daf-12 , and hbl-1 , and indirect targets of let-7 repression . To further investigate the regulatory relationships between let-7 and the up-regulated genes , a combination of computational and molecular-genetic criteria were used to enrich for direct target candidates among the up-regulated genes . Direct mRNA targets of miRNAs typically have partially complementary miRNA binding sites , making prediction of miRNA targets from genomic sequence difficult [42] , and many groups have developed a variety of rules for target recognition [11] , [36] , [38] , [39] , [40] , [41] , [69] . To enrich for biologically relevant candidates and allow for non-canonical binding sites , we searched for enriched 6-mer sequences in the 3′ UTRs of the genes up-regulated in let-7 mutants . Two conserved 6-mers complementary to let-7 mature sequence were enriched in the 3′ UTRs in the up-regulated gene set ( Table S1 ) . As expected , the nucleotides TACCTC , which are complementary to the let-7 seed sequence ( nucleotides 2–7 of a mature miRNA ) , were enriched , consistent with the prevailing model for miRNA target recognition [42] . Also enriched was AACCTA , complementary to nucleotides 9–14 of let-7 , which overlaps with the newly described “centered sites” observed for some miRNA target interactions [43] . 158 genes that were up-regulated in let-7 mutants had at least one of these two 6-mers in their 3′ UTRs . The presence of strong seed enrichment in the up-regulated gene set led us to include an additional 8 and 5 up-regulated , predicted targets found by the seed based algorithms PicTar and TargetScan respectively , for further analysis . From the three prediction methods , there were 167 unique direct target candidates , including the known targets lin-41 , daf-12 , and hbl-1 . We also employed an alternative filter to select potential let-7 targets independent of preconceptions about base pairing requirements . let-7 is near the end of a genetic pathway controlling developmental timing in C . elegans [71] . Negative regulation of lin-41 by let-7 in late larval stages allows the transcription factor LIN-29 to accumulate and to directly control the terminal differentiation of multiple cell types [6] , [7] , [72] , [73] . In let-7 mutants , lin-41 persists in late larval stages where it can continue to negatively regulate lin-29 [6] , [7] . Thus , in let-7 mutants , larval genes turned off by lin-29 will be up-regulated in addition to direct targets of let-7 . In lin-29 mutants , the same downstream larval genes should be up-regulated , yet the upstream direct targets of let-7 should be unaffected . By analyzing gene-expression in lin-29 versus let-7 mutants , novel targets can be found that may not have obvious binding sites . Three lin-29 ( n333 ) mutant L4 RNA samples paired with wildtype and let-7 ( n2853 ) samples were collected , labeled and hybridized to Affymetrix microarrays . In lin-29 ( n333 ) , 3030 genes were up-regulated and 1994 genes were down-regulated relative to WT samples ( Table S2 ) . Consistent with a role for lin-29 in directing terminal differentiation and adult fates , genes up-regulated in lin-29 mutants were enriched for GO terms for larval development ( Table S2 ) . In comparison to WT , 930 common genes were up-regulated in both let-7 ( n2853 ) and lin-29 ( n333 ) and 649 common genes were down-regulated in both . We selected the 192 genes that were up-regulated in both of the let-7 ( n2853 ) vs . WT and the let-7 ( n2853 ) vs . lin-29 ( n333 ) pairs , which included lin-41 , and daf-12 , as possible direct targets ( Tables S1 and S3 ) . Combining the candidates that emerged from the computational and mRNA expression analyses , there were 340 candidates to test for genetic interactions with let-7 . To identify functionally important genes among the list of candidates , we used RNAi screens to find genetic interactions by suppression of let-7 mutant phenotypes . The let-7 mutant worms display an array of developmental timing defects at the larval to adult transition including rupturing ( Rup ) of the intestine and gonads through the vulva [6] , [7] . The developmental defects observed in let-7 mutants are caused by the over-expression of direct regulatory targets such as lin-41 and hbl-1 , and some of these defects can be suppressed by RNAi knockdown of these targets in let-7 mutants [6] , [7] , [9] , [10] . RNAi mediated suppression of vulval rupturing in let-7 mutants has been used to find new genetic interactions in sets of computationally predicted targets and in genes on chromosome I [11] , [38] , [74] . However , many of the candidate genes from our global expression analyses have not been assayed for vulval rupture and , thus , we were able to discover novel genetic suppressors . Using the Ahringer feeding RNAi library [75] , the Vidal feeding RNAi library [76] and a few clones we generated , 308 genes out of the 340 candidates were tested for suppression of vulval rupturing in the let-7 ( mn112 ) null strain . Homozygous let-7 ( mn112 ) mutants die at the late larval stages and must be maintained by a wild-type copy of the let-7 gene coming from a balanced translocation or a rescuing transgene [6] , [7] . To grow a population of let-7 ( mn112 ) mutants to be able to score suppression , we generated a transgenic strain in which the worms were maintained by the presence of an extrachromosomal array ( Ex[let-7 ( + ) ; myo-2::GFP] ) , which contains a let-7 rescue fragment , allowing the mutants to survive , and the myo-2 promoter driving expression of a GFP marker in the pharynx to indicate the presence of the array ( Figure 2A ) . To identify new suppressors of vulval rupturing , worms were grown synchronously from the L1 stage on bacteria expressing dsRNA targeting candidate genes or empty vector , as a negative control , and populations of non-transgenic animals were scored for the rate of vulval rupturing at the late larval and young adult stages ( Figure 2B–2C ) . Nine clones exhibited larval growth arrest and therefore could not be scored for suppression . Empty vector clones were scored eight independent times as a negative control and 86–97% of these non-rescued worms ruptured at the time of scoring . We considered clones in which less than 75% of the population exhibited rupturing as suppressors ( Figure 2D ) , consistent with a previous screen [11] . From this , 22 suppressors were retested and 16 clones again met the suppression threshold , including known suppressors lin-41 , daf-12 , and hbl-1 ( Table 3 ) ( Figure 2C–2D ) . Transcription factors constitute approximately half of the rupturing suppressors ( 7 of 16 ) , several of which are involved in development including fos-1 , lin-11 , and sox-2 [77] , [78] , [79] , [80] , [81] . Enrichment of a different set of transcription factors was also noted by the Slack lab as genetic suppressors of their computational let-7 predictions [11] . To broaden the search for genes that interact with let-7 beyond those involved in vulval rupture , we reasoned that novel targets might control other phenotypes found in let-7 mutants . In addition to the rupturing phenotype , let-7 mutants also have defects in the terminal differentiation of their seam cells , a specialized type of hypodermal cell [6] , [7] , [82] . Seam cells undergo significant changes during the larval to adult transition , including fusion of the seam cells , cessation of division , and the secretion of the adult cuticular structure known as alae [83] . Exit of the seam cells from the cell cycle and secretion of alae have been shown to be retarded in let-7 mutants [6] , [7] , [82] . Interestingly , seam cell fusion was unaffected in let-7 ( mn112 ) null mutants , suggesting that some aspects of seam cell terminal differentiation are let-7 independent ( Figure S1 ) . We chose to focus on the cell cycle exit defect , in which the seam cells fail to stop dividing at the larval to adult transition [82] , as this would be the first screen for suppression of this phenotype and likely to uncover novel genetic interactions . Candidate RNAi clones from the rupturing suppression screen were tested for suppression of the cell cycle exit defect in let-7 ( n2853 ) mutants also carrying the integrated transgene Int[scm::GFP] , which expresses a nuclear localized GFP specifically expressed in seam cells . The number of GFP positive seam cell nuclei were counted in at least 20 young adult worms ( Figure 3A ) . Candidates were considered suppressed if they had significantly less nuclei than empty vector grown at the same time , p<0 . 05 using a Mann-Whitney U test . The 23 suppressing clones yielded 10 reproducible suppressors upon retest ( Figure 3B and Table 3 ) . Among the suppressors were lin-41 and daf-12 , which suppress two other let-7 phenotypes , vulval rupture and alae formation [6] , [7] , [11] . Thus lin-41 and daf-12 RNAi are sufficient to suppress all previously described phenotypes of let-7 mutants . Though hbl-1 RNAi also suppresses rupturing and alae formation defects , it is not surprising that it does not suppress the extra seam cell nuclei defect because hbl-1 loss of function mutants also have an increase in the seam cell nuclei number [10] . Of the 306 clones screened , 7 clones caused larval arrest and could not be scored . Consistent with previous work by the Gilleard lab [84] , elt-1 RNAi led to the loss of most of the seam cells during larval development , rendering it inconclusive for suppression . Suppressors of the supernumerary seam cell divisions in let-7 ( n2853 ) represent a diverse set of gene functions and there is only modest overlap with the rupturing suppressors , suggesting that the two phenotypes are likely under separate genetic control ( Table 3 ) . The twenty-three candidate let-7 targets were also tested for potential roles in a vulva formation abnormality due to precocious let-7 expression . The loss of function lin-28 ( n719 ) mutants exhibit a partially penetrant temperature-sensitive protruding multiple vulva ( pmuv ) phenotype that is dependent on let-7 . At 25°C , this phenotype is expressed in ∼67% of the lin-28 ( n719 ) population with the remaining worms displaying a single protruding vulva ( pvul ) ( Figure 4A ) . In the presence of the let-7 ( mn112 ) null allele , the pmuv phenotype is no longer observed in lin-28 ( n719 ) worms , and 100% of the double mutant population expresses the pvul phenotype ( Figure 4A ) . Thus , the pmuv phenotype is dependent on let-7 , and suggests that the precocious expression of let-7 in the lin-28 mutants might prematurely repress targets needed to regulate vulval cell patterning . We predicted that further suppression of such targets by RNAi would enhance the pmuv phenotype in lin-28 ( n719 ) worms . To identify potential targets that act in this pathway , the percent of the population exhibiting pmuv was scored for lin-28 ( n719 ) mutants subjected to RNAi of the 23 candidates . RNAi of three genes produced the expected enhancement of the pmuv phenotype ( Figure 4B ) , suggesting that inappropriate down-regulation of these candidates in lin-28 mutants contributes to mis-specification of vulval cell fates . This enhanced phenotype is dependent on let-7 because the pmuv phenotype is almost entirely absent in lin-28 mutant worms that also lack let-7 activity ( lin-28 ( n719 ) ;let-7 ( mn112 ) ) ( Figure 4C ) . Surprisingly , another set of genes significantly decreased the incidence of pmuv in lin-28 ( n719 ) ( Figure 4B ) and , in the case of nhr-25 , the pvul phenotype was also suppressed in the lin-28 ( n719 ) ;let-7 ( mn112 ) double mutants ( Figure 4C ) . These results suggest that some of the candidate genes may have a more complicated relationship with let-7 , possibly affecting let-7 expression or activity in tissue-specific feedback loops . miRNAs repress target mRNA expression through their association with Argonaute proteins allowing them to act as sequence-specific guides for the RISC complex [4] , [5] . Taking advantage of the recent global map of Argonaute Like Gene 1 ( ALG-1 ) binding sites in C . elegans [66] , we searched for these sites in the twenty-three suppressors . Eight of the twenty-three suppressing genes had significant ALG-1 binding sites within their 3′ UTRs and coding regions . This group included the known let-7 targets , such as daf-12 and lin-41 , as well as hbl-1 , which is also a target of other let-7 miRNA family members ( Table 3 ) [7] , [9] , [10] , [11] , [85] , [86] , [87] . To test if let-7 is responsible for the interaction of ALG-1 with these genes , we analyzed their association with ALG-1 using RNA immunoprecipitation ( RIP ) in wild-type and let-7 ( n2853 ) worms ( Figure 5A ) . Genes regulated by let-7 are expected to be enriched in wild-type samples versus let-7 mutant samples , while genes targeted by other miRNAs should be amplified similarly in both strains . Four independent RIPs were analyzed , and targets enriched in the wild-type for at least 2 of the 4 replicates were considered to be dependent on let-7 for ALG-1 association . The known targets lin-41 and daf-12 , served as positive controls with both showing let-7-dependent enrichment in the ALG-1 IP . fos-1 was used as a negative control as it did not have any significant CLIP reads nor did fos-1 sequences amplify from the RIPs in either worm strain . lin-14 was also used as a negative control because it is a known target of a different miRNA , lin-4 , and as expected there was no significant change in ALG-1 binding in let-7 mutants compared to WT . daf-9 and adt-2 had significant CLIP reads but could not be verified as targets through the RIP analysis . adt-2 had similar levels in the WT and let-7 ( n2853 ) mutant strains suggesting it may be targeted by a different miRNA , which could mask any let-7 dependent RISC association . Three novel targets were identified: prmt-1 , opt-2 , and T27D12 . 1 . They were all enriched in the WT compared to the let-7 ( n2853 ) RIP ( Figure 5A ) and are , therefore , associated with ALG-1 in a let-7-dependent manner . Furthermore , we found let-7 complementary sites ( LCS ) within the ALG-1 binding sites of these targets ( Figure 5B ) , supporting these genes as new direct targets of let-7 . Interestingly , T27D12 . 1 and opt-2 , which contain predicted target sites in coding exon sequences , showed weak mis-regulation at the mRNA level in let-7 ( n2853 ) versus WT worms ( Figure 5C ) . In contrast , prmt-1 and the positive control lin-41 , which contain 3′UTR target sites , were up-regulated over three-fold at the mRNA level in the let-7 mutant worms . These data are consistent with the global correlation observed between changes in mRNA levels and ALG-1 binding to 3′UTR , but not coding exon sequences [88] .
The let-7 miRNA is exceptional in its conservation and essential role in cellular differentiation across species [13] . Loss of let-7 activity results in lethality in worms and contributes to oncogenesis in mammalian tissues [14] , [89] . Since these effects are due to mis-regulation of let-7 targets , identification of the biologically relevant genes regulated by this miRNA has been a paramount research goal . Through a combination of genetic and molecular screens in C . elegans , we have uncovered twenty-three genes that are up-regulated in let-7 mutants and contribute to the developmental abnormalities characteristic of these mutants . Three of these genes , lin-41 , daf-12 and hbl-1 , are the best previously characterized let-7 targets in C . elegans , validating the sensitivity of our approach [6] , [7] , [9] , [10] , [11] . Unexpectedly , a subset of the genes that suppressed let-7 mutant phenotypes also suppressed a lin-28 phenotype that is due to up-regulation of let-7 expression , suggesting nonlinear pathways between these targets and let-7 in vulval precursor cells . Three genes , prmt-1 , opt-2 , and T27D12 . 1 , were found to associate with the miRNA complex in a let-7 dependent manner and , thus , emerged as likely novel direct targets of let-7 . A large fraction of the transcriptome is mis-regulated in let-7 ( n2853 ) worms . Based on microarray analyses , most of these changes are less than two-fold . However , let-7 ( n2853 ) is a temperature sensitive loss of function strain that maintains some let-7 activity even at the non-permissive temperatures . Accordingly , the fold change in let-7 target mRNA expression for lin-41 , for example , is less dramatic in let-7 ( n2853 ) compared to wild type at the L4 stage than it is in stages before ( L2 ) and after ( L4 ) let-7 expression in wild type worms [67] . By using reproducibility in the direction of change , instead of the absolute fold difference in mRNA levels , we identified twenty new genes in the let-7 pathway that exhibited only modest expression differences in let-7 mutants . In fact of our list of let-7 suppressors , only lin-41 and daf-12 were mis-regulated by more than two-fold by microarray analyses . The large number of down-regulated genes in let-7 ( n2853 ) mutants likely represents indirect targets , reflecting mis-regulation of direct targets that transcriptionally regulate some of these genes . Over one-third of the genes up and down-regulated in let-7 ( n2853 ) were changed in the same direction in lin-29 mutants , indicating that failure to trigger the lin-29-dependent transcriptional program also accounts for many of the mis-regulated genes in let-7 mutants . Considering that the two well-established targets of let-7 , lin-41 , and daf-12 , suppress both the rupturing vulva and extra seam cell phenotypes of let-7 mutants , it was surprising to find almost entirely distinct sets of new genes affecting one phenotype versus the other . The opt-2 gene was the only additional suppressor of both phenotypes , suggesting that different pathways largely control maturation of the vulva and seam cells . While it is not entirely understood why let-7 mutants rupture through the vulva , it has been postulated that improper cell fusions during vulva formation cause weakening and destabilization of this structure . Fourteen new genes were found to suppress the bursting vulva phenotype when subjected to RNAi conditions , none of which overlapped with the previously described suppressors of this let-7 phenotype [11] , [74] . A distinction from these studies is that we screened for suppression in null let-7 ( mn112 ) worms as opposed to the weaker let-7 ( n2853 ) strain . Two of the let-7 ( n2853 ) suppressors identified in Grosshans et al . , 2005 , lin-59 and lss-18 , were found to be up-regulated in let-7 mutants by our microarray analyses . However , these candidates failed to suppress the rupturing of let-7 ( mn112 ) worms , in agreement with the previous study [11] . Many of the genes we identified as suppressors of vulva rupturing encode transcription factors , a category also prominent on the list of potential let-7 targets described in Grosshans et al . , 2005 [11] . Genes involved in translation make up another class of let-7 ( n2853 ) suppressors [74] . A combined approach , incorporating let-7 target predictions by PicTar , reporter assays and screens for suppression of rupturing in let-7 ( n2853 ) , resulted in twelve potential new targets [38] . Of the genes that passed the genetic test , only fos-1 is in common with our list of bursting suppressors . Another group tested 181 genes with various criteria for being potential let-7 targets for changes in protein levels in WT versus let-7 ( n2853 ) worms [54] . Of the nineteen candidates up-regulated in let-7 mutants , nine also suppressed rupturing in let-7 ( n2853 ) . Three of these suppressors , T19A6 . 2 , Y47GA . 10 , and F46B6 . 7 , were up-regulated in our microarray data . However , they failed suppress vulva rupturing in the null let-7 ( mn112 ) background and , thus , did not appear on our final list of candidates . An important consideration when screening for suppression of vulva rupturing is that in some cases the effect may be indirect due to slow or halted development or the absence of vulva formation . These caveats were avoided by using the let-7 ( mn112 ) strain containing the extrachromosomal let-7 rescue construct , as RNAi clones that affected development regardless of the presence of the let-7 transgene could be flagged . Nonetheless , the observation that RNAi of many different genes results in suppression of the rupturing phenotype in let-7 mutants points to the existence of cross-regulatory pathways that are sensitive to down-regulation of a single target . Reiteration of seam cell nuclear divisions at the transition to adulthood is another characteristic of let-7 mutants [11] , [82] . In C . elegans , the lateral seam cells undergo an asymmetric division in which one daughter cell differentiates while the other repeats this pattern at each larval stage [83] . In let-7 mutants , the seam cells inappropriately undergo the larval type division instead of differentiating to the adult fate , where the cells normally fuse and cease dividing [6] . The heterochronic gene lin-29 is downstream of let-7 and is a master regulator of seam cell differentiation [6] , [73] . The failure of seam cells to properly differentiate in let-7 mutants seems to be largely due to a lack of lin-29 activity [6] , [7] . How let-7 positively regulates the expression of LIN-29 protein is presently unknown . Our screen identified eight new genes that suppress the supernumerary seam cell divisions of let-7 ( n2853 ) mutants . Three of these suppressors , opt-2 , prmt-1 , T27D12 . 1 , are likely direct targets of let-7 since their association with Argonaute is dependent on this miRNA . The group of extra seam cell suppressors includes factors with a variety of predicted functions that could potentially contribute to mis-regulation of lin-29 . In C . elegans , processing of the let-7 miRNA early in larval development is inhibited by LIN-28 protein [21] , [23] . In lin-28 ( n719 ) mutants , let-7 miRNA is expressed precociously , resulting in premature repression of its targets . One effect of this mis-regulation is the development of protruding multiple vulvas in lin-28 mutants grown at 25°C . This partially penetrant pmuv phenotype is dependent on let-7 because lin-28 ( n719 ) ;let-7 ( mn112 ) strains only produce single protruding vulvas . Since early accumulation of let-7 miRNA is expected to cause premature down-regulation of targets , we anticipated that further silencing of potential targets by RNAi would enhance the pmuv phenotype in lin-28 ( n719 ) worms . Three candidates , fos-1 , ZK1236 . 1 and T08B2 . 8 , emerged as enhancers , pointing to roles for these genes in vulval fate specification . Surprisingly , there were also several candidates that decreased the percentage of pmuv in lin-28 ( n719 ) worms including , nhr-25 , hbl-1 , sox-1 , prmt-1 , and nduf-7 . Since this effect is also observed when let-7 is removed from lin-28 ( n719 ) , these suppressors potentially feedback to regulate the expression or function of let-7 in vulval precursor cells . Feedback loops between let-7 family members and targets , such as daf-12 and hbl-1 , in other tissues have been previously demonstrated [9] , [10] , [85] , [86] , [87] , [90] . Multiple lines of molecular and genetic evidence support opt-2 , prmt-1 and T27D12 . 1 as new direct targets of let-7 regulation . One of the targets , opt-2 , may be a general downstream effector in the let-7 pathway as down-regulation of opt-2 suppresses phenotypes in the vulva and seam cells . Before this study , opt-2 was not a predicted let-7 target because it lacks complementarity to the 5′ end of the miRNA ( seed ) in its 3′UTR . However , a single ALG-1 binding site is present in the second last exon of opt-2 and this region includes a predicted let-7 binding site . opt-2 ( also known as pept-1 ) is a member of the peptide transporter family and facilitates uptake of di- and tri-peptides in the intestine [91] , [92] . Loss of opt-2 activity slows development , alters fat accumulation and enhances stress resistance 91 , 93 . Although opt-2 appears to be exclusively expressed in the intestine , loss of this factor causes global changes in gene expression [94] , [95] . Reporters driven by the let-7 promoter also show intestinal expression , suggesting that let-7 miRNA is available for directly regulating opt-2 in this tissue [96] , [97] , [98] . The ability of opt-2 RNAi to suppress let-7 phenotypes in vulval and seam cells suggests that signaling from the intestine influences development of these tissues . Another likely direct target , T27D12 . 1 , also seems to be regulated by let-7 through sequences in its open reading frame . This gene lacks predicted target sites for let-7 in its 3′UTR but came through our screen as a modestly up-regulated gene in let-7 ( n2853 ) that was capable of suppressing the extra seam cell phenotype of these mutants . T27D12 . 1 contains one ALG-1 binding site in its 3′UTR and one in a coding exon , but only the exonic region includes an obvious LCS , which conforms to seed-pairing with the allowance of a G-U pair . T27D12 . 1 is predicted to encode a sodium/phosphate transporter protein but little else is known about this factor . The more conventional miRNA target , prmt-1 , has an LCS within its 3′UTR and was previously predicted by the mirWIP and PITA algorithms as a let-7 target [40] , [41] . While prmt-1 has ALG-1 binding sites in its 3′UTR as well as coding exon sequences , only the 3′UTR site includes an obvious let-7 complementary site . Although there is not a canonical LCS in the 3′UTRs of mammalian homologs of prmt-1 , there are several well conserved potential let-7 binding sites ( Figure S2 ) . prmt-1 encodes a protein arginine methyltransferase , and it has been shown in mammalian cells to be a major contributor to methylation of histone 4 at arg-3 , leading to transcriptional activation [99] , [100] . Additionally , PRMT-1 has been shown to methylate arginine residues on other types of proteins in mammalian cells and C . elegans [93] , [101] . Recently , it was discovered that PRMT-1 methylates DAF-16 , a key transcription factor in the insulin pathway [101] . This modification prevents phosphorylation of DAF-16 by AKT , thus , keeping it in an active state to promote the expression of longevity-related genes . prmt-1 has a broad expression pattern that is largely overlapping with let-7 transcriptional reporters [96] , [97] , [98] , [101] . Down-regulation of prmt-1 by let-7 in late larval stages could influence the lifespan of worms by causing reduced methylation and , hence , activity of DAF-16 . Our combination of molecular and genetic screens revealed a complex network of genes that interact with let-7 in C . elegans . This approach was sensitive enough to detect the established let-7 targets , lin-41 , daf-12 and hbl-1 . While these genes are regulated at the mRNA level , other targets that are only subject to translational repression would be missed by focusing on transcripts up-regulated in let-7 mutants . However , the microarray data revealed that thousands of genes are mis-regulated when there is insufficient let-7 activity , supporting a widespread role for this miRNA in regulating , directly and indirectly , gene expression . A set of the up-regulated genes proved to be biologically relevant for the developmental abnormalities that arise in the absence of let-7 activity . At least three of these genes , which encode transport proteins and a modifying enzyme , appear to be new direct targets of let-7 . In conclusion , let-7 appears to regulate a variety of direct targets , which in turn influences the expression of hundreds of other genes . Loss of this miRNA alone results in extensive changes in gene expression and abnormal development in multiple tissues , supporting the role of let-7 as a master gene regulator .
The C . elegans strains were cultured at 15°C or 25°C under standard conditions [102] . Worms were synchronized by hypochlorite treatment and development was initiated by plating arrested L1 hatchlings on NGM plates seeded with OP50 bacteria or RNAi bacteria on RNAi plates . Strains used in this study include the following: wild type ( WT ) Bristol N2 , MT7626 let-7 ( n2853 ) , MT333 lin-29 ( n333 ) , MT1524 lin-28 ( n719 ) , PQ79 mnDp1 ( X/V ) /+; unc-3 ( ed151 ) let-7 ( mn112 ) ; Ex[let-7 ( + ) ; myo-2::GFP] , PQ270 mnDp1 ( X/V ) /+; unc-3 ( ed151 ) let-7 ( mn112 ) ; lin-28 ( n719 ) , PQ293 let-7 ( n2853 ) ; Int[scm::GFP] . Seam cell nuclei were counted at 40 hr ( 25°C ) in 20 adult PQ293 let-7 ( n2853 ) ; Int[scm::GFP] worms grown on vector control or gene specific RNAi plates for one generation . Suppression was determined by a Mann-Whitney U test comparing worms on each RNAi vector to those on the empty L4440 control vector grown on the same day . Bursting suppression was scored as more than 25% non-bursting , non-green ( non-rescued ) 40 hr adult PQ79 mnDp1 ( X/V ) /+; unc-3 ( ed151 ) let-7 ( mn112 ) ; Ex[let-7 ( + ) ; myo-2::GFP] worms grown at 25°C . All suppressing clones were retested using the same criteria for reproducibility . All clones suppressing at least one phenotype were verified by sequencing . Fifty to one hundred lin-28 ( n719 ) or lin-28 ( n719 ) ;let-7 ( mn112 ) worms were grown on RNAi until 48 hr ( 25°C ) adults and then scored for the protruding multivulva ( Pmuv ) or protruding single vulva ( Pvul ) phenotypes . Suppression/enhancement was determined by a Ttest comparing worms on each RNAi clone to those on the empty L4440 control vector grown at the same time for 4 or 5 independent RNAi experiments . Six paired replicates of L4 RNA from WT or let-7 ( n2853 ) worms were prepared and labeled as per manufacturer's instructions ( Affymetrix , Santa Clara ) and hybridized to Affymetrix C . elegans Gene microarrays . Three of the paired replicates of WT and let-7 ( n2853 ) were also paired with lin-29 ( n333 ) replicates for array analysis . To assess the significance of differential gene expression between the two groups , a paired t-statistic was computed . CEL files obtained after scanning were analyzed by using Affymetrix APT tools and Robust Multi-array Average ( RMA ) -sketch normalized [103] . Annotation files for the probe sets were obtained from Affymetrix . The paired t-test statistic was utilized to compute differences between groups for each probe set . Probe sets were mapped to custom gene structures generated from Refseq annotations obtained from ce2 at the UCSC genome browser . Gene ontology analyses were performed using the database for annotation , visualization and integrated discovery ( DAVID ) and the Functional Annotation Clustering Tool [104] , [105] . Classifications were set to the highest stringency and the recommended enrichment score of ≥1 . 3 was applied . To search for enriched motifs in the gene lists , pair-wise alignments between C . briggsae ( cb1 ) and C . elegans ( ce2 ) were obtained from the UCSC genome browser . 3′UTR exons were spliced together to generate the sequence if necessary , and then extended to 2000 bases from the stop codon . 6-mer enrichment in genes up-regulated in let-7 ( n2853 ) versus non-regulated genes was computed using methods described in [106] . RIP assays were preformed as previously described [23] , [88] . Synchronized WT and let-7 ( n2853 ) worms were grown at 25°C for 29 hours before being cross-linked by UV treatment . Equal amounts of lysates were pre-cleared before immunoprecipitation with the anti-ALG-1 antibody ( Thermo Fisher Scientific ) or control IgG ( Caltag Laboratories ) and protein G Dynabeads ( Invitrogen ) . Immunoprecipitated material was subjected to Proteinase K treatment and RNA extraction before reverse transcription using random oligo priming . The resulting cDNA was used in PCR with the primers listed in Table S4 . RNA was isolated from WT and let-7 ( n2853 ) worms grown at 25°C for 28 hours . qPCR was performed on cDNA with SYBR green ( Applied Biosystems ) and 10 uM of each forward and reverse primer on an ABI Prism 7000 real time PCR machine . Primers are listed in Table S4 . | In the past decade , microRNAs ( miRNAs ) have become recognized as key regulators of gene expression in many biological pathways . These small , non-coding RNAs target specific protein-coding genes for repression . The specificity is mediated by partial base-pairing interactions between the 22 nucleotide miRNA and sequences in the target messenger RNA ( mRNA ) . The use of imperfect base-pairing means that a single miRNA can regulate many different mRNAs , but it also means that identifying these targets is not straightforward . One of the first discovered miRNAs , let-7 , generally promotes cellular differentiation pathways through a repertoire of targets that is yet to be fully described . Here we utilized molecular and genetic approaches to identify biologically relevant targets of the let-7 miRNA in Caenorhabditis elegans . Our analyses indicate that let-7 regulates a large cast of genes , both directly and indirectly . Loss of let-7 activity in C . elegans results in multiple developmental abnormalities and , ultimately , death . We uncovered new targets of let-7 that contribute to these phenotypes when they fail to be properly regulated . Given the highly conserved nature of let-7 from worms to humans , our studies highlight new genes and pathways potentially under let-7 regulation across species . | [
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| 2013 | Functional Genomic Analysis of the let-7 Regulatory Network in Caenorhabditis elegans |
Out of all the complex phenomena displayed in the behaviour of animal groups , many are thought to be emergent properties of rather simple decisions at the individual level . Some of these phenomena may also be explained by random processes only . Here we investigate to what extent the interaction dynamics of a population of wild house mice ( Mus domesticus ) in their natural environment can be explained by a simple stochastic model . We first introduce the notion of perceptual landscape , a novel tool used here to describe the utilisation of space by the mouse colony based on the sampling of individuals in discrete locations . We then implement the behavioural assumptions of the perceptual landscape in a multi-agent simulation to verify their accuracy in the reproduction of observed social patterns . We find that many high-level features – with the exception of territoriality – of our behavioural dataset can be accounted for at the population level through the use of this simplified representation . Our findings underline the potential importance of random factors in the apparent complexity of the mice's social structure . These results resonate in the general context of adaptive behaviour versus elementary environmental interactions .
In a famous passage of his book The Sciences of the Artificial [1] the sociologist Herbert Simon considers the winding , weaving path of an ant as it makes its journey home across the rugged landscape of a wind- and wave-beaten sandy beach . He notes that , whilst the homebound ant has a clear destination , its progression along the path that leads to it is far from a straight line , due to the numerous obstacles encountered on the way . The example inspires in him this startling observation: An ant , viewed as a behaving system , is quite simple . The apparent complexity of its behavior over time is largely a reflection of the complexity of the environment in which it finds itself . More than fifty years after the first mention of the parable of the ant , the general question is still very much alive . There is indeed an ongoing debate today as to what aspects of the behaviour , and especially the social behaviour , observed in an animal species can be explained as a specific adaptation versus an emergent property of simple behavioural rules when individuals interact with their environment . If some emerging properties of a group's social structure result from more simple behavioural mechanisms , it may be that what is thought to be an explicitly social behaviour does not require specific selective adaptations . Although animal groups often display astonishing emergent patterns in their collective behaviour [2] , recent research has shown that much of the complexity of some natural phenomena can be directly attributed to the collective dynamics of simple , self-organised processes and individuals [3]–[5] . In recent years , assumptions of random behaviour have been discussed at length in contexts such as animal movement and foraging [6] . There have also been specific investigations on the description of collective motion in biological systems based on stochastic processes , as exemplified by the concept of Brownian agents [7] , [8] . This framework has proven to be a versatile and practical approach to describe collective patterns of movement at different organisational levels , for systems ranging from bacteria [9] to crustaceans [10] and social insects [11] . However , clearly the behaviour of animal groups is not limited to collective motion , but also includes complex social interactions between individuals within the group . These interactions are often of a high-order and individually-differentiated nature , as illustrated by the long history of studying social relationships in animal species with high levels of cognitive development [12] , [13] . Despite the efforts mentioned above to describe collective behaviour by means of stochastic forces , little attention has been paid to the importance of randomness for the emergence of complex social patterns in animal groups . Here we use as a case study data from a population of wild house mice ( Mus domesticus ) , social rodents characterised by cooperative breeding , polygynandry , territorial defence against non-group members , high skew in reproductive success and rather short mean life expectancy in both sexes [14]–[16] . This may have led to high flexibility in behaviour and social organisation . Yet exactly what aspects of the social structure of house mice can be explained by the self-emergent properties of collective random behaviour is unknown . In this paper , we first develop the assumption of random behaviour by creating a perceptual landscape that mixes purely diffusive motion and advective-diffusive Brownian bridges to describe the movement of individual animals . We then proceed to build a simple stochastic model that implements the assumed movement dynamics , and discuss its accuracy in accounting for specific characteristics of the social structure of a population of house mice . The novelty of our approach lies in the application of a dynamical perspective on habitat use to the investigation of social structure in animal groups as an emergent property of random interactions .
Animal experimentation was approved by the Swiss Animal Experimentation Commission ( Kantonale Tierversuchskommission , no . 26/2002 , 210/2003 , 215/2006 ) . We study an established free-living population of wild house mice in a barn outside of Zurich , where mice can freely emigrate and immigrate . In the barn , the mice nest in artificial nest boxes , and are provided with straw as nesting material , and food and water outside the boxes . At four to six week intervals , comprehensive trapping is conducted to monitor the population , and adult mice are implanted with a transponder ( RFID tag ) so that they are individually identifiable . Transponder readers are installed in the tunnels that provide entrances to the nest boxes ( two readers per nest box make it possible to distinguish a mouse that leaves a box from a mouse that enters a box ) ; these readers connect to a computer and continuously track movements into and out of nest boxes . This provides 24-hour information on movements and social affiliations of adult mice ( for a detailed description of the barn population and the methods used , see [17] ) . Data collection started in May 2007 and totals around 29 million individual recordings as of June 2012 . Here we analyse the period ranging from Jan . 1 , 2008 to Dec . 31 , 2009 . Our 2-year dataset covers 11'259'557 location records for 508 mice , accounting for 1'376'720 stay events in 40 nest boxes , and leading to 1'064'695 one-to-one encounters inside nest boxes , whose frequency , context and duration we use as a proxy for the characterisation of social interactions ( the full Dataset S1 is available to download from the Supplementary Material page ) . Figure 1 shows the geographical positioning of the nextboxes , as well as the heterogeneity of their occupation pattern: indeed , the total occupation duration per box ranges from 264 to 22'332 hours ( the lowest figure may , however , be attributed to a malfunctioning RFID antenna; the maximum value is longer than our study period because some stays can overlap when two or more mice stay simultaneously in the same nest box ) . This aggregated view allows to identify the “hot spots” of the barn and the busiest routes between nest boxes , from a static perspective on the behavioural data . It also shows how geographically clustered the traffic between nest boxes is , as a result of the partitioning and the obstacles of the barn . Evidently the physical environment of the mice affects their movement , which in turn has an impact on their social contact patterns . However , the view presented in Figure 1 is insufficient to characterise the dynamics of movement of individual mice , and the link between these dynamics and their social behaviour . Indeed , it focuses on aggregated properties of the study system rather than dynamical ones .
Through the description of the perceptual landscape , we have developed the assumption of simplistic individual motion to reconstruct the environment of a wild house mouse . We now test this hypothesis by implementing the assumptions of the perceptual landscape in an elementary model of collective behaviour , in which all agents are governed by the principles of random motion we have introduced previously . We make the assumption that through the collective behaviour of those agents , whose complexity lies far below that of real mice , we can reproduce some of the global behavioural patterns we observe in the barn .
In this paper we have developed the assumption of simple stochastic processes as a driving force for social interaction in a population of wild house mice . We introduced the notion of perceptual landscape , which maps the patterns of movement of mice between nest boxes in a barn into the motion of stochastic particles within a potential field . We are well aware that our model ignores the fact that such patterns have resulted from natural selection and adapt mice to their environment . Instead , we are interested in analysing whether and to what degree a general movement pattern alone can reflect important characteristics of the spatial and social behaviour of free-living mice . Our approach integrates two important facts , often neglected when mapping the home range or territory of wild animals: ( i ) the movement from one sampled location to another ought not to be thought of as a straight line , but instead may be better approximated by planar diffusion , and ( ii ) when an animal is resting in a safe area like a nest box ( or generally visiting an area with a strong potential of attraction ) it is less likely to exit and move on than if it were at another point within its home range . The parameters defining the landscape were obtained from the recorded data . The method has only one user-defined parameter , namely the assumed travelling speed of an animal . However , we detail a way to obtain this parameter from the data . Here we assumed a constant speed for a mouse moving along a Brownian bridge between two nest boxes . This may seem rather slow but is based on the fact that house mice spend a considerable part of their time outside nest boxes not moving , but instead feeding/drinking , marking their territory or partaking in social activities or territorial defence . Moreover , the mean radial diffusion coefficient obtained from this value of is ; this amounts to exploring an area of slightly less than one centimeter per second in each direction , which is a sensible figure in the case of small animals like house mice . As a tool to study animal behaviour , the perceptual landscape method scales linearly with the number of active paths , i . e . pairs of locations with a large number of transits . Therefore we argue that the method could scale properly to much larger systems , and provide a new way to analyse the spatial behaviour of animals on a large scale . Interestingly , the perceptual landscape contains several of the structural features observed in the real landscape of the mice . This confirms previous observations [30] , [31] that house mice use these structures to build up their own representation of the environment and navigate across it . From a formal point of view , it also reveals that the assumptions we made on the movement of random particles across the landscape yield meaningful behavioural patterns . Indeed , these patterns integrate important aspects of the decision rules guiding mice when they use their environment and defend their territory . Figure 3 illustrates the use of such a perspective for the study of individual home ranges ( panels B and C ) , as well as the movement patterns of the whole population studied ( panel A ) . We observed that the perceptual landscape is similar , but not identical , to the physical environment of the real mice . For example , the perceptual “wall” corresponding to the divider between segments A and B of the mouse barn is only weakly expressed . This may indicate that some mice regularly use nest boxes on both sides of the divider , and travel between them . Conversely to some physical features of the environment disappearing , some are overly expressed . For example , the entrance to the barn ( see inlay of Figure 3A ) is an area that mice could use , as it is open , but that they tend to avoid due to the presence of experimental equipment and the absence of protected nesting sites . As a result , the whole area appears in the perceptual landscape as a raised plateau , demonstrating little effective utilisation of that space . Generally , such discrepancies between the perceptual and the physical landscape may result from differences in the micro-environment of the animals . In practice , the mouse barn is not a homogeneous environment , but differs locally in humidity and temperature , in the degree of protection perceived by the mice ( suitable hides or other spatial structures outside the nest boxes ) , in the exposure to popular traffic routes used by many individuals , in the availability of food and water in close proximity to a nest box , or in the amount of light ( mice tend to avoid bright areas [14] ) , etc . In addition , the movement of mice between nest boxes will be influenced by their social environment . Since the perceptual landscape integrates all such factors , it may be seen as a cartographic tool of a much higher precision than a standard schematics or map of the environment of an animal population . In other words , this landscape is the combination of all dimensions that animals perceive in their environment ( be them physical boundaries , temperature , humidity or presence of conspecifics ) . This is especially important when considering that many animals view their environment in a way that is different from the way we see it . Indeed , house mice have poor visual acuity and their world is “dominated by smell” [14] . The representation of an animal's environment by simply mapping it as we would map our environment may thus be misleading . It is interesting to note that the perceptual landscape we described in this paper is a static construction , which represents the collective behaviour of the animals from a quasi-stationary perspective . As such , it results from the aggregate behaviour of the individuals of a population rather than adapts to it . However , the construction of this landscape by the animals is arguably a dynamical process: house mice , for example , alter their home range in response to the nearby presence of social partners [31] . In order to account for this dynamical aspect of the formation of the landscape , the Brownian agent framework [7] , [8] may be more suitable than simpler stochastic particles: indeed , Brownian agents move across an adaptive landscape , which builds up over time as a result of their behaviour . The Brownian agent paradigm constitutes a natural framework to study non-equilibrium systems , such as a population of interacting individuals . As such , this framework could be a logical extension to the perceptual landscape technique . In a second step , we tested the accuracy of the assumptions of our landscape . To this purpose , we implemented an agent-based model of the behaviour based on the landscape assumptions and tested its results against real observational data . As we assumed a very simple individual behaviour ( stochastic motion ) for the mice moving across the landscape , a simple approach was sufficient to reproduce it . We described the transitions between nest boxes by a set of stochastic processes , or zero-order Markov processes , effectively representing the set of nest boxes as a Markov chain ( this comparison is developed in Text S1 ) . In this description , the escape rates from each state ( inside a nest box or moving between two nest boxes ) were calculated from the real aggregated data . The underlying assumption of this Markov approach is that the process has no memory , the transition probability being only dependent on the current state . In this paradigm , individual mice are particles travelling along the Markov chain and all follow the same rules of motion . Each particle can be thought of as representing the “average mouse” , an individual who behaves as a composite of all the mice from the real population , without particular individual characteristics . We aimed to study how such an approximation performs in a social context , or how well it may reproduce the observed patterns of social encounters in a wild house mouse population . We used in our simulation as many average mice as the average number of RFID-tagged mice in the barn over the two-year study period . It should be noted that this approach implies no a priori assumption on the importance of the social interactions that can occur each time two mice meet inside or outside of a nest box , although it is well-known that social aspects play a crucial role in the behaviour of house mice , especially female [30] , [32]–[35] . Remarkably , we found that this simplistic , randow walk-like approach is sufficient to reproduce some features of the nest box occupation patterns , both at the population and the individual levels . In other words , the collective dynamics of the population as a whole , with its intrinsic fluctuations ( birth and death cycle ) and interindividual differences , may be well approximated by the behaviour of the average mouse . This is obvious from the match between experimental and simulated data in occupation density and transit frequencies between nest boxes . This accurate match should , however , come as little surprise: indeed , these features are aggregated observations on the behaviour of the whole population , and precisely those whose estimate we used to calibrate the stochastic model . Of more interest is the comparison between the model output and the observational data with regard to higher-level social features . The statistical test we ran on the simulation results amounts to asking whether the average mouse , moving randomly across the perceptual landscape , has a social behaviour ( defined as its pattern of encounters inside nest boxes ) consistent with the social behaviour of a real mouse from our study population . In agreement with the results obtained at the population level , we found that at the individual level most of the social features in the average mouse's behaviour were compatible with the behaviour of a real mouse , with the exception of the territorial aspect ( expressed in the number of nest boxes used per day ) . This is especially interesting when considering that we excluded any influence of conspecifics on an individual's probability to enter and/or stay in a nest box . Yet , the patterns of social interaction ( number of social encounters per individual , number of social partners , or duration of a social encounter ) did not differ significantly from those observed in the population of real house mice . It is important to note , however , that mice tagged in the study population are only adults , which typically had already established their territory and integrated in a social group . The behaviour of young , dispersing individuals that still move between groups or territories is thus underrepresented , although it may differ . Once integrated in a social group , however , mice seem to regularly meet with all group members , without pronounced individual preferences . Nevertheless , many real mice had fewer social partners and fewer social encounters than the average mouse from our model ( although the differences were not statistically significant from the global population ) . These discrepancies may , again , reflect territoriality , social preferences and differences in reproductive dominance [17] . Indeed , from a behavioural perspective , the assumption made for the average mouse , whose behaviour is the average of that of all other conspecifics , explicitly ignores any variability among individuals . However , it is well established – not only for house mice – that individuals within species and populations vary in their behaviour according to their sex , age , dominance or reproductive status , or personality . Even within individuals , behavioural performances can change over time due to individual experiences and modifications in physiology ( hunger , hormones , etc . ) . All such individual variability can explain differences in competitiveness , aggressiveness , social tolerance , or boldness , which will affect individual preferences towards conspecifics as well as towards nest boxes or other spatial structures . Furthermore , hindering non-group members from entering own nest boxes is very important due to the tendency of mice of both sexes to kill non-offspring . Evidently , the omission of all such factors in the model leads to different movement and social interaction patterns , changing the structure of the social network . Of course , it is highly implausible that a random particle may faithfully mimic a living mouse , and the simple modelling approach we have presented could not pretend to fully reproduce the complex , dynamical social network of a population of wild house mice . Yet , it points to the fundamental importance of simple , universal processes in the establishment of such a structure , and generally shows that the collective dynamics of stochastic processes are sufficient to reproduce some properties of a social system . This is clearly encouraging in the search for a more advanced cross-species model that could lead to a broader understanding of animal sociality . We proposed the perceptual landscape as a framework in which the complexity of the individual interactions is transferred to that of the environment ( the landscape ) . In doing so , we effectively simplified the analysis of collective social behaviour by moving from the study of many interacting individuals to the study of a single landscape object , whose properties can be quantified against those of the external environment ( such as its physical structure ) . Moreover , this approach provided us with a null model for the social behaviour of the individuals in the landscape , which we could use to characterise the social network built by a population of average mice . In this regard , the results of the simulations presented in Figure 4 represent a good null assumption for sociality in animal groups . Notwithstanding the apparent performance of such a simplifying approach , it should be noted that there are key differences at the individual level between the average mouse and an individual from the real population . This points to the existence of more sophisticated rules governing animal behaviour than merely random principles , as can be expected from complex creatures such as social mammals . We have applied methods from statistical physics to the understanding of the randomness underlying seemingly complex spatial and social animal behaviour . It appears that at least some elements of animal social behaviour can emerge from the collective dynamics of independent random processes . This complements recent work [36] which showed that such methods can be efficiently used to study the emergence of territoriality in animal populations . Such findings ultimately may parallel other examples of self-organisation in contexts as diverse as evolution [37] , speciation [38] and even human economic behaviour [39] . | From the synchronised beauty of fish schools to the rigorous hierarchy of ant colonies , animals often display awe-inspiring collective behaviour . In recent years , principles of statistical physics have helped to unveil some simple mechanisms behind the emergence of such collective dynamics . Among the most elementary tools used to explain group behaviour are random processes , a typical example being the so-called “random walk” . In this paper , we have developed a framework based on such random assumptions to study the spatial and social structure of a population of wild house mice . We introduce the concept of perceptual landscape to describe the spatial behaviour of animals , whilst including all sensory and social constraints they are subject to: the perceptual landscape effectively maps the environment of animals as they perceive it . By applying our assumptions to a multi-agent model , we are able to reveal that much of the high-level social behaviour observed in the mouse population can indeed be explained through the many interactions of randomly moving individuals . This raises the question of how much of what we often regard as complex natural phenomena may , in fact , be the result of exceedingly simple forces . | [
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| 2012 | How Random Is Social Behaviour? Disentangling Social Complexity through the Study of a Wild House Mouse Population |
The drug molecule PTC124 ( Ataluren ) has been described as a read-through agent , capable of suppressing premature termination codons ( PTCs ) and restoring functional protein production from genes disrupted by nonsense mutations . Following the discovery of PTC124 there was some controversy regarding its mechanism of action with two reports attributing its activity to an off-target effect on the Firefly luciferase ( FLuc ) reporter used in the development of the molecule . Despite questions remaining as to its mechanism of action , development of PTC124 continued into the clinic and it is being actively pursued as a potential nonsense mutation therapy . To thoroughly test the ability of PTC124 to read through nonsense mutations , we conducted a detailed assessment comparing the efficacy of PTC124 with the classical aminoglycoside antibiotic read-through agent geneticin ( G418 ) across a diverse range of in vitro reporter assays . We can confirm the off-target FLuc activity of PTC124 but found that , while G418 exhibits varying activity in every read-through assay , there is no evidence of activity for PTC124 .
Nonsense mutations are a type of genetic defect in which an amino acid codon is substituted by a TGA , TAG , or TAA stop codon , thereby interrupting the coding sequence of a protein-encoding gene . These mutations represent one major class of premature termination codon mutations ( PTCs ) ; out-of-frame insertion/deletion mutations can also lead to a PTC via a frameshift mechanism . Nonsense mutations prematurely terminate translation and result in production of either a truncated , non-functional protein , or , in many cases , pre-translational destruction of the transcript via nonsense-mediated mRNA decay [1] . PTC mutations are responsible for ∼10% of all human genetic disease and there is currently no available treatment [2] . As such , the discovery of the small molecule PTC124 provides hope for the development of a drug that can facilitate the read-through of PTCs and restore functional protein production [3] . Such a molecule would be applicable to a wide range of incurable hereditary diseases and some forms of cancer . The molecule was first described as effective in animal models of Duchenne muscular dystrophy ( DMD ) [3] , and the developers subsequently reported improvements in protein production in models of cystic fibrosis ( CF ) [4] and dysferlin deficiency [5] . This led to human clinical trials , where improvements in chloride channel conductance have been reported in CF patients [6] , [7] , [8] . Despite this clinical success , there have been studies that cast doubt upon the underlying mechanism of action of PTC124 [9] , [10] . Originally developed by optimising hit compounds identified following two high-throughput screening campaigns , the assay utilised in this effort was a cell-based firefly luciferase ( FLuc ) reporter containing an in-frame PTC , specifically the nonsense mutation TGA [3] . The authors describe the up-regulation of FLuc activity in response to PTC124 which they attribute to read-through of the PTC . However , it has subsequently been reported that the compound is a highly potent FLuc inhibitor , and the suggestion was made that this could be responsible for the up-regulation of luciferase signal that Welch et al . observed [9] , [10] . The rationale for this counterintuitive effect has been reviewed in detail [11] . Briefly , cells transfected with a FLuc construct containing a PTC in the reading frame can still produce a very small but steady amount of full-length FLuc due to natural read-through of the ‘leaky’ PTC . Incubation of the cells with inhibitor results in the compound binding and stabilising the protein , reducing its proteolytic degradation , increasing its half-life , and increasing the effective concentration of active FLuc over the course of the incubation period . The last step of the experiment is to dilute the medium with lysis buffer and add a very high concentration of the FLuc substrate luciferin . The luciferin essentially out-competes the reversible inhibitor and produces a luminescent signal which is mistakenly attributed to facilitated read-through of the PTC mutation . The molecular basis of PTC124 inhibition of FLuc was described in detail by Auld et al . with the X-ray crystal structure showing the enzyme binding and converting PTC124 to a PTC124-AMP adduct which has extremely high affinity for the enzyme [10] . Whilst these two studies described the mechanism by which PTC124 can up-regulate FLuc activity independently of PTC read-through , they only cursorily addressed whether the compound possesses genuine read-through activity . They generated a structurally unrelated Renilla luciferase ( RLuc ) construct containing a PTC and , using transient transfection , observed no activity with PTC124 and a modest two-fold increase in activity with the aminoglycoside gentamicin [9] . This low assay sensitivity , the potential for a discrepancy between transient and stably transfected reporters , and the positive , albeit qualitative , read-through data generated with human and mouse models of DMD [3] were used to argue that PTC124 may exhibit genuine read-through activity in spite of its interference in the FLuc assay [12] . To address some of the uncertainty surrounding the efficacy of PTC124 in promoting read-through of PTCs , we compared its activity with the well-characterised read-through agent geneticin ( G418 ) in multiple stably and transiently transfected PTC reporter cell lines . To be thorough , we used a diverse array of protein reporters and characterised compound activity across numerous PTC sequences .
We constructed a FLuc cell-based assay using AD293 ( ADXC8 ) cells stably transfected with a FLuc2P construct containing an in-frame nonsense mutation ( TGA ) at position 223 . The following ( +4 ) base position is a G residue . For ease of understanding the relative position of this PTC in the FLuc reading frame and for all constructs used in this study , a graphical summary is included in Figure S1 . Following 24-hour incubation with G418 , FLuc activity in ADXC8 cells dose-dependently increased to a maximum at 3 . 3 mM of 14 , 800%±600% of DMSO-only control ( Figure 1A , n = 5 ) . This is in contrast to a maximum response of only 174%±46% of DMSO-only control ( n = 5 ) with 0 . 15 µM PTC124 . At concentrations higher than this , PTC124 dose-dependently inhibits FLuc activity , most likely due to the continued presence of compound when the lysis/detection ( LD ) buffer is added to the cells . To investigate if this inhibition is masking more pronounced stimulation of FLuc activity due to PTC read-through , compound was removed from cells by washing with media ( three times , 10 minutes each ) before adding the LD buffer . Under these conditions G418 still produced a pronounced dose-dependent increase in signal , albeit with a 43% reduction in the maximum response ( 8 , 500%±800% of DMSO-only control , Figure 1B , n = 5 ) . The increase in signal caused by PTC124 was no longer inhibited at concentrations greater than 0 . 15 µM ( 171%±20% of DMSO-only control at 0 . 15 µM , n = 5 versus 170%±45% at 100 µM , n = 5 ) . However , neither was the maximum response significantly greater than that observed in the presence of PTC124 . To investigate the effects of PTC124 on FLuc activity independently of the PTC , we stably transfected AD293 cells with a wild-type FLuc2P construct under control of the same promotor ( CMV Luc2p WT ) . In this cell line , G418 did not affect FLuc activity , except for a slight reduction at 1 mM and above , most likely due to toxicity ( Figure 1C ) . In contrast , PTC124-treated CMV Luc2P WT cells behaved in the same manner as observed for ADXC8 cells , with a maximum response of 185%±10% ( n = 5 ) of control at 0 . 15 µM and marked inhibition at higher concentrations . Removal of compound prior to addition of LD buffer did not alter the lack of effect for G418-treated cells , but the PTC124-treated cells again exhibited the same profile of activity observed for ADXC8 cells ( 168%±9% at 0 . 15 µM , n = 5 versus 166%±32% at 100 µM , n = 5 , Figure 1D ) . These data show that the profile of PTC124 activity in two FLuc reporter assays is virtually identical whether or not a PTC is present . This is in agreement with Auld et al . that protein stabilisation is the mechanism through which PTC124 is increasing the FLuc signal . In contrast , the well-established read-through agent G418 produced a pronounced , dose-dependent increase in FLuc activity , orders of magnitude higher than that of PTC124 and had no effect in control cells . This positive control demonstrates that , for this cell type , bona fide read-through of PTCs increases protein levels to a greater extent than passive stabilisation from proteolytic degradation . To test for activity of PTC124 in a non-luciferase PTC reporter system we stably transfected AD293 cells with a β-galactosidase ( β-Gal ) construct containing an in-frame nonsense mutation ( TGA-G ) at position 320 within the lacZ gene ( Figure S1 ) . Incubation with G418 for 24 hours resulted in a dose-dependent increase in β-Gal activity ( 704%±16% at 3 . 3 mM , n = 5; Figure 2 ) . PTC124 however had no effect at concentrations between 0 . 1 nM up to 100 µM . We intentionally did not test PTC124 at higher concentrations , as this is in excess of the published maximally effective in vitro and in vivo concentration range of between 0 . 1 and 30 µM [3] , [4] , [13] . Interestingly , the minimal effective G418 concentration required to increase β-Gal activity appears greater than that observed for the FLuc reporter , indicating that the different PTC reporter systems are differentially sensitive to read-through . The reduced sensitivity of the β-Gal reporter to G418 may raise questions about the ability to accurately determine read-through with poorly efficacious compounds when switching between reporter assays . To ensure that a β-Gal assay–dependent shift in potency is not the reason for being unable to see activity with PTC124 , a third PTC reporter was constructed using AD293 cells transiently transfected with a RLuc construct containing an in-frame nonsense mutation ( TGA ) at position 21 ( Figure S1 ) . Incubation with G418 for 24 hours resulted in a dose-dependent increase in RLuc activity with a minimal effective concentration similar to that observed with the FLuc reporter ( Figure 3 ) . In contrast , PTC124 had no effect at any concentration tested ( Figure 3 ) . This is consistent with the results from Auld et al . albeit with a different RLuc construct [9] . In all of the assays tested so far we used non-mammalian reporter constructs . To determine if PTC124 is active against full-length mammalian proteins , we generated two FLAG-tagged collagen VII constructs based upon the two main PTC mutations responsible for severe dystrophic epidermolysis bullosa in the human population , Q251X ( TAG ) and R578X ( TGA ) [14] , [15] ( Figure S1 ) . For both constructs , transient transfection into AD293 cells and subsequent incubation with G418 resulted in detectable , dose-dependent increases in production of full-length protein as detected by ELISA , with maximal responses at 1 mM of 646%±48% of DMSO-only control ( n = 4 ) and 464%±43% of DMSO only control ( n = 4 ) , respectively ( Figure 4 ) . Only collagen VII protein secreted into the medium is detected in this assay , which requires an N-terminal signal sequence . Further , capture onto the ELISA plate requires the presence of the C-terminal FLAG tag , and the detection antibody recognises the N-terminal NC1 domain , so only full-length protein will be detected ( Figure S1 ) . As was observed in all other PTC reporter assays , PTC124 did not increase protein production at any concentration tested . The termination efficiency of stop codons is heavily dependent upon their sequence ( TGA , TAG , or TAA ) and the nature of the nucleotide following the nonsense codon ( +1 position ) [16] , [17] , [18] . To determine if the sequence context has some influence over the lack of efficacy of PTC124 we have observed so far , we generated a number of Keratin 6a-YFP fusion constructs ( K6a-YFP ) with the cysteine codon at position 533 replaced by a TGA , TAG , or TAA and all variants of the base in the +1 position ( Figure S1 ) . YFP production was then determined by Western blotting using transiently transfected AD293 cells that were incubated with either compound or DMSO-only control for 24 hours . We selected a concentration of 200 µM for G418 , which previous experiments suggest is tolerated by the cells but still elicits detectable read-through and 3 µM PTC124 , which was described as the maximally effective in vitro concentration by Welch et al . [3] . In DMSO-treated cells , K6a-YFP staining is predominantly very low level or undetectable for most PTC sequences ( Figure 5 ) . However , some PTCs are ‘leakier’ than others with a greater degree of basal protein production for TGA constructs , in particular TGAC . Exposure to G418 substantially increased K6a-YFP staining for all PTC sequences relative to DMSO-only controls ( Figure 5 ) . Again , the different PTC sequences appear to yield different degrees of read-through , with TAA being the most resistant ( TAAA and TAAG in particular ) . In contrast , the effect of PTC124 was indistinguishable from the effect of DMSO alone on the expression of the various K6a-YFP constructs . We present an in-depth study of the comparative efficacy of two published read-through agents , PTC124 and G418 . We used a diverse panel of PTC reporter assays , including transient transfection , stable cell lines , plate-based functional enzyme assays , and direct protein detection using ELISA and Western blotting . While we report activity with G418 in every assay , PTC124 exhibited no measurable effects . We also assessed the effects of sequence context upon read-through by testing 12 different constructs containing varying PTC sequences . In all cases G418 stimulated protein production , albeit to varying extent depending upon the sequence , whereas PTC124 had no effect . All of these reporter assays utilised cDNA constructs . An argument may be made that cDNAs lack relevance due to the potential absence of mRNA regulatory processes that are present in endogenous genes , one of which could be the mechanism through which PTC124 exerts its read-through activity . Although this argument is valid , it would not be compatible with the discovery and development of PTC124 , which was accomplished also using a cDNA construct . While we and others find no evidence of read-through efficacy for PTC124 [9] , [19] , [20] , [21] , [22] , there are independent studies detailing efficacy for PTC124 in non-FLuc read-through assays [13] , [23] , [24] . A greater understanding of this mechanistic discrepancy is very important for the future development of read-through drugs . Importantly , this should be accomplished by performing full and quantifiable dose–response studies of these drugs benchmarked against the activity of the aminoglycosides in multiple assays . Interestingly , the in vitro/ex vivo activity of PTC124 may be very different to that in vivo , as there are a number of reports for efficacy in in vivo models of disease and in clinical trials , in particular for CF [6] , [7] , [8] . In this regard a more thorough understanding of the mechanism of action of PTC124 would be highly beneficial .
The cytosine at positions 666 and 669 of the luc2P gene in pGL4 . 21[luc2P/puro] were both replaced with adenine using the QuickChange site-directed mutagenesis system ( Stratagene ) using the following primers: Luc2P-wtFLG . F 5′ CCG ATT CAG TCA TGC ACG AGA CCC CAT CTT CGG C 3′ and Luc2P-wtFLG . R 5′ GCC GAA GAT GGG GTC TCG TGC ATG ACT GAA TCG 3′ . The codon for arginine 223 was subsequently replaced by a premature TGA stop codon using the following primers: Luc2P-TGA . F 5′ CCG ATT CAG TCA TGC ATG AGA CCC CAT CTT CGG C 3′ and Luc2P-TGA . R 5′ GCC GAA GAT GGG GTC TCA TGC ATG ACT GAA TCG 3′ . A 0 . 9 kb BamH1/BglII fragment from pcDNA3 ( Invitrogen ) was subcloned into the mutated pGL4 . 21[luc2P/puro] plasmid so that the CMV promoter was inserted upstream of the luc2P coding sequence . AD293 cells were transfected with mutated pGL4 . 21[luc2P/puro] . The cells were placed under 1 µg/ml puromycin selection 24 hours post-transfection and grown until resistant foci were identified . Twenty-four puromycin-resistant clones were picked for further analysis . Cells were transfected with pRL-CMV ( Promega ) and treated with 600 µg/ml of gentamicin ( Invitrogen ) . Cells were incubated for 24 hours and then FLuc/RLuc activity tested . Cells were washed with PBS and lysed with Passive Lysis buffer ( Promega ) for 15 minutes with shaking . Luciferase activity was detected with the Dual Luciferase Reporter System ( Promega ) according to the manufacturer's instructions . Luminescence was detected with a LUMIstar OPTIMA luminometer ( BMG Labtech ) . The ratios of FLuc/RLuc activity were calculated and one clone was selected for further use . To test for read-through resulting in FLuc activity , cells were seeded into white 96-well plates ( Greiner ) and incubated for 24 hours with G418 or PTC124 added to concentrations as shown in Figure 1 . Luciferase activity was measured by monitoring luminescence on a TopCount luminometer ( PerkinElmer ) following the addition of 50 µl of lysis/detection buffer ( comprising: 25 mM Tris-Phosphate , 8 mM MgCl2 , 1 mM dithiothreitol , 0 . 5 mM ATP , 4 µM sodium pyrophosphate , 1% Triton X-100 ( v/v ) , 0 . 5% ( w/v ) BSA , 15% glycerol ( v/v ) , and 0 . 1 mg/ml luciferin ) . The cysteine 320 codon of the lacZ gene in the pSV-β-galactosidase vector ( Promega ) was replaced by a premature TGA stop codon using the QuickChange site-directed mutagenesis system ( Stratagene ) with the following primers: pSV-lacZ-TGA . F 5′ GAT TGA AGC AGA AGC ATG AGA TGT CGG TTT CCG CG 3′ and pSV-lacZ-TGA . R 5′ CGC GGA AAC CGA CAT CGC AGG CTT CTG CTT CAA TC 3′ . This reaction also replaced the cytosine at position 960 of the lacZ gene with adenine . A 3 , 738 bp BamH1/HindIII fragment containing the mutated lacZ construct was subcloned into the modified pGL4 . 21 vector described in ‘FLuc reporter assays’ ( above ) so that the mutated lacZ gene could be expressed from the CMV promoter . AD293 cells were transfected with the mutated pSV-β-galactosidase and grown under puromycin selection for one week . The cells were subsequently trypsinised and seeded at approximately one cell per well in 96-well plates . For characterisation of these clones , the cells were treated with 100 µg of geneticin ( Invitrogen ) for 24 hours , following which the cells were lysed with 1× Lysis Buffer ( Promega ) at room temperature for 1 hour with shaking at 900 rpm . 150 µl of assay buffer , consisting of 4 mg/ml CPRG ( Roche ) , 4 . 5 M β-mercaptoethanol ( Sigma-Aldrich ) , and 0 . 1 M magnesium chloride ( Invitrogen ) in 0 . 1 M phosphate buffer , pH 7 . 5 , was added to each well . After 5 hours of incubation with the assay buffer , absorbance at 580 nm was detected using the EnVision ( PerkinElmer ) , and the clone giving the strongest signal was selected for further use . To test for read-through resulting in β-Gal activity , cells were seeded into white 96-well plates ( Greiner ) and incubated for 24 hours with G418 or PTC124 added to concentrations as shown in Figure 2 . To determine β-Gal activity , the plates were first washed three times in fresh media before adding Promega β-Glo reagent according to manufacturer's instructions and monitoring luminescence using a TopCount luminometer ( PerkinElmer ) . AD293 cells were transformed in bulk with one of the plasmids , pJ609-R578X or pJ609-Q251X ( DNA 2 . 0 ) . These plasmids express human collagen VII containing the mutation R578X ( TCGA to TGAG ) or the mutation Q251X ( CAGT to TAGT ) with a C-terminal FLAG tag from the CMV promoter . Transformed cells were plated in 96-well plates from frozen stocks with G418 or PTC124 added to concentrations as shown in Figure 4 . Cell supernatants were taken for analysis following 3 days of growth , and collagen VII concentration in the supernatant analysed by ELISA . ELISA plates ( Nunc polysorp ) were coated with anti-FLAG antibody ( Abcam Ab1162 ) at a concentration of 2 . 5 µg/ml in carbonate buffer: ( 0 . 1 M NaHCO3/Na2CO3 pH 9 . 6 ) . 50 µl of antibody solution was added to each well and the plate incubated overnight at 4°C . Between each further step , the plate was washed extensively with PBST ( PBS with 0 . 1% Tween 20 ) . Each incubation step was at room temperature . Tissue culture supernatant was applied and the plate incubated for 1 hour . Following washing , the plate was blocked for 1 hour with 2% skimmed milk in PBST . The plate was washed again and the wells treated with the anti-collagen VII antibody LH7 . 2 ( hybridoma supernatant diluted 1 in 10 with 2% skimmed milk in PBST ) and the plate incubated for 1 hour . Following further washing , a secondary antibody was applied to the plate ( HRP-conjugated donkey anti-mouse , Jackson ) , diluted in PBST containing 2% skimmed milk , and the plate incubated for 1 hour . Following further washing , a detection reagent was added ( Pierce TMB substrate kit ) and the plate incubated until a blue colour was visually detectable . The reaction was stopped by adding an equal volume of 2 M H2SO4 and absorbance at 450 nm monitored . The plasmid pRL-CMV ( Promega ) was subjected to site-directed mutagenesis to create the read-through reporter plasmid pRL-mut4W . Codon 21 was mutated from TGG to TGA to give a W to X change . The primers used were as follows: 5′ GTC CGC AGT GGT GAG CCA GAT GTA AAC AAA TG 3′ and 5′ CAT TTG TTT ACA TCT GGC TCA CCA CTG CGG AC 3′ . This was carried out using a method similar to that described for the QuikChange site-directed mutagenesis kit ( Stratagene ) . AD293 cells were transfected with pRL-mut4W in 96-well plates from the same plasmid/transfection reagent mix . PTC124 or G418 was added 6 hours post-transfection and RLuc activity assayed 48 hours post-transfection . Cells were washed once in PBS and then lysed in 20 µl Passive Lysis buffer ( Promega ) with shaking for 30 minutes . RLuc activity was measured using “Stop and Glo” buffer ( Promega ) according to the manufacturer's instructions with a LUMIstar OPTIMA luminometer ( BMG Labtech ) . A wild-type K6a-YFP fusion construct was generated and subcloned into a pcDNA5/FRT vector ( Invitrogen ) as described previously [18] . The arginine 533 codon in K6a was replaced by a premature TGA stop codon using the QuickChange site-directed mutagenesis system ( Stratagene ) with the following primers: K6aTGAG . F 5′ AGT TCC AGC AGT GCA TGA GAC ATT GGG GGT GGC 3′ and K6aTGAG . R 5′ GCC CCC CCA ATG TCT CAT GCA CTG CTG GAA CT 3′ . An upstream GGC and a downstream GCC flanking the premature termination codon were also replaced by GCA and GAC respectively . AD293 cells were transiently transfected with the mutated K6a-YFP vector carrying R533X in the K6A gene and co-transfected with pEGFP-C1 ( Clontech ) as transfection control ( pEGFP-C1 was added to the transfection reagent mix then it was aliquoted to add the individual K6a-YFP plasmids ) . Six hours following transfection , the medium was replaced by fresh medium with 200 µM G418 or 3 µM PTC124 with a final DMSO concentration of 1% for each . After 24 hours' incubation with compounds , the cells were harvested in PBS and the pellet was resuspended in 150 µl of extraction buffer that consisted of NuPAGE LDS Sample Buffer ( Invitrogen ) and NuPAGE Sample Reducing Agent ( Invitrogen ) . Samples were analysed by electrophoresis using NuPAGE Novex 4%–12% Bis-Tris gels ( Invitrogen ) and transferred onto nitrocellulose membranes ( Whatman ) . Before loading , samples were sonicated and heated for 10 minutes at 70°C . YFP expression was detected by anti-GFP monoclonal antibody ( Roche ) and AlexaFluor 680 F ( ab′ ) fragment of goat anti-mouse IgG ( Invitrogen ) and visualised by Li-cor Imaging System using Odyssey 2 . 1 software . The blots were also probed with a primary antibody specific to keratin 18 ( Abcam Ab31844 ) as a loading control; for transfection control the same anti-GFP antibody as above was used to detect GFP . All blots were scanned and analysed with the same instrument settings . Termination efficiencies are influenced not only by the nature of the stop codon , but also the nature of the nucleotide following the nonsense codon ( +1 position ) . Therefore , a panel of constructs was created with every possible stop codon combined with every possible +1 nucleotide using the QuickChange site-directed mutagenesis system . The primers used for the mutagenesis are as follows . ( for TGAA ) K6aTGAA . F 5′ AGT TCC AGC AGT GCA TGA AAC ATT GGG GGT GGC 3′ K6aTGAA . R 5′ GCC ACC CCC AAT GTT TCA TGC ACT GCT GGA ACT 3′ ( for TGAC ) K6aTGAC . F 5′ AGT TCC AGC AGT GCA TGA CAC ATT GGG GGT GGC 3′ K6aTGAC . R 5′ GCC ACC CCC AAT GTG TCA TGC ACT GCT GGA ACT 3′ ( for TGAT ) K6aTGAT . F 5′ AGT TCC AGC AGT GCA TGA TAC ATT GGG GGT GGC 3′ K6aTGAT . R 5′ GCC ACC CCC AAT GTA TCA TGC ACT GCT GGA ACT 3′ ( for TAGG ) K6aTAGG . F 5′ AGT TCC AGC AGT GCA TAG GAC ATT GGG GGT GGC 3′ K6aTAGG . R 5′ GCC ACC CCC AAT GTC CTA TGC ACT GCT GGA ACT 3′ ( for TAGA ) K6aTAGA . F 5′ AGT TCC AGC AGT GCA TAG AAC ATT GGG GGT GGC 3′ K6aTAGA . R 5′ GCC ACC CCC AAT GTT CTA TGC ACT GCT GGA ACT 3′ ( for TAGC ) K6aTAGC . F 5′ AGT TCC AGC AGT GCA TAG CAC ATT GGG GGT GGC 3′ K6aTAGC . R 5′ GCC ACC CCC AAT GTG CTA TGC ACT GCT GGA ACT 3′ ( for TAGT ) K6aTAGT . F 5′ AGT TCC AGC AGT GCA TAG TAC ATT GGG GGT GGC 3′ K6aTAGT . R 5′ GCC ACC CCC AAT GTA CTA TGC ACT GCT GGA ACT 3′ ( for TAAG ) K6aTAAG . F 5′ AGT TCC AGC AGT GCA TAA GAC ATT GGG GGT GGC 3′ K6aTAAG . R 5′ GCC ACC CCC AAT GTC TTA TGC ACT GCT GGA ACT 3′ ( for TAAA ) K6aTAAA . F 5′ AGT TCC AGC AGT GCA TAA AAC ATT GGG GGT GGC 3′ K6aTAAA . R 5′ GCC ACC CCC AAT GTT TTA TGC ACT GCT GGA ACT 3′ ( for TAAC ) K6aTAAC . F 5′ AGT TCC AGC AGT GCA TAA CAC ATT GGG GGT GGC 3′ K6aTAAC . R 5′ GCC ACC CCC AAT GTG TTA TGC ACT GCT GGA ACT 3′ ( for TAAT ) K6aTAAT . F 5′ AGT TCC AGC AGT GCA TAA TAC ATT GGG GGT GGC 3′ K6aTAAT . R 5′ GCC ACC CCC AAT GTA TTA TGC ACT GCT GGA ACT 3′ | Ten percent of all single-gene hereditary diseases are caused by nonsense mutations . These are alterations in the DNA sequence of a protein-coding gene that cause the ribosome to prematurely finish translating the gene transcript before a full-length , active protein can be produced . In 2007 a drug was developed called PTC124 ( latterly known as Ataluren ) , which was reported to help the ribosome skip over the premature stop , restore production of functional protein , and thereby potentially treat these genetic diseases . In 2009 , however , questions were raised about the initial discovery of this drug; PTC124 was shown to interfere with the assay used in its discovery in a way that might be mistaken for genuine activity . As doubts regarding PTC124's efficacy remain unresolved , here we conducted a thorough and systematic investigation of the proposed mechanism of action of PTC124 in a wide array of cell-based assays . We found no evidence of such translational read-through activity for PTC124 , suggesting that its development may indeed have been a consequence of the choice of assay used in the drug discovery process . | [
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| 2013 | A Lack of Premature Termination Codon Read-Through Efficacy of PTC124 (Ataluren) in a Diverse Array of Reporter Assays |
The emergence and prevalence of drug resistance demands streamlined strategies to identify drug resistant variants in a fast , systematic and cost-effective way . Methods commonly used to understand and predict drug resistance rely on limited clinical studies from patients who are refractory to drugs or on laborious evolution experiments with poor coverage of the gene variants . Here , we report an integrative functional variomics methodology combining deep sequencing and a Bayesian statistical model to provide a comprehensive list of drug resistance alleles from complex variant populations . Dihydrofolate reductase , the target of methotrexate chemotherapy drug , was used as a model to identify functional mutant alleles correlated with methotrexate resistance . This systematic approach identified previously reported resistance mutations , as well as novel point mutations that were validated in vivo . Use of this systematic strategy as a routine diagnostics tool widens the scope of successful drug research and development .
Drug resistance is a worldwide health concern that affects all drug classes , including anti-infectives and anti-cancer agents [1–3] . Recent reports illustrate that first-line antibiotic treatment failure rates have increased 12% from 1991–2012 [4] . Cancer drug resistance has increased , in part due to the use of highly specific targeted therapeutics [1 , 5] . While attempts to combine drugs into “smart cocktails” hold some promise to reduce emergence of resistance , in the majority of cases drug resistance is inevitable . Therefore , it is important to understand the causative mechanisms of resistance to improve the use and targeting of therapeutics . Current strategies for understanding the mechanisms of resistance include: i ) observational trials [1 , 6] , ii ) in situ mutagenesis [7–10] and iii ) computational approaches [7 , 11–13] . However , each of these methods suffer from limitations with respect to throughput , resolution and accuracy . Hence , a rapid , systematic and cost-effective strategy to identify gene variants that modulate drug resistance over time is required to improve our understanding of resistance mechanisms . Here , we present such a streamlined method to identify the emergence and persistence of modulators of drug resistance . Our integrative approach combines a strategic parallel competitive in vivo resistance assay with a Bayesian statistical model [14 , 15] that is both systematic and quantitative . We applied this assay to the anti-cancer drug methotrexate ( MTX ) in its well-characterized target , dihydrofolate reductase . Our pipeline takes advantage of the S . cerevisiae variomics collection , which contains libraries of 2 x 105 random plasmid-borne point mutation alleles for every yeast gene [16] . These alleles are packaged within haploid-convertible heterozygous diploid yeast gene knockouts which can be grown competitively and quantified with massively parallel sequencing . Yeast dihydrofolate reductase ( DFR1 ) is a validated functional orthologue of human dihydrofolate reductase ( hDHFR ) , which is commonly used to study the MTX mechanism of action and enzymology [15 , 17 , 18] . In previous work , yeast has been employed to study genome-wide gene-drug interactions [19–23] , and is a well-established model for anticancer drug research [6 , 17 , 24 , 25] . Methotrexate acts as an antimetabolite that targets the enzyme dihydrofolate reductase , which functions to maintain folate homeostasis in nucleus and mitochondria by reducing dihydrofolate into tetrahydrofolate as a key element of thymidylate and protein synthesis [15] . Due to the high degree of conservation between yeast and human cellular pathways , the results obtained for the yeast dihydrofolate reductase can provide insights into how tumors acquire drug resistance , which is a major barrier to effective cancer treatment [26–28] and point mutations in DHFR active site have been shown to affect MTX binding affinity altering in turn MTX efficacy [8–10 , 29–37] . Thus , systematically surveying the causative DFR1 point mutations that correlate with poor MTX response and understanding how resistant dfr1 alleles interact with MTX will help develop MTX analogues with a potentially lower likelihood of resistance .
The functional variomics technology was adapted in our study by using the original dfr1 variomics library , which contains 2 x 105 point mutations in DFR1 [16] . To recover as many distinct dfr1 MTX resistant-alleles as possible , we exploited the variomics tool by screening the diploid and haploid dfr1 pools using an improved screening assay ( Fig 1 and Methods ) . Specifically , we wanted to test if the resulting alleles differed depending on if the wild-type DFR1 allele was present , as is the case for the DFR1/dfr1Δ heterozygote strain , or absent as in the haploid dfr1Δ strain . For haploids , the dfr1 allele must maintain viability and provide drug resistance whereas in the diploid case , the wild type allele can in principle allow separation-of-function alleles ( i . e . resistance without viability ) to be recovered . We tuned the parameters of the drug resistance assay to maximize for the enrichment of dfr1 alleles in parallel competitive conditions in an attempt to mimic the environment in which heterogeneous tumors are exposed to cytostatic drugs [38 , 39] ( Fig 1 ) . The dfr1 diploid library was first grown without drug selection to generate a dfr1 pool with ~50-fold coverage per variant for each of the 2 x 105 independent variants ( see Methods for details ) . The pool was then induced to sporulate to generate a haploid dfr1 pool of 2 . 2 x 104 viable dfr1 alleles which were then challenged with drug in liquid media . To minimize the loss of rare dfr1 alleles , drug exposure was limited to a 6-day treatment of the diploid and haploid pools in liquid media at a MEC100 dose of MTX ( Fig 1 and S1 Fig ) . Treated samples were collected every 2 days ( equivalent to 8 generations of growth ) and the remaining dfr1 pools were further propagated in fresh media with MTX ( S2 Fig ) . MTX-treated pools were harvested at each time point and plasmid-borne dfr1 alleles were PCR amplified and sequenced at a median coverage of 10K ( Fig 1 and S1 Table; Methods ) . The sequencing data was collected in separate runs for the diploid and haploid experiments and each processed independently ( see Methods for details ) . To call variants and estimate their associated allele frequencies in the mixed dfr1 pools , we used our previously published rare variant detection statistical model ( RVD2 ) [14] . We estimated the parameters of the model for each time point and for the wild-type control using the default Gibbs sampling and Markov chain monte carlo parameters ( 4000 Gibbs samples , 10 Metropolis-Hastings samples per gibbs sample , 20% warm-up , thinning rate of 2 ) . Finally , we called variants using the somatic test function in RVD2 . This test identifies variants where the difference in the non-reference read rate is between 0 . 1% and 100% between a designated case and control sample ( 95% posterior confidence ) . This test also filters for variant loci that have non-uniform , non-reference read counts to eliminate false-positive calls due to generally elevated sequencing error rates . We used RVD2 to compare the non-reference read rate at the starting time point “T0” to that of all later time points ( T1 , T2 , and T3 ) . We denote the model’s estimate of the true non-reference read rate at each locus the Variant Allele Frequency ( VAF ) at that locus . This analysis identified 66 variant positions in the DFR1 locus in the diploid pool and 49 variant positions in the haploid pool ( Fig 2; S2 and S3 Tables; and S3 Fig ) . Among the 35 ( 53% ) coding mutations in the diploid pool , 28 were missense mutations . Exactly 11 of these 28 mutations ( 39% ) correspond to highly conserved residues ( Fig 2; S2 Table; and S3 and S4 Figs ) . We noted that missense mutations that affect M35V and M35T residues , which were previously shown to affect MTX binding affinity and/or MTX resistance , were recovered in our screen [14] ( S2 Table ) . In the haploid pool in contrast , only 8 out of 17 coding mutations ( 47% ) were found to be missense mutations , 3 of which correspond to residues that are conserved in hDHFR ( Fig 2; S3 Table; S3 and S4 Figs ) . We estimated the diversity in the diploid and haploid pools at each time point by comparing the number and frequency of the variants under selection to the number and frequency of variants in the background strain , which carries only the wild-type allele on the parental plasmid . This procedure accounts for changes in the number and relative abundance of variant alleles , and sequencing variation ( see Methods for details ) . Because the wild-type allele-bearing strain was only sequenced with one replicate , the sensitivity of RVD is low and few variants were called . We record a—when fewer than two variants were called at a time point and no diversity score can be computed . For the haploid strain , the diversity scores for T0 - T3 were 1 . 37 , - , - , - respectively . For the diploid strain , the diversity scores for T0 –T3 were - , 9 . 20 , 7 . 13 , 4 . 66 respectively . The diversity of the haploid strain is lower than the diversity of the diploid strain at any time point , as would be expected due to the viability requirement imposed on any haploid allele . The diploid diversity score decreases monotonically from T1 to T3 . These results align with our expectation that diversity is higher for the diploid pool than the haploid pool and that diversity decreases with time under drug selection . The nucleotide coding sequence positions called in the diploid and haploid backgrounds do not overlap , except for one ( 627T>C ) ( S2 and S3 Tables ) . We reasoned this is likely due to the random genetic drifts introduced by the sporulation and haploid conversion events ( see Methods for details ) . Furthermore , two positions in the coding sequence identified in the diploid strain showed two minor allele changes over the MTX timecourse e . g . 187T>G ( at T1 time point ) and 187T>A ( at T2 time point ) , which result in non-synonymous mutations S63A and S63T , respectively ( S3 Fig and S2 Table ) . The emergence of all of these nucleotide changes at given time points suggests that these silent and non-synonymous mutations can have marginal effects in modulating MTX resistance in the diploid genetic background . We compared the spectrum of variants in the haploid pool to those in the diploid pool prior to selection ( T0 ) , because dfr1 mutants that survive conversion to the haploid state must be viable . We found some DFR1 positions had high variant allele frequencies ( VAFs ) in the diploid and haploid pools ( Fig 2 ) . The pre-existing dfr1 haploid allele frequency did not predict the emergence of MTX-resistant variants in later time points ( Fig 2; S3 Fig; and S3 Table ) . In contrast , in the diploid state , 5 out of 6 positions with high initial VAFs ( over 10% ) increased in abundance upon MTX exposure ( Fig 2; S3 Fig; and S2 Table ) . These observations are consistent with a model in which pre-existing mutations required for viability are no more likely to confer drug resistance , while resistant alleles can be found as pre-existing in the diploid state . Our strategy of surveying dfr1 alleles in both diploid and haploid backgrounds allowed us to distinguish dfr1 mutations with dominant resistance phenotypes regardless of whether or not such pre-existing mutants with competitive fitness advantages were already present . To validate the alleles identified by our high-throughput parallel assay as individual variants , all mutant alleles were reconstructed de novo and assayed for MTX resistance in individual growth assays ( Methods ) . We selected all of the coding ( non-synonymous and silent ) mutations that increased in VAF at the earliest time point ( S2 and S3 Tables ) and integrated a full length synthetic gene into the chromosomal DFR1 locus of the isogenic DFR1/dfr1Δ or dfr1Δ strains , such that these alleles were under the control of the endogenous promoter ( Methods ) . All of the DFR1 mutations in the haploid background were viable , however 6 out of 10 dfr1 mutants exhibited a slow growth phenotype ( Fig 3A and S5 Table ) . Also , 9 out of 10 dfr1 alleles ( not Q16Q ) were reproducibly resistant to a sublethal dose of MTX ( Fig 3A; Methods ) . The majority of the alleles ( 7 out of 10 ) are recessive based on our observation that they were no longer MTX resistant when a wild-type DFR1 copy was expressed ( Fig 3A; Methods ) . In the diploid background , 10 out of 27 DFR1/dfr1 mutants that express non-synonymous mutations were confirmed to exhibit strong resistance to MTX , with at least 80% retained growth relative to each corresponding DMSO-treated strain ( Fig 3B ) . Of these 10 DFR1/dfr1 mutants , 2 exhibit competitive fitness advantages while 3 represent hypomorphic alleles , ( with predicted DFR1 catalytic defects ) given their inability to survive under obligate respiratory conditions [40] ( Fig 3B; S4 Table and Methods ) . These results , combined with the experimental differences between initial screen vs . validation experiments ( e . g . re-screened individually vs . growth in competitive mixed culture ) ( Methods ) suggest that some of the DFR1 mutations with marginal MTX resistance , including I55M , F68L , T156A , F178F , E187K and N209H might manifest resistance only when expressed in combination . Ten of the validated MTX-resistant dfr1 alleles cluster in the functional binding pocket for folate , the substrate of Dfr1p , or its NADPH cofactor ( Fig 4 ) . Specifically , mutations in L27 , M35 , F38 and T141 correspond to residues that directly interact with MTX ( or folate ) in hDHFR ( Fig 4B and 4C ) . We hypothesize that these mutations likely reduce MTX affinity to render the drug ineffective . Similarly , mutations found in V127 and T156 correspond to residues situated in the NADPH binding cleft ( Fig 4C ) . Previous work has shown that specific C . galbrata DHFR inhibitors act by displacing the NADPH cofactor [41 , 42] , suggesting that a similar mechanism could be at work for the V127 and T156 mutations identified in our screen . Other non-synonymous mutations identified in our screen , including F38Y , M35T and M35V , have also been previously reported to lead to MTX resistance [30 , 32 , 33 , 35] . Further , we identified a W29R mutant , a residue known to be essential for enzyme function [43] . Specifically , the side chain of W25 ( W29 in yeast ) forms hydrophobic aromatic stacking interactions with both MTX ( PDB ID 1U72 ) [10] and folate ( Fig 5A ) ( PDB ID 1DHF ) [44] . In the latter case , the protein-ligand interaction is further stabilized by a hydrogen bond formed between the Nɛ of W25 and the hydroxyl group of the folate pteridine ring . We also noted that a Trp residue is conserved at this position , with the notable exception of the recently-identified hDHFR-like 1 ( hDHFRL1 ) protein [45] , which has an Arg residue at this position ( S3 and S4 Figs ) . This is the same substitution that we obtained in our screen ( W29R ) ( Fig 2 and S2 Table ) . An arginine at this position is unable to form the key hydrophobic interactions with MTX [46] and therefore we hypothesized that hDHFRL1 may be resistant to MTX . To test this and to explore the possibility that resistance arises from destabilizing hydrophobic contacts with antifolate [46] ( Fig 5A ) , we investigated the growth fitness of a wild-type human hDHFRL1 construct bearing an arginine at position 25 in a DFR1/dfr1Δ heterozygote strain ( Methods ) . This change did indeed render the cells resistant to MTX ( Fig 5B and S6 Fig ) . To extend this observation , we constructed a hDHFRL1 construct containing the putative loss-of-resistance allele , R25W , in a DFR1/dfr1Δ heterozygote strain ( Fig 5B and S6 Fig ) . Although this mutant DFR1/hDHFRL1 ( R25W ) has a comparable growth rate to its wild-type counterpart DFR1/hDHFRL1 , MTX resistance was abolished by introducing the R25W allele . Conversely , we tested the growth fitness of a hDHFR construct bearing the same mutation W25R ( equivalent to W29R in yeast ) in a DFR1/dfr1Δ heterozygote strain ( Methods ) . This human W25R variant was reproducibly MTX resistant ( Fig 5B ) . Of note , the W29R mutant in a yeast construct did not yield MTX resistance in our validation assay ( Fig 3B ) , likely due to the weak ability of W29R variant to persist and modulate MTX resistance in the pool over the timecourse ( Fig 2 and S3 Fig ) . Future work will address the mechanistic differences between the various MTX-resistant dfr1 alleles and their implications for folate metabolism .
Here we report a combined experimental and statistical approach capable of rapidly achieving high coverage of a targeted region to reliably identify bona fide drug resistant variants of dihydrofolate reductase . As a unique result of this strategy , we achieved increased throughput , resolution and sensitivity critical to detect dfr1 point mutation alleles that emerge or persist upon exposure to lethal MTX conditions over time . Although we cannot demonstrate we have exhausted all possible resistance conferring dfr1 mutations in the original dfr1 variomics library , our innovative approach proves to advance our understanding of the molecular basis of MTX resistance with the identification of a significant fraction of the resistance dfr1 variant space previously unknown in S . cerevisiae . By performing parallel competitive screening on diploid and haploid dfr1 libraries , we also uncovered pre-existing dfr1 hypomorphic alleles in the diploid state , which are as likely to modulate MTX resistance as haploid dfr1 mutations with dominant phenotypes . These observations further validate the relevance of interrogating gene variants in the diploid background given that in a human therapeutic target , mutations in only one allele suffice to provide drug resistance . Our variant calling algorithm , RVD2 , is specifically designed to call rare variants in pooled sequencing data . It does so by leveraging replicates of a sample to estimate a baseline error rate at each locus . Then the test sample error rates are compared , in a hypothesis testing framework , to the locus-specific baseline error rate . In previous work , we compared the performance of RVD2 to state-of-the-art variant calling methods using in vitro mixtures of synthesized DNA fragments at defined fractions . These experiments showed that RVD2 has higher sensitivity and specificity for detecting rare variants and equivalent sensitivity and specificity for higher frequency ( more than 10% ) variants . We also identified the emergence of rare variant alleles , at starting frequencies lower than 1% that are capable of conferring resistance to MTX over time . As variant diversity is lost upon lethal MTX selective pressure , many of these rare alleles do not persist in the pooled conditions suggesting that the presence of epistatic mutations that can affect the evolution trajectory of adaptive MTX resistant alleles [47] . In addition , several novel functional MTX-resistant dfr1 alleles that disrupt the conserved active-site residues were identified in vivo , providing additional genetic insights into the determinants of MTX resistance . Importantly , we show that the yeast-based assay used here is capable of interrogating functional homologs such as the human enzyme . Out of 42 Dfr1 residue changes identified in our screen , 14 ( 33% ) conserved residues are known to display key interactions with antifolate compounds and/or NADPH cofactor , which can affect the potency and selectivity of antifolates [29 , 33 , 35 , 42 , 48 , 49] . The remaining 28 ( 67% ) residues identified are novel sites capable of modulating MTX resistance . The catalytic function of such DHFR mutations remains to be explored . With current ever-improving gene synthesis approaches , determining the consequences of non-coding SNPs will become tenable as will assessing the concomitant effects of causative dfr1 mutations when expressed in combination . As sequencing technology becomes cheaper and more practical , this platform should in principle be extensible to uncovering linked mutations in small drug targets like DHFR that can confer specific resistance in yeast to address the growing problem of resistance to otherwise effective compounds and FDA drugs .
The homozygous diploid reference strain BY4743 MATa ( his3Δ1 leu2Δ0 LYS2 met15Δ0 ura3Δ0 ) /MATalpha ( his3Δ1 leu2Δ0 lys2Δ0 MET15 ura3Δ0 ) was used to determine the MEC100 ( minimum effective concentration to cause 100% cell killing ) of methotrexate ( MTX ) to use for resistance screening and validation growth assays . For MTX resistance screens , a dfr1 variomics library was used [50] . MTX was purchased from Sigma ( M9929 ) and single-use MTX aliquots were prepared by dissolving MTX in DMSO solvent to 100 mM and stored at -80°C . To counterselect cells carrying the plasmid-borne dfr1 mutations , 5-Fluoroorotic acid ( 5’-FOA; Sigma F6625 ) was added to the media at a final concentration of 1 g/L . To select DFR1/dfr1 cells with the correct dfr1 integration event , growth sensitivity to G418 sulphate ( Geneticin ) was verified by adding G418 ( Fisher 142480 ) to a final concentration of 400 mg/L to YPD agar plates . For assessing growth fitness in the presence of MTX , yeast strains and pools were cultured to mid-log phase ( OD600 ~0 . 5 ) in synthetic complete ( SC ) liquid media before adjusting the cultures to an initial OD600 of 0 . 0625 . Cells were then transferred to a 96-well microtiter plate containing liquid SC media with either MTX or DMSO solvent ( 2% v/v ) as control . To determine the MEC100 dose of MTX , a range of doses ( 0 . 025 , 0 . 05 , 0 . 1 , 0 . 3 and 2 mM ) were tested against the wild-type BY4743 strain . Cell growth upon MTX treatment relative to DMSO solvent was assayed in three biological replicates using a spectrophotometer Tecan shaker-reader that measured OD595 values over 24 hours at 30°C . Cell growth was inhibited at ~100% at 2 mM , which was the determined lethal dose for the MTX resistance assay ( S1 Fig ) . To confirm MTX resistance , the reconstructed yeast strains were cultured in rich YPD media at a sublethal dose ( 1 mM ) of MTX . Cell growth upon MTX treatment relative to DMSO solvent was assayed in three biological replicates using a Tecan shaker-reader over 24 hours at 30°C . For each mutant , the percent of growth rate in MTX relative to DMSO was calculated and the average and standard error of three biological replicates reported in S4 and S5 Tables . Mutant strains that showed a reproducible growth in the presence of the drug were confirmed to be true MTX-resistant strains ( Fig 3 ) . Cell growth in obligate respiratory media was used as a proxy to assess mitochondrial folate metabolism . Dfr1 mutant cells with non-synonymous mutations were cultured in obligate respiratory growth media using YPG media prepared with a non-fermentable carbon source ( glycerol at 3% v/v ) . Growth or lack of growth in YPG media was assayed in three independent assays . Cultures that displayed two doublings or fewer after 24 hours in YPG were scored as respiratory-defective . The respiratory-proficient strains DFR1/dfr1Δ and wild-type BY4742 were included , in parallel , as controls . The starting dfr1 variomics library consists of at least 2 x 105 independent dfr1 variant alleles , with single and multiple point mutations per allele . The variomics library is cloned into a CEN-based plasmid under control of native upstream and downstream regulatory regions , and transformed into a DFR1/dfr1Δ heterozygote convertible diploid strain [50] . The diploid variomics pool is cultured in synthetic dropout medium lacking uracil ( SD-URA ) at 30°C to generate a working stock of OD600 1 , equivalent to an average of 50-fold coverage ( independent cells ) for each of the 2 x 105 independent variants . The latter calculation assumes that each DFR1/dfr1Δ cell harbors one variant dfr1 allele and that all doubling times are similar . To generate a dfr1 pool in haploid MATa cells lacking the chromosomal wild-type DFR1 gene , the dfr1 diploid pool was sporulated and subsequently haploid converted using the previously described optimized procedures [16] , with the following modifications . Diploid cells were cultured in 200 ml sporulation medium at room temperature with vigorous shaking ( 200 rpm ) for 5 days in the dark to increase sporulation . ~ 2 x 105 sporulated cells , with an average 10-fold variant coverage were cultured in 400 ml haploid selection medium to enrich for MATa dfr1 G418R URA+ haploid cells at 30°C for 2 days [51] . Sporulation and haploid conversion recovered a dfr1 haploid pool with ~11% of the initial dfr1 alleles , which represents a total of 2 . 2 x 104 viable dfr1 alleles . The selection for haploid cells and genetic “bottle necks” introduced in the methodology ( Fig 1 and S2 Fig ) are likely to exert additional selective pressure on the haploid pool . Hence , we predict a smaller proportion of MTX-resistant variants that sustain cell viability is present in the haploid pool . The DFR1/dfr1Δ diploid and dfr1Δ haploid pools were cultured to an initial OD600 of 0 . 01 , with an average 10-fold variant coverage observed in the dfr1 variomics library . Each pool was screened in triplicate wells ( n1 , n2 , n3 technical replicates ) in 6-well microtiter plates over a 6-day time course in 10 ml of SD-URA media supplemented with MTX at 2 mM ( MEC100 ) ( Fig 1 and S2 Fig ) . Media supplemented with DMSO ( 2% v/v ) was prepared in parallel as a negative control . The screening assay consists of 3 time points , where the MTX and DMSO treated pools were propagated at 30°C with vigorous shaking ( 200 rpm ) . Sampling was done every 2 days , which typically resulted in 8-generation propagation for the DMSO-treated pools ( S2 Fig ) . The first replicate ( n1 ) set of cultures for MTX and DMSO was used for propagating the subsequent time point: at each time point , MTX-treated cells from the n1 replicate well were diluted to OD600 0 . 01 with fresh SD-URA medium supplemented with either MTX or DMSO , and transferred to the equivalent 3 replicate wells in a new microtiter plate ( S2 Fig ) . At each time point , MTX-treated cultures from all replicate wells were harvested for DFR1-targeted sequencing and analysis ( see below ) . The initial dfr1 variomics libraries of both diploid and haploid pools ( time point 0 ) were also split in three technical replicates to assess sample-to-sample variation . The sample size per condition ( n = 3 ) and expected sequencing median read depth ( 20 , 000x ) was selected based our published power analysis from a previous version of our statistical model [52] and experience with this version of the model [14] to detect a minimum variant allele frequency of 0 . 1% . Plasmids were extracted from harvested MTX-treated cell pools at each time point using the DNA extraction protocol described previously [50] . PCR reactions were performed using Phusion High Fidelity polymerase , according to the manufacturer’s instructions ( Thermo Fisher Scientific ) with the following modifications . To amplify the dfr1 amplicons from the plasmid-containing extracts , PCR reactions were performed in 50 μl containing 100 ng of plasmid extract and universal plasmid-specific oligonucleotides at 0 . 5 μM [16] . The cycling protocol was as follows: 1× ( 98°C for 30 sec ) , 30× ( 98°C for 10 sec , 52°C for 30 sec , 72°C for 45 sec ) , 1× ( 72°C for 5 min ) . For colony PCRs , a fraction of each yeast colony was picked using a plastic micropipet tip and placed at the bottom of the reaction tube containing 10 μl of 20 mM NaOH . Samples were boiled for 5 min and 1 μl of each sample was used for the PCR reactions in a total of 25 μl containing oligonucleotides ( 2 . 5 μM ) . For a complete list of oligonucleotides used , see S1 Table . The cycling protocol for colony PCR amplification was as follows: 1× ( 98°C for 30 sec ) , 30× ( 98°C for 10 sec , 48°C for 30 sec , 72°C for 10 sec ) , 1× ( 72°C for 5 min ) . All reaction products were analyzed on a 1% ( w/v ) agarose gel . Dfr1 amplicons prepared by PCR were first purified using the Thermo Fisher Scientific PCR purification kit , according to the manufacturer’s instructions , quantified using Qubit fluorometry ( Life Technologies ) and diluted for sequencing library preparation . Libraries were constructed using plexWell library kit technology ( seqWell , Beverly MA ) . In this approach , each 1+ kb pool of diverse amplicons is tagged with a pool-specific barcode via a transposase-mediated adapter addition at random locations . After this tagging , the pools of amplicons are then pooled into a single meta-pool , and subjected to a second transposase-mediated adapter addition . Fragments of this pool containing sequence from each of the two iterative adapter additions are then amplified to yield a final sequence library representing identifiable fragments from each original amplicon pool . Sequencing data is available upon request . We have deposited the raw fastq files at the NCBI SRA under the accession number SRP072709 . DFR1-targeted sequencing data was collected and processed separately for the diploid and haploid experiments . To align the raw ( fastq ) sequencing data to the S . cerevisiae genome , we first trimmed the Illumina adaptor sequences using cutadapt ( v 1 . 7 . 1 ) ( --anywhere AGATCGGAAGAGC ) . Then the paired-end reads were aligned to the April 2011 UCSC S . cerevisae reference genome ( sacCer3 ) using bwa ( v 0 . 7 . 12 ) mem with the -M flag set . Finally , the resulting bam files were indexed and sorted for subsequent processing and visualization . First , we used samtools ( v 1 . 2 ) mpileup to generate pileup files for the DFR1 gene region chrXV:780367–782084 . We also set the -A and -BQ0 flag to get high quality read depth estimates without discarding anomalous reads , and we set the maximum depth to 10 , 000 , 000 to ensure no truncation of read depth occurs . We used a custom program , described previously [14] , to provide the count of each base pair at each position in the region of interest . Then , we ran RVD2 gibbs on the wild-type , T0 , T1 , T2 , and T3 data sets separately with the default warm-up , thinning and sample size parameters to estimate the model parameters and latent variables in the RVD2 statistical model . Finally , we called variants between all pairs of data sets using RVD2 somatic test with an interval of [0 . 001 , 100] and a significance level ( α ) of 0 . 05 . The somatic test calls a provisional variant at a position if the Bayesian posterior probability ( estimated from a sample size of 1000 from the model posterior distribution ) that the difference between the VAF in two data sets ( e . g . T0 and T1 ) is in the interval is greater than 1-α ( two-sided ) . The provisional variant is called a variant if the distribution over the non-reference bases is non-uniform by a chi-squared test with a significance level of 0 . 05 . Calls based on these posterior credible intervals were not adjusted for multiple comparisons and we did not detect any gross deviations from the assumptions of the statistical model for this data . Further details on the estimation procedures and hypothesis test are provided in the RVD2 study [14] . Our statistical model and variant calling method as described previously [14] is publicly and freely available at https://bitbucket . org/flahertylab/rvd . Given a set of called variants , V , we compute the diversity as follows . To compute the diversity , we first compute the KL divergence from μ^j to p where p = 1/|V| where μ^j is the estimated non-reference read rate at called position j . The KL divergence is zero if and only if μ^j is equal to p . In that case , each variant is equally represented in the pool and the entire pool was made up of variant clones . Otherwise , the KL divergence is greater than zero . We compute the diversity as D=∑j∈V1+tanhDKL ( p‖μ^j ) . The tanh function is -1 when DKL ( p‖μ^j ) =∞ and 0 when DKL ( p‖μ^j ) =0 . So , 1+tanhDKL ( p‖μ^j ) is 0 when DKL ( p‖μ^j ) =∞ and 1 when DKL ( p‖μ^j ) =0 . Summing over all of the called variants means that the maximum value of the diversity grows with the number of variants . Therefore , this diversity measure captures both the uniformity of the distribution of the variants as well as the total number of variants . Gene fragments containing coding sequence point mutations flanked by DFR1 specific homology sequences were synthesized by IDT ( S6 Table ) . Gene fragments containing wild-type yeast DFR1 and human DHFR and DHFRL1 sequences were included as controls . Each gene fragment was resuspended in water ( molecular grade , Thermo Fisher Scientific ) to make a 10 ng/μl stock and stored at -20°C . Prior to yeast transformation , the gene fragments were PCR amplified using Phusion High Fidelity , according to the manufacturer’s instructions ( Thermo Fisher Scientific ) . For each PCR reaction , 10 ng of the IDT gene fragment was used as template in a 50 μl reaction and amplified with the oligonucleotides listed in S1 Table . To confirm MTX resistance , each dfr1 point mutant fragment was integrated into the dfr1:: kanMX locus of the haploid and diploid progenitor strains , which harbours the dfr1 variomics library . The diploid DFR1/dfr1Δ progenitor strain was first outgrown in YPD containing 5’-FOA for 2 days at 30°C to counterselect for the Ura+ dfr1 plasmids prior to the transformation . The haploid dfr1 strain was propagated in SD-URA medium to maintain the plasmid-borne dfr1 pool in order to maintain its viability . A high efficiency transformation protocol was used to create the mutants by mitotic recombination [53] . The progenitor strains were first cultured to mid-log phase in liquid SD-URA media and subsequently transformed with the dfr1 fragments according to the standard heat shock protocol [53] . Human DHFR and DHFRL1 point mutations were also integrated into the dfr1::kanMX locus to generate dfr1 yeast hybrid strains . To confirm that the yeast transformants have the correct integration , both diploid and haploid clones were 1 ) confirmed for the appearance of PCR products of the expected size using oligonucleotides that span the upstream and downstream junctions of the dfr1:: kanMX locus ( S1 Table ) ; 2 ) confirmed for loss of G418 resistance; and 3 ) confirmed for MTX resistance using a sublethal dose of MTX in liquid growth assays ( Fig 3 ) . Additionally , the haploid clones were counter-selected in 5’-FOA containing YPD agar plates to kill any cells carrying the plasmid-borne dfr1 mutations and confirmed for the absence of plasmid-borne dfr1 PCR products by colony PCR using plasmid specific oligonucleotides ( S1 Table ) . To make the yeast/human mutant constructs in the heterozygous diploid DFR1/dfr1 background , the haploid dfr1 mutants and wild-type BY4741 control were mated with the wild-type haploid BY4742 ( MATalpha his3Δ1 leu2Δ0 lys2Δ0 MET15 ura3Δ0 ) using standardized yeast manipulation procedures [54] . The diploid constructs were confirmed by selectively growing in agar plates containing synthetic dropout medium that lacks lysine and methionine amino acids ( SD-LYS-MET ) for 2 days at 30°C . The multiple sequence alignment for the dihydrofolate reductase protein was obtained with ClustalW [55] and the S4 Fig generated with ESPript [56] . The consensus sequence for all DHFR homologues ( S5 Fig ) was built using WebLogo [57] . The Saccharomyces cerevisiae DFR1 structural model was generated with Modeller [58] using the closest homologue of known structure ( Candida glabrata DHFR , 54% identity , PDB ID: 3CSE ) [41] as a template . The coordinates of NADPH were obtained by superimposing the structure of the C . glabrata DHFR structure in complex with NADPH ( PDB ID: 3CSE ) , and the coordinates of methotrexate were obtained by superimposing the structure of the E . coli DHFR in complex with methotrexate ( PDB ID: 4P66 ) [59] . The colour gradient for the sequence conservation was generated with ConSurf [60] , using the aforementioned multiple sequence alignment . All structure figures were obtained with PyMol ( Schrodinger , LLC ) . | One of the most profound outcomes of fast , reliable genome sequencing is the ability to tailor drug therapy to an individual’s genotype . This ‘personalized’ or ‘precision medicine’ is the realization of a decades-long effort to maximize drug effect and limit unwanted side effects . An undesirable consequence of such targeted therapies , however , is the emergence of drug resistance . This outcome is the result of an evolutionary process where mutations in the drug target render the drug perturbation allow such mutant cells to proliferate . Because of the unbiased , and stochastic nature of the emergence of drug resistance , it is impossible to predict . We developed a test where hundreds of thousands of mutant cells are exposed to a drug simultaneously and those cells that modulate resistance survive . This method is innovative because it partners a high-throughput experimental protocol with a tailored statistical model to identify all mutations that modulate resistance . Finally , we used synthetic biology to re-create these mutations and demonstrate that they were , in fact , bona fide drug-resistant variants . These mutations were further extended and confirmed to also be resistant in the human orthologue . This combined biological-computational approach allows one to identify drug’s degree of resistance to both guide treatments and future drug discovery . | [
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| 2016 | Reverse Chemical Genetics: Comprehensive Fitness Profiling Reveals the Spectrum of Drug Target Interactions |
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